cssi/SciSciGPT-SciSciNet huggingface dataset:
Is there still son preference in the United States

In this paper, we use 2008–2013 American Community Survey data to update and further probe evidence on son preference in the USA. In light of the substantial increase in immigration, we examine this question separately for natives and immigrants. Dahl and Moretti (Review of Economic Studies 75, 1085-1120, 2008) found earlier evidence consistent with son preference in that having a female first child raised fertility and increased the probability that the family was living without a father. We find that for our more recent period, having a female first child still raises the likelihood of living without a father, but is instead associated with lower fertility, particularly for natives. Thus, by the 2008–2013 period, any apparent son preference in fertility decisions appears to have been outweighed by factors such as cost concerns in raising girls or increased female bargaining power. In contrast, some evidence for son preference in fertility persists among immigrants. Immigrant families that have a female first child have significantly higher fertility and are more likely to be living without a father (though not significantly so). Further, gender inequity in source countries is associated with son preference in fertility among immigrants. For both first- and second-generation immigrants, the impact of a female first-born child on fertility is more pronounced for immigrants from source countries with less gender equity. Finally, we find no evidence of sex selection for the general population of natives and immigrants, suggesting that it does not provide an alternative mechanism to account for the disappearance of a positive fertility effect for natives.
High-precision measurements of ionospheric TEC gradients with the Very Large Array VHF system

[1] We have used a relatively long, contiguous VHF observation of a bright cosmic radio source (Cygnus A) with the Very Large Array (VLA) to demonstrate the capability of this instrument to study the ionosphere. This interferometer, and others like it, can observe ionospheric total electron content (TEC) fluctuations on a much wider range of scales than is possible with many other instruments. We have shown that with a bright source, the VLA can measure differential TEC values between pairs of antennas (δTEC) with a precision of 3 × 10−4 TECU. Here, we detail the data reduction and processing techniques used to achieve this level of precision. In addition, we demonstrate techniques for exploiting these high-precision δTEC measurements to compute the TEC gradient observed by the array as well as small-scale fluctuations within the TEC gradient surface. A companion paper details specialized spectral analysis techniques used to characterize the properties of wave-like fluctuations within this data.
The Role of Philanthropy in Local Government Finance

Nonprofit organizations thrive on the altruism of citizens, and actively court donors for major gifts. Yet individual gifts to government agencies are often unexpected, sporadic, and initiated by the donor. This article introduces the phenomenon of private giving to local governments and tests hypotheses regarding the expected forms of giving to public agencies. Results indicate that philanthropy is and will likely remain a minor and highly variable source of revenue, making it an ill-suited replacement for broad-based tax revenue. However, deliberate government efforts to provide a suitable environment for private donations appear to succeed in attracting more gifts per capita.
Imperfect information transmission and adverse selection in asset markets

Abstract This paper studies asset markets where owners of assets do not inherit private information from previous owners after a transaction and must learn about asset quality over time. Instantaneous learning by owners maximizes the number of owners with private information, but also maximizes the trading volume and welfare. This result is contrary to the traditional view that information asymmetry hurts trade efficiency. Public disclosure of information is not always optimal and its welfare implication depends crucially on the source of gains from trade.
Audit Partner Rotation and Financial Reporting Quality

SUMMARY: Audit partner rotation has received considerable attention globally and in the U.S. since the Sarbanes-Oxley Act of 2002 accelerated the rotation period from seven to five years and expanded the cooling-off period from two to five years. However, research on the effects of audit partner rotation on financial reporting quality in the U.S. is virtually non-existent, largely due to the absence of publicly available information on audit partners. Using a novel approach to determine audit partner rotation, we investigate the effect of rotation on financial reporting quality in the U.S. We find evidence of lower financial reporting quality following an audit partner change. Specifically, we find lower financial reporting quality during the first two years with a new audit partner relative to the final two years with the outgoing partner. We find the lower financial reporting quality to be more prevalent for larger clients. Further analyses suggest the initial year post-rotation presents audit challenge...
Jump-Robust Volatility Estimation using Nearest Neighbor Truncation

Abstract We propose two new jump-robust estimators of integrated variance that allow for an asymptotic limit theory in the presence of jumps. Specifically, our MedRV estimator has better efficiency properties than the tripower variation measure and displays better finite-sample robustness to jumps and small (“zero”) returns. We stress the benefits of local volatility measures using short return blocks, as this greatly alleviates the downward biases stemming from rapid fluctuations in volatility, including diurnal (intraday) U-shape patterns. An empirical investigation of the Dow Jones 30 stocks and extensive simulations corroborate the robustness and efficiency properties of our nearest neighbor truncation estimators.
The industry settings of leading organizational research: the role of economic and non-economic factors

Despite calls for attention to the role of context in organizational research, there have been no assessments of the distribution of industry contexts in organizational research. This paper explores that distribution in relation to the industry composition of the U.S. economy. Our analysis of 914 empirical field studies published in four leading journals from 1988 to 2002 reveals striking, persistent, and growing discrepancies between the industries that are economically important and the industries that have served as settings for organizational research. For example, education and manufacturing are oversampled in relation to their economic importance, while real estate, construction, wholesale, and retail are undersampled. We also develop and test a series of hypotheses predicting which industries serve as the contexts for leading research. Using negative binomial regression, we show that the percentage of recent MBA graduates in an industry and the percentage of employees with doctoral degrees in an industry predict the number of articles set in that industry, with the total number of employees and average establishment size in an industry enhancing the power of the model. We conclude by discussing the implications of our findings for advancing organizational theory and research methods. Copyright © 2008 John Wiley & Sons, Ltd.
Structural Estimation and Policy Evaluation in Developing Countries

This review discusses the use of discrete choice dynamic programming (DCDP) methods for evaluating policies of particular relevance to developing countries, such as policies to reduce child labor and increase school attendance, improve school quality, affect immigration flows, expand old-age pension benefits, or foster small-business investment through microfinance. We describe the DCDP framework and how it relates to static models, illustrate its application with an example related to conditional cash transfer programs, consider numerous empirical applications from the literature of how the DCDP methodology has been used to address substantively important policy issues, and discuss methods for model validation.
Moving Beyond “Reasonable”: Clarifying the FTC’s Use of Its Unfairness Authority in Data Security Enforcement Actions

Data security breaches, which compromise private consumer information, seem to be an ever-increasing threat. To stem this tide, the Federal Trade Commission (FTC) has been using its authority to enforce the prohibition against unfair business practices under Section 5 of the Federal Trade Commission Act (Section 5) to hold companies accountable when they fail to employ data security measures that could prevent breaches. Specifically, the FTC brings enforcement actions where it finds that companies have failed to implement “reasonable” data security measures. However, companies and scholars argue that the FTC has not provided adequate notice of what data security practices it considers “reasonable” for the purposes of Section 5.This Note first explains and critically analyzes several existing proposals that seek to bring clarity to the FTC’s application of its unfairness authority in the data security context. Then, this Note proposes a novel solution that encourages the FTC to explicitly outline its minimum data security requirements via nonlegislative rulemaking. Additionally, this Note contends that any FTC rulemaking should incorporate a principle of proportionality to ensure that companies know what data security measures they should implement based on the relative sensitivity of the consumer data that they retain. Lastly, this Note suggests that the FTC should also incorporate a safe harbor provision so that compliant companies know that, by following the FTC’s guidelines, they will be immune from Section 5 enforcement actions.
Information and Volatility

In an economy of interacting agents with both idiosyncratic and aggregate shocks, we examine how the structure of private information in‡uences aggregate volatility. The maximal aggregate volatility is attained in a noise free information structure in which the agents confound idiosyncratic and aggregate shocks, and display excess response to the aggregate shocks, as in Lucas [14]. For any given variance of aggregate shocks, the upper bound on aggregate volatility is linearly increasing in the variance of the idiosyncratic shocks. Our results hold in a setting of symmetric agents with linear best responses and normal uncertainty. We establish our results by providing a characterization of the set of all joint distributions over actions and states that can arise in equilibrium under any information structure. This tractable characterization, extending results in Bergemann and Morris [8], can be used to address a wide variety of questions linking information with the statistical moments of the economy.
Monetary Policy, Expected Inflation, and Inflation Risk Premia

We study variables that are normally unobservable, but very important for the conduct of monetary policy, expected inflation and inflation risk premia, within a New Keynesian business cycle model. We solve the model using a third-order approximation which allows us to study time-varying risk premia. Our model is consistent with rejection of the expectations hypothesis and the business-cycle behavior of nominal interest rates in the U.S. data. We find that inflation risk premia are very small and display little volatility. Hence, monetary policy authorities can use the difference between nominal and real interest rates from index-linked bonds as a proxy for inflation expectations. Moreover, for short maturities current inflation is a good predictor of inflation risk premia. We also find that short-term real interest rates and expected inflation are significantly negatively correlated and that short-term real interest rates display greater volatility than expected inflation. These results are consistent with empirical studies that use survey data and index-linked bonds to obtain measures of expected inflation and real interest rates. Finally, we show that our economy is consistent with the Mundell-Tobin effect, that is, increases in inflation are associated with higher nominal interest rates, but lower real interest rates.
Experimentation and the Returns to Entrepreneurship

Previous studies have argued that entrepreneurs earn less and bear more risk than salaried workers with otherwise similar characteristics. In a simple model of entrepreneurship, I show that estimates of mean and variance of returns to entrepreneurship used by these previous studies are biased, as they fail to account for the option value of experimenting with new ideas. Using longitudinal data, I find patterns that are consistent with entrepreneurship as experimentation and returns to entrepreneurship that are more attractive than established by previous research.
How Much Restructuring did the Transition Countries Experience? Evidence from Quality of their Exports

The increase in exports to market economies is a good sign, but it is not conclusive about the extent of restructuring of production technologies experienced in transition countries. This paper explores the source of the increase with an analysis of their exports' quality, interprets the results for the extent of restructuring, and discusses the potential factors behind them. Changes in factor intensity and unit values of both Central and Eastern European countries (CEEC) and Commonwealth of Independent States (CIS) exports in different manufacturing sectors during 1992–1999 are analysed. Although CEEC are in a significantly better position than CIS due to Europe Agreements, there is still large number of products with structural problems in CEEC. Insufficient foreign domestic investment, the Outward Processing Trade in European Agreements, and not fully exploited human capital are suggested as possible factors.
Network analysis of supply chain systems: A systematic review and future research

Supply chains are continuously evolving and adapting systems driven by complex sociotechnical interfirm interactions. Traditional engineering and operations management modeling approaches have primarily focused on technical issues and are not well suited to effectively capture the many complex structural and behavioral aspects of supply chain systems (SCSs). There is growing recognition by the supply chain community of the significant benefits a network analytic lens can provide to understand, design, and manage SCSs. We systematically review and analyze the relevant literature and, drawing on a multidisciplinary theoretical foundation, develop an integrative framework. Our framework identifies three distinct, but interdependent themes that characterize the study of SCSs: SCS network structure (i.e., system architecture), SCS network dynamics (i.e., system behavior), and SCS network strategy (i.e., system policy and control). We elaborate on these themes, review key findings, identify the current limitations and knowledge gaps, and discuss the fundamental benefits derived from adopting an integrated SCSs perspective. We conclude with future research directions for network analysis in SCS design and management, in particular, and complex enterprise systems, in general. ©2012 Wiley Periodicals, Inc. Syst Eng 16:
Vortex structure in superfluid color-flavor locked quark matter

The core region of a neutron star may feature quark matter in the color-flavor-locked (CFL) phase. The CFL condensate breaks the baryon number symmetry, such that the phenomenon of superfluidity arises. If the core of the star is rotating, vortices will form in the superfluid, carrying the quanta of angular momentum. In a previous study we have solved the question of stability of these vortices, where we found numerical proof of a conjectured instability, according to which superfluid vortices will decay into an arrangement of so-called semi-superfluid fluxtubes. Here we report first results of an extension of our framework that allows us to study multi-vortex dynamics. This will in turn enable us to investigate the structure of semi-superfluid string lattices, which could be relevant to study pinning phenomena at the boundary of the core.
Coaching Quality and Subordinate Work Attitudes in the Multiple Supervisor Audit Context

Audit subordinates typically work with multiple supervisors who are likely to vary in their level of coaching quality (CQ). While prior research suggests a low CQ supervisor could negatively affect a subordinate’s work attitudes, theory indicates that the presence of other positive coaching experiences may buffer against the negative influence of a low CQ supervisor. We investigate by asking participants to provide information on their coaching experiences with three supervisors. We then examine how perceptions of supervisors’ CQ interact to affect subordinates’ work attitudes. We find that the effect of a perceived low CQ supervisor on organizational commitment and turnover intention is mitigated when the CQ of another supervisor is high or when a relatively high CQ supervisor is also a mentor. Investigating factors that inhibit CQ, we find that supervisors’ lack of capability explains variation in lower CQ supervisors, whereas lack of presence explains variation for higher CQ supervisors.
From Immigrants to Americans: Race and Assimilation During the Great Migration

How does the appearance of a new immigrant group affect the integration of earlier generations of migrants? We study this question in the context of the first Great Migration (1915-1930), when 1.5 million African Americans moved from the US South to northern urban centers, where 30 million Europeans had arrived since 1850. We exploit plausibly exogenous variation induced by the interaction between 1900 settlements of southern-born blacks in northern cities and state-level outmigration from the US South after 1910. Black arrivals increased both the effort exerted by immigrants to assimilate and their eventual Americanization. These average effects mask substantial heterogeneity: while initially less integrated groups (i.e. Southern and Eastern Europeans) exerted more assimilation effort, assimilation success was larger for those culturally closer to native whites (i.e. Western and Northern Europeans). Labor market outcomes do not display similar heterogeneity, suggesting that these patterns cannot be entirely explained by economic forces. Our findings are instead more consistent with a framework in which changing perceptions of outgroup distance among native whites lowered the barriers to the assimilation of white immigrants.
Informed principal, moral hazard, and limited liability

I consider a moral hazard problem with risk neutral parties, limited liability, and an informed principal. The contractible outcome is correlated to both the principal’s private information and the agent’s hidden action. In contrast to a model without a privately informed principal or without limited liability, I show that the first-best payoff cannot be implemented by any equilibrium mechanism.
A Design Space Study for LISTA and Beyond

In recent years, great success has been witnessed in building problem-specific deep networks from unrolling iterative algorithms, for solving inverse problems and beyond. Unrolling is believed to incorporate the model-based prior with the learning capacity of deep learning. This paper revisits \textit{the role of unrolling as a design approach for deep networks}: to what extent its resulting special architecture is superior, and can we find better? Using LISTA for sparse recovery as a representative example, we conduct the first thorough \textit{design space study} for the unrolled models. Among all possible variations, we focus on extensively varying the connectivity patterns and neuron types, leading to a gigantic design space arising from LISTA. To efficiently explore this space and identify top performers, we leverage the emerging tool of neural architecture search (NAS). We carefully examine the searched top architectures in a number of settings, and are able to discover networks that consistently better than LISTA. We further present more visualization and analysis to ``open the black box", and find that the searched top architectures demonstrate highly consistent and potentially transferable patterns. We hope our study to spark more reflections and explorations on how to better mingle model-based optimization prior and data-driven learning.
Visualization of Intended Assistance for Acceptance of Shared Control

In shared control, advances in autonomous robotics are applied to help empower a human user in operating a robotic system. While these systems have been shown to improve efficiency and operation success, users are not always accepting of the new control paradigm produced by working with an assistive controller. This mismatch between performance and acceptance can prevent users from taking advantage of the benefits of shared control systems for robotic operation. To address this mismatch, we develop multiple types of visualizations for improving both the legibility and perceived predictability of assistive controllers, then conduct a user study to evaluate the impact that these visualizations have on user acceptance of shared control systems. Our results demonstrate that shared control visualizations must be designed carefully to be effective, with users requiring visualizations that improve both legibility and predictability of the assistive controller in order to voluntarily relinquish control.
Writhe-like invariants of alternating links

It is known that the writhe calculated from any reduced alternating link diagram of the same (alternating) link has the same value. That is, it is a link invariant if we restrict ourselves to reduc...
Guaranteed Phase & Topology Identification in Three Phase Distribution Grids

We present a method for joint phase identification and topology recovery in unbalanced three phase radial networks using only voltage measurements. By recovering phases and topology jointly, we utilize all three phase voltage measurements and can handle networks where some buses have a subset of three phases. Our method is theoretically justified by a novel linearized formulation of unbalanced three phase power flow and makes precisely defined and reasonable assumptions on line impedances and load statistics. We validate our method on three IEEE test networks simulated under realistic conditions in OpenDSS, comparing our performance to the state of the art. In addition to providing a new method for phase and topology recovery, our intuitively structured linearized model will provide a foundation for future work in this and other applications.
Optimal Sampling and Scheduling for Timely Status Updates in Multi-Source Networks

We consider a joint sampling and scheduling problem for optimizing data freshness in multi-source systems. Data freshness is measured by a non-decreasing penalty function of age of information , where all sources have the same age-penalty function. Sources take turns to generate update packets, and forward them to their destinations one-by-one through a shared channel with random delay. There is a scheduler, that chooses the update order of the sources, and a sampler, that determines when a source should generate a new packet in its turn. We aim to find the optimal scheduler-sampler pairs that minimize the total-average age-penalty at delivery times (Ta-APD) and the total-average age-penalty (Ta-AP). We prove that the Maximum Age First (MAF) scheduler and the zero-wait sampler are jointly optimal for minimizing the Ta-APD. Meanwhile, the MAF scheduler and a relative value iteration with reduced complexity (RVI-RC) sampler are jointly optimal for minimizing the Ta-AP. The RVI-RC sampler is based on a relative value iteration algorithm whose complexity is reduced by exploiting a threshold property in the optimal sampler. Finally, a low-complexity threshold-type sampler is devised via an approximate analysis of Bellman’s equation. This threshold-type sampler reduces to a simple water-filling sampler for a linear age-penalty function.
Eigenvectors from eigenvalues: A survey of a basic identity in linear algebra

If $A$ is an $n \times n$ Hermitian matrix with eigenvalues $\lambda_1(A),\dots,\lambda_n(A)$ and $i,j = 1,\dots,n$, then the $j^{\mathrm{th}}$ component $v_{i,j}$ of a unit eigenvector $v_i$ associated to the eigenvalue $\lambda_i(A)$ is related to the eigenvalues $\lambda_1(M_j),\dots,\lambda_{n-1}(M_j)$ of the minor $M_j$ of $A$ formed by removing the $j^{\mathrm{th}}$ row and column by the formula $$ |v_{i,j}|^2\prod_{k=1;k\neq i}^{n}\left(\lambda_i(A)-\lambda_k(A)\right)=\prod_{k=1}^{n-1}\left(\lambda_i(A)-\lambda_k(M_j)\right)\,.$$ We refer to this identity as the \emph{eigenvector-eigenvalue identity} and show how this identity can also be used to extract the relative phases between the components of any given eigenvector. Despite the simple nature of this identity and the extremely mature state of development of linear algebra, this identity was not widely known until very recently. In this survey we describe the many times that this identity, or variants thereof, have been discovered and rediscovered in the literature (with the earliest precursor we know of appearing in 1834). We also provide a number of proofs and generalizations of the identity.
Parts-Based Articulated Object Localization in Clutter Using Belief Propagation

Robots working in human environments must be able to perceive and act on challenging objects with articulations, such as a pile of tools. Articulated objects increase the dimensionality of the pose estimation problem, and partial observations under clutter create additional challenges. To address this problem, we present a generative-discriminative parts-based recognition and localization method for articulated objects in clutter. We formulate the problem of articulated object pose estimation as a Markov Random Field (MRF). Hidden nodes in this MRF express the pose of the object parts, and edges express the articulation constraints between parts. Localization is performed within the MRF using an efficient belief propagation method. The method is informed by both part segmentation heatmaps over the observation, generated by a neural network, and the articulation constraints between object parts. Our generative-discriminative approach allows the proposed method to function in cluttered environments by inferring the pose of occluded parts using hypotheses from the visible parts. We demonstrate the efficacy of our methods in a tabletop environment for recognizing and localizing hand tools in uncluttered and cluttered configurations.
Multi-Agent Safe Planning with Gaussian Processes

Multi-agent safe systems have become an increasingly important area of study as we can now easily have multiple AI-powered systems operating together. In such settings, we need to ensure the safety of not only each individual agent, but also the overall system. In this paper, we introduce a novel multi-agent safe learning algorithm that enables decentralized safe navigation when there are multiple different agents in the environment. This algorithm makes mild assumptions about other agents and is trained in a decentralized fashion, i.e. with very little prior knowledge about other agents’ policies. Experiments show our algorithm performs well with the robots running other algorithms when optimizing various objectives.
Cavity molecular dynamics simulations of vibrational polariton-enhanced molecular nonlinear absorption.

Recent experiments have observed that the chemical and photophysical properties of molecules can be modified inside an optical Fabry-Perot microcavity under collective vibrational strong coupling (VSC) conditions, and such modification is currently not well understood by theory. In an effort to understand the origin of such cavity-induced phenomena, some recent studies have focused on the effect of the cavity environment on the nonlinear optical response of the molecular subsystem. Here, we use a recently proposed protocol for classical cavity molecular dynamics simulations to numerically investigate the linear and the nonlinear response of liquid carbon dioxide under such VSC conditions following an optical pulse excitation. We find that applying a strong pulse of excitation to the lower hybrid light-matter state, i.e., the lower polariton (LP), can lead to an overall molecular nonlinear absorption that is enhanced by up to two orders of magnitude relative to the excitation outside the cavity. This polariton-enhanced multiphoton absorption also causes an ultrashort LP lifetime (0.2 ps) under strong illumination. Unlike usual polariton relaxation processes-whereby polaritonic energy transfers directly to the manifold of singly excited vibrational dark states-under the present mechanism, the LP transfers energy directly to the manifold of higher vibrationally excited dark states; these highly excited dark states subsequently relax to the manifold of singly excited states with a lifetime of tens of ps. Because the present mechanism is generic in nature, we expect these numerical predictions to be experimentally observed in different molecular systems and in cavities with different volumes.
Relaxing Common Belief for Social Networks.

We propose a relaxation of common belief called factional belief that is suitable for the analysis of strategic coordination on social networks. We show how this definition can be used to analyze revolt games on general graphs, including by giving an efficient algorithm that characterizes a structural result about the possible equilibria of such games. This extends prior work on common knowledge and common belief, which has been too restrictive for use in understanding strategic coordination and cooperation in social network settings.
Detection of dementia on raw voice recordings using deep learning: A Framingham Heart Study

Abstract Background Identification of reliable, affordable and easy-to-use strategies for detection of dementia are sorely needed. Digital technologies, such as individual voice recordings, offer an attractive modality to assess cognition but methods that could automatically analyze such data without any pre-processing are not readily available. Methods We used a subset of 1264 digital voice recordings of neuropsychological examinations administered to participants from the Framingham Heart Study (FHS), a community-based longitudinal observational study. The recordings were 73 minutes in duration, on average, and contained at least two speakers (participant and clinician). Of the total voice recordings, 483 were of participants with normal cognition (NC), 451 recordings were of participants with mild cognitive impairment (MCI), and 330 were of participants with dementia. We developed two deep learning models (a two-level long short-term memory (LSTM) network and a convolutional neural network (CNN)), which used the raw audio recordings to classify if the recording included a participant with only NC or only dementia, and also to differentiate between recordings corresponding to non-demented (NC+MCI) and demented participants. Findings Based on 5-fold cross-validation, the LSTM model achieved a mean (±std) area under the sensitivity-specificity curve (AUC) of 0.744±0.038, mean accuracy of 0.680±0.032, mean sensitivity of 0.719±0.112, and mean specificity of 0.652±0.089 in predicting cases with dementia from those with normal cognition. The CNN model achieved a mean AUC of 0.805±0.027, mean accuracy of 0.740±0.033, mean sensitivity of 0.735±0.094, and mean specificity of 0.750±0.083 in predicting cases with only dementia from those with only NC. For the task related to classification of demented participants from non-demented ones, the LSTM model achieved a mean AUC of 0.659±0.043, mean accuracy of 0.701±0.057, mean sensitivity of 0.245±0.161 and mean specificity of 0.856±0.105. The CNN model achieved a mean AUC of 0.730±0.039, mean accuracy of 0.735±0.046, mean sensitivity of 0.443±0.113, and mean specificity of 0.840±0.076 in predicting cases with dementia from those who were not demented. Interpretation This proof-of-concept study demonstrates the potential that raw audio recordings of neuropsychological testing performed on individuals recruited within a community cohort setting can provide a level of screening for dementia.
Fine-grained Vibration Based Sensing Using a Smartphone

Recognizing surfaces based on their vibration signatures is useful as it can enable tagging of different locations without requiring any additional hardware such as Near Field Communication (NFC) tags. However, previous vibration based surface recognition schemes either use custom hardware for creating and sensing vibration, which makes them difficult to adopt, or use intertial (IMU) sensors in commercial off-the-shelf (COTS) smartphones to sense movements produced due to vibrations, which makes them coarse-grained because of the low sampling rates of IMU sensors. The mainstream COTS smartphones based schemes are also susceptible to inherent hardware based irregularities in vibration mechanism of the smartphones. Moreover, the existing schemes that use microphones to sense vibration are prone to short-term and constant background noises (e.g. intermittent talking, exhaust fan, etc.) because microphones not only capture the sounds created by vibration but also other interfering sounds present in the environment. In this paper, we propose VibroTag, a practical vibration based sensing scheme that works with smartphones with different hardware, can extract fine-grained vibration signatures of different surfaces, and is robust to environmental noise and hardware based irregularities. We evaluated in multiple different environments where we collected data from 4 individuals for 5 to 20 consecutive days.
Do We Expect More from Radiology AI than from Radiologists

The expectations of radiology artificial intelligence do not match expectations of radiologists in terms of performance and explainability.
Heteroscedastic Exponomial Choice

We investigate analytical and empirical properties of the Heteroscedastic Exponomial Choice (HEC) model to lay the groundwork for its use in theoretical and empirical research that build demand models on a discrete choice foundation. The HEC model generalizes the Exponomial Choice (EC) model by including choice-specific variances for the random components of utility (the error terms). We show that the HEC model inherits some of the properties found in the EC model: closed-form choice probabilities, demand elasticities and consumer surplus; optimal monopoly prices that are increasing with ideal utilities in a hockey-stick pattern; and unique equilibrium oligopoly prices that are easily computed using a series of single-variable equations. However, the HEC model has several key differences with the EC model that show variances matter: the choice probabilities (market shares) as well as equilibrium oligopoly prices are not necessarily increasing with ideal utilities; and the new model can include choices with deterministic utility or choices with zero probability. However, because the HEC model uses more parameters, it is harder to estimate. To justify its use, we apply HEC to grocery purchase data for thirty product categories and find that it significantly improves model fit and generally improves out-of-sample prediction compared to EC. We go on to investigate the more nuanced impact of the variance parameters on oligopoly pricing. We find that the individual and collective incentives differ in equilibrium: Firms individually want lower error variability for their own product, but collectively prefer higher error variability for all products – including their own – because higher error variability softens the price competition.
Fine-grained lineage for safer notebook interactions

Computational notebooks have emerged as the platform of choice for data science and analytical workflows, enabling rapid iteration and exploration. By keeping intermediate program state in memory a...
Minority Investor Protections as Default Norms: Using Price to Illuminate the Deal in Close Corporations

This Article argues that legal protections for minority investors in close corporations should be interpreted as default rules. Currently, such protections are mandatory and thus impose on investors a uniform norm of conduct that restricts their freedom to bargain. Courts and scholars advocating such protections have so far been unwilling to permit their waiver primarily because of the difficulty of distinguishing between a knowledgeable waiver and an ignorant omission. A price-based approach solves that puzzle, however, by using the consideration paid by investors to illuminate their intentions. By permitting waiver only where there is clear evidence that the minority received a discounted purchase price, such an approach avoids the risk inherent in a system that treats legal protections as mere default rules: that an ignorant minority will be taken advantage of as a result of accidental waiver. Whether or not the investor was truly ignorant, there can be no injustice if she was adequately compensated for the risk of opportunistic behavior by her fellow shareholders. A price-based approach therefore provides knowledgeable parties with the ability to bargain over their preferred allocation of control while retaining legal protections for unsophisticated investors who fail to do so.
On the Analysis and Design of High-Frequency Transformers for Dual and Triple Active Bridge Converters in More Electric Aircraft

DC-DC converters with galvanic isolation are a key element in the aircraft DC distribution system and Dual and Triple Active Bridge converters are one of the most interesting candidates in this application. The high-frequency transformer leakage inductances play a key role in the AC Link of these converters. This leakage inductances determine the power transfer capability of the converter and shape the AC link currents. The leakage inductance value is related to the distribution of the transformer windings and it changes with the RMS value of the AC link current. In this paper, first a high frequency transformer is designed for a Dual and Triple Active Bridge converter for the More Electric Aircraft DC power system. Then, an Ansys/Maxwell Finite Element analysis is performed on the leakage inductances of the transformer in three different winding configurations and in different AC link RMS current values. Finally, the transient performance of the design is validated by the Ansys/Maxwell Transient.
Transverse Single Spin Asymmetries in Hadronic Interactions: An Experimental Overview and Outlook

Transverse single-spin asymmetries (SSA) are expected to be small in perturbative QCD because of the chiral nature of the theory. Experiment shows there are large transverse SSA for particles produced in special kinematics. This contribution reviews the experimental situation and provides an outlook for future measurements.
A Probabilistic Mechanism for Quark Confinement

The confinement of quarks is one of the enduring mysteries of modern physics. There is a longstanding physics heuristic that confinement is a consequence of ‘unbroken center symmetry’. This article gives mathematical confirmation of this heuristic, by rigorously defining of center symmetry in lattice gauge theories and proving that a theory is confining when center symmetry is unbroken. Furthermore, a sufficient condition for unbroken center symmetry is given: It is shown that if the center of the gauge group is nontrivial, and correlations decay exponentially under arbitrary boundary conditions, then center symmetry does not break.
Frequency Regulation With Heterogeneous Energy Resources: A Realization Using Distributed Control

This paper presents one of the first real-life demonstrations of coordinated and distributed resource control for secondary frequency response in a power distribution grid. A series of tests involved up to 69 heterogeneous active distributed energy resources consisting of air handling units, unidirectional and bidirectional electric vehicle charging stations, a battery energy storage system, and 107 passive distributed energy resources consisting of building loads and solar photovoltaic systems. The distributed control setup consists of a set of Raspberry Pi end-points exchanging messages via an Ethernet switch. Actuation commands for the distributed energy resources are obtained by solving a power allocation problem at every regulation instant using distributed ratio-consensus, primal-dual, and Newton-like algorithms. The problem formulation minimizes the sum of distributed energy resource costs while tracking the aggregate setpoint provided by the system operator. We demonstrate accurate and fast real-time distributed computation of the optimization solution and effective tracking of the regulation signal over 40 min time horizons. An economic benefit analysis confirms eligibility to participate in an ancillary services market and demonstrates up to $\$ $ 53k of potential annual revenue for the selected population of distributed energy resources.
Maximizing Marginal Fairness for Dynamic Learning to Rank

Rankings, especially those in search and recommendation systems, often determine how people access information and how information is exposed to people. Therefore, how to balance the relevance and fairness of information exposure is considered as one of the key problems for modern IR systems. As conventional ranking frameworks that myopically sorts documents with their relevance will inevitably introduce unfair result exposure, recent studies on ranking fairness mostly focus on dynamic ranking paradigms where result rankings can be adapted in real-time to support fairness in groups (i.e., races, genders, etc.). Existing studies on fairness in dynamic learning to rank, however, often achieve the overall fairness of document exposure in ranked lists by significantly sacrificing the performance of result relevance and fairness on the top results. To address this problem, we propose a fair and unbiased ranking method named Maximal Marginal Fairness (MMF). The algorithm integrates unbiased estimators for both relevance and merit-based fairness while providing an explicit controller that balances the selection of documents to maximize the marginal relevance and fairness in top-k results. Theoretical and empirical analysis shows that, with small compromises on long list fairness, our method achieves superior efficiency and effectiveness comparing to the state-of-the-art algorithms in both relevance and fairness for top-k rankings.
Generating explanations for answer set programming applications

We present an explanation system for applications that leverage Answer Set Programming (ASP). Given a program P, an answer set A of P, and an atom a in the program P, our system generates all explanation graphs of a which help explain why a is true (or false) given the program P and the answer set A. We illustrate the functionality of the system using some examples from the literature.
Inter-Cluster Transmission Control Using Graph Modal Barriers

In this paper we consider the problem of transmission across a graph and develop a method for effectively controlling/restricting it with limited resources. Transmission can represent information transfer across a social network, spread of a malicious virus across a computer network, or spread of an infectious disease across communities. The key insight is to appropriately reduce the capacity for transmission on inter-cluster edges of the graph. To that end, the key contribution of the paper is to develop algorithms for computing a real, positive-valued distribution over the edges that gives a measure of the role each edge plays in being an inter-cluster edge. We refer to this distribution as the “ resistance ” and its computation is based on the eigenvectors of the graph Laplacian. Selectively reducing the weights (implying reduced transmission rate) on the critical edges based on this computed distribution helps establish barriers between the clusters ( modal barriers ) and thus limit the transmission from one cluster to another. Our proposed method for edge weight reduction for inter-cluster transmission control depends only on the graph topology and not on the exact model of the transmission dynamics. Unlike other work on graph partitioning and clustering, we completely circumvent the associated computational complexities by assigning real values to edges instead of performing discrete graph cuts. This allows us to provide strong theoretical results on our proposed methods. We also develop approximations that allow low complexity distributed computation of the barrier weights using only neighborhood communication.
Zero-shot Neural Passage Retrieval via Domain-targeted Synthetic Question Generation

A major obstacle to the wide-spread adoption of neural retrieval models is that they require large supervised training sets to surpass traditional term-based techniques, which are constructed from raw corpora. In this paper, we propose an approach to zero-shot learning for passage retrieval that uses synthetic question generation to close this gap. The question generation system is trained on general domain data, but is applied to documents in the targeted domain. This allows us to create arbitrarily large, yet noisy, question-passage relevance pairs that are domain specific. Furthermore, when this is coupled with a simple hybrid term-neural model, first-stage retrieval performance can be improved further. Empirically, we show that this is an effective strategy for building neural passage retrieval models in the absence of large training corpora. Depending on the domain, this technique can even approach the accuracy of supervised models.
Uncertainty Quantification of Locally Nonlinear Dynamical Systems Using Neural Networks

AbstractModels are often given in terms of differential equations to represent physical systems. In the presence of uncertainty, accurate prediction of the behavior of these systems using the model...
A general theory of coexistence and extinction for stochastic ecological communities

We analyze a general theory for coexistence and extinction of ecological communities that are influenced by stochastic temporal environmental fluctuations. The results apply to discrete time (stochastic difference equations), continuous time (stochastic differential equations), compact and non-compact state spaces and degenerate or non-degenerate noise. In addition, we can also include in the dynamics auxiliary variables that model environmental fluctuations, population structure, eco-environmental feedbacks or other internal or external factors. We are able to significantly generalize the recent discrete time results by Benaim and Schreiber (J Math Biol 79:393-431, 2019) to non-compact state spaces, and we provide stronger persistence and extinction results. The continuous time results by Hening and Nguyen (Ann Appl Probab 28(3):1893-1942, 2018a) are strengthened to include degenerate noise and auxiliary variables. Using the general theory, we work out several examples. In discrete time, we classify the dynamics when there are one or two species, and look at the Ricker model, Log-normally distributed offspring models, lottery models, discrete Lotka-Volterra models as well as models of perennial and annual organisms. For the continuous time setting we explore models with a resource variable, stochastic replicator models, and three dimensional Lotka-Volterra models.
Voice of the Customers: Local Trust Culture and Consumer Complaints to the CFPB

We use complaints filed with the Consumer Financial Protection Bureau (CFPB) to study the interplay between social norms and the effectiveness of consumer protection laws. We find that a higher level of trust in a given location is associated with a lower number of complaints filed against financial institutions in that location. Employing a difference‐in‐differences approach, we further find that, after the establishment of the CFPB, banks in low‐trust areas reduce fees charged to consumers more compared to banks in high‐trust areas. Our results suggest that the threat of consumer complaints to a government agency affects how banks treat their customers, and they shed light on the interaction between informal culture and formal institutions, as well as on stakeholders’ influence in corporate policies.