cssi/SciSciGPT-SciSciNet huggingface dataset:
Intellectual Property and the Law of Contract: The Case Against ‘Efficient Breach’

In general sound general principles of contract law should carry over more or less seamlessly to the licensing of intellectual property. Those basic principles caution against attacks on standard form contracts that often contain liquidated damage provisions. This paper shows why over a broad range of cases involving two or more parties these liquidated provisions, properly calibrated, avoid the many disadvantages of using the theory of “efficient breach” to set damages. Chief among these difficulties are the high administrative costs of finding these damages, and the perverse incentives that they place on the defendant before breach and the plaintiff afterwards. Intellectual property licenses are not exempt from these many principles, which implies that these clear damage rules are typically optimal in first avoiding breach and then in resolve post‐breach disputes.
The First Amendment, Homosexual Unions, and "Newspeak": Has the Language Surrounding the Marriage Debate Altered the Nature of Marriage Itself, or Affected the Truth of the Issues Inherent in Alternative Marriage Demands?

This Article asserts that a special type of Family Law “Newspeak” has been a highly effective tactic used to destroy distinctions between dual-gendered marital families and same sex partnerships. Although marriage is a central target of rights litigation, there are other related areas where Family Newspeak is appearing. This Article reveals that traditionally protected political and religious speech is being undermined through a new expanded Family Newspeak. Newspeak is used to advance an expansive sexual agenda that not only will correspondingly infringe upon the First Amendment rights of others, but also is changing the lexicon of the English language.
Promises and Expectations

Abstract We investigate why people keep their promises in the absence of external enforcement mechanisms and reputational effects. In a controlled laboratory experiment we show that exogenous variation of second-order expectations (promisors' expectations about promisees' expectations) leads to a significant change in promisor behavior. We provide evidence that a promisor's aversion to disappointing a promisee's expectation leads her to behave more generously. We propose and estimate a simple model of conditional guilt aversion that is supported by our results and nests the findings of previous contributions as special cases.
The American Accounting Association: issues in governance and internal control

This article first presents a review and assessment of the American Accounting Association’s (AAA) governance and internal control. Second, it reports the results of a survey of members of the AAA that was conducted just prior to its annual meeting in August 2011 to solicit their opinions concerning the governance and internal control of the AAA. Thirty-seven and one-half percent of the respondents remained dissatisfied with the nomination, election and voting processes even after amendments to the bylaws were passed in spring 2011. Furthermore, although the AAA is a co-sponsor of the COSO framework of internal control, there is no indication that the AAA has adopted the COSO framework. Over 56% of the respondents believe the AAA should adopt COSO recommendations. The paper concludes that substantial reforms must be undertaken to improve the corporate governance and internal control functions of the AAA.
Who Gains from Buying Bad Bidders

We study the value gains from takeovers of firms with poor acquisition histories. We document that the premium received by target shareholders is higher when the value loss from the targets’ prior acquisitions is larger. However, the gains to target shareholders seem to be offset by losses to acquiring shareholders. The average announcement return to acquiring shareholders is negative and decreasing in the value loss from the targets’ prior acquisitions. Additionally, the combined acquirer-target value created in these takeovers is insignificant. These results suggest that the value lost from targets’ prior acquisitions is not recovered through changes in corporate control.
College Athletes as Employees: An Overflowing Quiver

This article discusses whether college athletes should be considered employees under a broad range of employment statutes. The central thesis is that, if college athletes are persistent, it is inevitable that some of them, somewhere, sometime, will be found to be employees. A major reason for this is that the basic rules for determining who is an employee lean in college athletes’ favor across a broad range of employment statutes, including privateand public-sector collective bargaining laws and laws protecting individual employment rights. College athletes are also likely to be classified as employees at some point because there are literally hundreds of different employment statutes. College athletes will have many independent opportunities to present their claims. Finally, claims by the NCAA and its member institutions to a special exemption for coverage under all these statutes are weak. The analogy to antitrust law, where the NCAA has been treated favorably, is inapt. Moreover, the courts will be reluctant to create non-statutory exceptions to important state and federal labor protections where the legislature has failed to do so.
The role of international design orientation and market intelligence in the export performance of US machine tool companies

This paper looks at the role of product design in the export performance of US manufacturing firms in the machine tool (MT) industry. Evidence from a survey of 173 MT companies points to stronger export results among firms that initiate the design process with respect to the needs of foreign buyers. In contrast, firms that enter foreign markets with products that were originally designed for domestic clients typically exhibit weaker export sales. Firms in the latter category spend less on market intelligence than their more internationally-oriented counterparts. For both groups of firms, however, a common finding is that recent interest in export expansion has been driven by rising import penetration (loss of domestic market share). The paper concludes with a brief discussion of the implications of the empirical results for future research on export marketing.
Securities law expertise and corporate disclosure

ABSTRACT We examine whether securities lawyers involved in SEC comment letter inquiries act as client advocates by resisting disclosure changes or as gatekeepers by encouraging disclosure transpare...
Global supply network health: Analysis and visualization

The health of a global supply network GSN plays an important role in the effective and timely delivery of complex products and services. Decision makers are in need of a holistic approach inclusive of several aspects affecting GSN performance, moving beyond traditional approaches that limit GSN analysis to a singular aspect, such as operational or financial performance. We argue that GSN health consists of four key aspects: operational, financial, collaboration, and strategic health. Using a network analytic approach, we propose a methodology to assess and visualize GSN health. We illustrate our methodology using a field study in the aerospace industry. Our contributions are multifold. Theoretically, we contribute to our understanding of GSNs as multimodal networks consisting of both technical and social relationships. From a managerial perspective, we provide a GSN health assessment and visualization framework that enables decision makers to identify risks, determine potential paths of their diffusion, and devise appropriate mitigation strategies.
Workplace Peers and Entrepreneurship

We examine whether the likelihood of entrepreneurial activity is related to the prior career experiences of an individual's coworkers, using a unique matched employer--employee panel data set. We argue that coworkers can increase the likelihood that an individual will perceive entrepreneurial opportunities as well as increase his or her motivation to pursue those opportunities. We find that an individual is more likely to become an entrepreneur if his or her coworkers have been entrepreneurs before. Peer influences also appear to be substitutes for other sources of entrepreneurial influence: we find that peer influences are strongest for those who have less exposure to entrepreneurship in other aspects of their lives.
Are company founders underpaid

This article examines the relation between founder status and CEO compensation in publicly listed US firms. The results suggest that CEO/founders receive lower cash pay and total compensation compared to professional managers. In contrast, CEOs who are relatives of the founders receive similar cash pay and total compensation to that of professional managers. Different compensation levels also emerge in terms of stock option awards. The findings underline the importance of distinguishing among these three CEO types when examining the determinants of executive compensation.
Three Concepts of Workplace Freedom of Association

This Article identifies three distinct concepts of workplace freedom of association (“FOA”) and traces their influence on labor law doctrine, focusing on the law of union security devices — contractual clauses that require workers, on pain of termination, to remit fees to unions. The “social democratic” concept informed the passage of the National Labor Relations Act (“NLRA” or “the Act”) and continues to inform social movement practice and some other countries’ jurisprudence. It views workplace freedom of association as a means to the end of ensuring economic equality and economic democracy, and generally endorses the so-called “union shop,” under which workers must contribute both to unions’ representational activities and to their legislative and organizing efforts. The “civil libertarian” concept was predominant in Supreme Court doctrine from the Warren Court era until recently. It emphasized individual rights of expression and political participation, and backstopped the line of cases declaring the union shop unlawful but requiring workers to help defray representational expenses. The “neoliberal” concept now appears ascendant. It views market behavior as a form of expressive behavior, and views compulsory payment of any union fees as unconstitutional. Disaggregating these concepts can enrich debates around workplace freedom of association in three ways. First, doing so illustrates that determining the scope of workplace freedom of association involves contestable value judgments about the goods and ends of unionization and association. Second, doing so illustrates that the Supreme Court’s recent union security cases reflect broader trends in the Court’s recent case law that constitutionalize a neoliberal political economy. Third, doing so suggests that the social democratic concept is both more coherent and more morally compelling than the civil libertarian concept, and may help it regain a foothold in debates around workplace freedom of association.
Biomembranes undergo complex, non-axisymmetric deformations governed by Kirchhoff-Love kinematics and revealed by a three dimensional computational framework

Biomembranes play a central role in various phenomena like locomotion of cells, cell-cell interactions, packaging and transport of nutrients, transmission of nerve impulses, and in maintaining organelle morphology and functionality. During these processes, the membranes undergo significant morphological changes through deformation, scission, and fusion. Modeling the underlying mechanics of such morphological changes has traditionally relied on reduced order axisymmetric representations of membrane geometry and deformation. Axisymmetric representations, while robust and extensively deployed, suffer from their inability to model symmetry breaking deformations and structural bifurcations. To address this limitation, a three-dimensional computational mechanics framework for high fidelity modeling of biomembrane deformation is presented. The proposed framework brings together Kirchhoff-Love thin-shell kinematics, Helfrich-energy based mechanics, and state-of-the-art numerical techniques for modeling deformation of surface geometries. Lipid bilayers are represented as spline-based surface discretizations immersed in a three-dimensional space; this enables modeling of a wide spectrum of membrane geometries, boundary conditions, and deformations that are physically admissible in a 3D space. The mathematical basis of the framework and its numerical machinery are presented, and their utility is demonstrated by modeling three classical, yet non-trivial, membrane deformation problems: formation of tubular shapes and their lateral constriction, Piezo1-induced membrane footprint generation and gating response, and the budding of membranes by protein coats during endocytosis. For each problem, the full three dimensional membrane deformation is captured, potential symmetry-breaking deformation paths identified, and various case studies of boundary and load conditions are presented. Using the endocytic vesicle budding as a case study, we also present a "phase diagram" for its symmetric and broken-symmetry states.
On the Characterization of Saddle Point Equilibrium for Security Games with Additive Utility

In this work, we investigate a security game between an attacker and a defender, originally proposed in [6]. As is well known, the combinatorial nature of security games leads to a large cost matrix. Therefore, computing the value and optimal strategy for the players becomes computationally expensive. In this work, we analyze a special class of zero-sum games in which the payoff matrix has a special structure which results from the additive property of the utility function. Based on variational principles, we present structural properties of optimal attacker as well as defender’s strategy. We propose a linear-time algorithm to compute the value based on the structural properties, which is an improvement from our previous result in [6], especially in the context of large-scale zero-sum games.
A practical two-sample test for weighted random graphs

Network (graph) data analysis is a popular research topic in statistics and machine learning. In application, one is frequently confronted with graph two-sample hypothesis testing where the goal is...
Reinforcement co-Learning of Deep and Spiking Neural Networks for Energy-Efficient Mapless Navigation with Neuromorphic Hardware

Energy-efficient mapless navigation is crucial for mobile robots as they explore unknown environments with limited on-board resources. Although the recent deep rein-forcement learning (DRL) approaches have been successfully applied to navigation, their high energy consumption limits their use in several robotic applications. Here, we propose a neuromorphic approach that combines the energy-efficiency of spiking neural networks with the optimality of DRL and benchmark it in learning control policies for mapless navigation. Our hybrid framework, spiking deep deterministic policy gradient (SDDPG), consists of a spiking actor network (SAN) and a deep critic network, where the two networks were trained jointly using gradient descent. The co-learning enabled synergistic information exchange between the two networks, allowing them to overcome each other’s limitations through a shared representation learning. To evaluate our approach, we deployed the trained SAN on Intel’s Loihi neuromorphic processor. When validated on simulated and real-world complex environments, our method on Loihi consumed 75 times less energy per inference as compared to DDPG on Jetson TX2, and also exhibited a higher rate of successful navigation to the goal, which ranged from 1% to 4.2% and depended on the forward-propagation timestep size. These results reinforce our ongoing efforts to design brain-inspired algorithms for controlling autonomous robots with neuromorphic hardware.
Dynamical uniform boundedness and the abc-conjecture

We address the Uniform Boundedness Conjecture of Morton and Silverman in the case of unicritical polynomials, assuming a generalization of the abc-conjecture. For unicritical polynomials of degree at least five, we require only the standard abc-conjecture.
Scalable Load Balancing in the Presence of Heterogeneous Servers

In large-scale computer systems, deciding how to dispatch arriving jobs to servers is a primary factor affecting system performance. Consequently, there is a wealth of literature on designing, analyzing, and evaluating the performance of load balancing policies. For analytical tractability, most existing work on dispatching in large-scale systems makes a key assumption: that the servers are homogeneous, meaning that they all have the same speeds, capabilities, and available resources. But this assumption is not accurate in practice. Modern computer systems are instead heterogeneous: server farms may consist of multiple generations of hardware, servers with varied resources, or even virtual machines running in a cloud environment. Given the ubiquity of heterogeneity in today's systems, it is critically important to develop load balancing policies that perform well in heterogeneous environments. In this paper, we focus on systems in which server speeds are heterogeneous.
Epistemic values in feature importance methods: Lessons from feminist epistemology

As the public seeks greater accountability and transparency from machine learning algorithms, the research literature on methods to explain algorithms and their outputs has rapidly expanded. Feature importance methods form a popular class of explanation methods. In this paper, we apply the lens of feminist epistemology to recent feature importance research. We investigate what epistemic values are implicitly embedded in feature importance methods and how or whether they are in conflict with feminist epistemology. We offer some suggestions on how to conduct research on explanations that respects feminist epistemic values, taking into account the importance of social context, the epistemic privileges of subjugated knowers, and adopting more interactional ways of knowing
The Threshold Effects on Consumer Choice and Pricing Decisions

Problem definition: This paper investigates the threshold effects on the consideration set formation for consumers and the associated pricing decisions for firms. Academic/practical relevance: In t...
Comparison between slow anisotropic LE4PD fluctuations and the principal component analysis modes of ubiquitin.

The biological function and folding mechanisms of proteins are often guided by large-scale slow motions, which involve crossing high energy barriers. In a simulation trajectory, these slow fluctuations are commonly identified using a principal component analysis (PCA). Despite the popularity of this method, a complete analysis of its predictions based on the physics of protein motion has been so far limited. This study formally connects the PCA to a Langevin model of protein dynamics and analyzes the contributions of energy barriers and hydrodynamic interactions to the slow PCA modes of motion. To do so, we introduce an anisotropic extension of the Langevin equation for protein dynamics, called the LE4PD-XYZ, which formally connects to the PCA “essential dynamics.” The LE4PD-XYZ is an accurate coarse-grained diffusive method to model protein motion, which describes anisotropic fluctuations in the alpha carbons of the protein. The LE4PD accounts for hydrodynamic effects and mode-dependent free-energy barriers. This study compares large-scale anisotropic fluctuations identified by the LE4PD-XYZ to the mode-dependent PCA predictions, starting from a microsecond-long alpha carbon molecular dynamics atomistic trajectory of the protein ubiquitin. We observe that the inclusion of free-energy barriers and hydrodynamic interactions has important effects on the identification and timescales of ubiquitin’s slow modes.
Surrounding the solution of a linear system of equations from all sides

Suppose $A \in \mathbb{R}^{n \times n}$ is invertible and we are looking for the solution of $Ax = b$. Given an initial guess $x_1 \in \mathbb{R}$, we show that by reflecting through hyperplanes generated by the rows of $A$, we can generate an infinite sequence $(x_k)_{k=1}^{\infty}$ such that all elements have the same distance to the solution, i.e. $\|x_k - x\| = \|x_1 - x\|$. If the hyperplanes are chosen at random, averages over the sequence converge and $$ \mathbb{E} \left\| x - \frac{1}{m} \sum_{k=1}^{m}{ x_k} \right\| \leq \frac{1 + \|A\|_F \|A^{-1}\|}{\sqrt{m}} \cdot\|x-x_1\|.$$ The bound does not depend on the dimension of the matrix. This introduces a purely geometric way of attacking the problem: are there fast ways of estimating the location of the center of a sphere from knowing many points on the sphere? Our convergence rate (coinciding with that of the Random Kaczmarz method) comes from averaging, can one do better?
The remarkable growth in financial economics, 1974–2020

Abstract The field of academic finance has grown and evolved in the 47 years since the Journal of Financial Economics (JFE) began publishing papers. This paper examines detailed data on the 3,003 papers written by 3,358 different authors published in the JFE over the period 1974–2020. Advances in computing power and electronic communication have driven trends toward more empirical work, more coauthorship, and more complex papers. The set of authors, referees, and editors has also evolved as the field spans a much larger geographic footprint and as women have come to play a larger role in all aspects of academic finance. Growth in the demand for finance faculty has driven up faculty compensation and the demand for scarce journal space.
Identifying risk of opioid use disorder for patients taking opioid medications with deep learning.

OBJECTIVE The United States is experiencing an opioid epidemic. In recent years, there were more than 10 million opioid misusers aged 12 years or older annually. Identifying patients at high risk of opioid use disorder (OUD) can help to make early clinical interventions to reduce the risk of OUD. Our goal is to develop and evaluate models to predict OUD for patients on opioid medications using electronic health records and deep learning methods. The resulting models help us to better understand OUD, providing new insights on the opioid epidemic. Further, these models provide a foundation for clinical tools to predict OUD before it occurs, permitting early interventions. METHODS Electronic health records of patients who have been prescribed with medications containing active opioid ingredients were extracted from Cerner's Health Facts database for encounters between January 1, 2008, and December 31, 2017. Long short-term memory models were applied to predict OUD risk based on five recent prior encounters before the target encounter and compared with logistic regression, random forest, decision tree, and dense neural network. Prediction performance was assessed using F1 score, precision, recall, and area under the receiver-operating characteristic curve. RESULTS The long short-term memory (LSTM) model provided promising prediction results which outperformed other methods, with an F1 score of 0.8023 (about 0.016 higher than dense neural network (DNN)) and an area under the receiver-operating characteristic curve (AUROC) of 0.9369 (about 0.145 higher than DNN). CONCLUSIONS LSTM-based sequential deep learning models can accurately predict OUD using a patient's history of electronic health records, with minimal prior domain knowledge. This tool has the potential to improve clinical decision support for early intervention and prevention to combat the opioid epidemic.
Clearance of Clostridioides difficile Colonization Is Associated with Antibiotic-Specific Bacterial Changes

The gut bacterial community prevents many pathogens from colonizing the intestine. Previous studies have associated specific bacteria with clearing Clostridioides difficile colonization across different community perturbations. However, those bacteria alone have been unable to clear C. difficile colonization. To elucidate the changes necessary to clear colonization, we compared differences in bacterial abundance between communities able and unable to clear C. difficile colonization. We treated mice with titrated doses of antibiotics prior to C. difficile challenge, resulting in no colonization, colonization and clearance, or persistent colonization. Previously, we observed that clindamycin-treated mice were susceptible to colonization but spontaneously cleared C. difficile Therefore, we investigated whether other antibiotics would show the same result. We found that reduced doses of cefoperazone and streptomycin permitted colonization and clearance of C. difficile Mice that cleared colonization had antibiotic-specific community changes and predicted interactions with C. difficile Clindamycin treatment led to a bloom in populations related to Enterobacteriaceae Clearance of C. difficile was concurrent with the reduction of those blooming populations and the restoration of community members related to the Porphyromonadaceae and Bacteroides Cefoperazone created a susceptible community characterized by drastic reductions in the community diversity and interactions and a sustained increase in the abundance of many facultative anaerobes. Lastly, clearance in streptomycin-treated mice was associated with the recovery of multiple members of the Porphyromonadaceae, with little overlap in the specific Porphyromonadaceae observed in the clindamycin treatment. Further elucidation of how C. difficile colonization is cleared from different gut bacterial communities will improve C. difficile infection treatments.IMPORTANCE The community of microorganisms, or microbiota, in our intestines prevents pathogens like C. difficile from colonizing and causing infection. However, antibiotics can disturb the gut microbiota, which allows C. difficile to colonize. C. difficile infections (CDI) are primarily treated with antibiotics, which frequently leads to recurrent infections because the microbiota has not yet returned to a resistant state. The recurrent infection cycle often ends when the fecal microbiota from a presumed resistant person is transplanted into the susceptible person. Although this treatment is highly effective, we do not understand the mechanism. We hope to improve the treatment of CDI through elucidating how the bacterial community eliminates CDI. We found that C. difficile colonized susceptible mice but was spontaneously eliminated in an antibiotic treatment-specific manner. These data indicate that each community had different requirements for clearing colonization. Understanding how different communities clear colonization will reveal targets to improve CDI treatments.
mfEGRA: Multifidelity efficient global reliability analysis through active learning for failure boundary location

This paper develops mfEGRA, a multifidelity active learning method using data-driven adaptively refined surrogates for failure boundary location in reliability analysis. This work addresses the issue of prohibitive cost of reliability analysis using Monte Carlo sampling for expensive-to-evaluate high-fidelity models by using cheaper-to-evaluate approximations of the high-fidelity model. The method builds on the efficient global reliability analysis (EGRA) method, which is a surrogate-based method that uses adaptive sampling for refining Gaussian process surrogates for failure boundary location using a single- fidelity model. Our method introduces a two-stage adaptive sampling criterion that uses a multifidelity Gaussian process surrogate to leverage multiple information sources with different fidelities. The method combines expected feasibility criterion from EGRA with one-step lookahead information gain to refine the surrogate around the failure boundary. The computational savings from mfEGRA depends on the discrepancy between the different models, and the relative cost of evaluating the different models as compared to the high-fidelity model. We show that accurate estimation of reliability using mfEGRA leads to computational savings of $\sim $ 46% for an analytic multimodal test problem and 24% for a three-dimensional acoustic horn problem, when compared to single-fidelity EGRA. We also show the effect of using a priori drawn Monte Carlo samples in the implementation for the acoustic horn problem, where mfEGRA leads to computational savings of 45% for the three-dimensional case and 48% for a rarer event four-dimensional case as compared to single-fidelity EGRA.
A Distributed Active Perception Strategy for Source Seeking and Level Curve Tracking

Algorithms for multi-agent systems to locate a source or to follow a desired level curve of spatially distributed scalar fields generally require sharing field measurements among the agents for gradient estimation. Yet, in this paper, we propose a distributed active perception strategy that enables swarms of various sizes and graph structures to perform source seeking and level curve tracking without the need to explicitly estimate the field gradient or explicitly share measurements. The proposed method utilizes a consensus-like Principal Component Analysis perception algorithm that does not require explicit communication in order to compute a local body frame. This body frame is used to design a distributed control law where each agent modulates its motion based only on its instantaneous field measurement. Several stability results are obtained within a singular perturbation framework which justifies the convergence and robustness of the strategy. Additionally, efficiency is validated through robots experiments.
It’s not so bad: Director bankruptcy experience and corporate risk-taking

Abstract We show that firms take more (but not necessarily excessive) risks when one of their directors experiences a corporate bankruptcy at another firm where they concurrently serve as a director. This increase in risk-taking is concentrated among firms where the director experiences a shorter, less-costly bankruptcy and where the affected director likely exerts greater influence and serves in an advisory role. The findings show that individual directors, not just CEOs, can influence a wide range of corporate outcomes. The findings also suggest that individuals actively learn from their experiences and that directors tend to lower their estimate of distress costs after participating in a bankruptcy firsthand.
Techniques for determining equality of the maximum nullity and the zero forcing number of a graph

It is known that the zero forcing number of a graph is an upper bound for the maximum nullity of the graph (see [AIM Minimum Rank - Special Graphs Work Group (F. Barioli, W. Barrett, S. Butler, S. Cioab$\breve{\text{a}}$, D. Cvetkovi$\acute{\text{c}}$, S. Fallat, C. Godsil, W. Haemers, L. Hogben, R. Mikkelson, S. Narayan, O. Pryporova, I. Sciriha, W. So, D. Stevanovi$\acute{\text{c}}$, H. van der Holst, K. Vander Meulen, and A. Wangsness). Linear Algebra Appl., 428(7):1628--1648, 2008]). In this paper, we search for characteristics of a graph that guarantee the maximum nullity of the graph and the zero forcing number of the graph are the same by studying a variety of graph parameters that give lower bounds on the maximum nullity of a graph. Inparticular, we introduce a new graph parameter which acts as a lower bound for the maximum nullity of the graph. As a result, we show that the Aztec Diamond graph's maximum nullity and zero forcing number are the same. Other graph parameters that are considered are a Colin de Verdiere type parameter and vertex connectivity. We also use matrices, such as a divisor matrix of a graph and an equitable partition of the adjacency matrix of a graph, to establish a lower bound for the nullity of the graph's adjacency matrix.
On stretch-limited elastic strings

Motivated by the increased interest in modelling non-dissipative materials by constitutive relations more general than those from Cauchy elasticity, we initiate the study of a class of stretch-limi...
Potentiation of glutamatergic synaptic transmission onto lateral habenula neurons following early life stress and intravenous morphine self-administration in rats.

Early life stress presents an important risk factor for drug addiction and comorbid depression and anxiety through persistent effects on the mesolimbic dopamine pathways. Using an early life stress model for child neglect (a single 24 h episode of maternal deprivation, MD) in rats, recent published works from our lab show that MD induces dysfunction in the ventral tegmental area and its negative controller, the lateral habenula (LHb). MD-induced potentiation of glutamatergic synaptic transmission onto LHb neurons shifts the coordination of excitation/inhibition (E/I) balance towards excitation, resulting in an increase in the overall spontaneous neuronal activity with elevation in bursting and tonic firing, and in the intrinsic excitability of LHb neurons in early adolescent male rats. Here, we explored how MD affects intravenous morphine self-administration (MSA) acquisition and sucrose preference as well as glutamatergic synaptic function in LHb neurons of adult male rats self-administering morphine. We found that MD-induced increases in LHb neuronal and glutamatergic synaptic activity and E/I ratio persisted into adulthood. Moreover, MD significantly reduced morphine intake, triggered anhedonia-like behaviour in the sucrose preference test and was associated with persistent glutamatergic potentiation 24 h after the last MSA session. MSA also altered the decay time kinetics of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor (AMPAR) currents in LHb neurons of control rats during this time period. Our data highlight that early life stress-induced glutamatergic plasticity in LHb may dampen the positive reinforcing and motivational properties of both natural rewards and opioids, and may contribute to the development of anhedonia and dysphoric states associated with opioids.
Robust and Verifiable Information Embedding Attacks to Deep Neural Networks via Error-Correcting Codes

In the era of deep learning, a user often leverages a third-party machine learning tool to train a deep neural network (DNN) classifier and then deploys the classifier as an end-user software product (e.g., a mobile app) or a cloud service. In an information embedding attack, an attacker is the provider of a malicious third-party machine learning tool. The attacker embeds a message into the DNN classifier during training and recovers the message via querying the API of the black-box classifier after the user deploys it. Information embedding attacks have attracted growing attention because of various applications such as watermarking DNN classifiers and compromising user privacy. State-of-the-art information embedding attacks have two key limitations: 1) they cannot verify the correctness of the recovered message, and 2) they are not robust against post-processing (e.g., compression) of the classifier. In this work, we aim to design information embedding attacks that are verifiable and robust against popular post-processing methods. Specifically, we leverage Cyclic Redundancy Check to verify the correctness of the recovered message. Moreover, to be robust against post-processing, we leverage Turbo codes, a type of error-correcting codes, to encode the message before embedding it to the DNN classifier. In order to save queries to the deployed classifier, we propose to recover the message via adaptively querying the classifier. Our adaptive recovery strategy leverages the property of Turbo codes that supports error correcting with a partial code. We evaluate our information embedding attacks using simulated messages and apply them to three applications (i.e., training data inference, property inference, DNN architecture inference), where messages have semantic interpretations. We consider 8 popular methods to post-process the classifier. Our results show that our attacks can accurately and verifiably recover the messages in all considered scenarios, while state-of-the-art attacks cannot accurately recover the messages in many scenarios.
Approximation of the inverse scattering Steklov eigenvalues and the inverse spectral problem

In this paper, we consider the numerical approximation of the Steklov eigenvalue problem that arises in inverse acoustic scattering. The underlying scattering problem is for an inhomogeneous isotropic medium. These eigenvalues have been proposed to be used as a target signature since they can be recovered from the scattering data. A Galerkin method is studied where the basis functions are the Neumann eigenfunctions of the Laplacian. Error estimates for the eigenvalues and eigenfunctions are proven by appealing to Weyl’s law. We will test this method against separation of variables in order to validate the theoretical convergence. We also consider the inverse spectral problem of estimating/recovering the refractive index from the knowledge of the Steklov eigenvalues, since the eigenvalues are monotone with respect to a real-valued refractive index implying that they can be used for nondestructive testing. Some numerical examples are provided for the inverse spectral problem.
Bringing Visual Inference to the Classroom

In the classroom, we traditionally visualize inferential concepts using static graphics or interactive apps. For example, there is a long history of using apps to visualize sampling distributions. ...
Capturing Row and Column Semantics in Transformer Based Question Answering over Tables

Transformer based architectures are recently used for the task of answering questions over tables. In order to improve the accuracy on this task, specialized pre-training techniques have been developed and applied on millions of open-domain web tables. In this paper, we propose two novel approaches demonstrating that one can achieve superior performance on table QA task without even using any of these specialized pre-training techniques. The first model, called RCI interaction, leverages a transformer based architecture that independently classifies rows and columns to identify relevant cells. While this model yields extremely high accuracy at finding cell values on recent benchmarks, a second model we propose, called RCI representation, provides a significant efficiency advantage for online QA systems over tables by materializing embeddings for existing tables. Experiments on recent benchmarks prove that the proposed methods can effectively locate cell values on tables (up to ~98% Hit@1 accuracy on WikiSQL lookup questions). Also, the interaction model outperforms the state-of-the-art transformer based approaches, pre-trained on very large table corpora (TAPAS and TaBERT), achieving ~3.4% and ~18.86% additional precision improvement on the standard WikiSQL benchmark.
Genetic and Physiological Characterization of the Antibacterial Activity of Bacillus subtilis subsp. inaquosorum Strain T1 Effective Against pirABVp-Bearing Vibrio parahaemolyticus

Acute hepatopancreatic necrosis disease (AHPND) is caused by PirAB toxin-producing Vibrio parahaemolyticus and has devastated the global shrimp aquaculture industry. One approach for preventing growth of AHPND-producing Vibrio spp. is through the application of beneficial bacteria capable of inhibiting these pathogens. In this study we focus on the inhibitory activity of Bacillus inaquosorum strain T1, which hinders V. parahaemolyticus growth in co-culture experiments in a density-dependent manner; inhibition was also observed using cell-free supernatants from T1 stationary phase cultures. Using mariner-based transposon mutagenesis, 17 mutants were identified having complete or partial loss of inhibitory activity. Of those displaying total loss of activity, 13 had insertions within a 42.6 kb DNA region comprising 15 genes whose deduced products were homologous to non-ribosomal polypeptide synthetases (NRPSs), polyketide synthases (PKSs) and related activities, which were mapped as one transcriptional unit. Mutants with partial activity contained insertions in spo0A and oppA, indicating stationary phase control. Expression of NRPS and PKS lacZ transcriptional fusions were negligible during growth and highest during early stationary phase. Inactivation of sigH resulted in loss of inhibitor activity, indicating a role for σH in transcription. Disruption of abrB resulted in NRPS and PKS gene overexpression during growth as well as enhanced growth inhibition. Our characterization of the expression and control of an NRPS-PKS gene cluster in B. inaquosorum T1 provides an understanding of factors involved in inhibitor production, enabling this strain9s development for use as a tool against AHPND Vibrio pathogens in shrimp aquaculture. IMPORTANCE The shrimp aquaculture industry has been significantly impacted by acute hepatopancreatic necrosis disease (AHPND), resulting in significant financial losses annually. Caused by strains of the bacterial pathogen, Vibrio parahaemolyticus, treatment of AHPND involves the use of antibiotics, which leads to a rise in antibiotic resistant strains. Alternative treatments include the application of beneficial microorganisms having inhibitory activities against AHPND pathogens. In this study, we examine the ability of Bacillus inaquosorum strain T1 to inhibit growth of an AHPND Vibrio strain and show that activity involves a gene cluster associated with antibacterial compound production. We found that gene expression is under stationary phase control and that enhanced activity occurred upon inactivation of a global transition state regulator. Our approach for understanding the factors involved in producing B. inaquosorum strain T1 inhibitory activity will allow for development of this strain for use as a tool for AHPND prevention and treatment.
General Health Checks in Adult Primary Care: A Review.

Importance General health checks, also known asgeneral medical examinations,periodic health evaluations,checkups,routine visits, orwellness visits, are commonly performed in adult primary care to identify and prevent disease. Although general health checks are often expected and advocated by patients, clinicians, insurers, and health systems, others question their value. Observations Randomized trials and observational studies with control groups reported in prior systematic reviews and an updated literature review through March 2021 were included. Among 19 randomized trials (906 to 59 616 participants; follow-up, 1 to 30 years), 5 evaluated a single general health check, 7 evaluated annual health checks, 1 evaluated biannual checks, and 6 evaluated health checks delivered at other frequencies. Twelve of 13 observational studies (240 to 471 415 participants; follow-up, cross-sectional to 5 years) evaluated a single general health check. General health checks were generally not associated with decreased mortality, cardiovascular events, or cardiovascular disease incidence. For example, in the South-East London Screening Study (n = 7229), adults aged 40 to 64 years who were invited to 2 health checks over 2 years, compared with adults not invited to screening, experienced no 8-year mortality benefit (6% vs 5%). General health checks were associated with increased detection of chronic diseases, such as depression and hypertension; moderate improvements in controlling risk factors, such as blood pressure and cholesterol; increased clinical preventive service uptake, such as colorectal and cervical cancer screening; and improvements in patient-reported outcomes, such as quality of life and self-rated health. In the Danish Check-In Study (n = 1104), more patients randomized to receive to a single health check, compared with those randomized to receive usual care, received a new antidepressant prescription over 1 year (5% vs 2%;P = .007). In a propensity score–matched analysis (n = 8917), a higher percentage of patients who attended a Medicare Annual Wellness Visit, compared with those who did not, underwent colorectal cancer screening (69% vs 60%;P Conclusions and Relevance General health checks were not associated with reduced mortality or cardiovascular events, but were associated with increased chronic disease recognition and treatment, risk factor control, preventive service uptake, and improved patient-reported outcomes. Primary care teams may reasonably offer general health checks, especially for groups at high risk of overdue preventive services, uncontrolled risk factors, low self-rated health, or poor connection or inadequate access to primary care.
Mitigating Smart Meter Asynchrony Error Via Multi-Objective Low Rank Matrix Recovery

Smart meters (SMs) are being widely deployed by distribution utilities across the U.S. Despite their benefits in real-time monitoring. SMs suffer from certain data quality issues; specifically, unlike phasor measurement units (PMUs) that use GPS for data synchronization, SMs are not perfectly synchronized. The asynchrony error can degrade the monitoring accuracy in distribution networks. To address this challenge, we propose a principal component pursuit (PCP)-based data recovery strategy. Since asynchrony results in a loss of temporal correlation among SMs, the key idea in our solution is to leverage a PCP-based low rank matrix recovery technique to maximize the temporal correlation between multiple data streams obtained from SMs. Further, our approach has a novel multi-objective structure, which allows utilities to precisely refine and recover all SM-measured variables, including voltage and power measurements, while incorporating their inherent dependencies through power flow equations. We have performed numerical experiments using real SM data to demonstrate the effectiveness of the proposed strategy in mitigating the impact of SM asynchrony on distribution grid monitoring.
Improve Learning from Crowds via Generative Augmentation

Crowdsourcing provides an efficient label collection schema for supervised machine learning. However, to control annotation cost, each instance in the crowdsourced data is typically annotated by a small number of annotators. This creates a sparsity issue and limits the quality of machine learning models trained on such data. In this paper, we study how to handle sparsity in crowdsourced data using data augmentation. Specifically, we propose to directly learn a classifier by augmenting the raw sparse annotations. We implement two principles of high-quality augmentation using Generative Adversarial Networks: 1) the generated annotations should follow the distribution of authentic ones, which is measured by a discriminator; 2) the generated annotations should have high mutual information with the ground-truth labels, which is measured by an auxiliary network. Extensive experiments and comparisons against an array of state-of-the-art learning from crowds methods on three real-world datasets proved the effectiveness of our data augmentation framework. It shows the potential of our algorithm for low-budget crowdsourcing in general.
A Broader Picture of Random-walk Based Graph Embedding

Graph embedding based on random-walks supports effective solutions for many graph-related downstream tasks. However, the abundance of embedding literature has made it increasingly difficult to compare existing methods and to identify opportunities to advance the state-of-the-art. Meanwhile, existing work has left several fundamental questions---such as how embeddings capture different structural scales and how they should be applied for effective link prediction---unanswered. This paper addresses these challenges with an analytical framework for random-walk based graph embedding that consists of three components: a random-walk process, a similarity function, and an embedding algorithm. Our framework not only categorizes many existing approaches but naturally motivates new ones. With it, we illustrate novel ways to incorporate embeddings at multiple scales to improve downstream task performance. We also show that embeddings based on autocovariance similarity, when paired with dot product ranking for link prediction, outperform state-of-the-art methods based on Pointwise Mutual Information similarity by up to 100%.