cssi/SciSciGPT-SciSciNet huggingface dataset:
On the limitations of first order approximation in GAN dynamics

While Generative Adversarial Networks (GANs) have demonstrated promising performance on multiple vision tasks, their learning dynamics are not yet well understood, both in theory and in practice. To address this issue, we study GAN dynamics in a simple yet rich parametric model that exhibits several of the common problematic convergence behaviors such as vanishing gradients, mode collapse, and diverging or oscillatory behavior. In spite of the non-convex nature of our model, we are able to perform a rigorous theoretical analysis of its convergence behavior. Our analysis reveals an interesting dichotomy: a GAN with an optimal discriminator provably converges, while first order approximations of the discriminator steps lead to unstable GAN dynamics and mode collapse. Our result suggests that using first order discriminator steps (the de-facto standard in most existing GAN setups) might be one of the factors that makes GAN training challenging in practice.
Using Online Labor Market Participants for Nonprofessional Investor Research: A Comparison of MTurk and Qualtrics Samples

ABSTRACT Recently, researchers have begun using online labor markets to recruit participants for experimental studies examining the judgments and decisions of nonprofessional investors. This study ...
Life in the Sweatbox

The time before a person files bankruptcy is sometimes called the financial “sweatbox.” Using original data from the Consumer Bankruptcy Project, we find that people are living longer in the sweatbox before filing bankruptcy than they have in the past. We also describe the depletion of wealth and well-being that defines people’s time in the sweatbox. For those people who struggle for more than two years before filing bankruptcy — the “long strugglers” — their time in the sweatbox is particularly damaging. During their years in the sweatbox, long strugglers deal with persistent collection calls, go without healthcare, food, and utilities, lose homes and other property, and yet remain ashamed of needing to file. For these people in particular, though time in the sweatbox undermines their ability to realize bankruptcy’s “fresh start,” they do not file until long after the costs outweigh the benefits. This Article’s findings challenge longstanding narratives about who files bankruptcy and why. These narratives underlie our laws, influence how judges rule in individual cases, and affect how attorneys interact with their clients.
Pricing for Profit in Internet of Things

The economic model of the Internet of Things (IoT) consists of end users, advertisers and three different kinds of providers–IoT service provider (IoTSP), Wireless service provider (WSP) and cloud service provider (CSP). We investigate three different kinds of interactions among the providers. First, we consider that the IoTSP prices a bundled service to the end-users, and the WSP and CSP pay the IoTSP (push model). Next, we consider the model where the end-users independently pay each provider (pull model). Finally, we consider a hybrid model of the above two where the IoTSP and WSP quote their prices to the end-users, but the CSP quotes its price to the IoTSP. We model different kinds of interaction among the providers as a combination of sequential and parallel non-cooperative games. We characterize and quantify the impact of the advertisement revenue on the equilibrium pricing strategy and payoff of providers, and corresponding demands of end users in each of the above interaction models. Our analysis reveals that the demand of end-users, and the payoffs of the providers are non decreasing functions of the advertisement revenue. For sufficiently high advertisement revenue, the IoTSP will offer its service free of cost in each interaction model. However, the payoffs of the providers, and the demand of end-users vary across different interaction models. Our analysis shows that the demand of end-users, and the payoff of the WSP are the highest in the pull (push, resp.) model in the low (high, resp.) advertisement revenue regime. The payoff of the IoTSP is always higher in the pull model irrespective of the advertisement revenue. The payoff of the CSP is the highest in the hybrid model in the low advertisement revenue regime. However, in the high advertisement revenue regime the payoff of the CSP in the hybrid model or in the push model can be higher depending on the equilibrium chosen in the push model.
Decision-making on the hot seat and the short list: Evidence from college football fourth down decisions

Abstract This study examines how career considerations influence risky decisions in the labor market for college football head coaches. We use historical data to predict, based on information available prior to the beginning of a given season, whether a coach will be fired or promoted after that season. Indices created from these models are used, along with other relevant data, to analyze the risky decision to attempt a fourth down conversion. We find that decision-making is sensitive to perceived job stability. Coaches who are more likely to be fired become more conservative, attempting fewer fourth down conversions. Conversely, coaches who are more likely to be promoted undertake more risk by attempting to convert more fourth downs. The result is that coaches with less job security are more likely to make decisions that are sub-optimal from the perspective of win-maximization.
The real effects of forced sales of corporate bonds

Abstract What are the real effects of forced sales of corporate securities? Our theoretical analysis shows that model uncertainty can generate distorted negative (positive) capital investment effects during price declines (reversals) in equilibrium when there is information feedback from financial markets. Empirically, we find that forced sales of corporate bonds by financial institutions had a significant negative impact on the capital investment and product market competitiveness — measured by market shares and price-cost margins — of exposed firms during the financial crisis. These adverse real effects on exposed firms were also vertically transmitted to their suppliers and customers.
Learning Not to Diversify: The Transformation of Graduate Business Education and the Decline of Diversifying Acquisitions:

Once a preferred strategy, corporate diversification into disparate lines of business has gradually declined in the U.S. over the past several decades. We argue that changes that occurred in a closely related domain—graduate business education—are important in understanding variation in de-diversification across firms. Building on a historical account of the transformation of business education, we explain how the rise of financial economics and agency-theoretic logic in business education changed students’ views about diversification. Nearly 20 years later, these MBA graduates rose to top decision-making positions and put the brakes on diversification. Using data on CEOs who ran 640 large U.S. corporations from 1985 to 2015, we show that CEOs who earned an MBA before the 1970s actively pursued diversification, whereas the next cohort of CEOs, who had been exposed to agency-theoretic logic in financial economics, refrained from it. We also demonstrate that the degree of managerial discretion moderated the...
Autonomous Flightworthiness Determination for Modular Vertical Lift Vehicles

Payload transportation via connected modular unmanned aerial vehicles is an emerging new area that offers unique advantages over other forms of aerial logistics. When considering rigidly attached, ...
Inference in structural vector autoregressions when the identifying assumptions are not fully believed: Re-evaluating the role of monetary policy in economic fluctuations

Abstract Point estimates and error bands for SVARs that are set identified are only justified if the researcher is persuaded that some parameter values are a priori more plausible than others. When such prior information exists, traditional approaches can be generalized to allow for doubts about the identifying assumptions. We use information about both structural coefficients and impacts of shocks and propose a new asymmetric t-distribution for incorporating information about signs in a nondogmatic way. We apply these methods to a three-variable macroeconomic model and conclude that monetary policy shocks are not the major driver of output, inflation, or interest rates.
The rationality of literal Tide Pod consumption

At the conclusion of 2017, to the dismay of journalists, pundits, and academics, large numbers of adolescents began consuming Tide Pods, a form of laundry detergent that is candy-like in appearance. This paper argues that purposeful consumption of laundry detergent may in fact be individually rational for adolescents. The consumption of Tide Pods may allow adolescents to successfully signal status in accordance with the Handicap Principle, which explains the beauty of a peacock’s tail and the practice of stotting by gazelles in the wild. The Handicap Principle is also a common explanation of adolescents’ willingness to engage in dangerous activities, like drug use. A subtext of the thesis of this paper is the veracity of rational choice explanations in unconventional contexts distant from its original applications.
An Information-Theoretic Analysis of Thompson Sampling for Large Action Spaces

Information-theoretic Bayesian regret bounds of Russo and Van Roy capture the dependence of regret on prior uncertainty. However, this dependence is through entropy, which can become arbitrarily large as the number of actions increases. We establish new bounds that depend instead on a notion of rate-distortion. Among other things, this allows us to recover through information-theoretic arguments a near-optimal bound for the linear bandit. We also offer a bound for the logistic bandit that dramatically improves on the best previously available, though this bound depends on an information-theoretic statistic that we have only been able to quantify via computation.
Load Balancing User Association in Millimeter Wave MIMO Networks

User association is necessary in dense millimeter wave (mmWave) networks to determine which base station a user connects to in order to balance base station loads and maximize a network utility. Given that mmWave connections are highly directional and vulnerable to small channel variations, user association changes these connections and hence significantly affects the network interference and consequently the users’ instantaneous rates. In this paper, we introduce a new load balancing user association scheme for mmWave MIMO cellular networks which consider these dependencies. We formulate the user association problem as mixed integer nonlinear programming and design a polynomial-time algorithm, called worst connection swapping (WCS), to find a near-optimal solution. Simulation results confirm that the proposed user association scheme improves network performance significantly by adjusting the interference according to the association, and under the max-min fairness, also enhances cell-edge users’ transmission rates. We also show how the proposed algorithm can be applied under mobility. Furthermore, the proposed WCS algorithm outperforms other generic algorithms for combinatorial programming such as the genetic algorithm in both accuracy and speed at several orders of magnitude faster, and for small networks, where exhaustive search is possible, it reaches the optimal solution.
Optimal Fines under Announced and Surprise Inspections

This paper examines optimal fines in a regulatory framework where the regulator can choose either surprise or announced inspections to monitor a firm for compliance. The regulator hires a supervisor who exerts unobservable monitoring effort. The firm can invest in avoidance, but it receives a fine if the supervisor finds evidence of non-compliance. Our Principal-Supervisor-Agent model focuses on the trade-off between the frequency of inspections and the magnitude of the fine. We find that when inspections are unannounced, the optimal fine is maximal, but when they are announced, the optimal fine may be less than maximal.
Option Informed Stock Picking

A wide range of research has suggested that informed trading in options markets may effectively signal subsequent changes in equity prices. In this article, the authors analyze the performance of long/short strategies based on a number of signals from options markets. In addition, they create an easily implemented long-only strategy based on a subset of the signals (volatility risk premium, option/stock volume ratio, implied volatility skew, and realized volatility). In order to minimize transaction costs and liquidity issues, they restrict their analysis to S&P 500 constituents, rebalance the portfolio monthly, and limit the holdings to 50 individual stocks. The analysis of the period from 1996 through mid-2015 shows significant outperformance of a long-only, equal-weighted portfolio of 50 stocks (and found similar results when considering 10-stock portfolios), relative to the S&P 500 and the equal-weighted S&P 500. A return attribution analysis confirms that the outperformance is provided by individual stock selection rather than sector selection.
Cardioprotection by nicotinamide mononucleotide (NMN): Involvement of glycolysis and acidic pH

Stimulation of the cytosolic NAD+ dependent deacetylase SIRT1 is cardioprotective against ischemia-reperfusion (IR) injury. NAD+ precursors including nicotinamide mononucleotide (NMN) are thought to induce cardioprotection via SIRT1. Herein, while NMN protected perfused hearts against IR (functional recovery: NMN 42 ± 7% vs. vehicle 11 ± 3%), this protection was insensitive to the SIRT1 inhibitor splitomicin (recovery 47 ± 8%). Although NMN-induced cardioprotection was absent in Sirt3-/- hearts (recovery 9 ± 5%), this was likely due to enhanced baseline injury in Sirt3-/- (recovery 6 ± 2%), since similar injury levels in WT hearts also blunted the protective efficacy of NMN. Considering alternative cardiac effects of NMN, and the requirement of glycolysis for NAD+, we hypothesized NMN may confer protection in part via direct stimulation of cardiac glycolysis. In primary cardiomyocytes, NMN induced cytosolic and extracellular acidification and elevated lactate. In addition, [U-13C]glucose tracing in intact hearts revealed that NMN stimulated glycolytic flux. Consistent with a role for glycolysis in NMN-induced protection, hearts perfused without glucose (palmitate as fuel source), or hearts perfused with galactose (no ATP from glycolysis) exhibited no benefit from NMN (recovery 11 ± 4% and 15 ± 2% respectively). Acidosis during early reperfusion is known to be cardioprotective (i.e., acid post-conditioning), and we also found that NMN was cardioprotective when delivered acutely at reperfusion (recovery 39 ± 8%). This effect of NMN was not additive with acidosis, suggesting overlapping mechanisms. We conclude that the acute cardioprotective benefits of NMN are mediated in part via glycolytic stimulation, with the downstream protective mechanism involving enhanced ATP synthesis during ischemia and/or enhanced acidosis during reperfusion.
Sufficient Conditions for Temporal Logic Specifications in Hybrid Dynamical Systems

Author(s): Han, H; Sanfelice, RG | Abstract: In this paper, we introduce operators, semantics, and conditions that, when possible, are solution-independent to guarantee basic temporal logic specifications for hybrid dynamical systems. Employing sufficient conditions for forward invariance and finite time attractivity of sets for such systems, we derive such sufficient conditions for the satisfaction of formulas involving temporal operators and atomic propositions. Furthermore, we present how to certify formulas that have more than one operator. Academic examples illustrate the results throughout the paper.
Efficient Equilibrium Testing Under Adhesion and Anisotropy Using Empirical Contact Force Models

This paper presents a method for efficiently testing the stability of an object under contact that accommodates empirical models of admissible forces at individual contact points. It handles a diverse range of possible geometries of the admissible force volume, including anisotropy, adhesion, and even nonconvexity. The method discretizes the contact region into patches, performs a convex decomposition of a polyhedral approximation to each admissible force volume, and then formulates the problem as a mixed integer linear program. The model can also accommodate articulated robot hands with torque limits and joint frictions. Predictions of our method are evaluated experimentally in object lifting tasks using a gripper that exploits microspines to exert strongly anisotropic forces. The method is applied to calculate gripper loading capabilities and equilibrium predictions for a quadruped climbing robot on steep and overhanging terrain.
Simulated Clinical Encounters Using Patient-Operated mHealth: Experimental Study to Investigate Patient-Provider Communication.

Background: This study investigates patient-centered mobile health (mHealth) technology in terms of the secondary user experience (UX). Specifically, it examines how personal mobile technology, under patient control, can be used to improve patient-provider communication about the patient’s health care during their first visit to a provider. Common ground, a theory about language use, is used as the theoretical basis to examine interactions. A novel concept of this study is that it is one of the first empirical studies to explore the relative meaningfulness of a secondary UX for specific health care tasks. Objective: The objective of this study was to investigate the extent that patient-operated mHealth technology can be designed to improve the communication between the patient and provider during an initial face-to-face encounter. Methods: The experimental study was conducted in 2 large Midwestern cities from February 2016 to May 2016. A custom-designed smartphone app prototype was used as the study treatment. The experimental design was posttest-only control group and included video-recorded simulated face-to-face clinical encounters in which an actor role-played a patient. Experienced clinicians consisting of doctors (n=4) and nurses (n=8) were the study participants. A thematic analysis of qualitative data was performed. Quantitative data collected from time on task measurements were analyzed using descriptive statistics. Results: Three themes that represent how grounding manifested during the encounter, what it meant for communication during the encounter, and how it influenced the provider’s perception of the patient emerged from the qualitative analysis. The descriptive statistics were important for inferring evidence of efficiency and effectiveness of communication for providers. Overall, encounter and task times averaged slightly faster in almost every instance for the treatment group than that in the control group. Common ground clearly was better in the treatment group, indicating that the idea of designing for the secondary UX to improve provider outcomes has merit. Conclusions: Combining the notions of common ground, human-computer interaction design, and smartphone technology resulted in a prototype that improved the efficiency and effectiveness of face-to-face collaboration for secondary users. The experimental study is one of the first studies to demonstrate that an investment in the secondary UX for high payoff tasks has value but that not all secondary UXs are meaningful for design. This observation is useful for prioritizing how resources should be applied when considering the secondary UX.
Forward Invariance of Sets for Hybrid Dynamical Systems (Part II)

In this paper, tools to study forward invariance properties with robustness to disturbances, referred to as robust forward invariance , are proposed for hybrid dynamical systems modeled as hybrid inclusions. Hybrid inclusions are given in terms of differential and difference inclusions with state and disturbance constraints, for whose definition only four objects are required. The proposed robust forward invariance notions allow for the diverse type of solutions to such systems (with and without disturbances), including solutions that have persistent flows and jumps, that are Zeno, and that stop to exist after finite amount of (hybrid) time. Sufficient conditions for sets to enjoy such properties are presented. These conditions are given in terms of the objects defining the hybrid inclusions and the set to be rendered robust forward invariant. In addition, as special cases, these conditions are exploited to state results on nominal forward invariance for hybrid systems without disturbances. Furthermore, results that provide conditions to render the sublevel sets of Lyapunov-like functions forward invariant are established. Analysis of a controlled inverter system is presented as an application of our results. Academic examples are given throughout this paper to illustrate the main ideas.
Deep Sketch-Photo Face Recognition Assisted by Facial Attributes

In this paper, we present a deep coupled framework to address the problem of matching sketch image against a gallery of mugshots. Face sketches have the essential information about the spatial topology and geometric details of faces while missing some important facial attributes such as ethnicity, hair, eye, and skin color. We propose a coupled deep neural network architecture which utilizes facial attributes in order to improve the sketch-photo recognition performance. The proposed Attribute-Assisted Deep Convolutional Neural Network (AADCNN) method exploits the facial attributes and leverages the loss functions from the facial attributes identification and face verification tasks in order to learn rich discriminative features in a common embedding subspace. The facial attribute identification task increases the inter-personal variations by pushing apart the embedded features extracted from individuals with different facial attributes, while the verification task reduces the intra-personal variations by pulling together all the features that are related to one person. The learned discriminative features can be well generalized to new identities not seen in the training data. The proposed architecture is able to make full use of the sketch and complementary facial attribute information to train a deep model compared to the conventional sketch-photo recognition methods. Extensive experiments are performed on composite (E-PRIP) and semi-forensic (IIIT-D semi-forensic) datasets. The results show the superiority of our method compared to the state-of-the-art models in sketch-photo recognition algorithms.
Deep Structured Prediction with Nonlinear Output Transformations

Deep structured models are widely used for tasks like semantic segmentation, where explicit correlations between variables provide important prior information which generally helps to reduce the data needs of deep nets. However, current deep structured models are restricted by oftentimes very local neighborhood structure, which cannot be increased for computational complexity reasons, and by the fact that the output configuration, or a representation thereof, cannot be transformed further. Very recent approaches which address those issues include graphical model inference inside deep nets so as to permit subsequent non-linear output space transformations. However, optimization of those formulations is challenging and not well understood. Here, we develop a novel model which generalizes existing approaches, such as structured prediction energy networks, and discuss a formulation which maintains applicability of existing inference techniques.
Hardware-aware machine learning: modeling and optimization

Recent breakthroughs in Machine Learning (ML) applications, and especially in Deep Learning (DL), have made DL models a key component in almost every modern computing system. The increased popularity of DL applications deployed on a wide-spectrum of platforms (from mobile devices to datacenters) have resulted in a plethora of design challenges related to the constraints introduced by the hardware itself. “What is the latency or energy cost for an inference made by a Deep Neural Network (DNN)?” “Is it possible to predict this latency or energy consumption before a model is even trained?” “If yes, how can machine learners take advantage of these models to design the hardware-optimal DNN for deployment?” From lengthening battery life of mobile devices to reducing the runtime requirements of DL models executing in the cloud, the answers to these questions have drawn significant attention. One cannot optimize what isn't properly modeled. Therefore, it is important to understand the hardware efficiency of DL models during serving for making an inference, before even training the model. This key observation has motivated the use of predictive models to capture the hardware performance or energy efficiency of ML applications. Furthermore, ML practitioners are currently challenged with the task of designing the DNN model, i.e., of tuning the hyper-parameters of the DNN architecture, while optimizing for both accuracy of the DL model and its hardware efficiency. Therefore, state-of-the-art methodologies have proposed hardware-aware hyper-parameter optimization techniques. In this paper, we provide a comprehensive assessment of state-of-the-art work and selected results on the hardware-aware modeling and optimization for ML applications. We also highlight several open questions that are poised to give rise to novel hardware-aware designs in the next few years, as DL applications continue to significantly impact associated hardware systems and platforms.
Capital market frictions and human capital investment: Evidence from workplace safety around regulation SHO

This paper examines the effect of capital market frictions on firms’ workplace safety. Using Regulation SHO as a natural experiment, we find a significant increase in work‐related injury rates of pilot firms. The effect is stronger for firms in more competitive industries and with high financial constraints, and weaker for firms whose employees have high negotiating power and with good corporate governance. Further tests suggest that managers’ myopia shifts their focus away from investments in workplace safety when workplace safety is not related to firm performance. Overall, the results highlight how capital market frictions affect firms’ investment in human capital.
Functional Categories of Visuomotor Neurons in Macaque Frontal Eye Field.

Frontal eye field (FEF) in macaque monkeys contributes to visual attention, visual–motor transformations and production of eye movements. Traditionally, neurons in FEF have been classified by the magnitude of increased discharge rates following visual stimulus presentation, during a waiting period, and associated with eye movement production. However, considerable heterogeneity remains within the traditional visual, visuomovement, and movement categories. Cluster analysis is a data-driven method of identifying self-segregating groups within a dataset. Because many cluster analysis techniques exist and outcomes vary with analysis assumptions, consensus clustering aggregates over multiple analyses, identifying robust groups. To describe more comprehensively the neuronal composition of FEF, we applied a consensus clustering technique for unsupervised categorization of patterns of spike rate modulation measured during a memory-guided saccade task. We report 10 functional categories, expanding on the traditional 3 categories. Categories were distinguished by latency, magnitude, and sign of visual response; the presence of sustained activity; and the dynamics, magnitude and sign of saccade-related modulation. Consensus clustering can include other metrics and can be applied to datasets from other brain regions to provide better information guiding microcircuit models of cortical function.
Evaluating Statistical Multiple Sequence Alignment in Comparison to Other Alignment Methods on Protein Data Sets

The estimation of multiple sequence alignments of protein sequences is a basic step in many bioinformatics pipelines, including protein structure prediction, protein family identification, and phylogeny estimation. Statistical coestimation of alignments and trees under stochastic models of sequence evolution has long been considered the most rigorous technique for estimating alignments and trees, but little is known about the accuracy of such methods on biological benchmarks. We report the results of an extensive study evaluating the most popular protein alignment methods as well as the statistical coestimation method BAli-Phy on 1192 protein data sets from established benchmarks as well as on 120 simulated data sets. Our study (which used more than 230 CPU years for the BAli-Phy analyses alone) shows that BAli-Phy has better precision and recall (with respect to the true alignments) than the other alignment methods on the simulated data sets but has consistently lower recall on the biological benchmarks (with respect to the reference alignments) than many of the other methods. In other words, we find that BAli-Phy systematically underaligns when operating on biological sequence data but shows no sign of this on simulated data. There are several potential causes for this change in performance, including model misspecification, errors in the reference alignments, and conflicts between structural alignment and evolutionary alignments, and future research is needed to determine the most likely explanation. We conclude with a discussion of the potential ramifications for each of these possibilities. [BAli-Phy; homology; multiple sequence alignment; protein sequences; structural alignment.].
Perspectives on Expanded Scope of Practice in Cytotechnology.

Introduction In an effort to identify professional trends while offering meaningful resources to support decision making in the cytopathology community, the ASC/ASCP Workgroup: Focusing on Emerging Roles in Cytopathology conducted several data collection activities to assess the current state and professional trends of cytotechnologist (CT) practice. This information is intended to inform evidence- based development of education and workforce model(s). Materials and Methods Research was conducted through mixed-method data collection processes. These included the ASCP Board of Certiﬁcation (BOC) Practice Analysis, focus groups used to gather qualitative data regarding the perceptions and experiences of current stakeholders in cytopathology through face-to-face discussion, and a RAND Delphi study conducted to gather qualitative data regarding the perspectives and "pulse" of decision makers inﬂuencing cytopathology practice. Results Research ﬁndings reveal that practice patterns with new and emerging technologies are changing the workplace for many cytotechnologists. Cytotechnologists are increasingly performing tasks within the laboratory that extend beyond their formal training and are looking to professional societies to bridge the gap. Although many laboratory leaders embrace the use of cytotechnologists in expanded roles, regulatory restrictions and reimbursement rules are among acknowledged barriers to change. Conclusions This study examines current marketplace needs and cytotechnologists' perceptions of their evolving workplace demands through qualitative data collection. This study provides a snapshot of the cur- rent climate of cytopathology and data that will help direct future education, personnel training needs, and stafﬁng decisions.
Identifying Unreported Opioid Deaths Through Toxicology Data and Vital Records Linkage: Case Study in Marion County, Indiana, 2011-2016.

Objectives. To demonstrate the severity of undercounting opioid-involved deaths in a local jurisdiction with a high proportion of unspecified accidental poisoning deaths.Methods. We matched toxicol...
Proteostasis Environment Shapes Higher-Order Epistasis Operating on Antibiotic Resistance.

Recent studies have affirmed that higher-order epistasis is ubiquitous and can have large effects on complex traits. Yet, we lack frameworks for understanding how epistatic interactions are influenced by central features of cell physiology. In this study, we assess how protein quality control machinery-a critical component of cell physiology-affects epistasis for different traits related to bacterial resistance to antibiotics. Specifically, we disentangle the interactions between different protein quality control genetic backgrounds and two sets of mutations: (i) SNPs associated with resistance to antibiotics in an essential bacterial enzyme (dihydrofolate reductase, or DHFR) and (ii) differing DHFR bacterial species-specific amino acid background sequences ( Escherichia coli , Listeria grayi , Chlamydia muridarum ). In doing so, we improve on generic observations that epistasis is widespread by discussing how patterns of epistasis can be partly explained by specific interactions between mutations in an essential enzyme and genes associated with proteostasis. These findings speak to the role of environmental and genotypic context in modulating higher-order epistasis, with direct implications for evolutionary theory, genetic modification technology, and efforts to manage antimicrobial resistance.
HOUSEHOLD SAVING, FINANCIAL CONSTRAINTS, AND THE CURRENT ACCOUNT IN CHINA

In this article, we present a model that can account for the changes in the current account balance in China since the 2000s. Our results suggest that inadequate insurance through government programs for the elderly and the decline in family insurance due to the one‐child policy led to large increases in the household saving rate. These increases coupled with the financial frictions preventing the household saving from being invested in domestic firms resulted in large current account surpluses until 2008. Relaxation of financial constraints, on the other hand, was responsible for the decline in the current account surplus after 2008.
Neutral technical progress and the measure of value: along the Kaldor–Kennedy line

In this paper, I investigate the question of "the effect of progress upon distribution" based on the analyses of Hicks, Robinson, Harrod, Salter, Kaldor, Samuelson, and Kennedy. The paper aims to address a neglected and controversial theoretical argument on neutral technical progress related to the measure of value that preceded and then continued to the period of the Cambridge Capital Theory Controversy. I focus on Kennedy's writings and his solutions to the complications between the measure of value and technical progress. Important intuitions behind the measure of value are crucial to the formulation of neutral technical progress in both the post-Keynesian and the neoclassical-Keynesian endogenous growth models. The paper concludes with mathematical illustrations of neutral technical progress theories.
Near-optimal Smooth Path Planning for Multisection Continuum Arms

We study the path planning problem for continuum-arm robots, in which we are given a starting and an end point, and we need to compute a path for the tip of the continuum arm between the two points. We consider both cases where obstacles are present and where they are not. We demonstrate how to leverage the continuum arm features to introduce a new model that enables a path planning approach based on the configurations graph, for a continuum arm consisting of three sections, each consisting of three muscle actuators. The algorithm we apply to the configurations graph allows us to exploit parallelism in the computation to obtain efficient implementation. We conducted extensive tests, and the obtained results show the completeness of the proposed algorithm under the considered discretizations, in both cases where obstacles are present and where they are not. We compared our approach to the standard inverse kinematics approach. While the inverse kinematics approach is much faster when successful, our algorithm always succeeds in finding a path or reporting that no path exists, compared to a roughly 70% success rate of the inverse kinematics approach (when a path exists).
Relevance, probative value, and explanatory considerations:

In my short contribution to this issue, I focus on Allen and Pardo proposal to redefine key evidentiary concepts in explanatory terms, with special attention to ‘relevancy’ and ‘probative value’. I...
Robot Eyes Wide Shut: Understanding Dishonest Anthropomorphism

The goal of this paper is to advance design, policy, and ethics scholarship on how engineers and regulators can protect consumers from deceptive robots and artificial intelligences that exhibit the problem of dishonest anthropomorphism. The analysis expands upon ideas surrounding the principle of honest anthropomorphism originally formulated by Margot Kaminsky, Mathew Ruben, William D. Smart, and Cindy M. Grimm in their groundbreaking Maryland Law Review article, "Averting Robot Eyes." Applying boundary management theory and philosophical insights into prediction and perception, we create a new taxonomy that identifies fundamental types of dishonest anthropomorphism and pinpoints harms that they can cause. To demonstrate how the taxonomy can be applied as well as clarify the scope of the problems that it can cover, we critically consider a representative series of ethical issues, proposals, and questions concerning whether the principle of honest anthropomorphism has been violated.
Proceedings of the 4th ACM workshop on Recurring malcode

It is my great pleasure to welcome you to the 4th ACM Workshop on Recurring Malcode (WORM'06). Internet-wide infectious epidemics have emerged as one of the leading threats to information security and service availability. Self-propagating malicious programs -- generally termed 'worms' -- exploit software weaknesses, hardware limitations, Internet topology, and the open Internet communication model to compromise large numbers of networked systems. Internet worms are increasingly being used as delivery mechanisms for malicious payloads such as spyware, phishing servers, spam relays, denial-of-service attacks, and information espionage. Current operational practices still face significant challenges in containing these threats as evidenced by the rise in automated botnet networks and the continued presence of worms released years ago.This year's workshop continues the efforts of the previous three years by providing a forum for exchanging ideas, increasing understanding, and relating experiences on self-propagating malicious software. We invited participation from researchers and practitioners from academic, commercial and government communities. The program committee selected ten papers and one panel based on quality, focus, and the likelihood of stimulating productive discussion at the workshop. The program committee also solicited invited talks from two leading practitioners to share their perspectives.
Long-Term Engraftment of ESC-Derived B-1 Progenitor Cells Supports HSC-Independent Lymphopoiesis.

Summary It is generally considered that mouse embryonic stem cell (ESC) differentiation into blood cells in vitro recapitulates yolk sac (YS) hematopoiesis. As such, similar to YS-derived B-progenitors, we demonstrate here that ESC-derived B-progenitors differentiate into B-1 and marginal zone B cells, but not B-2 cells in immunodeficient mice after transplantation. ESC-derived B-1 cells were maintained in the recipients for more than 6 months, secreting natural IgM antibodies in vivo. Gene expression profiling displayed a close relationship between ESC- and YS-derived B-1 progenitors. Because there are no hematopoietic stem cells (HSCs) detectable in our ESC differentiation culture, successful long-term engraftment of ESC-derived functional B-1 cells supports the presence of HSC-independent B-1 cell development.
Zigzag Persistence

We describe a new methodology for studying persistence of topological features across a family of spaces or point-cloud data sets, called zigzag persistence. Building on classical results about quiver representations, zigzag persistence generalises the highly successful theory of persistent homology and addresses several situations which are not covered by that theory. In this paper we develop theoretical and algorithmic foundations with a view towards applications in topological statistics.
Chapter 15 – Business Alliances: Joint Ventures, Partnerships, Strategic Alliances, and Licensing

This chapter describes the various forms of cooperative relationships common in business today, including joint ventures, partnerships, strategic alliances, equity partnerships, licensing agreements, and franchise alliances. The primary theme of this chapter is that well-constructed business alliances often represent viable alternatives to mergers and acquisitions, and they always should be considered one of the many options for achieving strategic business objectives. This chapter details the wide variety of motives for business alliances and the factors common to most successful alliances. Also addressed are the advantages and disadvantages of alternative legal structures, important deal-structuring issues, and empirical studies that purport to measure the contribution of business alliances to creating shareholder wealth.
Development and qualification of additively manufactured parts for space

Additive manufacturing fabricates the desired final part by depositing and fusing layer upon layer of the source material and offers both benefits and disadvantages compared to traditional manufacturing. New engineering designs are possible in which a single optimized part with topology can replace several traditional parts. The complex physics of metal deposition leads to variations in quality and to new flaws and residual stresses not seen in traditional manufacturing. Additive manufacturing currently has gaps in knowledge. Mission assurance for the space industry will require: qualification and certification standards; sharing of data in handbooks; predictive models relating processing, microstructure and properties; and development of closed loop process control and nondestructive evaluation to reduce variability. A tailored qualification strategy for additive manufacturing accounts for the manufacturing readiness level, mission risk class, and the knowledge of material properties. Three case studies are presented on the development and qualification of AM for the space industry with the common goals of maturing the technology and improving its reliability.
Estimate of disease heritability using 7.4 million familial relationships inferred from electronic health records

Heritability is essential for understanding the biological causes of disease, but requires laborious patient recruitment and phenotype ascertainment. Electronic health records (EHR) passively capture a wide range of clinically relevant data and provide a novel resource for studying the heritability of traits that are not typically accessible. EHRs contain next-of-kin information collected via patient emergency contact forms, but until now, these data have gone unused in research. We mined emergency contact data at three academic medical centers and identified millions of familial relationships while maintaining patient privacy. Identified relationships were consistent with genetically-derived relatedness. We used EHR data to compute heritability estimates for 500 disease phenotypes. Overall, estimates were consistent with literature and between sites. Inconsistencies were indicative of limitations and opportunities unique to EHR research. These analyses provide a novel validation of the use of EHRs for genetics and disease research.
Termite: a system for tunneling through heterogeneous data

Data-driven analysis is important in virtually every modern organization. Yet, most data is underutilized because it remains locked in silos inside of organizations; large organizations have thousands of databases, and billions of files that are not integrated together in a single, queryable repository. Despite 40+ years of continuous effort by the database community, data integration still remains an open challenge.In this paper, we advocate a different approach: rather than trying to infer a common schema, we aim to find another common representation for diverse, heterogeneous data. Specifically, we argue for an embedding (i.e., a vector space) in which all entities, rows, columns, and paragraphs are represented as points. In the embedding, the distance between points indicates their degree of relatedness. We present Termite, a prototype we have built to learn the best embedding from the data. Because the best representation is learned, this allows Termite to avoid much of the human effort associated with traditional data integration tasks. On top of Termite, we have implemented a Termite-Join operator, which allows people to identify related concepts, even when these are stored in databases with different schemas and in unstructured data such as text files, webpages, etc. Finally, we show preliminary evaluation results of our prototype via a user study, and describe a list of future directions we have identified.
Hyperbolicity of the partition Jensen polynomials

Given an arithmetic function $$a: \mathbb {N}\rightarrow \mathbb {R}$$ , one can associate a naturally defined, doubly infinite family of Jensen polynomials. Recent work of Griffin et al. shows that for certain families of functions $$a: \mathbb {N}\rightarrow \mathbb {R}$$ , the associated Jensen polynomials are eventually hyperbolic (i.e., eventually all of their roots are real). This work proves Chen et al. conjecture that the partition Jensen polynomials are eventually hyperbolic as a special case. Here, we make this result explicit. Let N(d) be the minimal number such that for all $$n \ge N(d)$$ , the partition Jensen polynomial of degree d and shift n is hyperbolic. We prove that $$N(3)=94$$ , $$N(4)=206$$ , and $$N(5)=381$$ , and in general, that $$N(d) \le (3d)^{24d} (50d)^{3d^{2}}$$ .
Hybrid endovascular exclusion of a bleeding innominate artery pseudoaneurysm in a patient with no open surgical options.

Abstract Mycotic pseudoaneurysms (MPs) rarely affect the aortic arch vessels and usually require surgical resection for definitive treatment. In this case, a 58-year-old woman developed a bleeding innominate artery MP after primary lung cancer resection complicated by an infected chest wound. Because of her previous surgery, irradiation, and chest wall reconstruction, she was not a candidate for open resection. A hybrid endovascular approach successfully excluded her innominate artery MP through placement of an aortic arch stent graft. Cerebral circulation was maintained through a periscoped left common carotid artery stent graft to the descending thoracic aorta graft, which supplied a left-to-right carotid-carotid bypass.
Accelerated Discovery of High-Refractive-Index Polyimides via First-Principles Molecular Modeling, Virtual High-Throughput Screening, and Data Mining

We present a high-throughput computational study to identify novel polyimides (PIs) with exceptional refractive index (RI) values for use as optic or optoelectronic materials. Our study utilizes an...
College Access and Adult Health

We investigate the relationship between college openings, college attainment, and health behaviors and outcomes later in life. Though a large prior literature attempts to isolate the causal effect of education on health via instrumental variables (IV), most studies use instruments that affect schooling behavior in childhood or adolescence, i.e. before the college enrollment decision. Our paper examines whether an increase in 2- and 4-year institutions per capita (“college accessibility”) in a state contributes to higher college attainment and better health later in life. Using 1980-2015 Census and American Community Survey data, we find consistent evidence that accessibility of public 2-year institutions positively affects schooling attainment and subsequent employment and earnings levels, particularly among whites and Hispanics. With restricted-use 1984-2000 National Health Interview Survey data, we again find that public 2-year accessibility increases schooling and benefits a host of health behaviors and outcomes in adulthood: it deters smoking, raises exercise levels, and improves self-reported health. However, most long-term health conditions are unaffected, which may be partially due to the age of our sample.