cssi/SciSciGPT-SciSciNet huggingface dataset:
p-Curve and Effect Size: Correcting for Publication Bias Using Only Significant Results

Journals tend to publish only statistically significant evidence, creating a scientific record that markedly overstates the size of effects. We provide a new tool that corrects for this bias without requiring access to nonsignificant results. It capitalizes on the fact that the distribution of significant p values, p-curve, is a function of the true underlying effect. Researchers armed only with sample sizes and test results of the published findings can correct for publication bias. We validate the technique with simulations and by reanalyzing data from the Many-Labs Replication project. We demonstrate that p-curve can arrive at conclusions opposite that of existing tools by reanalyzing the meta-analysis of the “choice overload” literature.
FDI and immigration: a regional analysis

Does immigration affect foreign direct investment? Existing studies on immigration and FDI have all looked at aggregate flows at the national level, arguing that immigrant networks lower the risk of foreign investment through increased information flows and a built in market. However, these national-level studies suffer from identification problems since many of the factors that attract immigrants also attract FDI. This study improves upon identification by looking at the regional distribution of both FDI and immigration from 10 source countries to the 50 US states. Using a unique measure of immigrant network size in each state, I find that immigration is not only positively correlated with FDI, it tends to lead it as well. Comparing a state with an average sized immigrant network to one with a network twice as large, I estimate that the stronger network state will get on average 20 more foreign-owned affiliates opening per year, an effect that is quite persistent over time. On average, more skilled immigrant communities attract more FDI, while the pull effect of immigration on FDI also increases with immigrant ties to native countries and with immigrant influence in local communities. These results suggest that immigration creates a positive externality in foreign investment that must be considered when assessing the costs and benefits of labor mobility.
Fighting Piracy in Somalia (and Elsewhere): Why More Is Needed

This Article argues that pirates should be treated as terrorists and that piracy-fighting countries should rely on a variety of antiterrorist conventions to justify the capture and prosecution of pirates. To provide a comprehensive outlook on piracy, Part I of this Article describes the history of piracy and its reappearance in the modern world. Part II briefly describes the resurgence of modern-day piracy, first in Southeast Asia and then in Somalia. Part III provides the current international legal framework for battling piracy, by focusing first on the definition of piracy in international law, and then on the existing international legal authority to apprehend and prosecute pirates. Part IV describes the existing options and solutions for fighting pirates, including domestic prosecutions, prosecutions in ad hoc tribuals, regional partnerships, and the aid of international maritime organizations. Part V advocates the need to fight piracy more aggressively, explaining the similarity between pirates and terorists. Finally, Part V also advocates the need to rebuild Somalia and its institutions, as this is the only permanent solution to eradicate piracy in this region of the world. FIGHTING PIRACY IN SOMALIA (AND ELSEWHERE): WHY MORE IS NEEDED
The ‘Principles’ Paradox

This essay, prepared for a University of Cambridge conference on ‘Principles Versus Rules in Financial Regulation’, posits a new issue in that debate. Although principles-based regulation is thought to more closely achieve normative goals than rules, the extent to which that occurs can depend on the enforcement regime. A person who is subject to unpredictable liability is likely to hew to the most conservative interpretation of the principle, especially where that person would be a potential deep pocket in litigation. This creates a paradox: unless protected by a regime enabling one in good faith to exercise judgment without fear of liability, such a person will effectively act as if subject to a rule and, even worse, an unintended rule.
Vertical Antitrust Policy as a Problem of Inference

The legality of nonprice vertical practices in the U.S. is determined by their likely competitive effects. An optimal enforcement rule combines evidence with theory to update prior beliefs, and specifies a decision that minimizes the expected loss. Because the welfare effects of vertical practices are theoretically ambiguous, optimal decisions depend heavily on prior beliefs, which should be guided by empirical evidence. Empirically, vertical restraints appear to reduce price and/or increase output. Thus, absent a good natural experiment to evaluate a particular restraint’s effect, an optimal policy places a heavy burden on plaintiffs to show that a restraint is anticompetitive.
Gender, Income Levels, and the Demand for Cigarettes

This paper uses data from the Current Population Survey to analyze determinants of cigarette demand. Price elasticities for smoking participation and quantity of cigarettes smoked are between −0.4 and −0.6 for both men and women. These effects diminish for high-income individuals. The family earnings elasticity of demand is weak, but education has strong negative effects on smoking, especially for high-income respondents. Own-earnings decrease individuals' price sensitivity. Employment status is influential even after controlling for income, education, and other factors. The presence of young children reduces smoking, with the effect most pronounced for women.
Normative behavioral economics

Abstract This paper addresses the question of why, in spite of its recent success, behavioral economics does not influence most discussions about how economic policy ought to be made. Failing to penetrate into contemporary discourse on leading policy issues is a serious problem, because behavioral techniques often point to policy prescriptions that are at odds with the prescriptions which follow from models using more standard behavioral assumptions. By comparing how the universe of possible policy implications changes when different methodological approaches are used, this paper demonstrates a systematic link between methodology and the range of policy prescriptions that can be socially desirable. Because of this link, the methodological multiplicity of behavioral economics, and the ideological pluralism which it supports, favor the use of normative behavioral economics. This follows from the basic economic principle of diversification: a policy prescription that reflects averaging over a number of distinct kinds of errors (one for each methodology) is less likely to wander far off target than one generated by a single method.
The Labour Market in the New Information Economy

The extension of information and communication technologies to economic activity is changing the labour market in important ways. This article shows that computerization and use of the Internet are associated with greater hours worked as well as higher wages; that IT occupations are rapidly increasing their share of employment; that job search and recruitment are moving rapidly to the Web, with consequences for matching employers and employees; and possibly most important of all, that trade unions have begun to use the Internet as a tool for servicing members and carrying their message to the public, raising the possibility of a major change in the nature of the union movement.
Does eliminating the Form 20-F reconciliation from IFRS to U.S. GAAP have capital market consequences?

Abstract This paper investigates the capital market consequences of the SEC's decision to eliminate the reconciliation requirement for cross-listed companies following International Financial Reporting Standards (IFRS). We find no evidence that the elimination has a negative impact on firms' market liquidity or probability of informed trading (PIN). We also find no evidence of a significant impact on cost of equity, analyst forecasts, institutional ownership, stock price efficiency and synchronicity. Moreover, IFRS users do not increase disclosure frequency nor supply the reconciliation voluntarily. Our results do not support the argument that eliminating the reconciliation results in information loss or greater information asymmetry.
Goodbye to All That, or a Fool's Errand, By One of the Fools: How I Stopped Worrying about Court Responses to Handwriting Identification (and Forensic Science in General) and Learned to Love Misinterpretations of Kumho Tire v. Carmichael

This article is a picaresque romp through the author's career, much of which has been spent coming to grips with the realities of forensic science, and the courts' abdication of their role as gatekeepers in judging the reliability of prosecution-proffered expertise. The reader follows the author from Bruno Hauptmann's cramped attic, through the Mayflower Madam case and into the era of Daubert and beyond. There is a serious academic point to all this, for the article illustrates how the lower federal courts have managed to ignore or misinterpret Kumho Tire v. Carmichael in such a way as to create a jurisprudence of expertise wholly at odds with the clear mandate of the Supreme Court, often by converting decisions with no precedential status into precedents of breathtaking breadth. In this regard, the article is as much about legal process in general as it is about expert evidence in criminal cases.
Before and After: Temporal Anomalies in Legal Doctrine

Legal doctrine exhibits some striking temporal anomalies, previously not much adverted to. Wrongdoing looked at before it has occurred, and after is has occurred, is apt to look very different. I take up the two key components of wrongdoing seriatim, the harm-portion and the misconduct-portion: the "damage" part and the "liability" part. We tend to look at harm in a harm-agnifying way before it has occurred, and in a harm-inimizing way afterwards. We thus tend to think about negligence and the harm it wreaks in seemingly inconsistent ways. I examine and reject some possible explanations of this. Misconduct too looks different before and after, and the reason seems to be a certain spacing phenomenon: space blameworthy actions closely together and their overall blameworthiness can turn out to be greater or less than the sum of its individual blameworthy parts. The ex ante perspective involves looking at how much the piece contributes to the overall blameworthiness of the defendant's actions. If, therefore, for some reason the sum of the blameworthy acts is greater or less than the overall blameworthiness of the defendant, we get an ex ante/ex post inconsistency. I offer a sketchy and tentative account of the likely bases of this spacing effect.
Position Auctions with Consumer Search

This paper examines a model in which advertisers bid for \sponsored-link" positions on a search engine. The value advertisers derive from each position is endogenized as coming from sales to a population of consumers who make rational inferences about rm qualities and search optimally. Consumer search strategies, equilibrium bidding, and the welfare benets of position auctions are analyzed. Implications for reserve prices and a number of other auction design questions are discussed.
Three-stage semi-parametric inference: Control variables and differentiability

Abstract We show the usefulness of the path-derivative calculations that were introduced in econometrics by Newey (1994) for multi-step semi-parametric estimators. These estimators estimate a finite-dimensional parameter using moment conditions that depend on nonparametric regressions on observed and estimated regressors that are estimated in the second and first step of the estimation procedure, respectively. Our earlier paper showed that Newey’s calculations can be extended to three-step estimators. In the current paper we consider the control variable (CV) estimator and related statistics in semi-parametric econometric models with non-separable errors and regressors that are correlated with these errors. Non-separable econometric models with endogenous regressors are often identified by average moment restrictions that average over control variables, and these control variables are estimated in a first stage by (non)parametric regression. We study aspects of inference for such estimators where we focus on a finite-dimensional parameter vector or statistic. The asymptotic distribution and a closed-form expression for the asymptotic variance of the CV estimator were not available until now. Our path derivative calculations are much simpler than the derivation of the asymptotic distribution by a stochastic expansion that is particularly complicated for multi-step semi-parametric estimators. We also consider just- and overidentification of the parameters and we propose a diagnostic test for overidentifying restrictions in models with non-separable errors and endogenous regressors. Finally, the path-derivative calculation breaks down if the moment condition is not differentiable. In an example we show that non-differentiability is associated with irregular behavior of the estimator.
The Social Production of National Security

This Article analyzes a recent policy innovation offered by governments on both sides of the Atlantic as a means of mitigating one form of national security risk: the idea that private individuals and voluntary associations have an untapped capacity for combating terrorism and in particular al Qaeda. Bold assertions in recent strategy statements mooting this possibility have wanted for any supporting account of how private behavior conduces to security. Even if the claimed social production of security against terrorism is causally well founded, it is unclear how the state can elicit desirable private conduct. Consequently, the proposal’s legal and policy ramifications remain elusive. To begin to address these gaps, this Article develops a comprehensive analysis of three plausible causal mechanisms that might yield the putative security-related benefits of social action. I label these ideological competition, ethical anchoring and cooperative coproduction. Drawing on legal, economic, and social psychology scholarship to illuminate these three mechanisms, this Article further investigates the state’s role in eliciting potentially desirable private action against terrorism risk. The Article concludes by highlighting threshold legal, strategic, and ethical puzzles in designing policies to elicit counterterrorism’s social production — puzzles that to date have received short shrift in American counterterrorism debates.
Stable Behavioral and Neural Responses to Thermal Stimulation Despite Large Changes in the Hydra vulgaris Nervous System

Many animals that lose neural tissue due to injury or disease have the ability to maintain their behavioral abilities by regenerating new neurons or reorganizing existing neural circuits. However, most small model organisms used for neuroscience like nematodes and flies lack this high degree of neural plasticity. Here, we show that the small freshwater cnidarian Hydra vulgaris can maintain stable sensory motor behaviors even after losing half of the neurons in its body. Specifically, we find that both the behavioral and neural response to a rapid change in temperature is maintained if we make Hydra’s nervous system roughly 50% smaller by caloric restriction or surgery. We further describe possible mechanisms for the observed neural activity. These results suggest that Hydra provides a powerful model for studying how animals maintain stable sensory-motor responses within dynamic neural circuit architectures, and may lead to the development of general principles for neural circuit plasticity and stability.
Choice of Majors: Are Women Really Different from Men?

Abstract The gender wage gap persists in the workplace in part because women major in fields that lead them into lower-paid occupations than fields associated with majors men choose. An open question is why women are more likely than men to switch towards majors that lead to lower-paying careers. Drawing on research suggesting that women are affected more by negative feedback than men, we use unique administrative data from a large private university on the East Coast from 2008 to 2016 to test whether women are more likely than men to switch majors in response to low grades in major-related classes. We also test whether the prevalence of men in a major or a major’s reputation for being stereotypically male-oriented induces women to switch out of that major more readily than men. We control for other factors that may affect a student’s final major, including high school performance, gender composition of faculty and peers, and economic returns of majors. The results show that overall women and men are equally likely to change their majors in response to poor grades in major-related courses. We also find no evidence that women in male-dominated majors are more likely than men to switch out, but we find that men with very low grades in female-dominated majors are more likely to switch out of these majors than women. In addition, we find that women are more likely to switch out of majors that are both male-dominated and STEM in response to poor performance compared to men. Thus, our results suggest that only when women students experience multiple signals suggesting their lack of fit in a field (i.e., low grades combined with gender peer composition, and external stereotyping signals) they tend to switch out of the major more often than male students. We find that men exhibit this tendency in response to even fewer overlapping signals about misfit than women.
Electronic Structure of Grain Boundaries and Interfaces in Polycrystalline Zinc Oxide.

Abstract : Preliminary theoretical model for the electronic structure of grain boundaries and interfaces in polycrystalline ZnO have been constructed on the basis of self-consistent-field-X-alpha scattered-wave (SCF-X alpha-SW) cluster molecular-orbital calculations. The disposition and character of the interface states, relative to the valence and conduction bands of the otherwise perfect crystalline material, have been studied for clusters representing coordinatively unsaturated Zn surface sites and molecular O2 chemisorption thereon. The possible effects of the resulting interface states on electron transport at grain boundaries in ZnO varistors have been addressed.
Backward Reachability for Polynomial Systems on A Finite Horizon

A method is presented to obtain an inner-approximation of the backward reachable set (BRS) of a given target tube, along with an admissible controller that maintains trajectories inside this tube. The proposed optimization algorithms are formulated as nonlinear optimization problems, which are decoupled into tractable subproblems and solved by an iterative algorithm using the polynomial S-procedure and sum-of-squares techniques. This framework is also extended to uncertain nonlinear systems with L_2 disturbances and L_{\infty} parametric uncertainties. The effectiveness of the method is demonstrated on several nonlinear robotics and aircraft systems with control saturation.
Is Face Recognition Sexist? No, Gendered Hairstyles and Biology Are.

Recent news articles have accused face recognition of being "biased", "sexist" or "racist". There is consensus in the research literature that face recognition accuracy is lower for females, who often have both a higher false match rate and a higher false non-match rate. However, there is little published research aimed at identifying the cause of lower accuracy for females. For instance, the 2019 Face Recognition Vendor Test that documents lower female accuracy across a broad range of algorithms and datasets also lists "Analyze cause and effect" under the heading "What we did not do". We present the first experimental analysis to identify major causes of lower face recognition accuracy for females on datasets where previous research has observed this result. Controlling for equal amount of visible face in the test images reverses the apparent higher false non-match rate for females. Also, principal component analysis indicates that images of two different females are inherently more similar than of two different males, potentially accounting for a difference in false match rates.
Self-supervised Deformation Modeling for Facial Expression Editing

Deep generative models have recently demonstrated impressive results in photo-realistic facial image synthesis and editing. Existing neural network-based approaches usually only rely on texture generation to edit expressions and largely neglect the motion information. However, facial expressions are inherently the result of muscle movement. In this work, we propose a novel end-to-end network that disentangles the task of facial editing into two steps: a “motionediting” step and a “texture-editing” step. In the “motionediting” step, we explicitly model facial movement through an image deformation, warping the image into the desired expression. In the “texture-editing” step, we generate the necessary textures, such as teeth and shading effects, for a photorealistic result. Our physically-based task-disentanglement system design allows each step to learn a focused task, and thus need not generate texture to hallucinate motion. Our system is trained in a self-supervised manner, requiring no ground truth deformation annotation. Using Action Units [8] as the representation for facial expression, our method improves the state-of-the-art facial expression editing performance in both qualitative and quantitative evaluations.1.
Explaining Learning Models in Manufacturing Processes

Abstract The use of advanced machine learning (ML) models for manufacturing could potentially reduce the pre-production testing and validation time for new processes. Once we decide that ML is indeed a suitable tool to apply in smart manufacturing processes, the challenge lies in training, validating, and testing an ML model in a pre-production environment so that engineers can be confident that the model building effort can be successfully transitioned to actual production. This paper aims at explaining the in-works of a given in-situ classifier for predicting the quality welds in ultrasonic welded battery tabs. Predicting the quality of new samples cannot attain full certainty due to characteristics of the data the model was trained on (e.g., noisy or wrongly labeled). By developing explainable methods to such connectionist learning models (also known as black boxes), we show why the classifier outputs were predicted, making these predictions better understood and trustworthy.
Bayesian Persuasion with State-Dependent Quadratic Cost Measures

We address Bayesian persuasion between a sender and a receiver with state-dependent quadratic cost measures for general classes of distributions. The receiver seeks to make mean-square-error estimate of a state based on a signal sent by the sender while the sender signals strategically in order to control the receiver's estimate in a certain way. Such a scheme could model, e.g., deception and privacy, problems in multi-agent systems. Existing solution concepts are not viable since here the receiver has continuous action space. We show that for finite state spaces, optimal signaling strategies can be computed through an equivalent linear optimization problem over the cone of completely positive matrices. We then establish its strong duality to a copositive program. To exemplify the effectiveness of this equivalence result, we adopt sequential polyhedral approximation of completely-positive cones and analyze its performance numerically. We also quantify the approximation error for a quantized version of a continuous distribution and show that a semi-definite program relaxation of the equivalent problem could be a benchmark lower bound for the sender's cost for large state spaces.
What to Do When You Can’t Do It All: Temporal Logic Planning with Soft Temporal Logic Constraints

In this paper, we consider a temporal logic planning problem in which the objective is to find an infinite trajectory that satisfies an optimal selection from a set of soft specifications expressed in linear temporal logic (LTL) while nevertheless satisfying a hard specification expressed in LTL. Our previous work considered a similar problem in which linear dynamic logic for finite traces (LDL f ), rather than LTL, was used to express the soft constraints. In that work, LDL f was used to impose constraints on finite prefixes of the infinite trajectory. By using LTL, one is able not only to impose constraints on the finite prefixes of the trajectory, but also to set ‘soft’ goals across the entirety of the infinite trajectory. Our algorithm first constructs a product automaton, on which the planning problem is reduced to computing a lasso with minimum cost. Among all such lassos, it is desirable to compute a shortest one. Though we prove that computing such a shortest lasso is computationally hard, we also introduce an efficient greedy approach to synthesize short lassos nonetheless. We present two case studies describing an implementation of this approach, and report results of our experiment comparing our greedy algorithm with an optimal baseline.
Spatially interacting phosphorylation sites and mutations in cancer

Advances in mass-spectrometry have generated increasingly large-scale proteomics datasets containing tens of thousands of phosphorylation sites (phosphosites) that require prioritization. We develop a bioinformatics tool called HotPho and systematically discover 3D co-clustering of phosphosites and cancer mutations on protein structures. HotPho identifies 474 such hybrid clusters containing 1255 co-clustering phosphosites, including RET p.S904/Y928, the conserved HRAS/KRAS p.Y96, and IDH1 p.Y139/IDH2 p.Y179 that are adjacent to recurrent mutations on protein structures not found by linear proximity approaches. Hybrid clusters, enriched in histone and kinase domains, frequently include expression-associated mutations experimentally shown as activating and conferring genetic dependency. Approximately 300 co-clustering phosphosites are verified in patient samples of 5 cancer types or previously implicated in cancer, including CTNNB1 p.S29/Y30, EGFR p.S720, MAPK1 p.S142, and PTPN12 p.S275. In summary, systematic 3D clustering analysis highlights nearly 3,000 likely functional mutations and over 1000 cancer phosphosites for downstream investigation and evaluation of potential clinical relevance.
On Banach algebras of band-dominated operators and their order structure

Abstract The goal of this paper is to study band-dominated operators on Banach spaces with Schauder basis with respect to uniformly locally finite metric spaces as well as the Banach algebras generated by them: the so called uniform Roe algebras. We investigate several kinds of isomorphisms between those Banach spaces (e.g., isomorphisms preserving norm, order, algebraic structure, etc) and prove several rigidity results on when a certain kind of isomorphism between the uniform Roe algebras implies that the base metric spaces are (bijectively) coarsely equivalent.
Generalized Paley graphs and their complete subgraphs of orders three and four

Let $$k \ge 2$$ be an integer. Let q be a prime power such that $$q \equiv 1 ({\mathrm{mod}}\,\,k)$$ if q is even, or, $$q \equiv 1 ({\mathrm{mod}}\,\,2k)$$ if q is odd. The generalized Paley graph of order q, $$G_k(q)$$ , is the graph with vertex set $$\mathbb {F}_q$$ where ab is an edge if and only if $${a-b}$$ is a kth power residue. We provide a formula, in terms of finite field hypergeometric functions, for the number of complete subgraphs of order four contained in $$G_k(q)$$ , $$\mathcal {K}_4(G_k(q))$$ , which holds for all k. This generalizes the results of Evans, Pulham and Sheehan on the original ( $$k=2$$ ) Paley graph. We also provide a formula, in terms of Jacobi sums, for the number of complete subgraphs of order three contained in $$G_k(q)$$ , $$\mathcal {K}_3(G_k(q))$$ . In both cases, we give explicit determinations of these formulae for small k. We show that zero values of $$\mathcal {K}_4(G_k(q))$$ (resp. $$\mathcal {K}_3(G_k(q))$$ ) yield lower bounds for the multicolor diagonal Ramsey numbers $$R_k(4)=R(4,4,\ldots ,4)$$ (resp. $$R_k(3)$$ ). We state explicitly these lower bounds for small k and compare to known bounds. We also examine the relationship between both $$\mathcal {K}_4(G_k(q))$$ and $$\mathcal {K}_3(G_k(q))$$ , when q is prime, and Fourier coefficients of modular forms.
Modular Simulation Framework for Process Variation Analysis of MRAM-based Deep Belief Networks

Magnetic Random-Access Memory (MRAM) based p-bit neuromorphic computing devices are garnering increasing interest as a means to compactly and efficiently realize machine learning operations in Restricted Boltzmann Machines (RBMs). When embedded within an RBM resistive crossbar array, the p-bit based neuron realizes a tunable sigmoidal activation function. Since the stochasticity of activation is dependent on the energy barrier of the MRAM device, it is essential to assess the impact of process variation on the voltage-dependent behavior of the sigmoid function. Other influential performance factors arise from varying energy barriers on power consumption requiring a simulation environment to facilitate the multi-objective optimization of device and network parameters. Herein, transportable Python scripts are developed to analyze the output variation under changes in device dimensions on the accuracy of machine learning applications. Evaluation with RBM circuits using the MNIST dataset reveal impacts and limits for processing variation of device fabrication in terms of the resulting energy vs. accuracy tradeoffs, and the resulting simulation framework is available via a Creative Commons license.
Application of adaptive ANOVA and reduced basis methods to the stochastic Stokes-Brinkman problem

The Stokes-Brinkman equations model fluid flow in highly heterogeneous porous media. In this paper, we consider the numerical solution of the Stokes-Brinkman equations with stochastic permeabilities, where the permeabilities in subdomains are assumed to be independent and uniformly distributed within a known interval. We employ a truncated anchored ANOVA decomposition alongside stochastic collocation to estimate the moments of the velocity and pressure solutions. Through an adaptive procedure selecting only the most important ANOVA directions, we reduce the number of collocation points needed for accurate estimation of the statistical moments. However, for even modest stochastic dimensions, the number of collocation points remains too large to perform high-fidelity solves at each point. We use reduced basis methods to alleviate the computational burden by approximating the expensive high-fidelity solves with inexpensive approximate solutions on a low-dimensional space. We furthermore develop and analyze rigorous a posteriori error estimates for the reduced basis approximation. We apply these methods to 2D problems considering both isotropic and anisotropic permeabilities.
A one-dimensional flow model enhanced by machine learning for simulation of vocal fold vibration.

A one-dimensional (1D) unsteady and viscous flow model that is derived from the momentum and mass conservation equations is described, and to enhance this physics-based model, a machine learning approach is used to determine the unknown modeling parameters. Specifically, an idealized larynx model is constructed and ten cases of three-dimensional (3D) fluid–structure interaction (FSI) simulations are performed. The flow data are then extracted to train the 1D flow model using a sparse identification approach for nonlinear dynamical systems. As a result of training, we obtain the analytical expressions for the entrance effect and pressure loss in the glottis, which are then incorporated in the flow model to conveniently handle different glottal shapes due to vocal fold vibration. We apply the enhanced 1D flow model in the FSI simulation of both idealized vocal fold geometries and subject-specific anatomical geometries reconstructed from the magnetic resonance imaging images of rabbits' larynges. The 1D flow model is evaluated in both of these setups and shown to have robust performance. Therefore, it provides a fast simulation tool that is superior to the previous 1D models.
Breakdown of Regularity of Scattering for Mass-Subcritical NLS

We study the scattering problem for the nonlinear Schrodinger equation $i\partial_t u + \Delta u = |u|^p u$ on $\mathbb{R}^d$, $d\geq 1$, with a mass-subcritical nonlinearity above the Strauss exponent. For this equation, it is known that asymptotic completeness in $L^2$ with initial data in $\Sigma$ holds and the wave operator is well-defined on $\Sigma$. We show that there exists $0<\beta<p$ such that the wave operator and the data-to-scattering-state map do not admit extensions to maps $L^2\to L^2$ of class $C^{1+\beta}$ near the origin. This constitutes a mild form of ill-posedness for the scattering problem in the $L^2$ topology.
Are PDFs still consistent with Tevatron data

As active data taking has moved to the LHC at CERN, more and more LHC data have been included into fits of parton distribution functions. An anomaly has arisen where formerly excellent agreement between theoretical predictions and experiment in single-top-quark production at the Tevatron is no longer quite as good. Is this indicative of a deeper issue?
Estimating knots and their association in parallel bilinear spline growth curve models in the framework of individual measurement occasions.

Latent growth curve models with spline functions are flexible and accessible statistical tools for investigating nonlinear change patterns that exhibit distinct phases of development in manifested variables. Among such models, the bilinear spline growth model (BLSGM) is the most straightforward and intuitive but useful. An existing study has demonstrated that the BLSGM allows the knot (or change-point), at which two linear segments join together, to be an additional growth factor other than the intercept and slopes so that researchers can estimate the knot and its variability in the framework of individual measurement occasions. However, developmental processes usually unfold in a joint development where two or more outcomes and their change patterns are correlated over time. As an extension of the existing BLSGM with an unknown knot, this study considers a parallel BLSGM (PBLSGM) for investigating multiple nonlinear growth processes and estimating the knot with its variability of each process as well as the knot-knot association in the framework of individual measurement occasions. We present the proposed model by simulation studies and a real-world data analysis. Our simulation studies demonstrate that the proposed PBLSGM generally estimate the parameters of interest unbiasedly, precisely and exhibit appropriate confidence interval coverage. An empirical example using longitudinal reading scores, mathematics scores, and science scores shows that the model can estimate the knot with its variance for each growth curve and the covariance between two knots. We also provide the corresponding code for the proposed model. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Whose Choice Are We Talking About: The Exclusion of Students with Disabilities from for-Profit Online Charter Schools

By examining the history of special education law against the emergence of the for-profit and online education movements, this paper explores the development of charter school policy from a consumer law perspective. It aims to explain why much of the current debate over test scores, “accountability,” and teacher evaluation obscures other systemic faults that implicate the very reasons we have a public education system in the first place. In the last fifteen years, information technologies have fostered the emergence of a new kind of school: the fully-online “cyber” or “virtual” charter. These schools, operated almost exclusively by for-profit, publicly-traded companies, are growing rapidly. As in the consumer finance industry, where the development of derivatives moved faster than the ability of regulators to ensure their safety, the virtual education world is largely unregulated. Over the past decade, clear and convincing evidence has emerged that for-profit charter schools are not adequately maintaining their fiscal and educational responsibilities to students with disabilities. Online schools are both knowingly and unknowingly discriminating against students with severe disabilities. The profit motive at online schools incentivizes them to do whatever they can to avoid serving the students who cost the most to educate. This kind of discrimination is broadly illegal and threatens the development of the American public school system as a whole. Unless we make a conscious effort to holistically reform the public education system, we will not be able to adequately uphold the democratic principles on which American education is based.
Structure and Randomness in Combinatorics

Combinatorics, like computer science, often has to deal with large objects of unspecified (or unusable) structure. One powerful way to deal with such an arbitrary object is to decompose it into more usable components. In particular, it has proven profitable to decompose such objects into a structured component, a pseudo-random component, and a small component (i.e. an error term): in many cases it is the structured component which then dominates. We illustrate this philosophy in a number of model cases.
Weak and Semi-Contraction for Network Systems and Diffusively-Coupled Oscillators

We develop two generalizations of contraction theory, namely, semi-contraction and weak contraction theory. First, using the notion of semi-norm, we propose a geometric framework for semi-contraction theory. We introduce matrix semi-measures and characterize their properties. We show that the spectral abscissa of a matrix is the infimum over weighted semi-measures. For dynamical systems, we use the semi-measure of their Jacobian to characterize the contractivity properties of their trajectories. Second, for weakly contracting systems, we prove a dichotomy for the asymptotic behavior of their trajectories and novel sufficient conditions for convergence to an equilibrium. Third, we show that every trajectory of a doubly-contracting system, i.e., a system that is both weakly and semi-contracting, converges to an equilibrium point. Finally, we apply our results to various important network systems including affine averaging and affine flow systems, continuous-time distributed primal-dual algorithms, and networks of diffusively-coupled dynamical systems. For diffusively-coupled systems, the semi-contraction theory leads to a sufficient condition for synchronization that
CutQC: using small Quantum computers for large Quantum circuit evaluations

Quantum computing (QC) is a new paradigm offering the potential of exponential speedups over classical computing for certain computational problems. Each additional qubit doubles the size of the computational state space available to a QC algorithm. This exponential scaling underlies QC’s power, but today’s Noisy Intermediate-Scale Quantum (NISQ) devices face significant engineering challenges in scalability. The set of quantum circuits that can be reliably run on NISQ devices is limited by their noisy operations and low qubit counts. This paper introduces CutQC, a scalable hybrid computing approach that combines classical computers and quantum computers to enable evaluation of quantum circuits that cannot be run on classical or quantum computers alone. CutQC cuts large quantum circuits into smaller subcircuits, allowing them to be executed on smaller quantum devices. Classical postprocessing can then reconstruct the output of the original circuit. This approach offers significant runtime speedup compared with the only viable current alternative—purely classical simulations—and demonstrates evaluation of quantum circuits that are larger than the limit of QC or classical simulation. Furthermore, in real-system runs, CutQC achieves much higher quantum circuit evaluation fidelity using small prototype quantum computers than the state-of-the-art large NISQ devices achieve. Overall, this hybrid approach allows users to leverage classical and quantum computing resources to evaluate quantum programs far beyond the reach of either one alone.
Non-Parametric Quickest Detection of a Change in the Mean of an Observation Sequence

We study the problem of quickest detection of a change in the mean of an observation sequence, under the assumption that both the pre- and post-change distributions have bounded support. We first study the case where the pre-change distribution is known, and then study the extension where only the mean and variance of the pre-change distribution are known. In both cases, no knowledge of the post-change distribution is assumed other than that it has bounded support. For the case where the pre-change distribution is known, we derive a test that asymptotically minimizes the worst-case detection delay over all post-change distributions, as the false alarm rate goes to zero. We then study the limiting form of the optimal test as the gap between the pre- and post-change means goes to zero, which we call the Mean-Change Test (MCT). We show that the MCT can be designed with only knowledge of the mean and variance of the pre-change distribution. We validate our analysis through numerical results for detecting a change in the mean of a beta distribution. We also demonstrate the use of the MCT for pandemic monitoring.
Transport of Alzheimer's associated amyloid-β catalyzed by P-glycoprotein

P-glycoprotein (P-gp) is a critical membrane transporter in the blood brain barrier (BBB) and is implicated in Alzheimer’s disease (AD). However, previous studies on the ability of P-gp to directly transport the Alzheimer’s associated amyloid-β (Aβ) protein have produced contradictory results. Here we use molecular dynamics (MD) simulations, transport substrate accumulation studies in cell culture, and biochemical activity assays to show that P-gp actively transports Aβ. We observed transport of Aβ40 and Aβ42 monomers by P-gp in explicit MD simulations of a putative catalytic cycle. In in vitro assays with P-gp overexpressing cells, we observed enhanced accumulation of fluorescently labeled Aβ42 in the presence of Tariquidar, a potent P-gp inhibitor. We also showed that Aβ42 stimulated the ATP hydrolysis activity of isolated P-gp in nanodiscs. Our findings expand the substrate profile of P-gp, and suggest that P-gp may contribute to the onset and progression of AD.
An ergodic-averaging method to differentiate covariant Lyapunov vectors

Covariant Lyapunov vectors or CLVs span the expanding and contracting directions of perturbations along trajectories in a chaotic dynamical system. Due to efficient algorithms to compute them that only utilize trajectory information, they have been widely applied across scientific disciplines, principally for sensitivity analysis and predictions under uncertainty. In this paper, we develop a numerical method to compute the directional derivatives of the first CLV along its own direction; the norm of this derivative is also the curvature of one-dimensional unstable manifolds. Similar to the computation of CLVs, the present method for their derivatives is iterative and analogously uses the second-order derivative of the chaotic map along trajectories, in addition to the Jacobian. We validate the new method on a super-contracting Smale–Williams Solenoid attractor. We also demonstrate the algorithm on several other examples including smoothly perturbed Arnold Cat maps, and the Lorenz’63 attractor, obtaining visualizations of the curvature of each attractor. Furthermore, we reveal a fundamental connection of the derivation of the CLV self-derivative computation with an efficient computation of linear response of chaotic systems.
Generalized Spectral Clustering via Gromov-Wasserstein Learning

We establish a bridge between spectral clustering and Gromov-Wasserstein Learning (GWL), a recent optimal transport-based approach to graph partitioning. This connection both explains and improves upon the state-of-the-art performance of GWL. The Gromov-Wasserstein framework provides probabilistic correspondences between nodes of source and target graphs via a quadratic programming relaxation of the node matching problem. Our results utilize and connect the observations that the GW geometric structure remains valid for any rank-2 tensor, in particular the adjacency, distance, and various kernel matrices on graphs, and that the heat kernel outperforms the adjacency matrix in producing stable and informative node correspondences. Using the heat kernel in the GWL framework provides new multiscale graph comparisons without compromising theoretical guarantees, while immediately yielding improved empirical results. A key insight of the GWL framework toward graph partitioning was to compute GW correspondences from a source graph to a template graph with isolated, self-connected nodes. We show that when comparing against a two-node template graph using the heat kernel at the infinite time limit, the resulting partition agrees with the partition produced by the Fiedler vector. This in turn yields a new insight into the $k$-cut graph partitioning problem through the lens of optimal transport. Our experiments on a range of real-world networks achieve comparable results to, and in many cases outperform, the state-of-the-art achieved by GWL.
Thermal transport dynamics in active heat transfer fluids (AHTF)

We present results of molecular dynamics calculations of the effective thermal conductivity of nanofluids containing self-propelled nanoparticles. The translational and rotational dynamics observed in the simulations follow the behavior expected from the standard theoretical analysis of Brownian and self-propelled nanoparticles. The superposition of self-propulsion and rotational Brownian motion causes the behavior of the self-propelled nanoparticles to resemble Brownian diffusion with an effective diffusivity that is larger than the standard Brownian value by a factor of several thousand. As a result of the enhanced diffusion (and the convective mixing resulting from the motion), we observe a discriminable increase of the effective thermal conductivity of the solution containing self-propelled nanoparticles. While the increases we observe are in the range of several percent, they are significant considering that, without propulsion, the nanofluid thermal conductivity is essentially not affected by the Brownian motion and can be understood within the effective medium theory of thermal conduction. Our results constitute a proof of concept that self-propelled particles have the potential to enhance thermal conductivity of the liquid in which they are immersed, an idea that could ultimately be implemented in a broad variety of cooling applications.
Acutely Triangulated, Stacked, and Very Ununfoldable Polyhedra

We present new examples of topologically convex edge-ununfoldable polyhedra, i.e., polyhedra that are combinatorially equivalent to convex polyhedra, yet cannot be cut along their edges and unfolded into one planar piece without overlap. One family of examples is acutely triangulated, i.e., every face is an acute triangle. Another family of examples is stacked, i.e., the result of face-to-face gluings of tetrahedra. Both families achieve another natural property, which we call very ununfoldable: for every $k$, there is an example such that every nonoverlapping multipiece edge unfolding has at least $k$ pieces.
A general Bayesian model for heteroskedastic data with fully conjugate full-conditional distributions

Models for heteroskedastic data are relevant in a variety of applications ranging from financial time series to environmental statistics. However, the topic of modelling the variance function condi...