cssi/SciSciGPT-SciSciNet huggingface dataset:
Reverse Regulatory Arbitrage: An Auction Approach to Regulatory Assignments

In the years before the Financial Crisis, banks got to pick their regulators, engaging in a form of regulatory arbitrage that we now know was a race to the bottom. We propose to turn the tables on the banks by allowing regulators, specifically, bank examiners, to choose the banks they regulate. We call this “reverse regulatory arbitrage,” and we think it can help improve regulatory outcomes. Building on our prior work that proposes to pay bank examiners for performance—by giving them financial incentives to avoid bank failures—we argue that bank supervisory assignments should be set through an auction among examiners. Examiner bidding would generate information about examiners’ skills, experience and preferences, as well as information about each bank. Provided examiners bear the upside and downside of their regulatory behavior, a bidding system for regulatory assignments could improve the fit between examiners and the banks they supervise, thereby enhancing regulatory efficiency.
The Many Faces of the Market

While markets are all around us, not all markets are the same. Markets come in a variety of colors based on the legality of activities in the specific market. As such, there is no market economy per se, but instead various shades of markets. The different shades of markets that are evidenced in practice directly depend on the institutional environment that makes certain activities legal or illegal. Shifts in the institutional environment are a result of entrepreneurial activity over the rules of the game. The rules of the game and resulting shade of the market in turn impact entrepreneurs acting within those rules and hence economic development or the lack thereof.Leconomie de marche est en realite une economie des marches. La variete des marches sexplique par la legalite des activites sur chacun dentre eux. Ainsi il nexiste pas une economie de marche en soi, mais plutot plusieurs formes de marche. Ces formes manifestes de marche sont fonction de lenvironnement institutionnel qui rend certaines activites legales ou illegales. Les changements de lenvironnement institutionnel sont le resultat de lactivite entrepreneuriale en dehors des regles du jeu. Les regles du jeu ainsi que la forme du marche qui leur est associee influencent a leur tour les actions entrepreneuriales et donc le developpement economique.
Canada: Foreclosures, Freemen, Foreign Law Schools and the Continuing Search for Meaningful Access to Justice

In this Correspondent's Report, the issue of access to lawyers in Canada is explored through the lens of several high-profile news stories and developments that have captured the attention of the public and the profession over the last several months.
BOOTSTRAPPING DENSITY-WEIGHTED AVERAGE DERIVATIVES

Employing the "small bandwidth"asymptotic framework of Cat- taneo, Crump, and Jansson (2009), this paper studies the properties of a variety of bootstrap-based inference procedures associated with the kernel-based density-weighted averaged derivative estimator proposed by Powell, Stock, and Stoker (1989). In many cases validity of bootstrap-based inference procedures is found to depend crucially on whether the bandwidth sequence satis…es a particular (asymptotic linearity) condition. An exception to this rule occurs for inference procedures involving a studentized esti- mator employing a "robust"variance estimator derived from the "small bandwidth" asymptotic framework. The results of a small-scale Monte Carlo experiment are found to be consistent with the theory and indicate in particular that sensitivity with respect to the bandwidth choice can be ameliorated by using the "robust"variance estimator.
Protecting Brand Image or Gaming the System? Consumer “Gag” Contracts in an Age of Crowdsourced Ratings and Reviews

Traditionally, businesses developed and controlled brand image through company-sponsored advertising and marketing campaigns. With the rise of social media, brand communications have become more interactive, especially on crowdsourced review sites. This increased interactivity helps companies to gain valuable insight into the consumer experience and to improve their brand image and customer engagement. Businesses soon learned that positive consumer ratings and reviews often translated into enhanced brand reputation and increased revenues. Some merchants and professionals seek to burnish their brand image by paying for positive reviews while others try to silence disgruntled customers through adhesive nondisparagement clauses. These gag clauses may rely upon dubious intellectual property claims, monetary penalty clauses, and other financial threats in order to prevent unhappy consumers from posting negative reviews. Certain businesses justify these provisions as legitimate actions needed to protect their brand image and goodwill in a social media environment fraught with fake negative reviews from unscrupulous competitors and libelous consumers. This paper examines the rise of consumer nondisparagement clauses and considers the legality of such agreements under contract, free speech, and intellectual property principles. Issues of freedom of contract and concerns about deceptive advertising, fair use, and copyright misuse are also addressed. This article concludes with a review of self-regulatory proposals that balance freedom of contract and legitimate branding concerns with customer speech interests and issues of good faith and fair dealing.
What Does Social Equality Require of Employers? A Response to Professor Bagenstos

In response to Samuel Bagenstos' recent article "Employment Law and Social Equality," this short essay considers the relationship between social equality and other aspects of individual employment law, including minimum wage laws and regulations on employer control of employee political speech. It also asks whether social equality may provide a consistent normative foundation for all three sub-fields of work law, namely labor law, employment discrimination, and individual employment law.
Explaining the Key Elements of Information Systems-Based Supply-Chain Strategy That Are Necessary for Business-to-Business Electronic Marketplace Survival

Business-to-business electronic marketplaces (EMs) were hyped as the future of efficient supply chains and as essential to the growth of e-commerce, but in spite of this promise, many EMs failed in the first few years after 2000. Although numerous studies have involved EMs, little research has focused on the degree to which an EM’s automated supply- chain strategy contributed to its survival or failure. Accordingly, this study examines 400 EMs through an extensive survey of the strategic supply- chain capabilities in conjunction with McKinsey & Company and CAPS Research. These results are interpreted using existing strategy literature. The following factors were found to be positively related to EM survival: capturing detailed spend data, providing sourcing and process change recommendations, allowing international contracts, offering the ability to track supplier product availability, and having more than 100 employees. In contrast, the following factors led to EM failure: providing warranties, employing...
IPO Liability and Entrepreneurial Response

This Article explores how legal liability in the IPO context can affect an entrepreneur's decision of whether and how to take a firm public. Liability under the Securities Act of 1933 effectively embeds a put option in an IPO security, forcing the entrepreneur to insure shareholders against poor firm performance, inflating the price of the security, and exposing the entrepreneur to risk. This may cause IPO firms to appear to underperform relative to non-IPO firms as the option value decays, and may lead the entrepreneur to undertake strategic (but destructive) responses to minimize the put value and his exposure to risk. Because of the value-destroying characteristics of these responses which include initial underpricing, entrenchment, lower net present value projects, asset partitioning, and reduced disclosure the present state of affairs is inefficient compared to a system where the entrepreneur can simply allocate the risk to shareholders.
Market Definition: An Analytical Overview

This survey addresses the reasons for undertaking market definition in antitrust analysis and methods of doing so.2 It discusses how the process of market definition should be conducted, with attention to the practical difficulties of doing so. Among other things, it explains why markets should be defined based solely with reference to demand substi tution, leaving supply substitution considerations for other steps of com petitive effects analysis, and identifies five types of evidence as to buyer substitution. The article also discusses the appropriate scope of markets (including submarkets) and explains why four approaches sometimes employed for market definition (price correlations, shipment flows, criti cal loss analysis, and cluster markets) should be avoided. In addition, it analyzes how market definition differs depending on whether the alleged harm is prospective or retrospective and depending upon whether the anticompetitive theory involves collusion or exclusion.
ABCs of Trading: Behavioral Biases affect Stock Turnover and Value

Psychological research suggests that individuals are satisficers. That is, when confronted with a large number of options, individuals often choose the first acceptable option, rather than the best possible option (Simon, 1957). Given the vast quantity of information available and the widespread convention of listing stocks in alphabetical order, we conjecture that investors are more likely to buy and sell stocks with early alphabet names. Consistent with this view, we find that early alphabet stocks are traded more frequently than later alphabet stocks and that alphabeticity also affects firm value. We also document how these effects have changed over time.
Genetic Variation in Political Participation

The decision to vote has puzzled scholars for decades. Theoretical models predict little or no variation in participation in large population elections and empirical models have typically accounted for only a relatively small portion of individual-level variance in turnout behavior. However, these models have not considered the hypothesis that part of the variation in voting behavior can be attributed to genetic effects. Matching public voter turnout records in Los Angeles to a twin registry, we study the heritability of political behavior in monozygotic and dizygotic twins. The results show that a significant proportion of the variation in voting turnout can be accounted for by genes. We also replicate these results with data from the National Longitudinal Study of Adolescent Health and show that they extend to a broad class of acts of political participation. These are the first findings to suggest that humans exhibit genetic variation in their tendency to participate in political activities.
Has Katz Become Quaint? Use of Big Data to Outflank the Fourth Amendment

INTRODUCTION On December 14, 2010, a federal court, upon a government motion, entered an order pursuant to the Stored Communications Act (“SCA”) requiring Twitter to turn over to the government subscriber information concerning the accounts of three Twitter users. The order demanded only “non-content” data: names, addresses, and all records of user activity, including dates, times, and IP address data for all subscriber activity since November 1, 2009. The subscribers filed a motion to vacate the order on grounds that it was insufficient under the SCA and violated both the First and Fourth Amendments. The motion was denied by the magistrate judge. 1 The subscribers then filed objections to the magistrate judge’s ruling. 2 The district judge denied the subscribers’ objections, agreeing with the magistrate judge that the subscribers lacked standing to challenge the SCA-based order on non-Constitutional grounds. The court also rejected the subscribers’ Fourth Amendment challenge, stating that “any privacy concerns were the result of private action, not government action,” and thus the “mere
Aggregate Earnings, Firm-Level Earnings, and Expected Stock Returns

This paper provides an analysis of the predictability of stock returns using market-, industry-, and firm-level earnings. Contrary to Lamont (1998), we find that neither dividend payout ratio nor the level of aggregate earnings can forecast the excess market return. We show that these variables do not have robust predictive power across different stock portfolios and sample periods. In contrast to the aggregate-level findings, earnings yield has significant explanatory power for the time-series and cross-sectional variation in firmlevel stock returns and the 48 industry portfolio returns. The mean reversion of stock prices as well as the earnings' correlation with expected stock returns are responsible for the forecasting power of earnings yield. These results are robust after controlling for bookto-market, size, price momentum, and post-earnings announcement drift. At the aggregate level, the information content of firm-level earnings about future cash flows is diversified away and higher aggregate earnings do not forecast higher returns.
Trade-In Rebates for Price Discrimination and Product Recovery

We investigate when and how an original equipment manufacturer (OEM) should offer a trade-in rebate to recover used products in order to achieve better price discrimination and weaken competition from third-party remanufacturers (3PRs). This paper is motivated by a major IT equipment OEM, which negotiates with customers to offer them personalized trade-in rebates to induce them to return their old products and purchase new units. The company also faces increasing competition from 3PRs. We model such a trade-in program with negotiated rebates through a generalized Nash bargaining framework. Our main research question is whether the OEM should compete with a 3PR using only a trade-in program or by offering remanufactured products, or through both options. In the absence of 3PRs, the OEM always prefers to offer the trade-in program compared with not offering a trade-in program. As a trade-in program also helps to restrict the supply of used products to 3PRs, one would expect that offering a trade-in program would be more attractive in the presence of a 3PR. We show, however, that the OEM may find it detrimental to offer a trade-in program when faced with competition from a 3PR. We also show that despite the fact that cores are readily available via the trade-ins, the trade-in program makes it less attractive for the OEM to remanufacture. Finally, we show that offering a trade-in program may also lead to lower total environmental impact, but only in the presence of remanufactured products.
Nationalism and anti-immigrant movements

Wrapping themselves in the flag, anti-immigrant movements proclaim nationalism, but real nationalism often requires immigration even when the immigration damages the interest of some non-migrant groups or categories.
Disagreement and the Allocation of Control

This article studies the allocation of control when there is disagreement—in the sense of differing priors—about the right course of action. People then value control rights since they believe that their decisions are better than those of others. More disagreement (due to, e.g., fundamental uncertainty) increases the value that players attach to control. The article shows that all income and control of a project should then be concentrated in one hand: income rights should go more to people with more control since such people value income higher (because they have a higher opinion of the decisions made); control rights should go more to people with more income since they care more (and believe that they make better decisions). Different projects may be optimally ‘‘owned’’ by different people. Furthermore—with residual income exogenously allocated—complementary decisions should be more co-located, whereas substitute decisions should be more distributed. Confident people with a lot at stake should—in a wide range of settings—get more control. (JEL D7, D8, L2, M1)
Do Oil Shocks Drive Business Cycles? Some U.S. and International Evidence

Oil prices rose sharply prior to the onset of the 2007–2009 recession. Hamilton [in the Palgrave Dictionary of Macroeconomics (2008)] noted that nine of the last ten recessions in the United States were preceded by a substantial increases in the price of oil. In this paper, we consider whether oil price shocks significantly increase the probability of recessions in a number of countries. Because business cycle turning points generally are not available for other countries, we estimate the turning points together with oil's effect in a Markov-switching model with time-varying transition probabilities. We find that, for most countries, oil shocks do affect the likelihood of entering a recession. In particular, for a constant, zero-term spread, an average-sized shock to WTI oil prices increases the probability of recession in the United States by nearly 50 percentage points after one year and nearly 90 percentage points after two years.
Outsourcing Tariff Evasion: A New Explanation for Entrepot Trade

Abstract Traditional explanations for indirect trade through an entrepot focus on savings in transport costs and the role of specialized agents in processing and distribution. We provide an alternative perspective based on the potential for entrepots to facilitate tariff evasion. Using data on direct exports to mainland China and indirect exports via Hong Kong SAR, we find that the indirect export rate rises with the Chinese tariff rate, despite the absence of any legal tax advantage to sending goods via Hong Kong SAR. We present several robustness tests to rule out plausible alternative hypotheses based on existing explanations for entrepot trade.
Is There a “Singles Tax”? The Relative Income Tax Treatment of Single Households

The existence of a “marriage tax,” in which many married couples pay more taxes when married than their combined taxes as single individuals, is well known. However, largely lost in the attention devoted to married taxpayers is the treatment of single taxpayers. This article examines the relative tax treatment of single and married taxpayers. Various types of representative taxpayers are constructed, and the difference in income taxes paid by single taxpayers and married taxpayers is calculated. These calculations show that there is a “singles tax”; that is, a single individual typically pays a greater income tax liability than a married couple with identical income, especially when the main transfer programs are considered.
Auctions with Dynamic Populations: Efficiency and Revenue Maximization

We study a stochastic sequential allocation problem with a dynamic population of privately-informed buyers. We characterize the set of efficient allocation rules and show that a dynamic VCG mechanism is both efficient and periodic ex post incentive compatible; we also show that the revenue-maximizing direct mechanism is a pivot mechanism with a reserve price. We then consider sequential ascending auctions in this setting, both with and without a reserve price. We construct equilibrium bidding strategies in this indirect mechanism where bidders reveal their private information in every period, yielding the same outcomes as the direct mechanisms. Thus, the sequential ascending auction is a natural institution for achieving either efficient or optimal outcomes.
In search of statistically valid risk factors

An influential observation has the potential to render a model unsuitable for estimation with an OLS regression. This is well known in the statistics literature. However, the use of standard measures for detecting and managing influential observations is not common practice in empirical finance. In this article, we examine a leverage point, which is an outlier in an independent variable, from an economic perspective since it represents an actual market event. It is also interesting and subtle from a statistical perspective: it materially deflates the volume of regression confidence ellipsoids but has a limited effect on the regression coefficients, thereby enabling pure noise to masquerade as statistical significance.
The Decisions Job Applicants Must Make: Insights From a Bayesian Perspective

The application/selection process can be thought of as a set of Bayesian opinion revision tasks, in which applicants obtain new information about the organization at each stage of the process and must integrate this information with their prior perceptions of the organization and the jobs. The Bayesian perspective provides useful insights for understanding serial decisions of this type. It suggests that real-world decision-makers are too sensitive to the valence and insufficiently sensitive to the diagnosticity of the information they obtain from interviewers, assessors, etc., and that the effects of information obtained early in the process depends on both the applicant’s state of perceived uncertainty and on the relationship between the applicant’s preconceptions and this early information.
Excavating Constitutional Antecedents in Asia: An Essay on the Potential and Perils

This essay appeared in a symposium issue titled 'Methodological appraches to Asian Constitutionalism'. Written as a response to a main article authored by Tom Ginsburg, it seeks to endorse Tom Ginsburg's call for studies that expand the relatively limited range of historically informed scholarship on constitutional law in Asia. Such a trend will no doubt also broaden the focus of the discipline of contemporary constitutional scholarship, which remains unjustifiably narrow and excludes many regions of the globe. While appreciating the virtues of Ginsburg' broader analysis, the essay also seeks to draw attention to the potential pitfalls of such historically-oriented inquiry. I emphasize the fact that in many Asian societies, contemporary constitutional practice marks radical departures from pre-existing traditions of law and constitutionalism. Drawing upon an example cited by Ginsburg and three others from debates in contemporary Indian constitutionalism, I seek to highlight potential problems with seeking to draw connections between the past and contemporary constitutional practice in Asia. Scholars seeking to follow Ginsburg's call should bear in mind the considerations of methodology and interpretation that this essay highlights. The version of the article uploaded here is the one that is availabe for free on the website of the Chicago-Kent Law Review.
Submillimeter-wave properties of thermospheric rocket plumes

The problem of detecting rocket plumes at thermospheric altitudes with satellite-borne submillimeter-wave radiometers is examined theoretically. To estimate the sizes of plume signatures contrasted against a 250-K earth background or in self-emission against the cold sky, a computer program has been developed to predict plume brightness temperatures and optical depths of rotational lines of plume molecular constituents (e.g., H2O) as a function of distance from the nozzle. The methods employed in the computations are described in general terms, and examples are presented to indicate that detectable H2O signatures extending to several thousand nozzle diameters should exist at plume altitudes above 250 km.
Leverage and Asset Prices: An Experiment.

Abstract We develop a model of leverage that is amenable to laboratory implementation and gather experimental data. We compare two economies that only differ in one dimension: in one economy, agents cannot borrow; in the other, they can leverage a risky asset to issue debt. Leverage increases asset prices in the laboratory. This increase is significant and quantitatively close to what theory predicts. Moreover, also as theory suggests, leverage allows gains from trade to be realized in the laboratory. Finally, the mechanism generating the price increase in the lab is due to the asset role as collateral, and different from what we would observe with a simple credit line or bigger cash endowments.
Algorithms and Hardness for Linear Algebra on Geometric Graphs

For a function $\mathrm{K}:\mathbb{R}^{d}\times \mathbb{R}^{d}\rightarrow \mathbb{R}_{\geq 0}$ , and a set $P=\{x_{1},\ldots, x_{n}\}\subset \mathbb{R}^{d}$ of $n$ points, the K graph $G_{P}$ of $P$ is the complete graph on $n$ nodes where the weight between nodes $i$ and $j$ is given by $\mathrm{K}(x_{i}, x_{j})$ . In this paper, we initiate the study of when efficient spectral graph theory is possible on these graphs. We investigate whether or not it is possible to solve the following problems in $n^{1+o(1)}$ time for a K-graph $G_{P}$ when $d : •Multiply a given vector by the adjacency matrix or Laplacian matrix of $G_{P}$ •Find a spectral sparsifier of $G_{P}$ •Solve a Laplacian system in $G_{P}$ 's Laplacian matrix For each of these problems, we consider all functions of the form $\mathrm{K}(u, v)=f(\Vert u-v\Vert_{2}^{2})$ for a function $f:\mathbb{R}\rightarrow \mathbb{R}$ . We provide algorithms and comparable hardness results for many such K, including the Gaussian kernel, Neural tangent kernels, and more. For example, in dimension $d=\Omega(\log n)$ , we show that there is a parameter associated with the function $f$ for which low parameter values imply $n^{1+o(1)}$ time algorithms for all three of these problems and high parameter values imply the nonexistence of subquadratic time algorithms assuming Strong Exponential Time Hypothesis (SETH), given natural assumptions on $f$ . As part of our results, we also show that the exponential dependence on the dimension $d$ in the celebrated fast multi-pole method of Greengard and Rokhlin cannot be improved, assuming SETH, for a broad class of functions $f$ . To the best of our knowledge, this is the first formal limitation proven about fast multipole methods.
On managing vulnerabilities in AI/ML systems

This paper explores how the current paradigm of vulnerability management might adapt to include machine learning systems through a thought experiment: what if flaws in machine learning (ML) were assigned Common Vulnerabilities and Exposures (CVE) identifiers (CVE-IDs)? We consider both ML algorithms and model objects. The hypothetical scenario is structured around exploring the changes to the six areas of vulnerability management: discovery, report intake, analysis, coordination, disclosure, and response. While algorithm flaws are well-known in academic research community, there is no apparent clear line of communication between this research community and the operational communities that deploy and manage systems that use ML. The thought experiments identify some ways in which CVE-IDs may establish some useful lines of communication between these two communities. In particular, it would start to introduce the research community to operational security concepts, which appears to be a gap left by existing efforts.
On Distributed Non-convex Optimization: Projected Subgradient Method For Weakly Convex Problems in Networks

The stochastic subgradient method is a widely-used algorithm for solving large-scale optimization problems arising in machine learning. Often these problems are neither smooth nor convex. Recently, [1-2] characterized the convergence of the stochastic subgradient method for the weakly convex case, which encompasses many important applications (e.g., robust phase retrieval, blind deconvolution, biconvex compressive sensing, and dictionary learning). In practice, distributed implementations of the projected stochastic subgradient method (stoDPSM) are used to speed-up risk minimization. In this paper, we propose a distributed implementation of the stochastic subgradient method with a theoretical guarantee. Specifically, we show the global convergence of stoDPSM using the Moreau envelope stationarity measure. Furthermore, under a so-called sharpness condition, we show that deterministic DPSM (with a proper initialization) converges linearly to the sharp minima, using geometrically diminishing step-size. We provide numerical experiments to support our theoretical analysis.
Charge radii of the nucleon from its flavor dependent Dirac form factors

We have determined the proton and the neutron charge radii from a global analysis of the proton and the neutron elastic form factors, after first performing a flavor decomposition of these form factors under charge symmetry in the light cone frame formulation. We then extracted the transverse mean-square radii of the flavor dependent quark distributions. In turn, these are related in a model-independent way to the proton and neutron charge radii but allow us to take into account motion effects of the recoiling nucleon for data at finite but high momentum transfer. In the proton case we find $$\langle r_p \rangle = 0.852 \pm 0.002_{\mathrm{(stat.)}} \pm 0.009_{\mathrm{(syst.)}}~({\mathrm{fm}})$$ , consistent with the proton charge radius obtained from muonic hydrogen spectroscopy [1, 2]. The current method improves on the precision of the $$\langle r_p \rangle $$ extraction based on the form factor measurements. Furthermore, we find no discrepancy in the $$\langle r_p \rangle $$ determination among the different electron scattering measurements, all of which, utilizing the current method of extraction, result in a value that is consistent with the smallest $$\langle r_p \rangle $$ extraction from the electron scattering measurements [3]. Concerning the neutron case, past results relied solely on the neutron-electron scattering length measurements, which suffer from an underestimation of underlying systematic uncertainties inherent to the extraction technique. Utilizing the present method we have performed the first extraction of the neutron charge radius based on nucleon form factor data, and we find $$\langle r_n^2 \rangle = -0.122 \pm 0.004_\mathrm{(stat.)} \pm 0.010_\mathrm{(syst.)}~(\mathrm{fm}^2)$$ .
Corrected score methods for estimating Bayesian networks with error‐prone nodes

Motivated by inferring cellular signaling networks using noisy flow cytometry data, we develop procedures to draw inference for Bayesian networks based on error-prone data. Two methods for inferring causal relationships between nodes in a network are proposed based on penalized estimation methods that account for measurement error and encourage sparsity. We discuss consistency of the proposed network estimators and develop an approach for selecting the tuning parameter in the penalized estimation methods. Empirical studies are carried out to compare the proposed methods with a naive method that ignores measurement error. Finally, we apply these methods to infer signaling networks using single cell flow cytometry data.
Construction of linearly independent non-orthogonal AGP states

We show how to construct a linearly independent set of antisymmetrized geminal power (AGP) states, which allows us to rewrite our recently introduced geminal replacement models as linear combinations of non-orthogonal AGPs. This greatly simplifies the evaluation of matrix elements and permits us to introduce an AGP-based selective configuration interaction method, which can reach arbitrary excitation levels relative to a reference AGP, balancing accuracy and cost as we see fit.
Simpler proof for nonlinearity of majority function

Abstract Given a Boolean function f , the (Hamming) weight w t ( f ) and the nonlinearity N ( f ) are well known to be important in designing functions that are useful in cryptography. The nonlinearity is expensive to compute, in general, so any formulas giving the nonlinearity for particular functions f are significant. The well known majority function has been extensively studied in a cryptographic context for the last dozen years or so, and there is a formula for its nonlinearity. The known proofs for this formula rely on many detailed results for the Krawtchouk polynomials. This paper gives a much simpler proof.
Independent sets in hypergraphs with a forbidden link

We give a probabilistic construction of a $3$-uniform hypergraph on $N$ vertices with independence number $O(\log N / \log \log N)$ in which there are at most two edges among any four vertices. This bound is tight and solves a longstanding open problem of Erd\H{o}s and Hajnal in Ramsey theory. We further extend this result to prove tight bounds on various other hypergraph Ramsey numbers.
Rapid Tilt-Series Method for Cryo-Electron Tomography: Characterizing Stage Behavior During FISE Acquisition

We and others recently developed rapid tilt-series acquisition methods for cryo-electron tomography on a Titan Krios G3i equipped with a single axis holder and a K-series direct electron detector and showed that one of these, the fast-incremental single exposure (FISE) method, significantly accelerates tilt-series acquisition when compared to traditional methods while preserving the quality of the images. Here, we characterize the behavior of our single axis holder in detail during a FISE experiment to optimally balance data quality with speed. We explain our methodology in detail so others can characterize their own stages, and conclude with recommendations for projects with different resolution goals.
Understanding deep learning requires rethinking generalization.

Despite their massive size, successful deep artificial neural networks can exhibit a remarkably small difference between training and test performance. Conventional wisdom attributes small generalization error either to properties of the model family, or to the regularization techniques used during training. Through extensive systematic experiments, we show how these traditional approaches fail to explain why large neural networks generalize well in practice. Specifically, our experiments establish that state-of-the-art convolutional networks for image classification trained with stochastic gradient methods easily fit a random labeling of the training data. This phenomenon is qualitatively unaffected by explicit regularization, and occurs even if we replace the true images by completely unstructured random noise. We corroborate these experimental findings with a theoretical construction showing that simple depth two neural networks already have perfect finite sample expressivity as soon as the number of parameters exceeds the number of data points as it usually does in practice. We interpret our experimental findings by comparison with traditional models.
Cooperative Training of Fast Thinking Initializer and Slow Thinking Solver for Conditional Learning.

This paper studies the problem of learning the conditional distribution of a high-dimensional output given an input, where the output and input belong to two different domains, e.g., the output is a photo image and the input is a sketch image. We solve this problem by cooperative training of a fast thinking initializer and slow thinking solver. The initializer generates the output directly by a non-linear transformation of the input as well as a noise vector that accounts for latent variability in the output. The slow thinking solver learns an objective function in the form of a conditional energy function, so that the output can be generated by optimizing the objective function, or more rigorously by sampling from the conditional energy-based model. We propose to learn the two models jointly, where the fast thinking initializer serves to initialize the sampling of the slow thinking solver, and the solver refines the initial output by an iterative algorithm. The solver learns from the difference between the refined output and the observed output, while the initializer learns from how the solver refines its initial output. We demonstrate the effectiveness of the proposed method on various conditional learning tasks.
Simulation of cryogenic buffer gas beams

The authors combine molecular and fluid flow methods to numerically simulate cryogenic buffer gas beams used for cooling atoms and molecules.
Search for Muonic Dark Forces at BABAR

Many models of physics beyond Standard Model predict the existence of light Higgs states, dark photons, and new gauge bosons mediating interactions between dark sectors and the Standard Model. Using a full data sample collected with the BABAR detector at the PEP-II e + e − collider, we report searches for a light non-Standard Model Higgs boson, dark photon, and a new muonic dark force mediated by a gauge boson (Z ′) coupling only to the second and third lepton families. Our results significantly improve upon the current bounds and further constrain the remaining region of the allowed parameter space.
Discovering Phase Field Models from Image Data with the Pseudo-Spectral Physics Informed Neural Networks

In this paper, we introduce a new deep learning framework for discovering the phase-field models from existing image data. The new framework embraces the approximation power of physics informed neural networks (PINNs) and the computational efficiency of the pseudo-spectral methods, which we named pseudo-spectral PINN or SPINN. Unlike the baseline PINN, the pseudo-spectral PINN has several advantages. First of all, it requires less training data. A minimum of two temporal snapshots with uniform spatial resolution would be adequate. Secondly, it is computationally efficient, as the pseudo-spectral method is used for spatial discretization. Thirdly, it requires less trainable parameters compared with the baseline PINN, which significantly simplifies the training process and potentially assures fewer local minima or saddle points. We illustrate the effectiveness of pseudo-spectral PINN through several numerical examples. The newly proposed pseudo-spectral PINN is rather general, and it can be readily applied to discover other PDE-based models from image data.
Network representation using graph root distributions

Exchangeable random graphs serve as an important probabilistic framework for the statistical analysis of network data. In this work, we develop an alternative parameterization for a large class of exchangeable random graphs, where the nodes are independent random vectors in a linear space equipped with an indefinite inner product, and the edge probability between two nodes equals the inner product of the corresponding node vectors. Therefore, the distribution of exchangeable random graphs in this subclass can be represented by a node sampling distribution on this linear space, which we call the graph root distribution. We study existence and identifiability of such representations, the topological relationship between the graph root distribution and the exchangeable random graph sampling distribution and estimation of graph root distributions.
Optimal Exchange Rate Policy: The Influence of Price Setting and Asset Markets

This paper examines optimal exchange-rate policy in two-country sticky-price general equilibrium models in which households and firms optimize over an infinite horizon in an environment of uncertainty. The models are in the vein of the new open-economy macroeconomics' as exemplified by Obstfeld and Rogoff (1995, 1998, 2000). The conditions under which fixed or floating exchange rates yield higher welfare depend on the exact nature of price stickiness and on the degree of risk-sharing opportunities. This paper presents some preliminary empirical evidence on the behavior of consumer prices in Mexico that suggests failures of the law of one price are important. The evidence on price setting and risk-sharing opportunities is not refined enough to make definitive conclusions about the optimal exchange-rate regime for that country.
Joint Sensor Node Selection and State Estimation for Nonlinear Networks and Systems

State estimation and sensor selection problems for nonlinear networks and systems are ubiquitous problems that are important for the control, monitoring, analysis, and prediction of a large number of engineered and physical systems. Sensor selection problems are extensively studied for linear networks. However, less attention has been dedicated to networks with nonlinear dynamics. Furthermore, widely used sensor selection methods relying on structural (graph-based) observability approaches might produce far from optimal results when applied to nonlinear network dynamics. In addition, state estimation and sensor selection problems are often treated separately, and this might decrease the overall estimation performance. To address these challenges, we develop a novel methodology for selecting sensor nodes for networks with nonlinear dynamics. Our main idea is to incorporate the sensor selection problem into an initial state estimation problem. The resulting mixed-integer nonlinear optimization problem is approximately solved using three methods. The good numerical performance of our approach is demonstrated by testing the algorithms on prototypical Duffing oscillator, associative memory, and chemical reaction networks. The developed codes are available online.
Bridging the gap between optimal trajectory planning and safety-critical control with applications to autonomous vehicles

Abstract We address the problem of optimizing the performance of a dynamic system while satisfying hard safety constraints at all times. Implementing an optimal control solution is limited by the computational cost required to derive it in real time, especially when constraints become active, as well as the need to rely on simple linear dynamics, simple objective functions, and ignoring noise. The recently proposed Control Barrier Function (CBF) method may be used for safety-critical control at the expense of sub-optimal performance. In this paper, we develop a real-time control framework that combines optimal trajectories generated through optimal control with the computationally efficient CBF method providing safety guarantees. We use Hamiltonian analysis to obtain a tractable optimal solution for a linear or linearized system , then employ High Order CBFs (HOCBFs) and Control Lyapunov Functions (CLFs) to account for constraints with arbitrary relative degrees and to track the optimal state, respectively. We further show how to deal with noise in arbitrary relative degree systems. The proposed framework is then applied to the optimal traffic merging problem for Connected and Automated Vehicles (CAVs) where the objective is to jointly minimize the travel time and energy consumption of each CAV subject to speed, acceleration, and speed-dependent safety constraints. In addition, when considering more complex objective functions, nonlinear dynamics and passenger comfort requirements for which analytical optimal control solutions are unavailable, we adapt the HOCBF method to such problems. Simulation examples are included to compare the performance of the proposed framework to optimal solutions (when available) and to a baseline provided by human-driven vehicles with results showing significant improvements in all metrics.
Low-force elastocaloric refrigeration via bending

Elastocaloric cooling has been identified as a promising alternative to high global warming potential vapor compression cooling. Two key bottlenecks to adoption are the need for bulky/expensive actuators to provide sufficient uniaxial stress and inadequate elastocaloric material fatigue life. This paper defines the physics that govern the performance of axisymmetric flexural bending for use as an emerging low-force and low-fatigue elastocaloric heating and cooling mechanisms and further demonstrates a continuous rotary-driven cooling prototype using polycrystalline Ni50.75Ti48.74. Elastocaloric material performance is determined using infrared thermography during uniaxial-tension and four-point bending thermomechanical testing. A systematic study reveals the effects of strain rate (from 0.001 to 0.025 s−1), maximum strain (from 2 to 8%), and strain mode on the temperature evolution, mechanical response, and coefficient of performance. Four-point bending experiments demonstrate a temperature reduction of up to 11.3 °C, material coefficients of performance between 2.31 and 21.71, and a 6.09- to 7.75-fold reduction in the required actuation force compared to uniaxial tension. The absence of localized macroscopic martensite domains and reduced mechanical dissipation during flexure represent reduced microstructure degradation and improved fatigue life. The rotary-based elastocaloric cooling prototype is shown to provide similar thermomechanical performance with the added benefit of discrete hot and cold zones, continuous cooling, inexpensive rotary actuation, and scalability, which represents a significant advancement for compact, long lifetime, and inexpensive elastocaloric cooling.
Enhancing the formation of ionic defects to study the ice Ih/XI transition with molecular dynamics simulations

Ice Ih, the common form of ice in the biosphere, contains proton disorder. Its proton-ordered counterpart, ice XI, is thermodynamically stable below 72 K. However, the formation of ice XI is kineti...
The Stability of Immigration Attitudes: Evidence and Implications

Do voters have stable immigration views? While any account of immigration politics must make an assumption about whether underlying attitudes are stable, the literature has been ambiguous regarding...
Case Study: Disclosure of Indirect Device Fingerprinting in Privacy Policies

Recent developments in online tracking make it harder for individuals to detect and block trackers. This is especially true for device fingerprinting techniques that websites use to identify and track individual devices. Direct trackers – those that directly ask the device for identifying information – can often be blocked with browser configurations or other simple techniques. However, some sites have shifted to indirect tracking methods, which attempt to uniquely identify a device by asking the browser to perform a seemingly-unrelated task. One type of indirect tracking known as Canvas fingerprinting causes the browser to render a graphic recording rendering statistics as a unique identifier. Even experts find it challenging to discern some indirect fingerprinting methods. In this work, we aim to observe how indirect device fingerprinting methods are disclosed in privacy policies, and consider whether the disclosures are sufficient to enable website visitors to block the tracking methods. We compare these disclosures to the disclosure of direct fingerprinting methods on the same websites.
Strategic coalitions in stochastic games

The article compares two different approaches of incorporating probability into coalition logics. One is based on the semantics of games with stochastic transitions and the other on games with the stochastic failures. The work gives an example of a non-trivial property of coalition power for the first approach and a complete axiomatization for the second approach. It turns out that the logical properties of the coalition power modality under the second approach depend on whether the modal language allows the empty coalition. The main technical results for the games with stochastic failures are a strong completeness theorem for the logical system without the empty coalition and an incompleteness theorem which shows that there is no strongly complete logical system in the language with the empty coalition.
Outside the Echo Chamber: Optimizing the Performative Risk

In performative prediction, predictions guide decision-making and hence can influence the distribution of future data. To date, work on performative prediction has focused on finding performatively stable models, which are the fixed points of repeated retraining. However, stable solutions can be far from optimal when evaluated in terms of the performative risk, the loss experienced by the decision maker when deploying a model. In this paper, we shift attention beyond performative stability and focus on optimizing the performative risk directly. We identify a natural set of properties of the loss function and model-induced distribution shift under which the performative risk is convex, a property which does not follow from convexity of the loss alone. Furthermore, we develop algorithms that leverage our structural assumptions to optimize the performative risk with better sample efficiency than generic methods for derivative-free convex optimization.