cssi/SciSciGPT-SciSciNet huggingface dataset:
The impact of geographic diversification on firm performance

Abstract This paper empirically examines the economic effects of both corporate industrial and geographic diversifications. Using a sample of 28,050 firm-year observations from 1990 to 1998, we find that industrial and geographic diversifications are associated with firm value decrease. Consistent with Denis et al. [Denis, D. J., Denis, D. K., and Yost, K. (2002). Global diversification, industrial diversification, and firm value. Journal of Finance, 57, 1951–1979], the costs of corporate diversification may outweigh the benefits of diversification. We find that geographically diversified firms have higher R&D expenditures, advertising expenses, operating income, ROE and ROA than industrially diversified firms. In addition, higher R&D expenditures create value for multi-segment global firms, but not for single-segment global firms. This result implies that there exists an interaction effect between industrial and geographic diversification. We also examine the effects of agency cost issues, as characterized by the diversification discount, on both industrial and geographic diversification. Consistent with the agency explanation, firms with high equity-based compensation are associated with higher firm value than firms with low equity-based compensation. Also, we find that firms with a higher insider ownership percentage are associated with higher excess value.
FREEDOM OF CONSCIENCE AS RELIGIOUS AND MORAL FREEDOM

In another essay being published contemporaneously with this one, I have explained that as the concept “human right” is understood both in the Universal Declaration of Human Rights and in all the various international human rights treaties that have followed in the Universal Declaration's wake, a right is a human right if the rationale for establishing and protecting the right—for example, as a treaty-based right—is, in part, that conduct that violates the right violates the imperative, articulated in Article 1 of the Universal Declaration, to “act towards all human beings in a spirit of brotherhood.” Each of the human rights articulated in the Universal Declaration and/or in one or more international human rights treaties—for example, the right, articulated in Article 5 of the Universal Declaration and elsewhere, not to be subjected to “cruel, inhuman or degrading treatment or punishment”—is a specification of what, in conjunction with other considerations, the imperative—which functions in the morality of human rights as the normative ground of human rights—is thought to forbid (or to require). A particular specification is controversial if and to the extent the supporting claim—a claim to the effect that the “act towards all human beings in a spirit of brotherhood” imperative forbids (or requires) X—is controversial. My aim in this essay is to elaborate and defend a particular specification: the right, internationally recognized as a human right, to freedom of conscience—to freedom, that is, to live one's life in accord with the deliverances of one's conscience.
Did blacklisting hurt the tax havens

Purpose – The purpose of this paper is to test the widely‐held assumption that blacklisting, such as that practiced by the Organization for Economic Cooperation and Development (OECD) and the Financial Action Task Force (FATF), affects the volume of financial activity associated with a tax haven.Design/methodology/approach – ARIMA (autoregressive integrated moving average) analysis is used to explore changes across eight measures of in banking‐associated activity that occurred when a tax haven was placed on, or removed from, one FATF and two OECD blacklists.Findings – The results are highly varied. Most importantly, no substantial and consistent impact of blacklisting on banking investment in and out of the tax havens was found across 38 jurisdictions.Practical implications – The role of “speech acts” – unconnected with other developments in the havens or foreign actions beyond rhetoric – may not be as important for tax haven investment as previously thought.Originality/value – No rigorous and comprehensi...
Firm Turnover in Imperfectly Competitive Markets

This paper is motivated by the empirical regularity that industries differ greatly in the level of firm turnover and that entry and exit rates are positively correlated across industries. Our objective is to investigate the effect of fixed costs and, in particular. market size on entry and exit rates and hence on the age distribution of firms. We analyse a stochastic dynamic model of a monopolistically competitive industry. Each firm's efficiency is assumed to follow a Markov process. We show existence and uniqueness of a stationary equilibrium with simultaneous entry and exit: efficient firms survive, while inefficient ones leave the market and are replaced by new entrants. We perform comparative dynamics with respect to the level of fixed costs: entry costs are negatively related and fixed production costs positively related to entry and exit rates. A central empirical prediction of the model is that the level of firm turnover is increasing in market size. In larger markets, competition is endogenously more intense than in smaller markets, and so price-cost margins are smaller. This price competition effect implies that the marginal surviving firm has to be more efficient than in smaller markets. Hence, in larger markets, the expected lifespan of firms is shorter, and the age distribution of firms is first-order stochastically dominated by that in smaller markets. In the empirical part, the prediction on market size and firm turnover is tested on an industry where firms compete in well-defined geographical markets of different sizes. Using data on hair salons in Sweden, we show that an increase in market size or fixed costs shifts the a-e distribution of firms towards younger firms, as predicted by the model.
Limited Investor Attention and Stock Market Misreactions to Accounting Information

We provide a model in which a single psychological constraint, limited attention, explains both under- and overreaction to different earnings components. Investor neglect of earnings induces post-earnings announcement drift and the profit anomaly. Neglect of earnings components causes accrual and cash flow anomalies. The model offers empirical implications relating the strength of earnings-related anomalies to the forecasting power of current earnings-related information for future earnings, investor attentiveness, and the volatilities of and correlation between accruals and cash flows. We also show that, owing to attention costs, in equilibrium not all investors choose to attend to earnings or its components. (JEL G12, G14, M41, M43)
Lies Without Liars? Janus Capital and Conservative Securities Jurisprudence

The Supreme Court’s recent Janus Capital case offers a reading of the word “make” in Rule 10b-5 that speaks to ultimate legal authority over the communication in question. This creates the real possibility that we can have lies without liars, an entirely perplexing result in terms of any purposive meaning of the rule. In so holding, Justice Thomas joined a seemingly short list of judges who suggest that legal formalism is a particularly good weapon with which to fight securities fraud. This paper explores Janus through the lens of conservative textualism, which takes us through a much longer intellectual history with respect to secondary liability (well beyond Central Bank of Denver) than is generally acknowledged. It makes two important claims: first, that a better reading of the opinion is one that limits it to private securities litigation, not SEC enforcement, even though the early precedent tilts in the other direction; and second, that Janus also plants seeds for a conservative retrenchment on the meaning of the “in connection with” requirement under Section 10(b) and Rule 10b-5, at least as applied to private securities litigation. It then examines some open questions under the “ultimate authority” test — the ability to reach officers and directors of the issuer, the problem of informal publicity, attribution of knowledge and “scheme” pleading, and considers whether the SEC can overturn Janus on its own by a simple clarifying amendment to its rule.
The Efficacy of Sexualized Female Models in Young Adult-Male Oriented Cigarette Advertising

This paper explores the portrayal (efficacy) of female models in male-targeted cigarette print advertisements through a content analysis (experiment). We first describe the presence and portrayal of females through a content analysis on male-targeted cigarette advertisements in magazines; we find that, in aggregate, females are often used as sexually attractive decoration, and the overall female portrayal is fundamentally different (and unequal) to that of the male. We next investigate the efficacy of these portrayals through an experiment among young adult-aged male smokers and nonsmokers. Our experimental findings indicate that these sexually charged advertisements generate superior attitudinal results in non-smoking males than smoking males, a finding that – in conjunction with the portrayal of females in the content analysis – suggests potential societal and public policy implications.
Semi-Analytical Valuation of Basket Credit Derivatives in Intensity-Based Models

Credit risk models today mostly come from two different families: structural models and reduced-form, or intensity-based, models. In the former, default probabilities are derived from estimates of how far a given firm9s net asset value is above a critical level at which it would default. Default correlations come from correlation in the underlying firm values. In intensity-based models, default is modeled as the first jump in a pure jump process, with a jump intensity that may be a function of exogenous factors. Default correlation arises from correlation in those factors. The expanding market for “correlation products,” such as basket credit derivatives, raises the need to deal explicitly with default correlation; so far, there has been relatively greater emphasis on the structural approach exemplified by the Gaussian copula model. In this article, Mortensen argues that an intensity-based approach can work just as well, or better. He presents a general model in which default intensities are driven by affine jump-diffusion processes, with a common factor that produces correlation among issuers, and one independent idiosyncratic factor per issuer. Calibrating against tranche prices from CDX and iTraxx CDOs, the intensity-based model is shown to be capable of fitting the market better than standard copula methods.
Empirical Implications of Equilibrium Bidding in First-Price, Symmetric, Common Value Auctions

This paper studies federal auctions for wildcat leases on the Outer Continental Shelf from 1954 to 1970. These are leases where bidders privately acquire (at some cost) noisy, but equally informative, signals about the amount of oil and gas that may be present. We develop tests of rational and equilibrium bidding in a common values model that are implemented using data on bids and ex post values. We also use data on tract location and exposr values to test the comparative static prediction that bidders may bid less aggressively in common value auctions when they expect more competition. We find that bidders are aware of the "winner's curse" and their bidding is largely consistent with equilibrium.
Hume's Internalism Reconsidered

A standard reading of Hume on the nature of practical reasons holds that he is a normative internalist; that, for Hume, legitimate practical reasons must be linked to an agent’s set of desires or motivating passions. Though the internalist reading of Hume is popular, it gives rise to serious puzzles of interpretation. To pick one nearly at random, it appears that, on an internalist reading, Hume has serious difficulties in establishing that the so-called “artificial" virtues of justice and promise-keeping are reason-giving, especially when it comes to characters like the sensible knave. Some, skeptical of the internalist reading, have argued that Hume is in fact a nihilist about practical reasons, and admits no reasons for action at all. Against the internalist and skeptical readings, I argue that there is substantial reason to believe that Hume’s corpus is compatible with a far more robust account of normativity than even internalism allows. If so, I argue, Hume has a genuine response to the sensible knave that establishes the knave’s obligation to justice. As it turns out, Hume’s considered view is unique, underexplored, and merits further philosophical investigation.
Robust Trajectory Optimization Over Uncertain Terrain With Stochastic Complementarity

Trajectory optimization with contact-rich behaviors has recently gained attention for generating diverse locomotion behaviors without pre-specified ground contact sequences. However, these approaches rely on precise models of robot dynamics and the terrain and are susceptible to uncertainty. Recent works have attempted to handle uncertainties in the system model, but few have investigated uncertainty in contact dynamics. In this letter, we model uncertainty stemming from the terrain and design corresponding risk-sensitive objectives for contact-implicit trajectory optimization. In particular, we parameterize uncertainties from the terrain contact distance and friction coefficients using probability distributions and propose a corresponding expected residual minimization cost approach. We evaluate our method in three simple robotic examples, including a legged hopping robot, and we benchmark one of our examples in simulation against a robust worst-case solution. We show that our risk-sensitive method produces contact-averse trajectories that are robust to terrain perturbations. Moreover, we demonstrate that the resulting trajectories converge to those generated by a traditional, non-robust method as the terrain model becomes more certain. Our study marks an important step towards a fully robust, contact-implicit approach suitable for deploying robots on real-world terrain.
Development of an Array of Kinetic Inductance Magnetometers (KIMs)

We describe optimization of a cryogenic magnetometer that uses nonlinear kinetic inductance in superconducting nanowires as the sensitive element instead of a superconducting quantum interference device (SQUID). The circuit design consists of a loop geometry with two nanowires in parallel, serving as the inductive section of a lumped LC resonator similar to a kinetic inductance detector (KID). This device takes advantage of the multiplexing capability of the KID, allowing for a natural frequency multiplexed readout. The Kinetic Inductance Magnetometer (KIM) is biased with a DC magnetic flux through the inductive loop. A perturbing signal will cause a flux change through the loop, and thus a change in the induced current, which alters the kinetic inductance of the nanowires, causing the resonant frequency of the KIM to shift. This technology has applications in astrophysics, material science, and the medical field for readout of Metallic Magnetic Calorimeters (MMCs), axion detection, and magnetoencephalography (MEG).
ATPG-Guided Fault Injection Attacks on Logic Locking

Logic Locking is a well-accepted protection technique to enable trust in the outsourced design and fabrication processes of integrated circuits (ICs) where the original design is modified by incorporating additional key gates in the netlist, resulting in a key-dependent functional circuit. The original functionality of the chip is recovered once it is programmed with the secret key, otherwise, it produces incorrect results for some input patterns. Over the past decade, different attacks have been proposed to break logic locking, simultaneously motivating researchers to develop more secure countermeasures. In this paper, we propose a novel stuck-at fault-based differential fault analysis (DFA) attack, which can be used to break logic locking that relies on a stored secret key. This proposed attack is based on self-referencing, where the secret key is determined by injecting faults in the key lines and comparing the response with its fault-free counterpart. A commercial ATPG tool can be used to generate test patterns that detect these faults, which will be used in DFA to determine the secret key. One test pattern is sufficient to determine one key bit, which results in at most $\vert\pmb{K}\vert$ test patterns to determine the entire secret key of size $\vert\pmb{K}\vert$ . The proposed attack is generic and can be extended to break any logic locked circuits.
A spectral approach to the shortest path problem

Abstract Let G = ( V , E ) be a simple, connected graph. One is often interested in a short path between two vertices u , v ∈ V . We propose a spectral algorithm: construct the function ϕ : V → R ≥ 0 ϕ = arg ⁡ min f : V → R f ( u ) = 0 , f ≢ 0 ⁡ ∑ ( w 1 , w 2 ) ∈ E ( f ( w 1 ) − f ( w 2 ) ) 2 ∑ w ∈ V f ( w ) 2 . ϕ can also be understood as the smallest eigenvector of the Laplacian Matrix L = D − A after the u-th row and column have been removed. We start in the point v and construct a path from v to u: at each step, we move to the neighbor for which ϕ is the smallest. This algorithm provably terminates and results in a short path from v to u, often the shortest. The efficiency of this method is due to a discrete analogue of a phenomenon in Partial Differential Equations that is not well understood. We prove optimality for trees and discuss a number of open questions.
Operando Electrochemical Kinetics in Particulate Porous Electrodes by Quantifying the Mesoscale Spatiotemporal Heterogeneities

Electrochemical energy systems rely on particulate porous electrodes to store or convert energies. While the three-dimensional porous structures were introduced to maximize the interfacial area for better overall performance of the system, spatiotemporal heterogeneities arose from materials thermodynamics localize the charge transfer processes onto a limited portion of the available interfaces. Here, we demonstrate a simple but precision method that can directly track and analyze the operando (i.e. local and reacting) interfaces at the mesoscale in a practical graphite porous electrode to obtain the true local current density, which turned out to be two orders of magnitude higher than the globally averaged current density adopted by existing studies. Our results resolve the long-standing discrepancies between kinetics parameters derived from electroanalytical measurements and from first principles predictions. Contradictory to prevailing beliefs, the electrochemical dynamics is not controlled by the solid-state diffusion process once the spatiotemporal reaction heterogeneities emerge in porous electrodes.
Finite horizon robust synthesis using integral quadratic constraints

We present a robust synthesis algorithm for uncertain linear time-varying (LTV) systems on finite horizons. The uncertain system is described as an interconnection of a known LTV system and a perturbation. The input-output behavior of the perturbation is specified by time-domain Integral Quadratic Constraints (IQCs). The objective is to synthesize a controller to minimize the worst-case performance. This leads to a non-convex optimization. The proposed approach alternates between an LTV synthesis step and an IQC analysis step. Both induced $\mathcal{L}_2$ and terminal Euclidean norm penalties on output are considered for finite horizon performance. The proposed algorithm ensures that the robust performance is non-increasing at each iteration step. The effectiveness of this method is demonstrated using numerical examples.
Agonist efficiency from concentration-response curves: Structural implications and applications

Abstract Agonists are evaluated by a concentration-response curve (CRC), with a midpoint (EC50) that indicates potency, a high-concentration asymptote that indicates efficacy, and a low-concentration asymptote that indicates constitutive activity. A third agonist attribute, efficiency (η), is the fraction of binding energy that is applied to the conformational change that activates the receptor. We show that η can be calculated from EC50 and the asymptotes of a CRC derived from either single-channel or whole-cell responses. For 20 agonists of skeletal muscle nicotinic receptors, the distribution of η-values is bimodal with population means at 51% (including acetylcholine, nornicotine, and dimethylphenylpiperazinium) and 40% (including epibatidine, varenicline, and cytisine). The value of η is related inversely to the size of the agonist’s headgroup, with high- versus low-efficiency ligands having an average volume of 70 vs. 102 A3. Most binding site mutations have only a small effect on acetylcholine efficiency, except for αY190A (35%), αW149A (60%), and those at αG153 (42%). If η is known, the EC50 and high-concentration asymptote can be calculated from each other. Hence, an entire CRC can be estimated from the response to a single agonist concentration, and efficacy can be estimated from EC50 of a CRC that has been normalized to 1. Given η, the level of constitutive activity can be estimated from a single CRC.
Reducible Markov Decision Processes and Stochastic Games

Markov decision processes (MDPs) provide a powerful framework for analyzing dynamic decision making. However, their applications are significantly hindered by the difficulty of obtaining solutions. In this study, we introduce reducible MDPs whose exact solutions can be obtained by solving simpler MDPs, termed the coordinate MDPs. The value function and an optimal policy of a reducible MDP are linear functions of those of the associated coordinate MDP. Because the coordinate MDP does not involve the multi‐dimensional endogenous state, we achieve dimension reduction on a reducible MDP. Extending the MDP framework to multiple players, we introduce reducible stochastic games. We show that these games reduce to simpler coordinate games that do not involve the multi‐dimensional endogenous state. We specify sufficient conditions for the existence of a pure‐strategy Markov perfect equilibrium in reducible stochastic games and derive closed‐form expressions for the players’ equilibrium values. The reducible framework encompasses a variety of linear and nonlinear models and offers substantial simplification in analysis and computation. We provide guidelines and illustrative examples on formulating problems as reducible models. We demonstrate the applicability and modeling flexibility of reducible models in a wide range of contexts including capacity and inventory management and duopoly competition.
Cautious active clustering

Abstract We consider the problem of classification of points sampled from an unknown probability measure on a Euclidean space. We study the question of querying the class label at a very small number of judiciously chosen points so as to be able to attach the appropriate class label to every point in the set. Our approach is to consider the unknown probability measure as a convex combination of the conditional probabilities for each class. Our technique involves the use of a highly localized kernel constructed from Hermite polynomials, in order to create a hierarchical estimate of the supports of the constituent probability measures. We do not need to make any assumptions on the nature of any of the probability measures nor know in advance the number of classes involved. We give theoretical guarantees measured by the F-score for our classification scheme. Examples include classification in hyper-spectral images and MNIST classification.
Nonprofit board turnover and financial performance: Examining optimal board turnover rate in United Way organizations

This article examines the effects of board turnover on nonprofit financial performance: resource acquisition and utilization. Governing board members play key roles in connecting organizations with external environments and ensuring that executives properly manage the organizations to achieve organizational missions. They also help in effectively attracting and appropriately utilizing financial resources. Given the importance of governing board members, any turnover occurring in the board should affect nonprofit financial performance. Using insights from organizational theories, we argue that the relationship between board turnover and the ability of UW organizations to acquire and utilize funds is nonlinear (first positive and then negative). We find general support for the hypothesis, which yields implications for both research and practice of board governance and human resources management in the nonprofit sector.
CRYLOGGER: Detecting Crypto Misuses Dynamically

Cryptographic (crypto) algorithms are the essential ingredients of all secure systems: crypto hash functions and encryption algorithms, for example, can guarantee properties such as integrity and confidentiality. Developers, however, can misuse the application programming interfaces (API) of such algorithms by using constant keys and weak passwords. This paper presents CRYLOGGER, the first open-source tool to detect crypto misuses dynamically. CRYLOGGER logs the parameters that are passed to the crypto APIs during the execution and checks their legitimacy offline by using a list of crypto rules. We compared CRYLOGGER with CryptoGuard, one of the most effective static tools to detect crypto misuses. We show that our tool complements the results of CryptoGuard, making the case for combining static and dynamic approaches. We analyzed 1780 popular Android apps downloaded from the Google Play Store to show that CRYLOGGER can detect crypto misuses on thousands of apps dynamically and automatically. We reverse-engineered 28 Android apps and confirmed the issues flagged by CRYLOGGER. We also disclosed the most critical vulnerabilities to app developers and collected their feedback.
Improved lower bounds for the number of fields with alternating Galois group

Let $n \geq 6$ be an integer. We prove that the number of number fields with Galois group $A_n$ and absolute discriminant at most $X$ is asymptotically at least $X^{1/8 + O(1/n)}$. For $n \geq 8$ this improves upon the previously best known lower bound of $X^{(1 - \frac{2}{n!})/(4n - 4) - \epsilon}$, due to Pierce, Turnage-Butterbaugh, and Wood.
Brain network mechanisms of visual shape completion

Visual shape completion recovers object shape, size, and number from spatially segregated edges. Despite being extensively investigated, the process's underlying brain regions, networks, and functional connections are still not well understood. To shed light on the topic, we scanned (fMRI) healthy adults during rest and during a task in which they discriminated pac-man configurations that formed or failed to form completed shapes (illusory and fragmented condition, respectively). Task activation differences (illusory-fragmented), resting-state functional connectivity, and multivariate pattern differences were identified on the cortical surface using 360 predefined parcels and 12 functional networks composed of such parcels. Brain activity flow mapping (ActFlow) was used to evaluate the likely involvement of resting-state connections for shape completion. We identified 36 differentially-active parcels including a posterior temporal region, PH, whose activity was consistent across 95% of observers. Significant task regions primarily occupied the secondary visual network but also incorporated the frontoparietal, dorsal attention, default mode, and cingulo-opercular networks. Each parcel's task activation difference could be modeled via its resting-state connections with the remaining parcels (r=.62, p<10-9), suggesting that such connections undergird shape completion. Functional connections from the dorsal attention network were key in modelling task activation differences in the secondary visual network. Dorsal attention and frontoparietal connections could also model activations in the remaining networks. Taken together, these results suggest that shape completion relies upon a sparsely distributed but densely interconnected network coalition that is centered in the secondary visual network, coordinated by the dorsal attention network, and inclusive of at least three other networks.
Exact Christoffel-Darboux expansions: A new, multidimensional, algebraic, eigenenergy bounding method

Although the Christoffel-Darboux representation (CDR) plays an important role within the theory of orthogonal polynomials, and many important bosonic and fermionic multidimensional Schrodinger equation systems can be transformed into a moment equation representation (MER), the union of the two into an effective, algebraic, eigenenergy bounding method has been overlooked. This particular fusion of the two representations, suitable for bounding bosonic or fermionic systems, defines the Orthonormal Polynomial Projection Quantization - Bounding Method (OPPQ-BM), as developed here. We use it to analyze several one dimensional and two dimensional systems, including the quadratic Zeeman effect for strong-superstrong magnetic fields. For this problem, we match or surpass the excellent, but intricate, results of Kravchenko et al (1996 Phys. Rev. A 54287) for a broad range of magnetic fields, without the need for any truncations or approximations.
Quasiparticle electronic structure of two-dimensional heterotriangulene-based covalent organic frameworks adsorbed on Au(111).

The modular nature and unique electronic properties of two-dimensional (2D) covalent organic frameworks (COFs) make them an attractive option for applications in catalysis, optoelectronics, and spintronics. The fabrications of such devices often involve interfaces formed between COFs and substrates. In this work, we employ the first-principles GW approach to accurately determine the quasiparticle electronic structure of three 2D carbonyl bridged heterotriangulene-based COFs featuring kagome lattice, with their properties ranging from a semi-metal to a wide-gap semiconductor. Moreover, we study the adsorption of these COFs on Au(111) surface and characterize the quasiparticle electronic structure at the heterogeneous COF/Au(111) interfaces. To reduce the computational cost, we apply the recently developed dielectric embedding GW approach and show that our results agree with existing experimental measurement on the interfacial energy level alignment. Our calculations illustrate how the many-body dielectric screening at the interface modulates the energies and shapes of the kagome bands, the effective masses of semiconducting COFs, as well as the Fermi velocity of the semi-metallic COF.
Estimating the Cumulative Incidence of SARS-CoV-2 Infection and the Infection Fatality Ratio in Light of Waning Antibodies.

BACKGROUND: Serology tests can identify previous infections and facilitate estimation of the number of total infections. However, immunoglobulins targeting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been reported to wane below the detectable level of serologic assays (which is not necessarily equivalent to the duration of protective immunity). We estimate the cumulative incidence of SARS-CoV-2 infection from serology studies, accounting for expected levels of antibody acquisition (seroconversion) and waning (seroreversion), and apply this framework using data from New York City and Connecticut. METHODS: We estimated time from seroconversion to seroreversion and infection fatality ratio (IFR) using mortality data from March to October 2020 and population-level cross-sectional seroprevalence data from April to August 2020 in New York City and Connecticut. We then estimated the daily seroprevalence and cumulative incidence of SARS-CoV-2 infection. RESULTS: The estimated average time from seroconversion to seroreversion was 3-4 months. The estimated IFR was 1.1% (95% credible interval, 1.0%, 1.2%) in New York City and 1.4% (1.1, 1.7%) in Connecticut. The estimated daily seroprevalence declined after a peak in the spring. The estimated cumulative incidence reached 26.8% (24.2%, 29.7%) at the end of September in New York City and 8.8% (7.1%, 11.3%) in Connecticut, higher than maximum seroprevalence measures (22.1% and 6.1%), respectively. CONCLUSIONS: The cumulative incidence of SARS-CoV-2 infection is underestimated using cross-sectional serology data without adjustment for waning antibodies. Our approach can help quantify the magnitude of underestimation and adjust estimates for waning antibodies.
The application of blockchain-based crypto assets for integrating the physical and financial supply chains in the construction & engineering industry

Abstract Supply chain integration remains an elusive goal for the construction and engineering industry. The high degree of fragmentation and the reliance on financial institutions has pushed the physical and financial supply chains apart. The paper demonstrates how blockchain-based crypto assets address this limitation when used for conditioning payments on the flow of products. The integration of flows is contrasted in today's supply chains and those using crypto assets for their payment settlement. The thesis is validated in two construction projects where a smart contract administered payments in crypto currencies and crypto tokens. The findings reveal that crypto assets can enhance both the granularity and the atomicity of the integration between the cash and product flows; the on- and off-chain realities can be directly linked in crypto asset-enabled supply chains. The paper concludes with a discussion of risks such as regulatory landscapes, security, and price volatility and offers potential solutions.
What philosophy of biology is not

Periodically through the history of biology, biologists have tried to do a little philosophy and occasionally a philosopher has turned his attention to biology. In the past decade or so a body of literature has arisen which might legitimately be called 'philosophy of biology'. The purpose of this paper will be to review the contributions made to this literature by phi losophers during the past 10 or 15 years. Earlier work will be discussed only if it has proven especially influential. The contributions made by biologists to the philosophy of biology will be touched on only briefly, both because the biological literature is too vast to permit anything like a fair summary in the confines of a short paper and because the strengths and weaknesses of this literature tend to be quite different from the efforts of philosophers.1 In this paper, though it is sure to result in acrimony, I have not refrained from criticism. There is too great a discrepancy be tween what philosophers produce under the guise of philosophy of biology and what philosophy of biology could be or, in my opinion, should be to pass over without comment. One striking feature of the remarks made by philosophers about biology is how frequently they are misinformed. For example, Mario Bunge in a paper on the weight of simplicity in the construction and assaying of scientific theories asks the following question:
NullaNet Tiny: Ultra-low-latency DNN Inference Through Fixed-function Combinational Logic

While there is a large body of research on efficient processing of deep neural networks (DNNs) [1]–[31], ultra-low-latency realization of these models for applications with stringent, sub-microsecond latency requirements continues to be an unresolved, challenging problem. Field-programmable gate array (FPGA)-based DNN accelerators are gaining traction as a serious contender to replace graphics processing unit/central processing unit-based platforms considering their performance, flexibility, and energy efficiency. NullaNet (2018) [32], LUTNet (2019) [33], and LogicNets (2020) [34] are among accelerators specifically designed to benefit from FPGAs’ capabilities.
Geodesic interpolation on Sierpiński gaskets

We study the analogue of a convex interpolant of two sets on Sierpinski gaskets and an associated notion of measure transport. The structure of a natural family of interpolating measures is described and an interpolation inequality is established. A key tool is a good description of geodesics on these gaskets, some results on which have previously appeared in the literature.
Segmentor: a tool for manual refinement of 3D microscopy annotations

Recent advances in tissue clearing techniques, combined with high-speed image acquisition through light sheet microscopy, enable rapid three-dimensional (3D) imaging of biological specimens, such as whole mouse brains, in a matter of hours. Quantitative analysis of such 3D images can help us understand how changes in brain structure lead to differences in behavior or cognition, but distinguishing densely packed features of interest, such as nuclei, from background can be challenging. Recent deep learning-based nuclear segmentation algorithms show great promise for automated segmentation, but require large numbers of accurate manually labeled nuclei as training data. We present Segmentor, an open-source tool for reliable, efficient, and user-friendly manual annotation and refinement of objects (e.g., nuclei) within 3D light sheet microscopy images. Segmentor employs a hybrid 2D-3D approach for visualizing and segmenting objects and contains features for automatic region splitting, designed specifically for streamlining the process of 3D segmentation of nuclei. We show that editing simultaneously in 2D and 3D using Segmentor significantly decreases time spent on manual annotations without affecting accuracy as compared to editing the same set of images with only 2D capabilities. Segmentor is a tool for increased efficiency of manual annotation and refinement of 3D objects that can be used to train deep learning segmentation algorithms, and is available at https://www.nucleininja.org/ and https://github.com/RENCI/Segmentor .
Gravitational waves from dark Yang-Mills sectors

Dark Yang-Mills sectors, which are ubiquitous in the string landscape, may be reheated above their critical temperature and subsequently go through a confining first-order phase transition that produces stochastic gravitational waves in the early universe. Taking into account constraints from lattice and from Yang-Mills (center and Weyl) symmetries, we use a phenomenological model to construct an effective potential of the semi quark-gluon plasma phase, from which we compute the gravitational wave signal produced during confinement for numerous gauge groups. The signal is maximized when the dark sector dominates the energy density of the universe at the time of the phase transition. In that case, we find that it is within reach of the next-to-next generation of experiments (BBO, DECIGO) for a range of dark confinement scales near the weak scale.
An Empirical Comparison of Bias Reduction Methods on Real-World Problems in High-Stakes Policy Settings

Applications of machine learning (ML) to high-stakes policy settings - such as education, criminal justice, healthcare, and social service delivery - have grown rapidly in recent years, sparking important conversations about how to ensure fair outcomes from these systems. The machine learning research community has responded to this challenge with a wide array of proposed fairness-enhancing strategies for ML models, but despite the large number of methods that have been developed, little empirical work exists evaluating these methods in real-world settings. Here, we seek to fill this research gap by investigating the performance of several methods that operate at different points in the ML pipeline across four real-world public policy and social good problems. Across these problems, we find a wide degree of variability and inconsistency in the ability of many of these methods to improve model fairness, but postprocessing by choosing group-specific score thresholds consistently removes disparities, with important implications for both the ML research community and practitioners deploying machine learning to inform consequential policy decisions.
Inhomogeneous exponential jump model

We introduce and study the inhomogeneous exponential jump model—an integrable stochastic interacting particle system on the continuous half line evolving in continuous time. An important feature of the system is the presence of arbitrary spatial inhomogeneity on the half line which does not break the integrability. We completely characterize the macroscopic limit shape and asymptotic fluctuations of the height function (= integrated current) in the model. In particular, we explain how the presence of inhomogeneity may lead to macroscopic phase transitions in the limit shape such as shocks or traffic jams. Away from these singularities the asymptotic fluctuations of the height function around its macroscopic limit shape are governed by the GUE Tracy–Widom distribution. A surprising result is that while the limit shape is discontinuous at a traffic jam caused by a macroscopic slowdown in the inhomogeneity, fluctuations on both sides of such a traffic jam still have the GUE Tracy–Widom distribution (but with different non-universal normalizations). The integrability of the model comes from the fact that it is a degeneration of the inhomogeneous stochastic higher spin six vertex models studied earlier in Borodin and Petrov (Higher spin six vertex model and symmetric rational functions, doi: 10.1007/s00029-016-0301-7 , arXiv:1601.05770 [math.PR], 2016). Our results on fluctuations are obtained via an asymptotic analysis of Fredholm determinantal formulas arising from contour integral expressions for the q-moments in the stochastic higher spin six vertex model. We also discuss “product-form” translation invariant stationary distributions of the exponential jump model which lead to an alternative hydrodynamic-type heuristic derivation of the macroscopic limit shape.
Estimation and Inference about Tail Features with Tail Censored Data

Abstract This paper considers estimation and inference about tail features such as tail index and extreme quantile when the observations beyond some threshold are censored. Ignoring such tail censoring could lead to substantial bias and size distortion, even if the censored probability is tiny. We first propose a new maximum likelihood estimator (MLE) based on the Pareto tail approximation and derive its asymptotic properties. Then, we propose an alternative method of constructing confidence intervals by resorting to extreme value theory. The MLE and the confidence intervals deliver excellent small sample performance, as shown by Monte Carlo simulations. Finally, we apply the proposed methods to estimate and construct confidence intervals for the tail index of the distribution of macroeconomic disasters and the coefficient of risk aversion using the dataset collected by Barro and Ursua (2008). Our empirical findings are substantially different from those obtained from the existing methods.
Preliminary Computational Assessment of Disk Rotating Detonation Engine Configurations

A rotating detonation engine (RDE) configuration whereby the working fluid enters and exits in a predominantly radial manner is examined using a quasi-two-dimensional computational fluid dynamic simulation. The simulation, based on a Cartesian coordinate system, was originally developed to examine the physics and performance of the more typical annular RDE. Modifications required to accommodate the radial and circumferential flowfield are discussed. The centripetal forces that arise in this disk RDE (DRDE) configuration create a different wave structure than that seen in the annular RDE. They also give rise to markedly different fluid behavior depending on whether the flow is radially inward or radially outward. Using an entropy-based measure of pressure gain, it is found that for the preliminary idealized calculations performed in this paper, the inward flowing DRDE outperforms the outward flowing variant. The inward flowing DRDE is further shown to outperform the equivalent annular RDE. The effects on performance of several parameters are examined, including inner-to-outer diameter ratio, inner-to-outer cross-sectional area ratio, and inlet throat-to-channel area ratio.
Multilayer shallow-water model with stratification and shear

The purpose of this paper is to present a shallow-water-type model with multiple inhomogeneous layers featuring variable linear velocity vertical shear and startificaion in horizontal space and time. This is achieved by writing the layer velocity and buoyancy fields as linear functions of depth, with coefficients that depend arbitrarily on horizontal position and time. The model is a generalization of Ripa's (1995) single-layer model to an arbitrary number of layers. Unlike models with homogeneous layers the present model is able to represent thermodynamics processes driven by heat and freshwater fluxes through the surface or mixing processes resulting from fluid exchanges across contiguous layers. By contrast with inhomogeneous-layer models with depth-independent velocity and buoyancy, the model derived here can sustain explicitly at low frequency a current in thermal wind balance (between the vertical vertical shear and the horizontal density gradient) within each layer. In the absence of external forcing and dissipation, energy, volume, mass, and buoyancy variance constrain the dynamics; conservation of total zonal momentum requires in addition the usual zonal symmetry of the topography and horizontal domain. The inviscid, unforced model admits a formulation suggestive of a generalized Hamiltonian structure, which enables the classical connection between symmetries and conservation laws via Noether's theorem. A steady solution to a system involving one Ripa-like layer and otherwise homogeneous layers can be proved formally (or Arnold) stable using the above invariants. A model configuration with only one layer has been shown previously to provide: a very good representation of the exact vertical normal modes up to the first internal mode; an exact representation of long-perturbation (free boundary) baroclinic instability; and a very reasonable representation of short-perturbation (classical Eady) baroclinic instability. Here it is shown that substantially more accurate overall results with respect to single-layer calculations can be achieved by considering a stack of only a few layers. A similar behavior is found in ageostrophic (classical Stone) baroclinic instability by describing accurately the dependence of the solutions on the Richardson number with only two layers.
Gene Conversion Facilitates the Adaptive Evolution of Self-Resistance in Highly Toxic Newts.

Reconstructing the histories of complex adaptations and identifying the evolutionary mechanisms underlying their origins are two of the primary goals of evolutionary biology. Taricha newts, which contain high concentrations of the deadly toxin tetrodotoxin (TTX) as an antipredator defense, have evolved resistance to self-intoxication, which is a complex adaptation requiring changes in six paralogs of the voltage-gated sodium channel (Nav) gene family, the physiological target of TTX. Here, we reconstruct the origins of TTX self-resistance by sequencing the entire Nav gene family in newts and related salamanders. We show that moderate TTX resistance evolved early in the salamander lineage in three of the six Nav paralogs, preceding the proposed appearance of tetrodotoxic newts by ∼100 million years. TTX-bearing newts possess additional unique substitutions across the entire Nav gene family that provide physiological TTX resistance. These substitutions coincide with signatures of positive selection and relaxed purifying selection, as well as gene conversion events, that together likely facilitated their evolution. We also identify a novel exon duplication within Nav1.4 encoding an expressed TTX-binding site. Two resistance-conferring changes within newts appear to have spread via nonallelic gene conversion: in one case, one codon was copied between paralogs, and in the second, multiple substitutions were homogenized between the duplicate exons of Nav1.4. Our results demonstrate that gene conversion can accelerate the coordinated evolution of gene families in response to a common selection pressure.