cssi/SciSciGPT-SciSciNet huggingface dataset:
The origins of American health libertarianism

This Article examines Americans' enduring demand for freedom of therapeutic choice as a popular constitutional movement originating in the United States' early years. In exploring extrajudicial advocacy for therapeutic choice between the American Revolution and the Civil War, this piece illustrates how multiple concepts of freedom in addition to bodily freedom bolstered the concept of a constitutional right to medical liberty. There is a deep current of belief in the United States that people have a right to choose their preferred treatments without government interference. Modern American history has given rise to movements for access to abortion, life-ending drugs, unapproved cancer treatments, and medical marijuana. Recently, cries of "Death Panels" have routinely been directed against health care reform proposals that citizens believe would limit the products and procedures covered by government health insurance. Some of the most prominent contemporary struggles for health freedom have been waged in court. But other important recent battles for freedom of therapeutic choice have taken place in other forums, from legislative hearings to Food and Drug Administration advisory committee meetings to public demonstrations. This attitude of therapeutic libertarianism is not new. Drawing mainly on primary historical sources, this Article examines arguments in favor of freedom of therapeutic choice voiced in antebellum America in the context of battles against state licensing regimes. After considering some anti-licensing arguments made before independence, it discusses the views and statements of Benjamin Rush, an influential founding father who was also the most prominent American physician of the early national period. The Article then analyzes the Jacksonian-era battle against medical licensing laws waged by the practitioners and supporters of a school of botanical medicine known as Thomsonianism. This triumphant struggle was waged in explicitly constitutional terms, even though it occurred entirely outside of the courts. The Thomsonian campaign thus offers one of the most striking examples of a successful popular constitutional movement in American history. This article shows that, at its origin, the American commitment to freedom of therapeutic choice was based on notions of not only bodily freedom, but also economic freedom, freedom of conscience, and freedom of injury. Finally, this Article considers ways in which this early history helps illuminate the nature of current struggles for freedom of therapeutic choice.
Optimal Health Insurance and the Distortionary Effects of the Tax Subsidy

Applies a model to understand how the income tax subsidy distorts optimal cost-sharing in health insurance.
Density zoning and class segregation in U.S. metropolitan areas.

Objectives Socioeconomic segregation rose substantially in U.S. cities during the final decades of the 20th century and we argue zoning regulations are an important cause for this increase.
Asset-Level Risk and Return in Real Estate Investments

Relatively little is known in the academic literature about the idiosyncratic returns of individual real estate investments, though quite a few commercial properties command prices commensurate with the market values of small to medium publicly traded companies. I use purchase and sale data from the National Council of Real Estate Investment Fiduciaries (NCREIF) to compute holding period price-appreciation returns for commercial properties. In stark contrast with liquid asset returns, idiosyncratic drift and volatility estimates diverge as the holding period shrinks. This puzzling phenomenon survives a variety of controls for vintage effects, systematic risk heterogeneity, and sample selection biases. I derive an equilibrium search-based illiquid asset pricing model which, when calibrated, fits the data very well. Thus a structural model of illiquidity seems crucial to a descriptive theory of real estate investment returns. The model can be extended to other illiquid asset classes and used to price derivatives such as debt claims.
The Limited Local Impacts of Ethnic and Racial Diversity

The United States has more immigrants than at any time since the 1920s and immigration rates remain high. Past research unequivocally predicts that the resulting increase in ethnic and racial diversity will reduce local investments in public goods. By analyzing a new, comprehensive data set on U.S. cities from 1950 to 2002, this article challenges those predictions. In the 1950s and early 1960s, the percent Black had no strong impacts on local public goods. Since the 1970s, the impact of diversity has been limited chiefly to criminal justice, an issue that has remained racially coded, nationally salient, and relevant to localities. Contrary to past work, diversity’s influence on local public goods is neither pervasive nor consistent. These findings challenge static conceptions of local ethnic and racial divisions, and they suggest a connection between diversity’s local impacts and trends in national politics.
Why Not A General Strike

Why Not a General Strike? proposes a - no surprise! - general strike, with the exception of essential services, for the Labor Day Weekend of 2006. With the waves of bankruptcies in the airline industry now spreading to the auto industry, and with the fracture of the AFL-CIO, workers are placed into increasingly desperate positions. Perhaps it is time for a weekend of deliberation about the future of jobs and about the lack of job and retirement and insurance security. The earlier history of the United States, and contemporary experiences in many other countries, demonstrate the intriguing viability of the general strike.
On Demographic Change and Competitive Equilibrium in American Politics

In their seminal analysis of American elections, Stokes and Iversen (1962) demonstrated that each party’s share of the vote never strays very far from a competitive equilibrium. However, it is difficult to envision how this equilibrium will maintain amid changing demographics. The Republican leaning white proportion of the electorate is shrinking while the Democratic leaning Latino and Asian proportion is rapidly growing. These demographic changes threaten to tip the partisan balance in favor of the Democrats. Can the competitive equilibrium hold amid changing demographics? I answer this question in three steps. First, I analyze presidential election returns since the end of the Civil War. I confirm the presence of a competitive equilibrium. I then use a set of simulations to establish that demographic changes will tip the partisan balance in favor of the Democrats. I then assess how much the Republican Party will have to increase its level of support among whites and/or other groups to remain competitive. I find that relatively modest changes in white and/or Latino and Asian voting behavior will be sufficient to give the Republican Party an even chance of winning well into the future.
Representative democracy and marginal rate progressive income taxation

Abstract This paper develops a political economy model that is consistent with the fact that democracies have a preference for increasing marginal tax rates on income. We present a model in which there is an exogenous set of political parties with preferences over the set of admissible tax schedules. This set contains virtually any increasing and piecewise linear continuous function. Each party decides whether or not to present a candidate for election. There is a fixed cost of running. The elected candidate implements one of her preferred tax policies. Our main results provide conditions under which a Strong Nash Equilibrium exists, and a tax schedule with increasing marginal tax rates is implemented in some Nash Equilibria and in any Strong Nash Equilibrium.
Takeover Defenses of IPO Firms

Many firms deploy takeover defenses when they go public. IPO managers tend to deploy defenses when their compensation is high, shareholdings are small, and oversight from nonmanagerial shareholders is weak. The presence of a defense is negatively related to subsequent acquisition likelihood, yet has no impact on takeover premiums for firms that are acquired. These results do not support arguments that takeover defenses facilitate the eventual sale of IPO firms at high takeover premiums. Rather, they suggest that managers shift the cost of takeover protection onto nonmanagerial shareholders. Thus, agency problems are important even for firms at the IPO stage.
Using the Market to Address Climate Change: Insights from Theory and Experience

Emissions of greenhouse gases linked with global climate change are affected by diverse aspects of economic activity, including individual consumption, business investment, and government spending. An effective climate policy will have to modify the decision calculus for these activities in the direction of more efficient generation and use of energy, lower carbon-intensity of energy, and – more broadly – a more carbon-lean economy. The only approach to doing this on a meaningful scale that would be technically feasible and cost-effective is carbon pricing, that is, market-based climate policies that place a shadow-price on carbon dioxide emissions. We examine alternative designs of three such instruments – carbon taxes, cap-and-trade, and clean energy standards. We note that the U.S. political response to possible market-based approaches to climate policy has been and will continue to be largely a function of issues and structural factors that transcend the scope of environmental and climate policy.
AMBIGUITY IN POLICY LESSONS: THE AGENCIFICATION EXPERIENCE

The policy transfer literature identifies the importance of context in shaping policy selection. However, countries with distinctly different contexts are pursuing the agencification of the public sector. Why? The solution to this puzzle lies in the ambiguity associated with public management ideas, which allows policy adopters room to interpret management doctrines and experience. The result is that public management ideas that carry the same identifying label can mask variation in the understanding of the policy, the motivation for adoption and in implementation outcomes. The process of interpretation allows policy-makers in different contexts to: (1) adopt superficially similar policy concepts; (2) overlook negative experiential learning that contradicts the policy doctrine; and (3) adopt policies unsuitable to the national context.
Expanding the Utility of Fair Use in Copyright: the Best Practices Model

Being able to access copyrighted material is fundamental to freedom of expression. This is ever more acutely obvious in a digital environment, in which exchange of quoted and often remixed copyrighted material is a daily feature of media use and social networking. But it is a basic right that requires better public education to balance the censorship effects of monopoly ownership rights.
Embodied Researchers: Gendered Bodies, Research Activity, and Pregnancy in the Field

Kathleen B. Jones, in her now famous essay about women-friendly polities, explains that that citizenship must be redefined to include a body that does not “easily fit military-corporate uniforms” (1990, 794). Jones calls theorists to recognize women's “embodied lives,” and in doing so, considers how “women's bodies are problematic” and “sex/gendered identity affects … life” (786). We argue here that recognizing women's embodied lives is similarly important to a discussion of gender and fieldwork. As researchers in the field, we have been defined by our social position as women, thus putting us at distinct disadvantages and advantages (Sundberg 2003).
Accessibility, vulnerability, and resilience in a stochastic model of sustainable ecotourism

Abstract We show how the notions of accessibility , vulnerability , and resilience can be used to shed light on the sustainable management of a natural area that is used for ecotourism. To this end, we construct and analyze two queuing-theoretic models that approach the problem of sustainable management in different ways. In the first model, there is a capacity constraint on the number of ecotourists that are permitted to visit the natural area and the optimal rate at which an ecotourist agency manager provides service to the ecotourists is endogenously determined. In the second model, there is no capacity constraint but the manager endogenously ascertains the optimal number of ecotourists who are allowed into the natural area before he provides service to these ecotourists. The sustainability aspect of the management problem is addressed in two ways. First, the conceptualizations of accessibility, vulnerability, and resilience depend on certain long run metrics. Second, the objective functions in the two models that the manager optimizes are formulated using these long run metrics.
Cell Size Regulation Through Tunable Geometric Localization of the Bacterial Actin Cytoskeleton

Virtually all cells have the ability to adjust their size in response to environmental cues, yet relatively little is known about the molecular factors responsible for tuning cell size or the mechanisms through which they reconfigure cellular dimensions. In the rod-shaped bacterium Escherichia coli, the actin-like protein MreB localizes in a curvaturedependent manner and spatially coordinates cell-wall insertion to maintain cell shape across changing environments. Here, we demonstrate that the MreB-binding protein RodZ regulates the biophysical properties of MreB filaments and thereby alters the spatial patterning of cell-wall growth to modulate cell size. The relative expression levels of MreB and RodZ changed in a manner commensurate with variations in growth rate and cell width, and single-cell analyses demonstrated that RodZ systematically alters the curvature-based localization of MreB and cell width in a manner dependent on the concentration of RodZ. We identified MreB mutants that we predict using molecular dynamics simulations to alter the bending properties of MreB filaments at the molecular scale in a manner similar to RodZ binding, and showed that these mutants rescue rod-like shape in the absence of RodZ alone or in combination with wild-type MreB. Together, our results show that E. coli controls its shape and dimensions by differentially regulating RodZ and MreB expression, highlighting the rich capacity for precise coordination of cell-shape changes with environmental conditions by exploiting cytoskeletal molecular biophysics.
Order Flows and Stock Returns: Compensation for Market Makers with Inventory Concerns

We investigate whether market makers with inventory concerns are compensated with subsequent monthly returns in the cross-section. We find a significant negative relation between order flows and monthly returns, “the order flow effect,” suggesting that market makers lower prices for stocks with sell order flows and demand a reward in the form of higher expected returns. Further, the order flow effect is stronger for high-volatility or high-volume stocks for which market makers have serious inventory concerns. Funding liquidity of market makers also affects the order flow effect. Finally, our finding is independent of existing regularities and robust to the decimalization.
Property and Intellectual Property in Vaccine Markets

As biopharmaceutical forms of technology, vaccines constitute one of the most important tools for the promotion and maintenance of public health. Tolstoy famously wrote that “[h]appy families are all alike; every unhappy family is unhappy in its own way.” Vaccine markets offer perhaps one of the most extreme embodiments of Tolstoy’s principle in the field of biopharmaceutical innovation. Vaccines are often described as one of the most unprofitable types of biopharmaceutical goods, under-incentivized from a research and development (R&D) perspective, and routinely failing to attract sufficient investment from traditional funders in biopharma. In this sense, and despite the scientifically well-established value of vaccines from a public health perspective, vaccine markets are often portrayed as a collection of unhappy families. Yet, at least throughout the developed world, there are plenty of examples of steadily profitable vaccine markets, as is the case of recently developed vaccines targeting the human papilloma virus (HPV). The Essay begins by mapping this dualism in vaccine R&D and commercialization, describing both “happy” and “unhappy” markets. It then connects the development of new vaccines with the default legal regime to promote innovation in the biopharmaceutical arena: the patent system. In exploring possible solutions for transactional problems arising in connection with the development of vaccine technology in the context of infectious disease outbreaks, the Essay asks whether the rights covering vaccine technologies are best understood as property rights or as something else. This inquiry is of course but a fragment of a much larger interrogation of the nature and mechanics of intellectual property systems: are intellectual property rights—and rights arising out of the grant of patents in particular—more like property or akin to something else? Arguing that under the current non-committal position of the Supreme Court there is room for understandings of patent rights that are not property-centric, the Essay concludes by exploring how less property-like protection—in the form of a liability regime for critical components of vaccine technology—can remove some of the most salient transactional obstacles to the development and commercialization of new vaccines targeting infectious disease pathogens like Ebola, Zika and COVID-19.
SeedExtractor: An Open-Source GUI for Seed Image Analysis

Accurate measurement of seed size parameters is essential for both breeding efforts aimed at enhancing yields and basic research focused on discovering genetic components that regulate seed size. To address this need, we have developed an open-source graphical user interface (GUI) software, SeedExtractor that determines seed size and shape (including area, perimeter, length, width, circularity, and centroid), and seed color with capability to process a large number of images in a time-efficient manner. In this context, our application takes ∼2 s for analyzing an image, i.e., significantly less compared to the other tools. As this software is open-source, it can be modified by users to serve more specific needs. The adaptability of SeedExtractor was demonstrated by analyzing scanned seeds from multiple crops. We further validated the utility of this application by analyzing mature-rice seeds from 231 accessions in Rice Diversity Panel 1. The derived seed-size traits, such as seed length, width, were used for genome-wide association analysis. We identified known loci for regulating seed length (GS3) and width (qSW5/GW5) in rice, which demonstrates the accuracy of this application to extract seed phenotypes and accelerate trait discovery. In summary, we present a publicly available application that can be used to determine key yield-related traits in crops.
Expansion of ultracold neutral plasmas with exponentially decaying density distributions

We present a study of the expansion of an ultracold neutral plasma (UCNP) with an initial density distribution that decays exponentially in space, created by photoionizing atoms shortly after their release from a quadrupole (or biconic cusp) magnetic trap. A characteristic ion acoustic timescale is evident in the evolution of the plasma size and velocity, indicating that the dynamics are reasonably well described by a model of hydrodynamic expansion of a quasi-neutral plasma. However, for low plasma density and high initial electron temperature, excess ion kinetic energy in the vicinity of the central density peak suggests significant local non-neutrality at early times. Observations are compared to the well-understood self-similar expansion of a UCNP with an initial Gaussian density distribution, and a similar scaling law describes the evolution of plasma size for both cases.
An in-plane $$J_2$$-invariance condition and control algorithm for highly elliptical satellite formations

Phased formations of satellites provide an important means of keeping multiple satellites in close proximity without becoming dangerously close to one another. In order to minimize the amount of propellant necessary to keep a set of satellites in a phased formation, this paper presents a new condition for in-plane linearized secular $$J_2$$ -invariance in highly eccentric orbits. A maintenance strategy outlined here combats the growth of out-of-plane motion, and the delta-v cost of this strategy is analyzed. For context, this paper also discusses relevant formations that benefit from this condition and maintenance strategy. A highly elliptical perching formation called the “boomerang perch” is also discussed.
Link Floer homology also detects split links

Inspired by work of Lipshitz-Sarkar, we show that the module structure on link Floer homology detects split links. Using results of Ni, Alishahi-Lipshitz, and Lipshitz-Sarkar, we establish an analogous detection result for sutured Floer homology.
Image-histogram-based secondary electron counting to evaluate detective quantum efficiency in SEM.

Abstract Scanning electron microscopy is a powerful tool for nanoscale imaging of organic and inorganic materials. An important metric for characterizing the limits of performance of these microscopes is the Detective Quantum Efficiency (DQE), which measures the fraction of emitted secondary electrons (SEs) that are detected by the SE detector. However, common techniques for measuring DQE approximate the SE emission process to be Poisson distributed, which can lead to incorrect DQE values. In this paper, we introduce a technique for measuring DQE in which we directly count the mean number of secondary electrons detected from a sample using image histograms. This technique does not assume Poisson distribution of SEs and makes it possible to accurately measure DQE for a wider range of imaging conditions. As a demonstration of our technique, we map the variation of DQE as a function of working distance in the microscope.
How simulation languages should report results: A modest proposal

The focus of simulation software environments is on developing simulation models; much less consideration is placed on reporting results. However, the quality of the simulation model is irrelevant if the results are not interpreted correctly. The manner in which results are reported, along with a lack of standardized guidelines for reports, could contribute to the misinterpretation of results. We propose a hierarchical report structure where each reporting level provides additional detail about the simulated performance. Our approach utilizes two recent developments in output analysis: a procedure for omitting statistically meaningless digits in point estimates, and a graphical display called a MORE Plot, which conveys operational risk and statistical error in an intuitive manner on a single graph. Our motivation for developing this approach is to prevent or reduce misinterpretation of simulation results and to provide a foundation for standardized guidelines for reporting.
Polyhedral properties of the induced cluster subgraphs

Abstract A cluster graph is a graph whose every connected component is a complete graph. Given a simple undirected graph G , a subset of vertices inducing a cluster graph is called an independent union of cliques (IUC), and the IUC polytope associated with G is defined as the convex hull of the incidence vectors of all IUCs in the graph. The Maximum IUC problem, which is to find a maximum-cardinality IUC in a graph, finds applications in network-based data analysis. In this paper, we derive several families of facet-defining valid inequalities for the IUC polytope. We also give a complete description of this polytope for some special classes of graphs. We establish computational complexity of the separation problem for most of the considered families of valid inequalities and explore the effectiveness of employing the corresponding cutting planes in an integer (linear) programming framework for the Maximum IUC problem through computational experiments.
Initial data in general relativity described by expansion, conformal deformation and drift

The conformal method is a technique for finding Cauchy data in general relativity solving the Einstein constraint equations, and its parameters include a conformal class, a conformal momentum (as measured by a densitized lapse), and a mean curvature. Although the conformal method is successful in generating constant mean curvature (CMC) solutions of the constraint equations, it is unknown how well it applies in the non-CMC setting, and there have been indications that it encounters difficulties there. We are therefore motivated to investigate alternative generalizations of the CMC conformal method. Introducing a densitized lapse into the ADM Lagrangian, we find that solutions of the momentum constraint can be described in terms of three parameters. The first is conformal momentum as it appears in the standard conformal method. The second is volumetric momentum, which appears as an explicit parameter in the CMC conformal method, but not in the non-CMC formulation. We have called the third parameter drift momentum, and it is the conjugate momentum to infinitesimal motions in superspace that preserve conformal class and volume form up to independent diffeomorphisms. This decomposition of solutions of the momentum constraint leads to extensions of the CMC conformal method where conformal and volumetric momenta both appear as parameters. There is more than one way to treat drift momentum, in part because of an interesting duality that emerges, and we identify three candidates for incorporating drift into a variation of the conformal method.
Long exit times near a repelling equilibrium

For a smooth vector field in a neighborhood of a critical point with all positive eigenvalues of the linearization, we consider the associated dynamics perturbed by white noise. Using Malliavin calculus tools, we obtain polynomial asymptotics for probabilities of atypically long exit times in the vanishing noise limit.
The Passions of the Earth

In our times, owing to the great advances of science and technology and to the enormous increase of our individual knowledge and awareness of the world around us, of our human nature, old visions of the “whole” that we humans had entertained and within which we felt englobed like a butterfly in a chrysalis have collapsed. We feel estranged from previously familiar surroundings. We feel uprooted as if thrown into the thin air, incapable of finding our bearings and our compass. In the midst of this disarray, we need to look into the situation of the human condition, the ontopoietic situation within which we subsist, unfold, generate. We need specifically to look into its deepest and most significant level — a level at which we throw hooks toward the circumambient world, nature, universe, and beyond and with which we maintain ties that sustain us — namely, the sphere of the ontopoietic becoming and existence that I have called elsewhere the realm of the “elements.” It is on the side of nature-life that we throw out our hooks, which are received. And on the side of the inner ontopoietic processes through which the living being filters the vital forces and distills a specifically human significance of life that makes it fully human there are corresponding human “elemental passions of the soul.”
On cyclic and nontransitive probabilities

Motivated by classical nontransitivity paradoxes, we call an n-tuple (x1,…,xn)∈[0,1]n cyclic if there exist independent random variables U1,…,Un with P(Ui=Uj)=0 for i≠j such that P(Ui+1>Ui)=xi for i=1,…,n−1 and P(U1>Un)=xn. We call the tuple (x1,…,xn) nontransitive if it is cyclic and in addition satisfies xi>12 for all i. Let pn (resp. pn∗) denote the probability that a randomly chosen n-tuple (x1,…,xn)∈[0,1]n is cyclic (resp. nontransitive). We determine p3 and p3∗ exactly, while for n≥4 we give upper and lower bounds for pn that show that pn converges to 1 as n→∞. We also determine the distribution of the smallest, middle, and largest elements in a cyclic triple.
Recovery RL: Safe Reinforcement Learning With Learned Recovery Zones

Safety remains a central obstacle preventing widespread use of RL in the real world: learning new tasks in uncertain environments requires extensive exploration, but safety requires limiting exploration. We propose Recovery RL, an algorithm which navigates this tradeoff by (1) leveraging offline data to learn about constraint violating zones before policy learning and (2) separating the goals of improving task performance and constraint satisfaction across two policies: a task policy that only optimizes the task reward and a recovery policy that guides the agent to safety when constraint violation is likely. We evaluate Recovery RL on 6 simulation domains, including two contact-rich manipulation tasks and an image-based navigation task, and an image-based obstacle avoidance task on a physical robot. We compare Recovery RL to 5 prior safe RL methods which jointly optimize for task performance and safety via constrained optimization or reward shaping and find that Recovery RL outperforms the next best prior method across all domains. Results suggest that Recovery RL trades off constraint violations and task successes 2–20 times more efficiently in simulation domains and 3 times more efficiently in physical experiments. See https://tinyurl.com/rl-recovery for videos and supplementary material.
Hierarchical Multi-head Attentive Network for Evidence-aware Fake News Detection.

The widespread of fake news and misinformation in various domains ranging from politics, economics to public health has posed an urgent need to automatically fact-check information. A recent trend in fake news detection is to utilize evidence from external sources. However, existing evidence-aware fake news detection methods focused on either only word-level attention or evidence-level attention, which may result in suboptimal performance. In this paper, we propose a Hierarchical Multi-head Attentive Network to fact-check textual claims. Our model jointly combines multi-head word-level attention and multi-head document-level attention, which aid explanation in both word-level and evidence-level. Experiments on two real-word datasets show that our model outperforms seven state-of-the-art baselines. Improvements over baselines are from 6% to 18%. Our source code and datasets are released at https://github.com/nguyenvo09/EACL2021.
Locally induced Gaussian processes for large-scale simulation experiments

Gaussian processes (GPs) serve as flexible surrogates for complex surfaces, but buckle under the cubic cost of matrix decompositions with big training data sizes. Geospatial and machine learning communities suggest pseudo-inputs, or inducing points, as one strategy to obtain an approximation easing that computational burden. However, we show how placement of inducing points and their multitude can be thwarted by pathologies, especially in large-scale dynamic response surface modeling tasks. As remedy, we suggest porting the inducing point idea, which is usually applied globally, over to a more local context where selection is both easier and faster. In this way, our proposed methodology hybridizes global inducing point and data subset-based local GP approximation. A cascade of strategies for planning the selection of local inducing points is provided, and comparisons are drawn to related methodology with emphasis on computer surrogate modeling applications. We show that local inducing points extend their global and data subset component parts on the accuracy–computational efficiency frontier. Illustrative examples are provided on benchmark data and a large-scale real-simulation satellite drag interpolation problem.
Positivity of Curvature On Manifolds With Boundary

Consider a compact manifold $M$ with smooth boundary $\partial M$. Suppose that $g$ and $\tilde{g}$ are two Riemannian metrics on $M$. We construct a family of metrics on $M$ which agrees with $g$ outside a neighborhood of $\partial M$ and agrees with $\tilde{g}$ in a neighborhood of $\partial M$. We prove that the family of metrics preserves various natural curvature conditions under suitable assumptions on the boundary data. Moreover, under suitable assumptions on the boundary data, we can deform a metric to one with totally geodesic boundary while preserving various natural curvature conditions.
Interplay between intraocular and intracranial pressure effects on the optic nerve head in vivo.

Abstract Intracranial pressure (ICP) has been proposed to play an important role in the sensitivity to intraocular pressure (IOP) and susceptibility to glaucoma. However, the in vivo effects of simultaneous, controlled, acute variations in ICP and IOP have not been directly measured. We quantified the deformations of the anterior lamina cribrosa (ALC) and scleral canal at Bruch's membrane opening (BMO) under acute elevation of IOP and/or ICP. Four eyes of three adult monkeys were imaged in vivo with OCT under four pressure conditions: IOP and ICP either at baseline or elevated. The BMO and ALC were reconstructed from manual delineations. From these, we determined canal area at the BMO (BMO area), BMO aspect ratio and planarity, and ALC median depth relative to the BMO plane. To better account for the pressure effects on the imaging, we also measured ALC visibility as a percent of the BMO area. Further, ALC depths were analyzed only in regions where the ALC was visible in all pressure conditions. Bootstrap sampling was used to obtain mean estimates and confidence intervals, which were then used to test for significant effects of IOP and ICP, independently and in interaction. Response to pressure manipulation was highly individualized between eyes, with significant changes detected in a majority of the parameters. Significant interactions between ICP and IOP occurred in all measures, except ALC visibility. On average, ICP elevation expanded BMO area by 0.17 mm2 at baseline IOP, and contracted BMO area by 0.02 mm2 at high IOP. ICP elevation decreased ALC depth by 10 μm at baseline IOP, but increased depth by 7 μm at high IOP. ALC visibility decreased as ICP increased, both at baseline (−10%) and high IOP (−17%). IOP elevation expanded BMO area by 0.04 mm2 at baseline ICP, and contracted BMO area by 0.09 mm2 at high ICP. On average, IOP elevation caused the ALC to displace 3.3 μm anteriorly at baseline ICP, and 22 μm posteriorly at high ICP. ALC visibility improved as IOP increased, both at baseline (5%) and high ICP (8%). In summary, changing IOP or ICP significantly deformed both the scleral canal and the lamina of the monkey ONH, regardless of the other pressure level. There were significant interactions between the effects of IOP and those of ICP on LC depth, BMO area, aspect ratio and planarity. On most eyes, elevating both pressures by the same amount did not cancel out the effects. Altogether our results show that ICP affects sensitivity to IOP, and thus that it can potentially also affect susceptibility to glaucoma.
DRAG: Director-Generator Language Modelling Framework for Non-Parallel Author Stylized Rewriting

Author stylized rewriting is the task of rewriting an input text in a particular author’s style. Recent works in this area have leveraged Transformer-based language models in a denoising autoencoder setup to generate author stylized text without relying on a parallel corpus of data. However, these approaches are limited by the lack of explicit control of target attributes and being entirely data-driven. In this paper, we propose a Director-Generator framework to rewrite content in the target author’s style, specifically focusing on certain target attributes. We show that our proposed framework works well even with a limited-sized target author corpus. Our experiments on corpora consisting of relatively small-sized text authored by three distinct authors show significant improvements upon existing works to rewrite input texts in target author’s style. Our quantitative and qualitative analyses further show that our model has better meaning retention and results in more fluent generations.
CalibDNN: multimodal sensor calibration for perception using deep neural networks

Current perception systems often carry multimodal imagers and sensors such as 2D cameras and 3D LiDAR sensors. To fuse and utilize the data for downstream perception tasks, robust and accurate calibration of the multimodal sensor data is essential. We propose a novel deep learning-driven technique (CalibDNN) for accurate calibration among multimodal sensor, specifically LiDAR-Camera pairs. The key innovation of the proposed work is that it does not require any specific calibration targets or hardware assistants, and the entire processing is fully automatic with a single model and single iteration. Results comparison among different methods and extensive experiments on different datasets demonstrates the state-of-the-art performance.
Probabilistic analysis of algorithms for cost constrained minimum weighted combinatorial objects

Abstract We consider cost constrained versions of the minimum spanning tree problem and the assignment problem. We assume edge weights are independent copies of a continuous random variable Z that satisfies F ( x ) = P ( Z ≤ x ) ≈ x α as x → 0 , where α ≥ 1 . Also, there are r = O ( 1 ) budget constraints with edge costs chosen from the same distribution. We use Lagrangean duality to construct polynomial time algorithms that produce asymptotically optimal solutions. For the spanning tree problem, we allow r > 1 , but for the assignment problem we can only analyse the case r = 1 .
Higher Dualizability and Singly-Generated Grothendieck Categories

Let k be a field. We show that locally presentable, k-linear categories $${\mathcal {C}}$$ dualizable in the sense that the identity functor can be recovered as $$\coprod _i x_i\otimes f_i$$ for objects $$x_i\in {\mathcal {C}}$$ and left adjoints $$f_i$$ from $${\mathcal {C}}$$ to $$\mathrm {Vect}_k$$ are products of copies of $$\mathrm {Vect}_k$$ . This partially confirms a conjecture by Brandenburg, the author and T. Johnson-Freyd. Motivated by this, we also characterize the Grothendieck categories containing an object x with the property that every object is a copower of x: they are precisely the categories of non-singular injective right modules over simple, regular, right self-injective rings of type I or III.
Self-intersections of closed parametrized minimal surfaces in generic Riemannian manifolds

This article shows that for generic choice of Riemannian metric on a compact oriented manifold M of dimension four, the tangent planes at any self-intersection $$p \in M$$ of any prime closed parametrized minimal surface in M are not simultaneously complex for any orthogonal complex structure on M at p. This implies via geometric measure theory that $$H_2(M;{{\mathbb {Z}}})$$ is generated by homology classes that are represented by oriented imbedded minimal surfaces.
Mitigating belief projection in explainable artificial intelligence via Bayesian teaching.

State-of-the-art deep-learning systems use decision rules that are challenging for humans to model. Explainable AI (XAI) attempts to improve human understanding but rarely accounts for how people typically reason about unfamiliar agents. We propose explicitly modelling the human explainee via Bayesian teaching, which evaluates explanations by how much they shift explainees’ inferences toward a desired goal. We assess Bayesian teaching in a binary image classification task across a variety of contexts. Absent intervention, participants predict that the AI’s classifications will match their own, but explanations generated by Bayesian teaching improve their ability to predict the AI’s judgements by moving them away from this prior belief. Bayesian teaching further allows each case to be broken down into sub-examples (here saliency maps). These sub-examples complement whole examples by improving error detection for familiar categories, whereas whole examples help predict correct AI judgements of unfamiliar cases.
Semi-Supervised Singing Voice Separation With Noisy Self-Training

Recent progress in singing voice separation has primarily focused on supervised deep learning methods. However, the scarcity of ground-truth data with clean musical sources has been a problem for long. Given a limited set of labeled data, we present a method to leverage a large volume of unlabeled data to improve the model’s performance. Following the noisy self-training framework, we first train a teacher network on the small labeled dataset and infer pseudo-labels from the large corpus of unlabeled mixtures. Then, a larger student network is trained on combined ground-truth and self-labeled datasets. Empirical results show that the proposed self-training scheme, along with data augmentation methods, effectively leverage the large unlabeled corpus and obtain superior performance compared to supervised methods.
An Algorithm for Traffic Grooming in WDM Optical Mesh Networks with Multiple Objectives

This paper studies a traffic grooming in wavelength-division multiplexing (WDM) mesh networks for the SONET/SDH streams requested between node pairs. The traffic could be groomed at the access node before converting to an optical signal carried in the all-optical network. We design a virtual topology with a given physical topology to satisfy multiple objectives and constraints. The grooming problem of a static demand is considered as an optimization problem. The traditional algorithms found in the literatures mostly focus on a single objective either to maximize the performance or to minimize the cost. We propose a multi-objective evolutionary algorithm to solve a grooming problem that optimizes multiple objectives all together at the same time. In this paper we consider the optimization of three objectives: maximize the traffic throughput, minimize the number of transceivers, and minimize the average propagation delay or average hop counts. The simulation results show that our approach is superior to an existing heuristic approaches in an acceptable running time.
QUBO formulations for training machine learning models.

Training machine learning models on classical computers is usually a time and compute intensive process. With Moore’s law nearing its inevitable end and an ever-increasing demand for large-scale data analysis using machine learning, we must leverage non-conventional computing paradigms like quantum computing to train machine learning models efficiently. Adiabatic quantum computers can approximately solve NP-hard problems, such as the quadratic unconstrained binary optimization (QUBO), faster than classical computers. Since many machine learning problems are also NP-hard, we believe adiabatic quantum computers might be instrumental in training machine learning models efficiently in the post Moore’s law era. In order to solve problems on adiabatic quantum computers, they must be formulated as QUBO problems, which is very challenging. In this paper, we formulate the training problems of three machine learning models—linear regression, support vector machine (SVM) and balanced k-means clustering—as QUBO problems, making them conducive to be trained on adiabatic quantum computers. We also analyze the computational complexities of our formulations and compare them to corresponding state-of-the-art classical approaches. We show that the time and space complexities of our formulations are better (in case of SVM and balanced k-means clustering) or equivalent (in case of linear regression) to their classical counterparts.
Predictive performance of international COVID-19 mortality forecasting models.

Forecasts and alternative scenarios of COVID-19 mortality have been critical inputs for pandemic response efforts, and decision-makers need information about predictive performance. We screen n = 386 public COVID-19 forecasting models, identifying n = 7 that are global in scope and provide public, date-versioned forecasts. We examine their predictive performance for mortality by weeks of extrapolation, world region, and estimation month. We additionally assess prediction of the timing of peak daily mortality. Globally, models released in October show a median absolute percent error (MAPE) of 7 to 13% at six weeks, reflecting surprisingly good performance despite the complexities of modelling human behavioural responses and government interventions. Median absolute error for peak timing increased from 8 days at one week of forecasting to 29 days at eight weeks and is similar for first and subsequent peaks. The framework and public codebase ( https://github.com/pyliu47/covidcompare ) can be used to compare predictions and evaluate predictive performance going forward.
Algorithmic monoculture and social welfare

As algorithms are increasingly applied to screen applicants for high-stakes decisions in employment, lending, and other domains, concerns have been raised about the effects of algorithmic monoculture, in which many decision-makers all rely on the same algorithm. This concern invokes analogies to agriculture, where a monocultural system runs the risk of severe harm from unexpected shocks. Here, we show that the dangers of algorithmic monoculture run much deeper, in that monocultural convergence on a single algorithm by a group of decision-making agents, even when the algorithm is more accurate for any one agent in isolation, can reduce the overall quality of the decisions being made by the full collection of agents. Unexpected shocks are therefore not needed to expose the risks of monoculture; it can hurt accuracy even under "normal" operations and even for algorithms that are more accurate when used by only a single decision-maker. Our results rely on minimal assumptions and involve the development of a probabilistic framework for analyzing systems that use multiple noisy estimates of a set of alternatives.
Solvated Nickel Complexes as Stoichiometric and Catalytic Perfluoroalkylation Agents.

The acetonitrile-solvated [(MeCN)Ni(C2 F5 )3 ]- was prepared in order to compare and contrast its reactivity with the known [(MeCN)Ni(CF3 )3 ]- towards organic electrophiles. Both [(MeCN)Ni(CF3 )3 ]- and [(MeCN)Ni(C2 F5 )3 ]- successfully react with aryl iodonium and diazonium salts as well as alkynyl iodonium salts to give fluoroalkylated organic products. Electrochemical analysis of [(MeCN)NiII (C2 F5 )3 ]- suggests that, upon electro-oxidation to [(MeCN)n NiIII (C2 F5 )3 ], reductive homolysis of a perfluoroethyl radical occurs, with the concomitant formation of [(MeCN)2 NiII (C2 F5 )2 ]. Catalytic C-H trifluoromethylations of electron-rich arenes were successfully achieved using either [(MeCN)Ni(CF3 )3 ]- or the related [Ni(CF3 )4 ]2- . Stoichiometric reactions of the solvated nickel complexes reveal that "ligandless" nickel is exceptionally capable of serving as reservoir of CF3 groups under catalytically relevant conditions.