cssi/SciSciGPT-SciSciNet huggingface dataset:
Beyond Aid: How Much Should African Countries Pay to Borrow?

Post debt relief, the number of African countries considering accessing international capital markets, often to fund large infrastructure projects, is increasing. Potential risks of capital inflows are well known but the literature offers little help to estimate the cost of borrowing internationally for the first time. This article proposes a two-step approach to estimate the sovereign credit rating and interest rate cost of a country considering borrowing externally. Estimates can be used to assess the costs and benefits of different financing options. The method can also be used to construct foreign currency as well as domestic local currency yield curves.
Does history matter? Colonial education investments in India

Can good policy overcome or alter the effects of history? This question is addressed in this article using unique district-level data for 148 districts of former British India. Controlling for observable differences in geography and income, the ordinary least squares estimates suggest a large and positive effect of colonial expenditures on rural primary education in 1911 on rural literacy up to 1991. However, instrumental variable estimates that control for the endogeneity of colonial investments suggest that the effects of historical spending are significant only up to 1971. Two policy changes can account for these findings: an increase in spending following the 1968 National Education Policy and a greater emphasis on the universal provision of public goods such as schools in the 1970s. Unlike recent studies documenting the persistent effects of historical investments on contemporary outcomes, this study emphasizes how effective policies can overturn the effects of history.
Medical futility and implications for physician autonomy.

The term medical futility has become a shorthand way to describe a situation in which a patient demands and a physician objects to the provision of certain medical treatment, on the ground that the treatment will provide no medical benefit to the patient. The dilemma of medically futile care has gained prominence as a subject for debate in medical and legal literature, has been discussed implicitly and explicitly in court opinions, and has weighed heavily in the debate surrounding health care reform and accompanying discussion of allocation of health care resources. Recently, the issues surrounding the provision of nonbeneficial care became the subject of a federal court opinion. On February 10, 1994, the United States Court of Appeals for the Fourth Circuit handed down its decision in In re Baby K, a case that has since served as the central focus for debate over the provision of medical treatment deemed inappropriate or futile by a patient’s physician.
How Do Accelerators Impact the Performance of High-Technology Ventures?

Accelerators aim to help nascent companies reach successful outcomes by providing capital, enabling industry connections, and increasing exposure to investors. Critically, however, accelerators als...
Under the Lender’s Looking Glass

This paper studies the impact of bank monitoring on the risk of US equity REITs. Using a unique, hand-collected data sample of mortgage balances, I show that bank screening and monitoring of REIT assets via utilizing secured mortgage financing (vs unsecured, public debt) lowers the overall company risk of a REIT. At the asset level, screening results in retail and office assets with higher acquisition values and located in primary markets, i.e., more transparent assets, being pledged as collateral. Further, I find evidence consistent with the role of lender monitoring for secured mortgage loans and show that properties located in closer proximity to a REIT’s headquarters are more likely to be pledged as collateral for a mortgage.
Monetary incentives in mail surveys

Eighteen empirical studies from fourteen different researchers provide evidence that prepaid monetary incentives have a strong positive impact on the response rate in mail surveys. One of these studies is described here and an attempt is made to generalize from all eighteen about the relationship between size of incentives and reduction in nonresponse. These generalizations should be of value for the design of mail survey studies.
The Effect of Issuer Conservatism on IPO Pricing and Performance

Based on a textual analysis of initial public offering (IPO) prospectuses, we obtain a number of important findings regarding the relation between the conservatism in prospectuses, IPO pricing, and subsequent operating and stock return performance. First, prospectus conservatism is positively related to underpricing, with the relation more pronounced for technology than nontechnology firms. Second, for nontechnology IPOs, prospectus conservatism is able to predict the firm's post-IPO operating performance. Specifically, we find that conservatism is inversely related to the firm's operating performance for the 3 years following the IPO. However, this predictability is limited to nontechnology IPOs. Finally, we find some evidence that for nontechnology IPOs conservatism is inversely related to the firm's post-IPO abnormal stock return. We conclude that the conservatism contained in an IPO's prospectus contains useful information about pricing and subsequent operating and stock return performance. Moreover, prospectus conservatism for nontechnology IPOs deserves more attention from investors. Copyright 2013, Oxford University Press.
The global market for OTC derivatives: An analysis of dealer holdings

We provide a descriptive examination of the trading activities of one of the most important intermediaries in global financial markets—the OTC derivatives dealer. These dealers play a central role in the provision of derivative products and in the intermediation of market risks faced by financial and nonfinancial firms alike. Utilizing a unique database, we analyze the derivatives holdings of 264 dealers spanning 34 countries over the period 1995–2001. We document the geographic composition of dealers on both country and regional levels as well as analyze trends in dealer holdings on an aggregate and individual product level. We further analyze the extent of global merger activity among dealers and resulting consolidation effects. Finally, we investigate at the individual dealer level the extent and evolution of their array of product offerings. © 2005 Wiley Periodicals, Inc. Jrl Fut Mark 25:39–77, 2005
FRICTIONAL GOODS MARKETS: THEORY AND APPLICATIONS

We analyse dynamic general equilibrium models with more-or-less directed search by informed buyers and random search by uninformed buyers. This nests existing specifications and generates new insights. A quantitative application concerns the welfare cost of inflation, which is known to be quite high with pure random search and low with pure directed search. Our calibration implies the impact of inflation is fairly low, in part because, in addition to the usual costs, it provides benefits by more heavily taxing high-price sellers that inefficiently profit from exploiting the uninformed. Other applications analyse analytically and numerically changes in credit conditions and information.
What sustains fiscal consolidations in emerging market countries

This paper examines the factors affecting the persistence of fiscal consolidation in 25 emerging market countries during 1980–2001. It proposes a new approach for defining spells of fiscal consolidation. The results indicate that the probability of ending a fiscal adjustment is affected by the legacy of previous fiscal failures, the size of the deficit, the composition of spending and level of total revenues. There is also some evidence that the initial debt stock, exchange rate developments, inflation and the unemployment rate have an impact on the persistence of adjustments. Copyright © 2005 John Wiley & Sons, Ltd.
Artificial Intelligence Assisted Collaborative Edge Caching in Small Cell Networks

Edge caching is a new paradigm that has been exploited over the past several years to reduce the load for the core network and to enhance the content delivery performance. Many existing caching solutions only consider homogeneous caching placement due to the immense complexity associated with the heterogeneous caching models. Unlike these legacy modeling paradigms, this paper considers heterogeneous content preference of the users with heterogeneous caching models at the edge nodes. Besides, aiming to maximize the cache hit ratio (CHR) in a two-tier heterogeneous network, we let the edge nodes collaborate. However, due to complex combinatorial decision variables, the formulated problem is hard to solve in the polynomial time. Moreover, there does not even exist a ready-touse tool or software to solve the problem. We propose a modified particle swarm optimization (M-PSO) algorithm that efficiently solves the complex constraint problem in a reasonable time. Using numerical analysis and simulation, we validate that the proposed algorithm significantly enhances the CHR performance when comparing to that of the existing baseline caching schemes.
Students with the initial “A” don’t get better grades

Abstract It has been claimed that students whose first or last name begins with the letters A or B have higher grade point averages than students whose first or last name begins with the letters C or D. We critique the analysis behind this claim, raising a number of issues that lead us to question the validity of the claim. We also test for a relation between student initial and GPA using two new datasets and find no evidence to support such a relation.
Citizenship and Protection

This Article discusses the role of U.S. citizenship in determining who would be protected by the Constitution, other domestic laws, and the courts. Traditionally, within the United States, both noncitizens and citizens have had more or less equal civil liberties protections. But outside the sovereign territory of the United States, noncitizens have historically lacked such protections. This Article sketches the traditional rules that demarcated the boundaries of protection, then addresses the functional and normative justifications for the very different treatment of noncitizens depending on whether or not they were present within the United States.
Does higher-frequency data always help to predict longer-horizon volatility?

No. Conditional autocorrelation in realized shocks due to misspecification in expected return process affects the relative performance of longer-horizon volatility predictions of models using different frequencies of data. This is because, for multi-step forecasts of volatility, small violations of residual serial independence are compounded in temporal aggregation. In this paper, we show that the conditional autocorrelation in return residuals is a strong predictor of the relative performance of different frequency models of volatility. When the conditional autocorrelation is high, the higher frequency model performs markedly worse than its lower frequency counterpart. Empirically, we show that residual autocorrelation exists in the large cross section of stocks at any given point in time, and that this misspecification can substantially decrease the accuracy of multi-step forecasts generated by higher frequency models. Comparing the monthly volatility predictions from daily models and monthly models, we show that there is a trade-off between the gains from high-frequency data and the susceptibility of its multi-period ahead forecasts to returns model misspecification.
The Demographic Transition and the Emergence of Sustained Economic Growth

The demographic transition that swept the world in the course of the last century has been identified as one of the prime forces in the transition from stagnation to growth. The unprecedented increase in population growth during the early stages of industrialization was ultimately reversed and the demographic transition brought about a significant reduction in fertility rates and population growth in various regions of the world, enabling economies to convert a larger share of the fruits of factor accumulation and technological progress into growth of income per capita. This paper examines various mechanisms that have been proposed as possible triggers for the demographic transition, assessing their empirical validity, and their potential role in the transition from stagnation to growth. (JEL: O11, O14, O33, O40, J11, J13)
Toward a Formal Semantics for Control-Flow Process Models

A number of information systems have been developed to automate business processes. For process modeling, verification, and automation in information systems, a formal semantics of control-flow process models is needed. Usually process modeling languages e.g., BPMN, EPC, IDEF3, UML, and WfMC standards are used to represent control-flow process models. When these process modeling languages are developed, their informal semantics are typically described using examples, but their formal semantics are not defined. Although many different semantics for control-flow process models have been proposed, the existing semantics specifications have limitations because they do not support certain desirable features. In this paper, we propose a new formal semantics for control-flow process models. We show that it is more accurate, complete, and applicable than the existing semantics specifications.
TREASURY BILLS AND/OR CENTRAL BANK BILLS FOR ABSORBING SURPLUS LIQUIDITY: THE MAIN CONSIDERATIONS

This paper discusses the challenging question of whether central banks should use Treasury bills or central bank bills for draining excess liquidity in the banking system. While recognizing that there are practical reasons for using central bank bills, the paper argues that Treasury bills are the first best option especially because of the positive externalities for the financial sector and the rest of the economy. However, the main considerations in the choice should be: (i) operational independence for the central bank; (ii) market development; and (iii) the strengthening of the transmission of monetary policy impulses.
Cosmological constant from quarks and torsion

We present a simple and natural way to derive the observed small, positive cosmological constant from the gravitational interaction of condensing fermions. In the Riemann-Cartan spacetime, torsion gives rise to the axial–axial vector four-fermion interaction term in the Dirac Lagrangian for spinor fields. We show that this nonlinear term acts like a cosmological constant if these fields have a nonzero vacuum expectation value. For quark fields in QCD, such a torsion-induced cosmological constant is positive and its energy scale is only about 8 times larger than the observed value. Adding leptons to this picture could lower this scale to the observed value.
What Cognitive Dissonance Tells Us About Tone in Persuasion

One of the toughest questions that lawyers face is how hard to push in persuasion. We want to advocate strongly enough so that our passion for our client’s cause, and our belief in its rightness, is apparent to the court. There is nothing worse than lukewarm advocacy. But we do not want to push so far that we cross from zealous advocacy into obnoxiousness. The problem is that the line between persuasion and coercion is a fine one. This essay takes an initial step toward thinking about where good advocacy should draw the line between zeal and coercion by looking to cognitive science for guidance. In particular, the paper looks at cognitive dissonance and related psychological processes to determine how decision-makers might react to different advocacy styles. The bottom line arrived at in the paper is that it may often be advisable for lawyers to present arguments in a tone that, while strong in pursuit of a favorable outcome, appears more gradual, objective and reasonable. In other words, in many cases, the most psychologically appealing advocacy approach is one that appears more balanced and reasonable rather than one that is aggressively pushy and one sided.
Medicaid Planning, Estate Recovery, and Alternatives for Long-Term Care Financing: Identifying the Ethical Issues

In anticipation of the escalating costs of long-term care, consumers and the federal and state governments are engaging in a variety of strategies intended to preserve the financial solvency of the respective parties. For the consumer, this may mean planning in a way designed to maximize one's potential future eligibility for government support, whereas the federal government tries to limit those planning activities inspired by public benefits considerations and the states attempt to recover their long-term care expenses from the estates of deceased consumers whose long-term care needs the state had earlier supported. All of these strategies have important and controversial public policy implications. The public policy debate surrounding these strategies ought to be ethically informed. This article sets out to identify and outline some of the main ethical questions engendered by individual Medicaid planning on one hand, and state estate recovery efforts on the other. It concludes that neither of these approaches to the challenge of long-term care financing is very ethically palatable.
Myopia, Discretion, and Commitment in a Two-Period AS/AD Model

A large body of pedagogical literature has recently emerged to place the New Keynesian framework for analyzing business cycle fluctuations and the conduct of monetary policy into undergraduate economics curricula. This article develops the graphical apparatus for the analysis of optimal monetary policy in the context of a two-period model under alternative assumptions about the formation of inflationary expectations. It demonstrates that differences in assumptions about the formation of inflationary expectations translate into quite different conclusions regarding the optimal conduct of monetary policy.
How political identity and charity positioning increase donations: Insights from Moral Foundations Theory

Abstract Marketers can strategically target potential donors based on their political identity. Drawing upon the moral foundations underlying political identity, we examine conditions in which a charity's positioning increases donations based on its alignment with a political identity. In doing so, we demonstrate the moderating role of moral identity internalization, which is a robust predictor of donation behavior. In three studies, our results reveal that when the moral foundations of a charity, as evidenced by the charity's management processes (Study 1) or mission (Studies 2 and 3), are aligned with the donor's political identity, donations increase. This effect is enhanced among those with higher moral identity internalization. These results offer theoretical contributions, as well as practical implications, for organizations soliciting donations, providing insight into the extent to which charity positioning based on moral foundations can differentially influence donations based on political identity.
The Antidiscrimination Paradox: Why Sex Before Race?

This paper seeks to explain a paradox: Why does Title VII's prohibition on sex discrimination currently look so much more expansive than its prohibition on race discrimination? Why in particular, do workers appear to be receiving greater protection for expressions of gender identity than for expressions of racial identity? I argue that as a doctrinal matter, the paradox is illusory - the product of a fundamental misinterpretation of recent sex discrimination case law by scholars. Rather than reflecting fundamentally distinct antidiscrimination principles, the race and sex cases in fact reflect the same traditional commitments to ending status discrimination and undermining group-based subordination. Nonetheless, as a practical matter, the paradox is real. Courts are more likely to protect workplace expressions of gender identity than racial identity. The divergence, I contend, flows not from law, but from culture - in particular society's ongoing commitment to racial transcendence and gender essentialism.
When Voters Make Laws: How Direct Democracy Is Shaping American Cities

Three challenges affect the use of initiatives and referendums concerning bonds to fund local infrastructure. First, public officials can often make take-it-or-leave-it offers to voters, conferring...
Hypothesis tests on Rayleigh wave radiation pattern shapes: a theoretical assessment of idealized source screening

Shallow seismic sources excite Rayleigh wave ground motion with azimuthally dependent radiation patterns. We place binary hypothesis tests on theoretical models of such radiation patterns to screen cylindrically symmetric sources (like explosions) from non-symmetric sources (like non-vertical dip-slip, or non-VDS faults). These models for data include sources with several unknown parameters, contaminated by Gaussian noise and embedded in a layered half-space. The generalized maximum likelihood ratio tests that we derive from these data models produce screening statistics and decision rules that depend on measured, noisy ground motion at discrete sensor locations. We explicitly quantify how the screening power of these statistics increase with the size of any dip-slip and strike-slip components of the source, relative to noise (faulting signal strength), and how they vary with network geometry. As applications of our theory, we apply these tests to (1) find optimal sensor locations that maximize the probability of screening non-circular radiation patterns, and (2) invert for the largest non-VDS faulting signal that could be mistakenly attributed to an explosion with damage, at a particular attribution probability. Lastly, we quantify how certain errors that are sourced by opening cracks increase screening rate errors. While such theoretical solutions are ideal and require future validation, they remain important in underground explosion monitoring scenarios because they provide fundamental physical limits on the discrimination power of tests that screen explosive from non-VDS faulting sources.
Constraints from Invariant Subtropical Vertical Velocities on the Scalings of Hadley Cell Strength and Downdraft Width with Rotation Rate

Weak-temperature-gradient influences from the tropics and quasigeostrophic influences from the extratropics plausibly constrain the subtropical-mean static stability in terrestrial atmospheres. Because mean descent acting on this static stability is a leading-order term in the thermodynamic balance, a state-invariant static stability would impose constraints on the Hadley cells, which this paper explores in simulations of varying planetary rotation rate. If downdraft-averaged effective heating (the sum of diabatic heating and eddy heat flux convergence) too is invariant, so must be vertical velocity -- an "omega governor." In that case, the Hadley circulation overturning strength and downdraft width must scale identically -- the cell can strengthen only by widening or weaken only by narrowing. Simulations in two idealized, dry GCMs with a wide range of planetary rotation rates exhibit nearly unchanging downdraft-averaged static stability, effective heating, and vertical velocity, as well as nearly identical scalings of the Hadley cell downdraft width and strength. In one, eddy stresses set this scaling directly (the Rossby number remains small); in the other, eddy stress and bulk Rossby number changes compensate to yield the same, ({\sim}\Omega^{-1/3}) scaling. The consistency of this power law for cell width and strength variations may indicate a common driver, and we speculate that Ekman pumping could be the mechanism responsible for this behavior. Extending to moist atmospheres, in an idealized aquaplanet GCM the subtropical static stability is also insensitive to rotation rate but the effective heating and vertical velocity are not.
Fusing Concurrent Orthogonal Wide-aperture Sonar Images for Dense Underwater 3D Reconstruction

We propose a novel approach to handling the ambiguity in elevation angle associated with the observations of a forward looking multi-beam imaging sonar, and the challenges it poses for performing an accurate 3D reconstruction. We utilize a pair of sonars with orthogonal axes of uncertainty to independently observe the same points in the environment from two different perspectives, and associate these observations. Using these concurrent observations, we can create a dense, fully defined point cloud at every time-step to aid in reconstructing the 3D geometry of underwater scenes. We will evaluate our method in the context of the current state of the art, for which strong assumptions on object geometry limit applicability to generalized 3D scenes. We will discuss results from laboratory tests that quantitatively benchmark our algorithm’s reconstruction capabilities, and results from a real-world, tidal river basin which qualitatively demonstrate our ability to reconstruct a cluttered field of underwater objects.
Computing High-Quality Clutter Removal Solutions for Multiple Robots

We investigate the task and motion planning problem of clearing clutter from a workspace with limited ingress/egress access for multiple robots. We call the problem multi-robot clutter removal (MRCR). Targeting practical applications where motion planning is non-trivial but is not a bottle-neck, we focus on finding high-quality solutions for feasible MRCR instances, which depends on the ability to efficiently compute high-quality object removal sequences. Despite the challenging multi-robot setting, our proposed search algorithms based on A*, dynamic programming, and best-first heuristics all produce solutions for tens of objects that significantly outperform single robot solutions. Realistic simulations with multiple Kuka youBots further confirms the effectiveness of our algorithmic solutions. In contrast, we also show that deciding the optimal object removal sequence for MRCR is computationally intractable.
Transitioning Between Tableaux and Spider Bases for Specht Modules

Regarding the Specht modules associated to the two-row partition (n, n), we provide a combinatorial path model to study the transitioning matrix from the tableau basis to the A1-web basis (i.e. cup diagrams), and prove that the entries in this matrix are positive in the upper-triangular portion with respect to a certain partial order.
Contrastive Similarity Matching for Supervised Learning.

We propose a novel biologically plausible solution to the credit assignment problem motivated by observations in the ventral visual pathway and trained deep neural networks. In both, representation...
Dirichlet process mixture models with shrinkage prior

We propose Dirichlet Process Mixture (DPM) models for prediction and cluster-wise variable selection, based on two choices of shrinkage baseline prior distributions for the linear regression coefficients, namely the Horseshoe prior and Normal-Gamma prior. We show in a simulation study that each of the two proposed DPM models tend to outperform the standard DPM model based on the non-shrinkage normal prior, in terms of predictive, variable selection, and clustering accuracy. This is especially true for the Horseshoe model, and when the number of covariates exceeds the within-cluster sample size. A real data set is analyzed to illustrate the proposed modeling methodology, where both proposed DPM models again attained better predictive accuracy.
Changes in partner traits drive variation in plant–nectar robber interactions across habitats

Abstract The frequency and outcome of biotic interactions commonly vary with environmental conditions, even without changes to community composition. Yet the drivers of such environmentally-mediated change in biotic interactions are poorly understood, limiting our ability to predict how environmental change will impact communities. Studying nectar robbery by stingless bees of Odontonema cuspidatum (Acanthaceae) in a coffee agroecosystem, we documented a temporally consistent difference in nectar robbing intensity between anthropogenic and seminatural habitats. Plants growing in coffee fields (anthropogenic habitat) experienced significantly more nectar robbery than plants growing in forest fragments (seminatural habitat). Using a combination of field surveys and manipulative experiments, we found that nectar robbery was higher in coffee fields primarily due to environmental effects on a) neighborhood floral context and b) O. cuspidatum floral traits. This led to both preferential foraging by nectar robbers in coffee fields, and to changes in foraging behavior on O. cuspidatum that increased robbery. Nectar robbery significantly reduced fruit set in O. cuspidatum. These results suggest that the effects of anthropogenic environmental change on species traits may be more important than its effect on species density in determining how interaction frequency and outcome are affected by such environmental change.
Face-Off: Adversarial Face Obfuscation

Advances in deep learning have made face recognition technologies pervasive. While useful to social media platforms and users, this technology carries significant privacy threats. Coupled with the abundant information they have about users, service providers can associate users with social interactions, visited places, activities, and preferences--some of which the user may not want to share. Additionally, facial recognition models used by various agencies are trained by data scraped from social media platforms. Existing approaches to mitigate these privacy risks from unwanted face recognition result in an imbalanced privacy-utility trade-off to users. In this paper, we address this trade-off by proposing Face-Off, a privacy-preserving framework that introduces strategic perturbations to the user's face to prevent it from being correctly recognized. To realize Face-Off, we overcome a set of challenges related to the black-box nature of commercial face recognition services, and the scarcity of literature for adversarial attacks on metric networks. We implement and evaluate Face-Off to find that it deceives three commercial face recognition services from Microsoft, Amazon, and Face++. Our user study with 423 participants further shows that the perturbations come at an acceptable cost for the users.
Outlining Traceability: A Principle for Operationalizing Accountability in Computing Systems

Accountability is widely understood as a goal for well governed computer systems, and is a sought-after value in many governance contexts. But how can it be achieved? Recent work on standards for governable artificial intelligence systems offers a related principle: traceability. Traceability requires establishing not only how a system worked but how it was created and for what purpose, in a way that explains why a system has particular dynamics or behaviors. It connects records of how the system was constructed and what the system did mechanically to the broader goals of governance, in a way that highlights human understanding of that mechanical operation and the decision processes underlying it. We examine the various ways in which the principle of traceability has been articulated in AI principles and other policy documents from around the world, distill from these a set of requirements on software systems driven by the principle, and systematize the technologies available to meet those requirements. From our map of requirements to supporting tools, techniques, and procedures, we identify gaps and needs separating what traceability requires from the toolbox available for practitioners. This map reframes existing discussions around accountability and transparency, using the principle of traceability to show how, when, and why transparency can be deployed to serve accountability goals and thereby improve the normative fidelity of systems and their development processes.
Rethinking growth and inequality in the US: what is the role of measurement of GDP?

Five sectors have increased their contribution to US GDP growth since 1973: professional-business services (PBS), finance, information, healthcare, and arts-entertainment. Among these, however, fin...
Axial Spectral Encoding of Space-Time Wave Packets

Space-time (ST) wave packets are propagation-invariant pulsed optical beams whose group velocity can be tuned in free space by tailoring their spatiotemporal spectral structure. To date, efforts on synthesizing ST wave packets have striven to maintain their propagation invariance. Here, we demonstrate that one degree of freedom of a ST wave packet---its on-axis spectrum---can be isolated and purposefully controlled independently of the others. Judicious spatiotemporal spectral amplitude and phase modulation yields ST wave packets with programmable spectral changes along the propagation axis, including red-shifting or blue-shifting spectra, or more sophisticated axial spectral encoding including bidirectional spectral shifts and accelerating spectra. In all cases, the spectral shift can be larger than the initial on-axis bandwidth, while preserving the propagation-invariance of the other degrees of freedom, including the wave packet spatiotemporal profile. These results may be useful in range finding in microscopy or remote sensing via spectral stamping.
Predicting the characteristics of defect transitions on curved surfaces

The energetically optimal position of lattice defects on intrinsically curved surfaces is a complex function of shape parameters. For open surfaces, a simple condition predicts the critical size for which a central disclination yields lower energy than a boundary disclination. In practice, this transition is modified by activation energies or more favorable intermediate defect positions. Here it is shown that these transition characteristics (continuous or discontinuous, first or second order) can also be inferred from analytical, general criteria evaluated from the surface shape. A universal scale of activation energy is found, and the criteria are generalized to predict transition order as surface shape symmetry is broken. The results give practical insight into structural transitions to disorder in many cellular materials of technological and biological importance.
Iterative Algorithms for Joint Scatter and Attenuation Estimation From Broken Ray Transform Data

The single-scatter approximation is fundamental in many tomographic imaging problems including x-ray scatter imaging and optical scatter imaging for certain media. In all cases, noisy measurements are affected by both local scatter events and nonlocal attenuation. Prior works focus on reconstructing one of two images: scatter density or total attenuation. However, both images are media specific and useful for object identification. Nonlocal effects of the attenuation image on the data are summarized by the broken ray transform (BRT). While analytic inversion formulas exist, poor conditioning of the inverse problem is only exacerbated by noisy measurements and sampling errors. This has motivated interest in the related star transforms incorporating BRT measurements from multiple source-detector pairs. However, all analytic methods operate on the log of the data. For media comprising regions with no scatter a new approach is required. We are the first to present a joint estimation algorithm based on Poisson data models for a single-scatter measurement geometry. Monotonic reduction of the log-likelihood function is guaranteed for our iterative algorithm while alternating image updates. We also present a fast algorithm for computing the discrete BRT forward operator. Our generalized approach can incorporate both transmission and scatter measurements from multiple source-detector pairs. Transmission measurements resolve low-frequency ambiguity in the joint image estimation problem, while multiple scatter measurements resolve the attenuation image. The benefits of joint estimation, over single-image estimation, vary with problem scaling. Our results quantify these benefits and should inform design of future acquisition systems.
Loop homology of bi-secondary structures

Abstract In this paper we compute the loop homology of bi-secondary structures. Bi-secondary structures were introduced by Haslinger and Stadler and are pairs of RNA secondary structures, i.e. diagrams having non-crossing arcs in the upper half-plane. A bi-secondary structure is represented by drawing its respective secondary structures in the upper and lower half-plane. An RNA secondary structure has a loop decomposition, where a loop corresponds to a boundary component, regarding the secondary structure as an orientable fatgraph. The loop-decomposition of secondary structures facilitates the computation of its free energy and any two loops intersect either trivially or in exactly two vertices. In bi-secondary structures the intersection of loops is more complex and is of importance in current algorithmic work in bio-informatics and evolutionary optimization. We shall construct a simplicial complex capturing the intersections of loops and compute its homology. We prove that only the zeroth and second homology groups are nontrivial and furthermore show that the second homology group is free. Finally, we provide evidence that the generators of the second homology group have a bio-physical interpretation: they correspond to pairs of mutually exclusive substructures.
La migración asiática libre al centro del virreinato novohispano, 1565-1700

The establishment of trans-Pacific communication between New Spain and the Philippine Islands in 1565 spurred the development of significant cultural, economic and migratory processes involving these two areas, including the trans-Pacific migration of free Asians to the central region of the Vice-Royalty of New Spain from 1565 to 1700. This article analyzes the diverse groups that participated in this complex migratory phenomenon and examines the factors that gave rise to this process. Also, it considers the important influence that free Asian immigrants had on the society of New Spain.
Multimodal mobility systems: joint optimization of transit network design and pricing

The performance of multimodal mobility systems relies on the seamless integration of conventional mass transit services and the advent of Mobility-on-Demand (MoD) services. Prior work is limited to individually improving various transport networks' operations or linking a new mode to an existing system. In this work, we attempt to solve transit network design and pricing problems of multimodal mobility systems en masse. An operator (public transit agency or private transit operator) determines frequency settings of the mass transit system, flows of the MoD service, and prices for each trip to optimize the overall welfare. A primal-dual approach, inspired by the market design literature, yields a compact mixed integer linear programming (MILP) formulation. However, a key computational challenge remains in allocating an exponential number of hybrid modes accessible to travelers. We provide a tractable solution approach through a decomposition scheme and approximation algorithm that accelerates the computation and enables optimization of large-scale problem instances. Using a case study in Nashville, Tennessee, we demonstrate the value of the proposed model. We also show that our algorithm reduces the average runtime by 60% compared to advanced MILP solvers. This result seeks to establish a generic and simple-to-implement way of revamping and redesigning regional mobility systems in order to meet the increase in travel demand and integrate traditional fixed-line mass transit systems with new demand-responsive services.
Is The United States Tax Court Exempt from Administrative Law Jurisprudence When Acting as a Reviewing Court

Taxes are the lifeblood that sustains a government; therefore, the U.S. Supreme Court has enforced the principle that the government must be unimpaired in its ability to collect taxes or it could be undermined by citizens attempting to evade their tax obligations. Taxpayers have always been permitted to dispute the amount of their liability, but had little opportunity to dispute collection methods. In 1998, Congress enacted the Internal Revenue Service Restructuring and Reform Act which represents a dramatic departure from this principle. The Act now permits taxpayers to protest the proposed collection method. Unfortunately, the statute is short on details, and the IRS and the Tax Court have struggled to create procedures that comport with the spirit of the 1aw within the confines of their respective authorities. The Tax Court has tried to create a review process by analogizing collection due process appeals to the Tax Court's deficiency cases. However, when the Tax Court hears deficiency cases it acts as a trial court and hears the matter de novo. When the Tax Court hears collection due process appeals it acts as an appellate court reviewing agency action to determine its propriety, a different role and from deficiency cases.Commentators have urged the Tax Court to fill in the gaps by turning to traditional administrative law jurisprudence, including the Administrative Procedure Act. In the absence of legislation specifying how a court is to review agency action, these bodies of law step into the breach and provide structure for the court's review process. As a result, participants in the process are assured of consistency and predictability regarding the review process, thus rendering the process fairer. However, the Tax Court insists that it has never been subject to administrative law jurisprudence or the APA, nor could it be. An exploration of both the Tax Court's and the APA’s history reveals
Online Discriminative Graph Learning from Multi-Class Smooth Signals

Abstract Graph signal processing (GSP) is a key tool for satisfying the growing demand for information processing over networks. However, the success of GSP in downstream learning and inference tasks such as supervised classification is heavily dependent on the prior identification of the relational structures. Graphs are natural descriptors of the relationships between entities of complex environments. The underlying graph is not readily detectable in many cases and one has to infer the topology from the observed signals which admit certain regularity over the sought representation. Assuming that signals are smooth over the latent class-specific graph is the notion we build upon. Firstly, we address the problem of graph signal classification by proposing a novel framework for discriminative graph learning. To learn discriminative graphs, we invoke the assumption that signals belonging to each class are smooth with respect to the corresponding graph while maintaining non-smoothness with respect to the graphs corresponding to other classes. Discriminative features are extracted via graph Fourier transform (GFT) of the learned representations and used in downstream learning tasks. Secondly, we extend our work to tackle increasingly dynamic environments and real-time topology inference. To this end, we adopt recent advances of GSP and time-varying convex optimization. We develop a proximal gradient (PG) method which can be adapted to situations where the data are acquired on-the-fly. Beyond discrimination, this is the first work that addresses the problem of dynamic graph learning from smooth signals where the sought network alters slowly. The introduced online framework is guaranteed to track the optimal time-varying batch solution under mild technical conditions. The validation of the proposed frameworks is comprehensively investigated using both synthetic and real data. The proposed classification pipeline outperforms the-state-of-the-art methods when applied to the problem of emotion classification based on electroencephalogram (EEG) data. We also perform network-based analysis of epileptic seizures using electrocorticography (ECoG) records. Moreover, by applying our method to financial data, our approach infers the relationships between the stock-price behavior of leading US companies and the recent events including the COVID-19 pandemic.
The whole is greater than its parts: ensembling improves protein contact prediction.

The prediction of amino acid contacts from protein sequence is an important problem, as protein contacts are a vital step towards the prediction of folded protein structures. We propose that a powerful concept from deep learning, called ensembling, can increase the accuracy of protein contact predictions by combining the outputs of different neural network models. We show that ensembling the predictions made by different groups at the recent Critical Assessment of Protein Structure Prediction (CASP13) outperforms all individual groups. Further, we show that contacts derived from the distance predictions of three additional deep neural networks—AlphaFold, trRosetta, and ProSPr—can be substantially improved by ensembling all three networks. We also show that ensembling these recent deep neural networks with the best CASP13 group creates a superior contact prediction tool. Finally, we demonstrate that two ensembled networks can successfully differentiate between the folds of two highly homologous sequences. In order to build further on these findings, we propose the creation of a better protein contact benchmark set and additional open-source contact prediction methods.
FEWS: Large-Scale, Low-Shot Word Sense Disambiguation with the Dictionary.

Current models for Word Sense Disambiguation (WSD) struggle to disambiguate rare senses, despite reaching human performance on global WSD metrics. This stems from a lack of data for both modeling and evaluating rare senses in existing WSD datasets. In this paper, we introduce FEWS (Few-shot Examples of Word Senses), a new low-shot WSD dataset automatically extracted from example sentences in Wiktionary. FEWS has high sense coverage across different natural language domains and provides: (1) a large training set that covers many more senses than previous datasets and (2) a comprehensive evaluation set containing few- and zero-shot examples of a wide variety of senses. We establish baselines on FEWS with knowledge-based and neural WSD approaches and present transfer learning experiments demonstrating that models additionally trained with FEWS better capture rare senses in existing WSD datasets. Finally, we find humans outperform the best baseline models on FEWS, indicating that FEWS will support significant future work on low-shot WSD.
Haplotype Explorer: an infection cluster visualization tool for spatiotemporal dissection of the COVID-19 pandemic.

SUMMARY: Many of software for network visualization are available, but existing software have not been optimized to infection cluster visualization, especially the current worldwide invasion of COVID-19 since 2019. To reach the spatiotemporal understanding of epidemics, we have developed Haplotype Explorer. In Haplotype Explorer, users can explore the network interactively with metadata like accession number, locations, and collection dates. Time dependent transition of the network can be exported as continuous sections for making a movie. Here, we introduce features and products of Haplotype Explorer, demonstrating time-dependent snapshots and a movie of haplotype networks inferred from total of 4,282 SARS-CoV-2 genomes. ABSTRACT: The worldwide eruption of COVID-19 that began in Wuhan, China in late 2019 reached 10 million cases by late June 2020. In order to understand the epidemiological landscape of the COVID-19 pandemic, many studies have attempted to elucidate phylogenetic relationships between collected viral genome sequences using haplotype networks. However, currently available applications for network visualization are not suited to understand the COVID-19 epidemic spatiotemporally due to functional limitations, that motivated us to develop Haplotype Explorer, an intuitive tool for visualizing and exploring haplotype networks. Haplotype Explorer enables to dissect epidemiological consequences via interactive node filters and provides the perspective on infectious disease dynamics depend on regions and time, such as introduction, outbreak, expansion, and containment. Here, we demonstrate the effectiveness of Haplotype Explorer by showing features and an example of visualization. The demo using SARS-CoV-2 genomes are available at https://github.com/TKSjp/HaplotypeExplorer/blob/master/Example/. There are several examples using SARS-CoV-2 genomes and Dengue virus serotype 1 E-genes sequence.