cssi/SciSciGPT-SciSciNet huggingface dataset:
Origins of the Social Function of Property in Chile

La version espanola de este articulo se puede encontrar en: http://ssrn.com/abstract=2307102Numerous countries in Latin America have constitutions that incorporate a social-function or social-obligation norm in their definitions of property. Scholars familiar with the broad-sweeping social legislation of the Mexican Constitution of 1917 might speculate that Mexican constitutional provisions were the intellectual source for other Latin American constitutions that define property in terms of a social function. In fact, the origin of the social function in property in the Southern Cone was not an intellectual imperial imposition from the North, in this case Mexico, but rather was the product of the transmission of European, notably French, ideas about the social function. These ideas migrated to the region through the works of Leon Duguit, a law professor from Bordeaux, who wrote and lectured extensively on law and constitutional theory in the early 1900s. Duguit’s lectures in Buenos Aires in 1911 and their subsequent publication are the earliest structured exposition of the social function of property. These lectures spread the idea of the social function of property to many areas of the world, and they produced direct effects in the Southern Cone. In 1925, Chile was one of the first countries in Latin America to adopt a social-function limitation on property. This study traces the importance of Duguit’s work in the construction of the property provisions of the Chilean Constitution of 1925. This contribution notes the shift from the earlier expressions of property as an absolute right, as found in the Constitution of 1833, to the language of the Constitution of 1925 that submits property to “the maintenance and progress of the social order.” It tracks the debates in the drafting committees to expose the various concepts of property open to the drafters of the constitution and links these concepts to various broader political stances. The intervention and guidance of President Alessandri in adopting a social-function definition of property are placed into their political context. In closing, the study contributes to the intellectual and political history of the social-function norm. It assesses the legacy of the social-function norm in Chile with particular reference to land reform or agrarian reform until the insertion of the term "social function" into Pinochet's Constitutional Act Number 3 of 1976 and the Chilean Constitution of 1980. It concludes that the social function of property was reinterpreted by both the left and right in Chile to meanings that had little to do with Duguit's original understanding of the concept.
Litigation, School Finance Reform, and Aggregate Educational Spending

In the past two decades, litigation in many U.S. stateshas triggered educational reform movements designed to reducethe inequalities in educational expenditures across school districts.This paper uses a panel data set across all the states from 1970–1990to examine the role of litigation and educational finance reformin determining the level of education funding in a flexible,dynamic setting. An important finding of our work is that litigationand reform have differential effects across the states, in somecases leading to increases while in other cases decreases inpredicted spending.
Skewness in Stock Returns, Periodic Cash Payouts, and Investor Heterogeneity

This paper analyzes the asset pricing implications of periodic cash payouts within the context of a stationary rational expectations model with heterogeneous investors. The periodicity of cash payouts provides a natural motivation for time-varying conditional volatility in stock returns. I show that the unconditional distribution of returns is a mixture of normals distribution, which has non-trivial skewness properties. I examine how conditional volatility, trading volume and skewness in stock returns are related to information dispersion and liquidity in the stock market. The model provides a rationale for why firm returns have positive skewness while market returns have negative skewness.
Retail Store Location and Market Analysis: A Review of the Research

Most research and writing in the area of retail store location and market analysis has been undertaken by marketing researchers and urban geographers. The purposes of this review are to present thi...
The Economics of Super Managers

We study a competitive model in which managers differ in ability and choose unobservable effort. Each firm chooses its size, how able a manager is to hire, and managerial compensation. The model can be considered an amalgam of agency and Superstars, where optimizing incentives enhances the firm's ability to provide a talented manager with greater resources. The model delivers many testable implications. Preliminary results show that the model is useful for understanding interesting compensation trends, for example, why CEO pay has recently become more closely associated with firm size. Allowing for firm productivity differences generally strengthens our results. The Author 2011. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com., Oxford University Press.
Weighted Estimation of Harmonic Components in a Musical Sound Signal

The study of musical sound has become a popular research field. Harmonic regression signal plus noise statistical models have been used to analyze sound signals. However, it is common to give estimates of harmonic parameters without indications of their uncertainties. Least squares estimates for harmonic models have been studied and asymptotic variance expression have been developed. In practice, window-based estimates are used. This paper studies the statistical properties of such estimates; in particular, we use asymptotic variance expressions to develop standard errors and construct confidence intervals. We present applications and examples of the statistical techniques to musical sound signal analysis.
It Takes a Village A Test of the Creative Class, Social Capital, and Human Capital Theories

Richard Florida argues that the “creative class” is inextricably connected with surges in urban growth. This article, using data from 276 metropolitan statistical areas, empirically tests the creat...
Does Sustainability Require a New Theory of Property Rights

By demanding stewardship of natural capital over exploitation, sustainability envisions a property regime less committed to individual property rights than are the traditional and economic theories of property. While the traditional property theories of Blackstone, Locke, and U.S. constitutional doctrine tolerate restrictions on private property rights for the sake of public welfare, they resist the strongest versions of sustainability, which promote generational and social justice. Similarly, an economic analysis of property recognizes the values of resource conservation and welfare for future generations, but only to the limited extent the economist can calculate future value. As a result, economic analysis may overlook or undervalue the interests of remote generations. Moreover, the goal of a more egalitarian distribution of resources, which informs the social justice model of sustainability theory, runs counter to the dominant economic analysis of property. Only relational principles at the fringes of property theory in the United States can fully embrace the strongest versions of sustainability. Thus, absent an unlikely theoretical revolution in the U.S., the sustainable development agenda cannot succeed in this country at the level required by the international community. Forthcoming in The Kansas Law Review, vol. 58, no. 1 (2009)
Portfolio and Consumption Decisions under Mean-Reverting Returns: An Exact Solution for Complete Markets

This paper solves, in closed form, the optimal portfolio choice problem for an investor with utility over consumption under mean-reverting returns. Previous solutions either require approximations, numerical methods, or the assumption that the investor does not consume over his lifetime. This paper breaks the impasse by assuming that markets are complete. The solution leads to a new understanding of hedging demand and of the behavior of the approximate log-linear solution. The portfolio allocation takes the form of a weighted average and is shown to be analogous to duration for coupon bonds. Through this analogy, the notion of investment horizon is extended to that of an investor who consumes at multiple points in time.
Managing for the Moment: The Role of Earnings Management via Real Activities versus Accruals in SEO Valuation

ABSTRACT We assess the role of both accruals manipulation (AM) and real activities manipulation (RAM) in inducing overvaluation at the time of a seasoned equity offering (SEO). Our results reveal t...
Ex-Urban Sprawl and Fire Response in the United States

Much has been written in the post-World War II era in the United States about the rise of suburbia and development beyond older city boundaries, whether such development has been called urban, suburban, or ex-urban sprawl. Many writers have focused on various issues concerning sprawl, especially on the unintended consequences that new development has had on (among other issues) municipal finances, neighborhood income and residential segregation, and transportation planning. This last one is important since post-World War II development has mostly centered around the automobile in the United States. Over the last decade, a new area in the literature of sprawl has focused on how the "built-environment" of residential areas can impact health. For example, authors have chronicled how sprawled regions have higher auto vehicle accidents per capita, greater obesity rates, worse carbon emissions (due to greater travel by automobile), and delays in emergency medical service responses. This article adds to the late...
De novo genome assembly of the tobacco hornworm moth (Manduca sexta).

The tobacco hornworm, Manduca sexta, is a lepidopteran insect that is used extensively as a model system for studying insect biology, development, neuroscience, and immunity. However, current studies rely on the highly fragmented reference genome Msex_1.0, which was created using now-outdated technologies and is hindered by a variety of deficiencies and inaccuracies. We present a new reference genome for M. sexta, JHU_Msex_v1.0, applying a combination of modern technologies in a de novo assembly to increase continuity, accuracy, and completeness. The assembly is 470 Mb and is ∼20× more continuous than the original assembly, with scaffold N50 > 14 Mb. We annotated the assembly by lifting over existing annotations and supplementing with additional supporting RNA-based data for a total of 25,256 genes. The new reference assembly is accessible in annotated form for public use. We demonstrate that improved continuity of the M. sexta genome improves resequencing studies and benefits future research on M. sexta as a model organism.
Connecting Disconnected Financial Markets

In most financial markets, securities are traded in isolation. Such a disconnected market design can be inefficient if agents trade more than one security. I assess welfare effects of connecting markets by allowing orders for one security to depend on prices of other securities. I show that everyone trades identical amounts under both market structures if and only if the clearing prices are perfectly correlated or all are price-takers. Prices in disconnected markets might allow strategic traders to extract higher rents from nonstrategic traders. In expectation, connected markets generate higher welfare, but all markets become efficient as they grow large.
FULLY CONVOLUTIONAL SPATIO-TEMPORAL MODELS FOR REPRESENTATION LEARNING IN PLASMA SCIENCE

We have trained a fully convolutional spatio-temporal model for fast and accurate representation learning in the challenging exemplar application area of fusion energy plasma science. The onset of major disruptions is a critically important fusion energy science (FES) issue that must be resolved for advanced tokamak. While a variety of statistical methods have been used to address the problem of tokamak disruption prediction and control, recent approaches based on deep learning have proven particularly compelling. In the present paper, we introduce further improvements to the fusion recurrent neural network (FRNN) software suite. Up to now, FRNN was based on the long short-term memory (LSTM) variant of recurrent neural networks to leverage the temporal information in the data. Here, we implement and apply the temporal convolutional neural network (TCN) architecture to the time-dependent input signals, thus rendering the FRNN architecture fully convolutional. This allows highly optimized convolution operations to carry the majority of the computational load of training, thus enabling a reduction in training time, and the effective use of high performance computing (HPC) resources for hyperparameter tuning. At the same time, the TCN based architecture achieves equal or better predictive performance when compared with the LSTM architecture for a large, representative fusion database. Across data-rich scientific disciplines, these results have implications for the resource-effective training of general spatio-temporal feature extractors based on deep learning. Moreover, this challenging exemplar case study illustrates the advantages of a predictive platform with flexible architecture selection options capable of being readily tuned and adapted for responding to prediction needs that increasingly arise in large modern observational dataset.
Regularity for $$C^{1,\alpha }$$ Interface Transmission Problems

We study the existence, uniqueness, and optimal regularity of solutions to transmission problems for harmonic functions with $$C^{1,\alpha }$$ interfaces. For this, we develop a novel geometric stability argument based on the mean value property.
The 2-Leg Vertex in K-theoretic DT Theory

K-theoretic Donaldson–Thomas counts of curves in toric and many related threefolds can be computed in terms of a certain canonical 3-valent tensor, the K-theoretic equivariant vertex. In this paper we derive a formula for the vertex in the case when two out of three entries are nontrivial. We also discuss some applications of this result.
Weakly-convex–concave min–max optimization: provable algorithms and applications in machine learning

Min–max problems have broad applications in machine learning, including learning with non-decomposable loss and learning with robustness to data distribution. Convex–concave min–max problem is an a...
Simulation in retail: A case study for process improvement in the receiving area

Simulation tools allow its users to computationally model real-life systems in order to determine their best future outcome. One real-life system that can benefit from simulation is that of the retail industry. This paper describes how simulation can be an effective tool for this type of industry, especially for process improvement projects. In addition, a small case study is presented to demonstrate the use of simulation for a large retailer which needs to improve its unloading and receiving processes. Among the future ideas for research, this paper shows that less obvious methods for process improvement, such as tracking customer loyalty, can be analyzed using simulation to determine which route a retailer should take in order to please its customers. Other topics on this subject are suggested at the conclusion of this paper.
Self-Supervised Learning of Audio Representations From Permutations With Differentiable Ranking

Self-supervised pre-training using so-called “pretext” tasks has recently shown impressive performance across a wide range of modalities. In this work, we advance self-supervised learning from permutations, by pre-training a model to reorder shuffled parts of the spectrogram of an audio signal, to improve downstream classification performance. We make two main contributions. First, we overcome the main challenges of integrating permutation inversions into an end-to-end training scheme, using recent advances in differentiable ranking. This was heretofore sidestepped by casting the reordering task as classification, fundamentally reducing the space of permutations that can be exploited. Our experiments validate that learning from all possible permutations improves the quality of the pre-trained representations over using a limited, fixed set. Second, we show that inverting permutations is a meaningful pretext task for learning audio representations in an unsupervised fashion. In particular, we improve instrument classification and pitch estimation of musical notes by reordering spectrogram patches in the time-frequency space.
Using the PALS Architecture to Verify a Distributed Topology Control Protocol for Wireless Multi-Hop Networks in the Presence of Node Failures

The PALS architecture reduces distributed, real-time asynchronous system design to the design of a synchronous system under reasonable requirements. Assuming logical synchrony leads to fewer system behaviors and provides a conceptually simpler paradigm for engineering purposes. One of the current limitations of the framework is that from a set of independent “synchronous machines”, one must compose the entire synchronous system by hand, which is tedious and error-prone. We use Maude’s meta-level to automatically generate a synchronous composition from user-provided component machines and a description of how the machines communicate with each other. We then use the new capabilities to verify the correctness of a distributed topology control protocol for wireless networks in the presence of nodes that may fail.
Zeros of complex random polynomials spanned by Bergman polynomials

We study the expected number of zeros of Pn(z)=∑k=0nηkpk(z), where {ηk} are complex-valued independent and identically distributed standard Gaussian random variables, and {pk(z)} are polynomials orthogonal on the unit disk. When pk(z)=(k+1)∕πzk, k∈{0,1,…,n}, we give an explicit formula for the expected number of zeros of Pn(z) in a disk of radius r∈(0,1) centered at the origin. From our formula we establish the limiting value of the expected number of zeros, the expected number of zeros in a radially expanding disk, and show that the expected number of zeros in the unit disk is 2n∕3. Generalizing our basis functions {pk(z)} to be regular in the sense of Ullman–Stahl–Totik and that the measure of orthogonality associated to polynomials is absolutely continuous with respect to planar Lebesgue measure, we give the limiting value of the expected number of zeros in a disk of radius r∈(0,1) centered at the origin, and show that asymptotically the expected number of zeros in the unit disk is 2n∕3.
Methadone and suboxone® mentions on twitter: thematic and sentiment analysis.

According to the latest medical evidence, Methadone and buprenorphine-naloxone (Suboxone®) are effective treatments for opioid use disorder (OUD). While the evidence basis for the use of these medi...
A Sparse Delaunay Filtration

We show how a filtration of Delaunay complexes can be used to approximate the persistence diagram of the distance to a point set in $R^d$. Whereas the full Delaunay complex can be used to compute this persistence diagram exactly, it may have size $O(n^{\lceil d/2 \rceil})$. In contrast, our construction uses only $O(n)$ simplices. The central idea is to connect Delaunay complexes on progressively denser subsamples by considering the flips in an incremental construction as simplices in $d+1$ dimensions. This approach leads to a very simple and straightforward proof of correctness in geometric terms, because the final filtration is dual to a $(d+1)$-dimensional Voronoi construction similar to the standard Delaunay filtration complex. We also, show how this complex can be efficiently constructed.
Environmental specialization and cryptic genetic divergence in two massive coral species from the Florida Keys Reef Tract

Broadcast-spawning coral species have wide geographical ranges spanning strong environmental gradients, but it is unclear how much spatially varying selection these gradients actually impose. Strong divergent selection might present a considerable barrier for demographic exchange between disparate reef habitats. We investigated whether the cross-shelf gradient is associated with spatially varying selection in two common coral species, Montastraea cavernosa and Siderastrea siderea, in the Florida Keys. To this end, we generated a de novo genome assembly for M. cavernosa and used 2bRAD to genotype 20 juveniles and 20 adults of both species from each of the three reef zones to identify signatures of selection occurring within a single generation. Unexpectedly, each species was found to be composed of four genetically distinct lineages, with gene flow between them still ongoing but highly reduced in 13.0%-54.7% of the genome. Each species includes two sympatric lineages that are only found in the deep (20 m) habitat, while the other lineages are found almost exclusively on the shallower reefs (3-10 m). The two "shallow" lineages of M. cavernosa are also specialized for either nearshore or offshore: comparison between adult and juvenile cohorts indicates that cross-shelf migrants are more than twice as likely to die before reaching adulthood than local recruits. S. siderea and M. cavernosa are among the most ecologically successful species on the Florida Keys Reef Tract, and this work offers important insight into the genomic background of divergent selection and environmental specialization that may in part explain their resilience and broad environmental range.
Simple Explanation of the No Free Lunch Theorem of Optimization

The No Free Lunch Theorem of Optimization (NFLT) is an impossibility theorem telling us that a general-purpose universal optimization strategy is impossible, and the only way one strategy can outperform another is if it is specialized to the structure of the specific problem under consideration. In this paper, a framework is presented for conceptualizing optimization problems that leads to useful insights and a simple explanation of the NFLT.
The short duration premium

Abstract Stocks of firms with cash flows concentrated in the short term (i.e., short duration stocks) pay a large premium over long duration stocks. I empirically demonstrate that this premium (i) is long-lived and strong even among large firms, (ii) subsumes the value and profitability premia, and (iii) exposes investors to variation in expected returns, especially in times when the premium is high. These facts are consistent with an intertemporal model in which the marginal (long-term) investor dislikes expected return declines as they lead to lower expected wealth growth. The model also captures the positive relation between risk premia and bond duration.
Limits of Blaschke metrics

We find a compactification of the SL(3,R)-Hitchin component by studying the degeneration of the Blaschke metrics on the associated equivariant affine spheres. In the process, we establish the closure in the space of projectivized geodesic currents of the space of flat metrics induced by holomorphic cubic differentials on a Riemann surface.
Genetic and Epidemiologic Analyses of an Outbreak of Pneumocystis jirovecii Pneumonia among Kidney Transplant Recipients in the United States.

Background Pneumocystis jiroveciii is an opportunistic fungus that causes Pneumocystis pneumonia (PCP) in immunocompromised hosts. Over an 11-month period, we observed a rise in cases of PCP among kidney-transplant recipients (KTR), prompting an outbreak investigation. Methods Clinical and epidemiologic data were collected for KTR diagnosed with PCP between July 2019 and May 2020. Pneumocystis strain typing was performed using restriction fragment length polymorphism analyses and multilocus sequence typing in combination with next-generation sequencing. A transmission map was drawn, and a case-control analysis was performed to determine risk factors associated with PCP. Results Nineteen cases of PCP in KTR were diagnosed at a median of 79 months post-transplantation; eight received monthly belatacept infusions. Baseline characteristics were similar for KTR on belatacept versus other regimens; the number of clinic visits was numerically higher for the belatacept group during the study period (median 7.5 vs 3). Molecular typing of respiratory specimens from nine patients revealed coinfection with up to seven P. jirovecii strains per patient. A transmission map suggested multiple clusters of interhuman transmission. In a case-control univariate analysis, belatacept, lower absolute lymphocyte count, non-White race, and more transplant clinic visits were associated with an increased risk of PCP. In multivariate and prediction power estimate analyses, frequent clinic visits was the strongest risk factor for PCP. Conclusion Increased clinic exposure appeared to facilitate multiple clusters of nosocomial PCP transmission among KTR. Belatacept was a risk factor for PCP, possibly by increasing clinic exposure through the need for frequent visits for monthly infusions.
On the inducibility problem for random Cayley graphs of abelian groups with a few deleted vertices

Given a $k$-vertex graph $H$ and an integer $n$, what are the $n$-vertex graphs with the maximum number of induced copies of $H$? This question is closely related to the inducibility problem introduced by Pippenger and Golumbic in 1975, which asks for the maximum possible fraction of $k$-vertex subsets of an $n$-vertex graph that induce a copy of $H$. Huang, Lee and the first author proved that for a random $k$-vertex graph $H$, almost surely the $n$-vertex graphs maximizing the number of induced copies of $H$ are the balanced iterated blow-ups of $H$. In this paper, we consider the case where the graph $H$ is obtained by deleting a small number of vertices from a random Cayley graph $\widetilde{H}$ of an abelian group. We prove that in this case, almost surely all $n$-vertex graphs maximizing the number of induced copies of $H$ are balanced iterated blow-ups of $\widetilde{H}$.
Direction Of Arrival Estimation For Non-Coherent Sub-Arrays Via Joint Sparse And Low-Rank Signal Recovery

Estimating the directions of arrival (DOAs) of multiple sources from a single snapshot obtained by a coherent antenna array is a well-known problem, which can be addressed by sparse signal reconstruction methods, where the DOAs are estimated from the peaks of the recovered high-dimensional signal. In this paper, we consider a more challenging DOA estimation task where the array is composed of non-coherent sub-arrays (i.e., sub-arrays that observe different unknown phase shifts due to using low-cost unsynchronized local oscillators). We formulate this problem as the reconstruction of a joint sparse and low-rank matrix and solve its convex relaxation. While the DOAs can be estimated from the solution of the convex problem, we further show how an improvement is obtained if, instead, one estimates from this solution only the phase shifts, creates "phase-corrected" observations and applies another final (plain, coherent) sparsity-based DOA estimation. Numerical experiments show that the proposed approach outperforms strategies that are based on non-coherent processing of the sub-arrays as well as other sparsity-based methods.
PANOPLY: a cloud-based platform for automated and reproducible proteogenomic data analysis.

ABSTRACT Proteogenomics involves the integrative analysis of genomic, transcriptomic, proteomic and post-translational modification data produced by next-generation sequencing and mass spectrometry-based proteomics. Several publications by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and others have highlighted the impact of proteogenomics in enabling deeper insight into the biology of cancer and identification of potential drug targets. In order to encapsulate the complex data processing required for proteogenomics, and provide a simple interface to deploy a range of algorithms developed for data analysis, we have developed PANOPLY—a cloud-based platform for automated and reproducible proteogenomic data analysis. A wide array of algorithms have been implemented, and we highlight the application of PANOPLY to the analysis of cancer proteogenomic data.
The limits of pan privacy and shuffle privacy for learning and estimation

There has been a recent wave of interest in intermediate trust models for differential privacy that eliminate the need for a fully trusted central data collector, but overcome the limitations of local differential privacy. This interest has led to the introduction of the shuffle model (Cheu et al., EUROCRYPT 2019; Erlingsson et al., SODA 2019) and revisiting the pan-private model (Dwork et al., ITCS 2010). The message of this line of work is that, for a variety of low-dimensional problems—such as counts, means, and histograms—these intermediate models offer nearly as much power as central differential privacy. However, there has been considerably less success using these models for high-dimensional learning and estimation problems. In this work we prove the first non-trivial lower bounds for high-dimensional learning and estimation in both the pan-private model and the general multi-message shuffle model. Our lower bounds apply to a variety of problems—for example, we show that, private agnostic learning of parity functions over d bits requires Ω(2d/2) samples in these models, and privately selecting the most common attribute from a set of d choices requires Ω(d1/2) samples, both of which are exponential separations from the central model.
Contributions of Transonic Dynamics Tunnel testing to airplane flutter clearance

The Transonic Dynamics Tunnel (TDT) became in operational in 1960, and since that time has achieved the status of the world's premier wind tunnel for testing large in aeroelastically scaled models at transonic speeds. The facility has many features that contribute to its uniqueness for aeroelastic testing. This paper will briefly describe these capabilities and features, and their relevance to aeroelastic testing. Contributions to specific airplane configurations and highlights from the flutter tests performed in the TDT aimed at investigating the aeroelastic characteristics of these configurations are presented.
Weak Geodesics for the deformed Hermitian-Yang-Mills equation

We study weak geodesics in the space of potentials for the deformed Hermitian–Yang–Mills equation. The geodesic equation can be formulated as a degenerate elliptic equation, allowing us to employ nonlinear Dirichlet duality theory, as developed by Harvey–Lawson. By exploiting the convexity of the level sets of the Lagrangian angle operator in the highest branch, we are able to construct $C^0$ solutions of the associated Dirichlet problem.
Conflict Resolution for Wind-Optimal Aircraft Trajectories in North Atlantic Oceanic Airspace with Wind Uncertainties

Air traffic in the North Atlantic oceanic airspace (NAT) experiences very strong winds caused by jet streams. Flying wind-optimal trajectories increases individual flight efficiency, which is advantageous when operating in the NAT. However, as the NAT is highly congested during peak hours, a large number of potential conflicts between flights are detected for the sets of wind-optimal trajectories. Conflict resolution performed at the strategic level of flight planning can significantly reduce the airspace congestion. However, being completed far in advance, strategic planning can only use predicted environmental conditions that may significantly differ from the real conditions experienced further by aircraft. The forecast uncertainties result in uncertainties in conflict prediction, and thus, conflict resolution becomes less efficient. This work considers wind uncertainties in order to improve the robustness of conflict resolution in the NAT. First, the influence of wind uncertainties on conflict prediction is investigated. Then, conflict resolution methods accounting for wind uncertainties are proposed.
Experimental Investigation of Oscillations in Flows Over Shallow Cavities

Wind tunnel experiments were carried out with three axisymmetric models to study laminar axisymmetric flows over shallow cavities at low subsonic speeds. The first model had a hemispherical nose, the second an ogive-shape nose, and the third an elliptic nose. Constant-temperature hot-wire anemometry was used in measuring both mean and fluctuating quantities. Flow around the cavity was visualized by heating the first model. To study the effect of mass injection on cavity oscillations, air was injected circumferentially all along the base of the cavity. Major results are that the cavity depth has little effect on oscillations in shallow cavities except when the depth is of the order of the thickness of the cavity shear flow, that downstream corner is the key factor in inducing self-sustained oscillations in the cavity shear layer, and that the presence of a back face results in an integral relation between wave length of the propagating disturbances and the cavity width in each mode of cavity operation.
Simplified Power Supplies for Ion Thrusters

A program addressing less complex and potentially lower cost ion thruster systems has been started at the NASA Lewis Research Center. This paper discusses the initial development and demonstration of power supplies with an order of magnitude reduction in parts count, leading to increased reliability at lower weight, while still maintaining thrust system performance. Two new self-regulating keeper power supply circuits were developed and tested. One supply comprises 14 parts and uses an input voltage range of 18 to 36 volts, the other operates from 200 to 400 volts and requires 22 components. A new technique for controlling heater power is also demonstrated.
Adaptive Discretization for Adversarial Lipschitz Bandits

Lipschitz bandits is a prominent version of multi-armed bandits that studies large, structured action spaces such as the [0,1] interval, where similar actions are guaranteed to have similar rewards. A central theme here is the adaptive discretization of the action space, which gradually "zooms in" on the more promising regions thereof. The goal is to take advantage of "nicer" problem instances, while retaining near-optimal worst-case performance. While the stochastic version of the problem is well-understood, the general version with adversarial rewards is not. We provide the first algorithm for adaptive discretization in the adversarial version, and derive instance-dependent regret bounds. In particular, we recover the worst-case optimal regret bound for the adversarial version, and the instance-dependent regret bound for the stochastic version. A version with full proofs (and additional results) appears at arxiv.org/abs/2006.12367v2.
How Do Teachers From Alternative Pathways Contribute to the Teaching Workforce in Urban Areas? Evidence From Kansas City:

We examine how teachers from two alternative preparation programs—Teach for America (TFA) and Kansas City Teacher Residency (KCTR)—contribute to the teacher labor market in and around Kansas City, ...
Yeast Nem1‐Spo7 Protein Phosphatase Activity on Pah1 Phosphatidate Phosphatase is Specific for the Pho85‐Pho80 Protein Kinase Phosphorylation Sites

Pah1 is the phosphatidate phosphatase in the yeast Saccharomyces cerevisiae that produces diacylglycerol for triacylglycerol synthesis and concurrently controls the levels of phosphatidate used for phospholipid synthesis. Phosphorylation and dephosphorylation of Pah1 regulate its subcellular location and phosphatidate phosphatase activity. Compared with its phosphorylation by multiple protein kinases, Pah1 is dephosphorylated by a protein phosphatase complex consisting of Nem1 (catalytic subunit) and Spo7 (regulatory subunit). In this work, we characterized the Nem1-Spo7 phosphatase complex for its enzymological, kinetic, and regulatory properties with phosphorylated Pah1. The dephosphorylation of Pah1 by Nem1-Spo7 phosphatase resulted in the stimulation (6-fold) of phosphatidate phosphatase activity. For Pah1 phosphorylated by the Pho85-Pho80 kinase complex, maximum Nem1-Spo7 phosphatase activity required Mg2+ ions (8 mm) and Triton X-100 (0.25 mm) at pH 5.0. The energy of activation for the reaction was 8.4 kcal/mol, and the enzyme was thermally labile at temperatures above 40 °C. The enzyme activity was inhibited by sodium vanadate, sodium fluoride, N-ethylmaleimide, and phenylglyoxal but was not significantly affected by lipids or nucleotides. Nem1-Spo7 phosphatase activity was dependent on the concentrations of Pah1 phosphorylated by Pho85-Pho80, Cdc28-cyclin B, PKA, and PKC with kcat and Km values of 0.29 s−1 and 81 nm, 0.11 s−1 and 127 nm, 0.10 s−1 and 46 nm, and 0.02 s−1 and 38 nm, respectively. Its specificity constant (kcat/Km) for Pah1 phosphorylated by Pho85-Pho80 was 1.6-, 4-, and 6-fold higher, respectively, than that phosphorylated by PKA, Cdc28-cyclin B, and PKC.
Optimal Algorithms for Multiwinner Elections and the Chamberlin-Courant Rule

We consider the algorithmic question of choosing a subset of candidates of a given size k from a set of m candidates, with knowledge of voters' ordinal rankings over all candidates. We consider the well-known and classic scoring rule for achieving diverse representation: the Chamberlin-Courant (CC) or 1-Borda rule, where the score of a committee is the average over the voters, of the rank of the best candidate in the committee for that voter; and its generalization to the average of the top s best candidates, called the s-Borda rule. Our first result is an improved analysis of the natural and well-studied greedy heuristic. We show that greedy achieves a (1 - 2/k+1)-approximation to the maximization (or satisfaction) version of CC rule, and a (1 - 2s/k+1)-approximation to the s-Borda score. This significantly improves the existing submodularity-based analysis of the greedy algorithm that only shows a (1-1/e)-approximation. Our result also improves on the best known approximation algorithm for this problem. We achieve this result by showing that the average dissatisfaction score for the greedy algorithm is at most 2 m+1/k+1 for the CC rule, and at most 2s2 m+1/k+1 for s-Borda. We show these dissatisfaction score bounds are tight up to constants, and even the constant factor of 2 in the case of the CC rule is almost tight. For the dissatisfaction (or minimization) version of the problem, it is known that the average dissatisfaction score of the best committee cannot be approximated in polynomial time to within any constant factor when s is a constant (under standard computational complexity assumptions). As our next result, we strengthen this to show that the score of m+1/k+1 can be viewed as an optimal benchmark for the CC rule, in the sense that it is essentially the best achievable score of any polynomial-time algorithm even when the optimal score is a polynomial factor smaller. We show that another well-studied algorithm for this problem, called the Banzhaf rule, attains this benchmark. We finally show that for the s-Borda rule, when the optimal value is small, these algorithms can be improved by a factor of ~Omega(√s) via LP rounding. Our upper and lower bounds are a significant improvement over previous results, and taken together, not only enable us to perform a finer comparison of greedy algorithms for these problems, but also provide analytic justification for using such algorithms in practice.
Towards an AI Coach to Infer Team Mental Model Alignment in Healthcare

Shared mental models are critical to team success; however, in practice, team members may have misaligned models due to a variety of factors. In safety-critical domains (e.g., aviation, healthcare), lack of shared mental models can lead to preventable errors and harm. Towards the goal of mitigating such preventable errors, here, we present a Bayesian approach to infer misalignment in team members’ mental models during complex healthcare task execution. As an exemplary application, we demonstrate our approach using two simulated team-based scenarios, derived from actual teamwork in cardiac surgery. In these simulated experiments, our approach inferred model misalignment with over 75% recall, thereby providing a building block for enabling computer-assisted interventions to augment human cognition in the operating room and improve teamwork.
Optical disks become erasable

The topics covered include the following: optical recording; how does it work?; why all the fuss?; state of the industry; sample applications; and future directions.
Dynamically Aggregating Diverse Information

An agent has access to multiple information sources, each modeled as a Brownian motion whose drift provides information about a different component of an unknown Gaussian state. Information is acquired continuously---where the agent chooses both which sources to sample from, and also how to allocate attention across them---until an endogenously chosen time, at which point a decision is taken. We demonstrate conditions on the agent's prior belief under which it is possible to exactly characterize the optimal information acquisition strategy. We then apply this characterization to derive new results regarding: (1) endogenous information acquisition for binary choice, (2) the dynamic consequences of attention manipulation, and (3) strategic information provision by biased news sources.
Behind the screen: Understanding national support for a foreign investment screening mechanism in the European Union

What determines national preferences for institutionalizing foreign direct investment (FDI) screening? Over the past decade, advanced economies worldwide have tightened their national investment screening mechanisms (ISMs). In March 2019, the European Union (EU) adopted its first common FDI screening framework. This article explores variations in Member State preferences for the creation and nature of a pan-European screening framework. Based on extensive interviews with high-level EU and country officials involved in the negotiation process, and using a unique measure of national support for the EU-wide ISM created through the first-ever elite survey on this subject matter, we find that countries with higher technological levels were more supportive of FDI screening due to concerns over unreciprocated technological transfer. We also show the effects of Chinese FDI on country-level support for FDI screening sector-dependent. We found no evidence that total Chinese FDI predicts preferences for ISM. Instead, countries with high levels of Chinese FDI in strategic sectors are more likely to support the ISM, while those with high levels of Chinese investment in low-tech sectors tend to oppose screening. Our overall findings suggest that EU investment screening, and national-level screening in general, might become more restrictive in the future, especially in light of the COVID-19 pandemic.
Scalability in Platforms for Local Groceries: An Examination of Indirect Network Economies

Despite a significant rise in consumer interest in local food, supply constraints limit consumer access to these products in many markets. Online platforms for local foods may help solve these cons...
Superconvergence of Discontinuous Galerkin Methods for Elliptic Boundary Value Problems

In this paper, we present a unified analysis of the superconvergence property for a large class of mixed discontinuous Galerkin methods. This analysis applies to both the Poisson equation and linear elasticity problems with symmetric stress formulations. Based on this result, some locally postprocess schemes are employed to improve the accuracy of displacement by order $$\min (k+1, 2)$$ if polynomials of degree k are employed for displacement. Some numerical experiments are carried out to validate the theoretical results.
Predicting 2D THz Spectra Due to Nonlinear Phononics with First-Principles Calculations

We present our method for creating first-principles models of two-dimensional (2D) terahertz spectra in solid materials. We demonstrate the accuracy of these models and their exciting potential in the growing field of nonlinear phononics.
On the Shape Fields Finiteness Principle

In this paper, we improve the finiteness constant for the finiteness principles for $C^m(\mathbb{R}^n,\mathbb{R}^d)$ and $C^{m-1,1}(\mathbb{R}^n,\mathbb{R}^D)$ selection proven by Fefferman, Israel, and the second author and extend the more general shape fields finiteness principle to the vector-valued case.
Visual Identification of Articulated Object Parts

As autonomous robots interact and navigate around real-world environments such as homes, it is useful to reliably identify and manipulate articulated objects, such as doors and cabinets. Many prior works in object articulation identification require manipulation of the object, either by the robot or a human. While recent works have addressed predicting articulation types from visual observations alone, they often assume prior knowledge of category-level kinematic motion models or sequence of observations where the articulated parts are moving according to their kinematic constraints. In this work, we propose FormNet, a neural network that identifies the articulation mechanisms between pairs of object parts from a single frame of an RGB-D image and segmentation masks. The network is trained on 100k synthetic images of 149 articulated objects from 6 categories. Synthetic images are rendered via a photorealistic simulator with domain randomization. Our proposed model predicts motion residual flows of object parts, and these flows are used to determine the articulation type and parameters. The network achieves an articulation type classification accuracy of 82.5% on novel object instances in trained categories. Experiments also show how this method enables generalization to novel categories and be applied to real-world images without fine-tuning.
Generative Max-Mahalanobis Classifiers for Image Classification, Generation and More

Joint Energy-based Model (JEM) of [11] shows that a standard softmax classifier can be reinterpreted as an energy-based model (EBM) for the joint distribution \(p(\boldsymbol{x}, y)\); the resulting model can be optimized to improve calibration, robustness and out-of-distribution detection, while generating samples rivaling the quality of recent GAN-based approaches. However, the softmax classifier that JEM exploits is inherently discriminative and its latent feature space is not well formulated as probabilistic distributions, which may hinder its potential for image generation and incur training instability. We hypothesize that generative classifiers, such as Linear Discriminant Analysis (LDA), might be more suitable for image generation since generative classifiers model the data generation process explicitly. This paper therefore investigates an LDA classifier for image classification and generation. In particular, the Max-Mahalanobis Classifier (MMC) [30], a special case of LDA, fits our goal very well. We show that our Generative MMC (GMMC) can be trained discriminatively, generatively or jointly for image classification and generation. Extensive experiments on multiple datasets show that GMMC achieves state-of-the-art discriminative and generative performances, while outperforming JEM in calibration, adversarial robustness and out-of-distribution detection by a significant margin. Our source code is available at https://github.com/sndnyang/GMMC.