cssi/SciSciGPT-SciSciNet huggingface dataset:
Reconsidering Gobitis: An Exercise in Presidential Leadership

In June of 1940, the Supreme Court ruled 8-1 in Minersville School District v. Gobitis that the First Amendment posed no barrier to the punishment of two school-age Jehovah’s Witnesses who refused to pay homage to the American flag. Three years later, the Justices reversed themselves in West Virginia State Board of Education v. Barnette. This sudden change has prompted a host of explanations. Some observers have stressed changes in judicial personnel in the intervening years; others have pointed to the wax and wane of general anxieties over the war; still others have emphasized the sympathy-inspiring acts of terror visited upon Jehovah’s Witnesses in the wake of Gobitis. Drawing upon previously unearthed archival material, this article for the first time attributes a major role to presidential initiative. A sophisticated strategy implemented by the Roosevelt administration systematically eroded the picture of political life constructed by Gobitis, presented an alternative reading of the First Amendment in urgent fashion, and rhetorically empowered advocates for the pro-rights position. Despite what many believed to be a deliberative moment, however, the Supreme Court incompletely memorialized the interaction between the branches of government. In copying the President’s words without attribution and purging the record of executive branch participation, the Barnette Court impoverished our appreciation of the constitutional system in action. Understanding the remarkable debate over the right of conscience within this paradigm sheds light on a variety of enduring questions, from the strategies utilized by ∗ Associate Professor, American University, Washington College of Law. This article was chosen for the 2008 Stanford/Yale Junior Faculty Forum in the field of constitutional history. I thank participants of faculty workshops at American University, the Georgetown University Law Center, University of Iowa School of Law, St. John’s University School of Law, Thomas Jefferson School of Law, and the May Gathering at the University of Virginia Law School. Penetrating critiques and helpful advice came from Bruce Ackerman, Randy Barnett, John Barrett, Deven Desai, Barry Friedman, Heather Gerken, Risa Goluboff, Mark Graber, Larry Kramer, Joe Lowndes, Steve Luther, Bill Nelson, Jeff Powell, Mike Seidman, Nelson Tebbe, Mark Tushnet, and Ted White. John Arsenault, Mallori Browne, Peter Fehrs, Amanda Garty, Brady Iandiorio, and Kaitlan Monroe provided invaluable research support. I am indebted to the archivists and librarians at the Franklin Delano Roosevelt Presidential Library, the Special Collections Reading Room of Georgetown University’s Lauinger Library, and the Manuscript Reading Room of the Library of Congress. Washington University Open Scholarship 364 WASHINGTON UNIVERSITY LAW REVIEW [VOL. 86:363 presidents to control political pathways, to the origins of the First Amendment’s centrality to the modern order.
Riding the Yield Curve: Reprise

We investigate the efficacy of riding the yield curve. This strategy dictates holding longer-term treasury bills when the yield curve is upwardsloping. We find that the strategy is surprisingly effective. it stochastically dominates buying and holding shorter-term bills for large subperiods, and nearly dominates for the entire sample period, 1949-1988. Our empirical results suggest that abnormal profit opportunities are available from selectively increasing the maturity of a short-term portfolio.
Firm-Level Productivity, Risk, and Return

This paper provides new evidence about the link between firm-level total factor productivity TFP and stock returns. We estimate firm-level TFP and show that it is strongly related to several firm characteristics such as size, the book-to-market ratio, investment, and hiring rate. Low productivity firms earn a significant premium over high productivity firms in the following year, and this premium is countercyclical. We show that a production-based asset pricing model calibrated to match the cross section of measured firm-level TFPs can replicate the empirical relationship between TFP, many firm characteristics, and stock returns. Our results offer an explanation as to how these firm characteristics rationally predict returns. This paper was accepted by Wei Jiang, finance.
A Return to the "Bright Line Rule" of Miranda

When deciding Miranda, the Supreme Court's intent was to create a workable rule to guide police officers largely untrained in the law in order to ensure voluntariness of confessions. The Justices also sought to eliminate confusion and lack of guidance created by the earlier Escobedo opinion. Unfortunately, the many exceptions and limitations on Miranda have undermined its value as a workable bright line rule, instead requiring precisely the type of fact specific case-by-case analysis the Court initially sought to avoid. The Court should return to a bright line rule for Miranda. A clear rule could be achieved by eliminating many of the exceptions in the determinations of custody, interrogation, the mandated warnings, resumption of questioning, and waiver. The effect would be to strike the appropriate balance between sufficient guidance to law enforcement officers and the protection of suspects' rights.
In Search of a Canon: Three Texts on Dispute Resolution

Is the dispute resolution field finding its canon? The recent publication of three similar academic texts in the field suggests a growing consensus around what dispute resolution is and what body of theory underlies it. Publication of these texts also suggests that the study of dispute resolution continues to move more centrally into legal, and other professional, education. Important scholarship, however, remains unexamined, affecting both the maturation of the field and its potential influence on future professionals.The three texts reviewed in this article include the following:1. Resolving Disputes: Theory, Practice and Law (2005) by Jay Folberg, Dwight Golann, Lisa Kloppenberg, and Thomas Stipanowich (eds.).2. Dispute Resolution: Beyond the Adversarial Model (2005) by Carrie J. Menkel-Meadow, Lela Porter Love, Andrea Kupfer Schneider, and Jean R. Sternlight (eds.).3. The Handbook of Dispute Resolution (2005) by Michael L. Moffitt and Robert C. Bordone (eds.).
A Tale of Two Sovereigns: Danger and Opportunity in Tribal-State Court Relations

As the many Native American nations garner economic strength and come into increasing contact with state and local forums, so do the chances that those forums will come face to face with questions of tribal law. The challenges posed by an AngloAmerican court answering questions of tribal law present both danger and opportunity. Opportunities come with strengthened inter-sovereign relationships and with the acknowledgement of tribal legal potency by other forums. Dangers inhere in crosscultural illiteracy and particularly in the temptation to ignore abstention principles and to attack tribal jurisdiction. It may be possible for tribal courts to gain some of the advantages and ameliorate some of the dangers by entering into a variety of cooperative arrangements with state courts. In an arrangement of true parity, these options may also provide avenues for tribal courts to obtain assistance in answering important questions of state law in tribal court cases. The purpose of this article is to bring together the various options for tribal and state court cooperation for the analysis of tribal law questions. They may include cooperative organizations, abstention, certification, and even formal consultation agreements between particular courts, such as the recent, historic accord between the high courts of New York and New South Wales. The article further concludes that each option has costs and benefits, and that individual tribal nations must each undertake their own cost/benefit analyses to determine which action or none at all is the most likely to benefit tribal sovereignty and institutions based on their goals, history, relationships with state and local entities, and even relationships between individual leaders in the state and tribal justice systems. * Associate Professor of Law, Washburn University School of Law; J.D., Duke University School of Law, 1995; B.A., University of Florida, 1992. For their valuable comments, I thank my Washburn Law colleagues, Prof Eric Jensen of Fordham University School of Law shared the very helpful news of the New York-Australian courts consultation agreement. For their insightful research support, I am also grateful to Creighton Miller, J.D., M.L.I.S., and Katy McMullen, J.D. 2012, Washburn University School of Law.
Paternalism, Self-Governance, and Public Health: The Case of E-Cigarettes

This article develops a normative framework for assessing public health laws, using the regulation of e-cigarettes as a case study. Although e-cigarettes are likely far less dangerous to individual users than traditional cigarettes, it remains uncertain whether their proliferation will lead to a reduction of smoking-related disease and deaths or to increased morbidity and mortality. This scientific uncertainty presents regulators with difficult challenges in determining whether and how to regulate e-cigarettes. This article presents a normative framework for analyzing such questions by offering three justifications for public health laws: impaired agency, harm to others, and self-governance. Each justification responds to the common charge that public health laws are impermissibly paternalistic. The self-governance rationale, which is the most robust, and most reflective of public health’s own population perspective, has been the least theorized. This article develops that theory, examining the basis for the justification as well as its limitations. The article then applies its normative framework to the regulation of e-cigarettes, focusing on the FDA’s so-called deeming regulations, which at the time the article was written were pending but have since been promulgated in a substantially similar form. The article supports the FDA’s ultimate decision to ban the sales of e-cigarettes to minors and to require the disclosure of warning labels based upon the impaired agency rationale. However, the scientific uncertainty renders the harm rationale inadequate. As a result, the regulations’ pre-market review requirement must rely on the self-governance rationale for its normative justification. Given the lack of clear legislative guidance and political engagement, the article concludes that the pre-market review provisions are normatively problematic: if public health advocates want to claim the mantle of self-governance, they must take it seriously.
Contracting Around Liability Rules

In his influential paper Contracting into Liability Rules: Intellectual Property Rights and Collective Rights Organizations, Robert Merges makes the case that intellectual property (IP) owners vested with property entitlements can and do contract away their right to an injunction when it is efficient for them to do so. The result was to cement for many the superiority of property over liability rules, since Merges demonstrated a seemingly critical asymmetry between the two. Merges’s evidence suggested that if a judge or a legislature got the damages calculation wrong, we were stuck with an inefficient liability rule, but that we weren’t similarly stuck with an inefficiently-allocated property rule.
The Dynamic Capabilities of Meta‐Multinationals

Plain language summary Today's multinational enterprises (MNEs) exhibit characteristics that have not been discussed very much in previous studies. They are stateless in the sense that operations are spread across nations, but also maintain some central authority. We call such MNEs ‘meta-MNEs’ and analyze them using the Dynamic Capabilities framework, a key framework in strategic management. We find that the capability of MNEs to shape and adapt to changing environments is necessary for the emergence and success of the meta-MNE. We also discuss prospects for meta-MNEs in the context of China. Technical summary Established theories of the multinational enterprise (MNE) were created for a different, less globally competitive world. Today's MNEs are moving toward the stateless ideal type known as a metanational. Because considerable central authority remains, we call the emerging model the meta-MNE and use the Dynamic Capabilities framework to analyze this phenomenon. Strong dynamic capabilities, including asset orchestration, are necessary both for the very existence of the meta-MNE and for sustaining its competitive advantage. We draw contrasts between the meta-MNE and the traditional, home-centric MNE. We consider the ways in which nations still matter and the policy implications of the emergence of the meta-MNE. We also discuss recent developments in China that have greatly shaped the environment facing existing and future meta-MNEs. Copyright © 2016 Strategic Management Society.
Expected returns and expected dividend growth

Abstract We investigate a consumption-based present-value relation that is a function of future dividend growth and find that changing forecasts of dividend growth are an important feature of the post-war U.S. stock market, despite the failure of the dividend–price ratio to uncover such variation. In addition, dividend forecasts are found to covary with changing forecasts of excess stock returns over business cycle frequencies. This covariation is important because positively correlated fluctuations in expected dividend growth and expected returns have offsetting effects on the log dividend–price ratio. The market risk premium and expected dividend growth thus vary considerably more than is apparent using the log divided–price ratio alone as a predictive variable.
WHAT DO WOMEN (AND MEN) WANT? ENTREPRENEURIAL EXPECTANCIES OF WOMEN AND MEN NASCENT ENTREPRENEURS

Women are the majority owners in 30% of all privately held firms in the United States. These firms have $2.5 trillion in revenues and employ 19.1 million individuals. However, despite the large number of women business owners, little is known about the motivations that women have for starting their own firms. This study uses an expectancy theory framework to examine the differences in motivations to start a firm between men and women. Our findings indicate that there are significant differences in motivations for starting a new business, with men being motivated by financial gains, self-realization and autonomy where for women status is a significant motivating factor. Implications are discussed.
Complexes from Complexes

This paper is concerned with the derivation and properties of differential complexes arising from a variety of problems in differential equations, with applications in continuum mechanics, relativity, and other fields. We present a systematic procedure which, starting from well-understood differential complexes such as the de Rham complex, derives new complexes and deduces the properties of the new complexes from the old. We relate the cohomology of the output complex to that of the input complexes and show that the new complex has closed ranges, and, consequently, satisfies a Hodge decomposition, Poincare-type inequalities, well-posed Hodge–Laplacian boundary value problems, regular decomposition, and compactness properties on general Lipschitz domains.
Using Pathway Modeling to Evaluate and Improve Student-Centered Teaching Practices in Co-Taught College Science Courses.

This Essay demonstrates how course pathway modeling can help co-instructors better represent the complexity of student-centered teaching practices. It discusses how this approach can improve curric...
Degradation of Photoreceptor Outer Segments by the Retinal Pigment Epithelium Requires Pigment Epithelium-Derived Factor Receptor (PEDF-R).

Purpose To examine the contribution of pigment epithelium-derived factor receptor (PEDF-R) to the phagocytosis process. Previously, we identified PEDF-R, the protein encoded by the PNPLA2 gene, as a phospholipase A2 in the retinal pigment epithelium (RPE). During phagocytosis, RPE cells ingest abundant phospholipids and protein in the form of photoreceptor outer segment (POS) tips, which are then hydrolyzed. The role of PEDF-R in RPE phagocytosis is not known. Methods Mice in which PNPLA2 was conditionally knocked out (cKO) in the RPE were generated. Mouse RPE/choroid explants were cultured. Human ARPE-19 cells were transfected with siPNPLA2 silencing duplexes. POSs were isolated from bovine retinas. The phospholipase A2 inhibitor bromoenol lactone was used. Transmission electron microscopy, immunofluorescence, lipid labeling, pulse-chase experiments, western blots, and free fatty acid and β-hydroxybutyrate assays were performed. Results The RPE of the cKO mice accumulated lipids, as well as more abundant and larger rhodopsin particles, compared to littermate controls. Upon POS exposure, RPE explants from cKO mice released less β-hydroxybutyrate compared to controls. After POS ingestion during phagocytosis, rhodopsin degradation was stalled both in cells treated with bromoenol lactone and in PNPLA2-knocked-down cells relative to their corresponding controls. Phospholipase A2 inhibition lowered β-hydroxybutyrate release from phagocytic RPE cells. PNPLA2 knockdown also resulted in a decline in fatty acids and β-hydroxybutyrate release from phagocytic RPE cells. Conclusions PEDF-R downregulation delayed POS digestion during phagocytosis. The findings imply that the efficiency of RPE phagocytosis depends on PEDF-R, thus identifying a novel contribution of this protein to POS degradation in the RPE.
BlueFeather, the singleton that wasn't: Shared gene content analysis supports expansion of Arthrobacter phage Cluster FE

Bacteriophages (phages) exhibit high genetic diversity, and the mosaic nature of the shared genetic pool makes quantifying phage relatedness a shifting target. Early parameters for clustering of related Mycobacteria and Arthrobacter phage genomes relied on nucleotide identity thresholds but, more recently, clustering of Gordonia and Microbacterium phages has been performed according to shared gene content. Singleton phages lack the nucleotide identity and/or shared gene content required for clustering newly sequenced genomes with known phages. Whole genome metrics of novel Arthrobacter phage BlueFeather, originally designated a putative singleton, showed low nucleotide identity but high amino acid and gene content similarity with Arthrobacter phages originally assigned to Clusters FE and FI. Gene content similarity revealed that BlueFeather shared genes with these phages in excess of the parameter for clustering Gordonia and Microbacterium phages. Single gene analyses revealed evidence of horizontal gene transfer between BlueFeather and phages in unique clusters that infect a variety of bacterial hosts. Our findings highlight the advantage of using shared gene content to study seemingly genetically isolated phages and have resulted in the reclustering of BlueFeather, a putative singleton, as well as former Cluster FI phages, into a newly expanded Cluster FE.
HGAN: Hybrid generative adversarial network

In this paper, we present a simple approach to train Generative Adversarial Networks (GANs) in order to avoid a \textit {mode collapse} issue. Implicit models such as GANs tend to generate better samples compared to explicit models that are trained on tractable data likelihood. However, GANs overlook the explicit data density characteristics which leads to undesirable quantitative evaluations and mode collapse. To bridge this gap, we propose a hybrid generative adversarial network (HGAN) for which we can enforce data density estimation via an autoregressive model and support both adversarial and likelihood framework in a joint training manner which diversify the estimated density in order to cover different modes. We propose to use an adversarial network to \textit {transfer knowledge} from an autoregressive model (teacher) to the generator (student) of a GAN model. A novel deep architecture within the GAN formulation is developed to adversarially distill the autoregressive model information in addition to simple GAN training approach. We conduct extensive experiments on real-world datasets (i.e., MNIST, CIFAR-10, STL-10) to demonstrate the effectiveness of the proposed HGAN under qualitative and quantitative evaluations. The experimental results show the superiority and competitiveness of our method compared to the baselines.
An Introduction to Machine Learning for Panel Data

Machine learning has dramatically expanded the range of tools for evaluating economic panel data. This paper applies a variety of machine-learning methods to the Boston housing dataset, an iconic proving ground for machine learning. Though machine learning often lacks the overt interpretability of linear regression, methods based on decision trees score the relative importance of dataset features. In addition to addressing the theoretical tradeoff between bias and variance, this paper discusses practices rarely followed in traditional economics: the splitting of data into training, validation, and test sets; the scaling of data; and the preference for retaining all data. The choice between traditional and machine-learning methods hinges on practical rather than mathematical considerations. In settings emphasizing interpretative clarity through the scale and sign of regression coefficients, machine learning may best play an ancillary role. Wherever predictive accuracy is paramount, however, or where heteroskedasticity or high dimensionality might impair the clarity of linear methods, machine learning can deliver superior results.
Revamping Cross-Modal Recipe Retrieval with Hierarchical Transformers and Self-supervised Learning

Cross-modal recipe retrieval has recently gained substantial attention due to the importance of food in people’s lives, as well as the availability of vast amounts of digital cooking recipes and food images to train machine learning models. In this work, we revisit existing approaches for cross-modal recipe retrieval and propose a simplified end-to-end model based on well established and high performing encoders for text and images. We introduce a hierarchical recipe Transformer which attentively encodes individual recipe components (titles, ingredients and instructions). Further, we propose a self-supervised loss function computed on top of pairs of individual recipe components, which is able to leverage semantic relationships within recipes, and enables training using both image-recipe and recipe-only samples. We conduct a thorough analysis and ablation studies to validate our design choices. As a result, our proposed method achieves state-of-the-art performance in the cross-modal recipe retrieval task on the Recipe1M dataset. We make code and models publicly available 1.
A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results

Inference is the process of using facts we know to learn about facts we do not know. A theory of inference gives assumptions necessary to get from the former to the latter, along with a definition ...
Targeted replacement of full-length CFTR in human airway stem cells by CRISPR-Cas9 for pan-mutation correction in the endogenous locus.

Cystic fibrosis (CF) is a monogenic disease caused by impaired production and/or function of the CF transmembrane conductance regulator (CFTR) protein. Although we have previously shown correction of the most common pathogenic mutation, there are many other pathogenic mutations throughout the CF gene. An autologous airway stem cell therapy in which the CFTR cDNA is precisely inserted into the CFTR locus may enable the development of a durable cure for almost all CF patients, irrespective of the causal mutation. Here, we use CRISPR-Cas9 and two adeno-associated viruses (AAVs) carrying the two halves of the CFTR cDNA to sequentially insert the full CFTR cDNA along with a truncated CD19 (tCD19) enrichment tag in upper airway basal stem cells (UABCs) and human bronchial epithelial cells (HBECs). The modified cells were enriched to obtain 60%–80% tCD19+ UABCs and HBECs from 11 different CF donors with a variety of mutations. Differentiated epithelial monolayers cultured at air-liquid interface showed restored CFTR function that was >70% of the CFTR function in non-CF controls. Thus, our study enables the development of a therapy for almost all CF patients, including patients who cannot be treated using recently approved modulator therapies.
Idiosyncratic Risk in Housing Markets

Most US households invest a substantial fraction of their wealth in an individual asset: their family home. This paper studies the empirical properties of idiosyncratic house price risk using a novel micro-dataset of house sales and remodeling permits from the metropolitan areas of Los Angeles, San Diego, and San Francisco. Unlike what is usually assumed in the literature, I find that idiosyncratic housing risk does not scale with time. Moreover, the fraction of capital gains variance determined by the idiosyncratic component decreases over a house holding period. I analyze the effects of idiosyncratic risk on household welfare, using a quantitative portfolio model. Exposure to idiosyncratic shocks has substantial effects on the trade-off between owning and renting, especially for short holding periods.
New ADS Functionality for the Curator

In this paper we provide an update concerning the operations of the NASA Astrophysics Data System (ADS), its services and user interface, and the content currently indexed in its database. As the primary information system used by researchers in Astronomy, the ADS aims to provide a comprehensive index of all scholarly resources appearing in the literature. With the current effort in our community to support data and software citations, we discuss what steps the ADS is taking to provide the needed infrastructurein collaboration with publishers and data providers. A new API provides accessto the ADS search interface, metrics, and libraries allowing users to programmatically automate discovery and curation tasks. The new ADS interface supports a greater integration of content and services with a variety of partners, including ORCID claiming, indexing of SIMBAD objects, and article graphics from a variety of publishers. Finally, we highlight how librarians can facilitate the ingest of gray literature that they curate into our system.
New methods to find patches of invisible integer lattice points

It is a surprising fact that the proportion of integer lattice points visible from the origin is exactly $\frac{6}{\pi^2}$, or approximately 60 percent. Hence, approximately 40 percent of the integer lattice is hidden from the origin. Since 1971, many have studied a variety of problems involving lattice point visibility, in particular, searching for patterns in that 40 percent of the lattice comprised of invisible points. One such pattern is a square patch, an $n \times n$ grid of $n^2$ invisible points, which we call a hidden forest. It is known that there exist arbitrarily large hidden forests in the integer lattice. However, the methods up to now involve the Chinese Remainder Theorem (CRT) on the rows and columns of matrices with prime number entries, and they have only been able to locate hidden forests very far from the origin. For example, using this method the closest known $4 \times 4$ hidden forest is over 3 quintillion, or $3 \times 10^{18}$, units away from the origin. We introduce the concept of quasiprime matrices and utilize a variety of computational and theoretical techniques to find some of the closest known hidden forests to this date. Using these new techniques, we find a $4 \times 4$ hidden forest that is merely 184 million units away from the origin. We conjecture that every hidden forest can be found via the CRT-algorithm on a quasiprime matrix.
Global convergence of Negative Correlation Extreme Learning Machine

Ensemble approaches introduced in the Extreme Learning Machine literature mainly come from methods that rely on data sampling procedures, under the assumption that the training data are heterogeneously enough to set up diverse base learners. To overcome this assumption, it was proposed an ELM ensemble method based on the Negative Correlation Learning framework, called Negative Correlation Extreme Learning Machine (NCELM). This model works in two stages: (i) different ELMs are generated as base learners with random weights in the hidden layer, and (ii) a NCL penalty term with the information of the ensemble prediction is introduced in each ELM minimization problem, updating the base learners, (iii) second step is iterated until the ensemble converges. Although this NCL ensemble method was validated by an experimental study with multiple benchmark datasets, no information was given on the conditions about this convergence. This paper mathematically presents sufficient conditions to guarantee the global convergence of NCELM. The update of the ensemble in each iteration is defined as a contraction mapping function, and through Banach theorem, global convergence of the ensemble is proved.
Flame stability analysis of flame spray pyrolysis by artificial intelligence

Flame spray pyrolysis (FSP) is a process used to synthesize nanoparticles through the combustion of an atomized precursor solution; this process has applications in catalysts, battery materials, and pigments. Current limitations revolve around understanding how to consistently achieve a stable flame and the reliable production of nanoparticles. Machine learning and artificial intelligence algorithms that detect unstable flame conditions in real time may be a means of streamlining the synthesis process and improving FSP efficiency. In this study, the FSP flame stability is first quantified by analyzing the brightness of the flame’s anchor point. This analysis is then used to label data for both unsupervised and supervised machine learning approaches. The unsupervised learning approach allows for autonomous labeling and classification of new data by representing data in a reduced dimensional space and identifying combinations of features that most effectively cluster it. The supervised learning approach, on the other hand, requires human labeling of training and test data but is able to classify multiple objects of interest (such as the burner and pilot flames) within the video feed. The accuracy of each of these techniques is compared against the evaluations of human experts. Both the unsupervised and supervised approaches can track and classify FSP flame conditions in real time to alert users of unstable flame conditions. This research has the potential to autonomously track and manage flame spray pyrolysis as well as other flame technologies by monitoring and classifying the flame stability.
Near-unity broadband omnidirectional emissivity via femtosecond laser surface processing

It is very challenging to achieve near perfect absorption or emission that is both broadband and omnidirectional while utilizing a scalable fabrication process. Femtosecond laser surface processing is an emerging low-cost and large-scale manufacturing technique used to directly and permanently modify the surface properties of a material. The versatility of this technique to produce tailored surface properties has resulted in a rapidly growing number of applications. Here, we demonstrate near perfect, broadband, omnidirectional emissivity from aluminum surfaces by tuning the laser surface processing parameters including fluence, pulse count, and the ambient gas. Full-wave simulations and experimental results prove that the obtained increase in emissivity is mainly a result of two distinct features produced by femtosecond laser surface processing: the introduction of microscale surface features and the thick oxide layer. This technique leads to functionalized metallic surfaces that are ideal for emerging applications, such as passive radiative cooling and thermal management of spacecraft. Surfaces with wide-angle high emissivity in the infrared spectrum are important for applications in radiative cooling, thermophotovoltaics, and spacecraft thermal management. Here, near perfect, broadband, omnidirectional emissivity is realized via scalable laser surface processing of aluminum.
High-dimensional index volatility models via Stein’s identity

We study the estimation of the parametric components of single and multiple index volatility models. Using the first- and second-order Stein’s identities, we develop methods that are applicable for the estimation of the variance index in the high-dimensional setting requiring finite moment condition, which allows for heavy-tailed data. Our approach complements the existing literature in the low-dimensional setting, while relaxing the conditions on estimation, and provides a novel approach in the high-dimensional setting. We prove that the statistical rate of convergence of our variance index estimators consists of a parametric rate and a nonparametric rate, where the latter appears from the estimation of the mean link function. However, under standard assumptions, the parametric rate dominates the rate of convergence and our results match the minimax optimal rate for the mean index estimation. Simulation results illustrate finite sample properties of our methodology and back our theoretical conclusions.
Impossibility of almost extension

Abstract Let ( X , ‖ ⋅ ‖ X ) , ( Y , ‖ ⋅ ‖ Y ) be normed spaces with dim ⁡ ( X ) = n . Bourgain's almost extension theorem asserts that for any e > 0 , if N is an e-net of the unit sphere of X and f : N → Y is 1-Lipschitz, then there exists an O ( 1 ) -Lipschitz F : X → Y such that ‖ F ( a ) − f ( a ) ‖ Y ≲ n e for all a ∈ N . We prove that this is optimal up to lower order factors, i.e., sometimes max a ∈ N ⁡ ‖ F ( a ) − f ( a ) ‖ Y ≳ n 1 − o ( 1 ) e for every O ( 1 ) -Lipschitz F : X → Y . This improves Bourgain's lower bound of max a ∈ N ⁡ ‖ F ( a ) − f ( a ) ‖ Y ≳ n c e for some 0 c 1 2 . If X = l 2 n , then the approximation in the almost extension theorem can be improved to max a ∈ N ⁡ ‖ F ( a ) − f ( a ) ‖ Y ≲ n e . We prove that this is sharp, i.e., sometimes max a ∈ N ⁡ ‖ F ( a ) − f ( a ) ‖ Y ≳ n e for every O ( 1 ) -Lipschitz F : l 2 n → Y .
Heterogeneous Intermediary Asset Pricing

Abstract I show that the composition of the financial sector has important asset pricing implications beyond the health of the aggregate financial sector. To assess the impact of massive balance sheet adjustments within the intermediary sector during the Great Recession and resolve conflicting asset pricing evidence, I propose a dynamic asset pricing model with heterogeneous intermediaries facing financial frictions. Asset flows between intermediaries are quantitatively important for both the level of and variation in the risk premium. An empirical measure of the composition of the intermediary sector negatively forecasts future excess returns and is priced in the cross-section with a positive price of risk.
ASTROPHYSICAL NEUTRINOS: 20TH CENTURY AND BEYOND

I summarize the first four decades of solar neutrino research and suggest what may be possible to learn with extragalactic neutrinos and with solar neutrinos in the next decade.
Iterative Paraphrastic Augmentation with Discriminative Span Alignment

We introduce a novel paraphrastic augmentation strategy based on sentence-level lexically constrained paraphrasing and discriminative span alignment. Our approach allows for the large-scale expansion of existing datasets or the rapid creation of new datasets using a small, manually-produced seed corpus. We demonstrate our approach with experiments on the Berkeley FrameNet Project, a large-scale language understanding effort spanning more than two decades of human labor. With four days of training data collection for a span alignment model and one day of parallel compute, we automatically generate and release to the community 495,300 unique (Frame, Trigger) pairs in diverse sentential contexts, a roughly 50-fold expansion atop FrameNet v1.7. The resulting dataset is intrinsically and extrinsically evaluated in detail, showing positive results on a downstream task.
Goal-Directed Design Agents: Integrating Visual Imitation with One-Step Lookahead Optimization for Generative Design

Engineering design problems often involve large state and action spaces along with highly sparse rewards. Since an exhaustive search of those spaces is not feasible, humans utilize relevant domain knowledge to condense the search space. Previously, deep learning agents (DLAgents) were introduced to use visual imitation learning to model design domain knowledge. This note builds on DLAgents and integrates them with one-step lookahead search to develop goal-directed agents capable of enhancing learned strategies for sequentially generating designs. Goal-directed DLAgents can employ human strategies learned from data along with optimizing an objective function. The visual imitation network from DLAgents is composed of a convolutional encoder-decoder network, acting as a rough planning step that is agnostic to feedback. Meanwhile, the lookahead search identifies the fine-tuned design action guided by an objective. These design agents are trained on an unconstrained truss design problem that is modeled as a sequential, action-based configuration design problem. The agents are then evaluated on two versions of the problem: the original version used for training and an unseen constrained version with an obstructed construction space. The goal-directed agents outperform the human designers used to train the network as well as the previous objective-agnostic versions of the agent in both scenarios. This illustrates a design agent framework that can efficiently use feedback to not only enhance learned design strategies but also adapt to unseen design problems.
Targeted RNA-seq improves efficiency, resolution, and accuracy of allele specific expression for human term placentas

Genomic imprinting is an epigenetic mechanism that results in allele-specific expression (ASE) based on the parent of origin. It is known to play a role in the prenatal and postnatal allocation of maternal resources in mammals. ASE detected by whole transcriptome RNA-seq (wht-RNAseq) has been widely used to analyze imprinted genes using reciprocal crosses in mice to generate large numbers of informative SNPs. Studies in humans are more challenging due to the paucity of SNPs and the poor preservation of RNA in term placentas and other tissues. Targeted RNA-seq (tar-RNAseq) can potentially mitigate these challenges by focusing sequencing resources on the regions of interest in the transcriptome. Here, we compared tar-RNAseq and wht-RNAseq in a study of ASE in known imprinted genes in placental tissue collected from a healthy human cohort in Mali, West Africa. As expected, tar-RNAseq substantially improved the coverage of SNPs. Compared to wht-RNAseq, tar-RNAseq produced on average four times more SNPs in twice as many genes per sample and read depth at the SNPs increased fourfold. In previous research on humans, discordant ASE values for SNPs of the same gene have limited the ability to accurately quantify ASE. We show that tar-RNAseq reduces this limitation as it unexpectedly increased the concordance of ASE between SNPs of the same gene, even in cases of degraded RNA. Studies aimed at discovering associations between individual variation in ASE and phenotypes in mammals and flowering plants will benefit from the improved power and accuracy of tar-RNAseq.
What We Can Learn From Visual Artists About Software Development

This paper explores software’s role in visual art production by examining how artists use and develop software. We conducted interviews with professional artists who were collaborating with software developers, learning software development, and building and maintaining software. We found artists were motivated to learn software development for intellectual growth and access to technical communities. Artists valued efficient workflows through skilled manual execution and personal software development, but avoided high-level forms of software automation. Artists identified conflicts between their priorities and those of professional developers and computational art communities, which influenced how they used computational aesthetics in their work. These findings contribute to efforts in systems engineering research to integrate end-user programming and creativity support across software and physical media, suggesting opportunities for artists as collaborators. Artists’ experiences writing software can guide technical implementations of domain-specific representations, and their experiences in interdisciplinary production can aid inclusive community building around computational tools.
Temperature Dependence of the Anisotropic Magnetoresistance of the Metallic Antiferromagnet Fe 2 As

Electrical readout of metallic antiferromagnet (AFM) memories is typically realized by measuring the anisotropic magnetoresistance (AMR), but the mechanisms for enhanced AMR are not yet established. We study AMR of single crystals of AFM ${\mathrm{Fe}}_{2}\mathrm{As}$ from $T=5$ K to above the N\'eel temperature, ${T}_{N}\ensuremath{\approx}353$ K. With an applied magnetic field $B$ rotating in the (001) plane, we observe a peak-to-peak AMR change of 1.3% for $Bg1$ T at $T=5$ K, one order of magnitude larger than reported in $\mathrm{Cu}\mathrm{Mn}\mathrm{As}$, a widely studied candidate for AFM spintronics. The AMR varies strongly with temperature, decreasing by a factor of approximately 10 at $T\ensuremath{\approx}200$ K. Our results suggest that large AMR in easy-plane AFMs may require N\'eel temperatures that greatly exceed room temperature.
Restricting Grammars with Tree Automata

Precedence and associativity declarations in systems like yacc resolve ambiguities in context-free grammars (CFGs) by specifying restrictions on allowed parses. However, they are special purpose and do not handle many other grammatical restrictions that language designers need in order to resolve ambiguities like dangling else, the interactions between binary operators and functional if expressions in ML, and the interactions between object allocation and function calls in JavaScript. Often, language designers resort to restructuring their grammars in order to encode these restrictions, but this obfuscates the designer's intent and makes grammars difficult to read, write, and maintain. In this paper, we show how tree automata can modularly and concisely encode such restrictions. We do this by reinterpreting CFGs as tree automata and intersecting them with tree automata encoding the desired restrictions. The results are then reinterpreted back into CFGs that encode the specified restrictions. This process can be used as a preprocessing step before other CFG manipulations and is well behaved. It performs well in practice and never introduces ambiguities or LR($k$) conflicts.
The role of interannual ENSO events in decadal timescale transitions of the Interdecadal Pacific Oscillation

The build-up of decadal timescale upper ocean heat content in the off-equatorial western tropical Pacific can provide necessary conditions for interannual El Nino/Southern Oscillation (ENSO) events to contribute to decadal timescale transitions of tropical Pacific SSTs to the opposite phase of the Interdecadal Pacific Oscillation (IPO). This can be viewed as a corollary to subseasonal westerly wind burst events contributing to El Nino interannual timescale transitions. A long pre-industrial control run with CESM1 is analyzed to show that there is a greater chance of ENSO activity to contribute to an IPO transition when off-equatorial western Pacific Ocean heat content reaches either a maximum (for El Nino to contribute to a transition to positive IPO) or minimum (for La Nina to contribute to a transition to negative IPO) as seen in observations. If above a necessary ocean heat content threshold, the convergence associated with westerly anomaly near-equatorial surface winds associated with El Nino activity can draw that heat content equatorward to sustain anomalously warm western and central Pacific SSTs. These are associated with positive precipitation and convective heating anomalies, a Gill-type response and wind stress curl anomalies that continue to feed warm water into the near-equatorial western Pacific. These conditions then sustain the decadal-timescale transition to positive IPO (with the opposite sign for transition to negative IPO). Associated central equatorial Pacific convective heating anomalies produce SLP and wind stress anomalies in the North and South Pacific that can excite westward-propagating off-equatorial oceanic Rossby waves to contribute to the western Pacific thermocline depth and consequent heat content anomalies.
Physical Constraints on Motility with Applications to Possible Life on Mars and Enceladus

Motility is a ubiquitous feature of microbial life on Earth, and is widely regarded as a promising biosignature candidate. In the search for motile organisms, it is therefore valuable to have rough estimates for the number of such microbes that one may expect to find in a given area or volume. In this work, we explore this question by employing a simple theoretical model that takes into account the amount of free energy available in a given environment and the energetic cost of motility. We present heuristic upper bounds for the average biomass density and the number density of motile lifeforms for the Martian subsurface and the ocean of Enceladus by presuming that the motile microbes in question derive their energy from methanogenesis. We consequently demonstrate that the resultant densities of motile organisms might be potentially comparable to, or much lower than, the total microbial densities documented in various extreme environments on Earth.
Numerical Simulation of Three-Dimensional Boattail Afterbody Flowfields

The thin shear-layer approximations of the three-dimensional, compressible Navier-Stokes equations are solved for subsonic, transonic, and supersonic now over axisymmetric boattail bodies at moderate angles of attack. The plume is simulated by a solid body configuration identical to those used In experimental tests. An implicit algorithm of second-order accuracy is used to solve the equations on the ILLIAC 4 computer. The turbulence is expressed by an algebraic model applicable to three-dimensional flowfields with moderate separation. The formulation used is attractive in its independence of boundary-layer parameters. Such a simple model, however, is incapable of supporting detailed quantitative descriptions of complex shear flows. Never-the-less, good qualitative comparisons are found with three different sets of experimental date. Quantitative improvement will depend on improved turbulence transport descriptions.
Making transport more robust and interpretable by moving data through a small number of anchor points

Optimal transport (OT) is a widely used technique for distribution alignment, with applications throughout the machine learning, graphics, and vision communities. Without any additional structural assumptions on transport, however, OT can be fragile to outliers or noise, especially in high dimensions. Here, we introduce Latent Optimal Transport (LOT), a new approach for OT that simultaneously learns low-dimensional structure in data while leveraging this structure to solve the alignment task. The idea behind our approach is to learn two sets of "anchors" that constrain the flow of transport between a source and target distribution. In both theoretical and empirical studies, we show that LOT regularizes the rank of transport and makes it more robust to outliers and the sampling density. We show that by allowing the source and target to have different anchors, and using LOT to align the latent spaces between anchors, the resulting transport plan has better structural interpretability and highlights connections between both the individual data points and the local geometry of the datasets.