cssi/SciSciGPT-SciSciNet huggingface dataset:
The Claim Construction Effect

The United States Court of Appeals for the Federal Circuit was uniquely created to guide the patent system. Since its creation the court has rapidly grown in importance, as has interest and debate over the success of the court as an institution. Some of the most controversial actions of the court involve its attempts to resolve uncertainty in the central patent law doctrine of claim construction - the interpretation of patent claims. This Article explores the consequences of the federal circuit’s efforts to reform claim construction jurisprudence. Using empirical evidence for support, it makes the argument that, as the federal circuit grappled with the problem of claim construction, a so-called claim construction effect developed in federal circuit patent decisions. The hypothesis of the claim construction effect states that as claim construction doctrine increased in prominence in federal circuit opinions, a concomitant increase in unpredictability in claim construction doctrine bled into other areas of patent law. The effect was to decrease the predictability of federal circuit decisions in other areas of patent law. Building on earlier empirical work, this Article finds an explanation for the claim construction effect in the addition of new judges in the year 2000 that can be associated to changes in claim construction outcomes and to a decrease in the predictability of federal circuit decisions in other areas of patent law. This Article also analyzes whether the federal circuit corrected the claim construction effect with its opinion in Phillips v. AWH Corp. Here, the results paint a decidedly mixed picture. Some stability appears to have returned after Phillips. But there is also evidence that the fundamentals of the claim construction effect remain strongly entrenched in federal circuit jurisprudence.
“You Might Be a Public Intellectual If…”: A Checklist for Political Scientists, a Challenge for Political Science

Amitai Etzioni has written something of a pocket guide for public intellectuals, proffering hard-won lessons from his own time in the trenches. I wish that I had read his primer before I tried to break into the club. And I hope that PS readers seeking to be PI players read his guide with the right mix of humor, curiosity, and skepticism about the rules of success that Etzioni evinces.
The effect of shill bidding upon prices: Experimental evidence

This paper explores, through a series of experiments, the effect of shill bidding upon revenues and prices in auctions. We study the practice of shill bidding in a common value framework. Our findings are consistent with the prediction that, if bidders are aware of the possibility of seller participation in an auction, profits will be reduced on average. We also study factors that affect bidder and seller participation decisions. Shill bidding can alleviate the problem of the winner's curse by lowering the price and it can, thus, provide benefits to bidders.
Political Control of Federal Prosecutions – Looking Back And Looking Forward

This Essay explores the mechanisms of control over federal criminal enforcement that the administration and Congress used or failed to use during George W. Bush’s presidency. It gives particular attention to Congress, not because legislators played a dominant role, but because they generally chose to play such a subordinate role. My fear is that the media focus on management inadequacies or abuses within the Justice Department during the Bush administration might lead policymakers and observers to overlook the hard questions that remain about how the federal criminal bureaucracy should be structured and guided during a period of rapidly shifting priorities and about the role Congress should play in this process.
Invariance and universality in social agent-based simulations

Agent-based simulation models have a promising future in the social sciences, from political science to anthropology, economics, and sociology. To realize their full scientific potential, however, these models must address a set of key problems, such as the number of interacting agents and their geometry, network topology, time calibration, phenomenological calibration, structural stability, power laws, and other substantive and methodological issues. This paper discusses and highlights these problems and outlines some solutions.
The Persistence of Dualism in Human Rights Treaty Implementation

A close look at the much-discussed state and municipal efforts to implement human rights treaties in the United States exposes an apparent paradox: subnational legislators and executive agencies engage more frequently with unratified human rights treaties than with those that have been ratified and are thus binding law under the Supremacy Clause. This Article explains this phenomenon as a manifestation of how states and localities view their roles and responsibilities in our federalist system. Despite increasing subnational acknowledgment of international law, states and cities continue to take a dualist view of the project of human rights treaty implementation, which places accountability for treaty implementation squarely with the federal government. Because these instruments guarantee broad rights that are not easily divisible along traditional jurisdictional lines, however, the perpetuation of this dualist perspective presents a significant barrier to their implementation. A more dynamic federal-subnational relationship that encourages and promotes multi-level collaboration and jurisdictional redundancy is necessary to realize the goals of these instruments. The Article concludes with strategies for creating this kind of cooperative partnership.
The Detention Power

This paper surveys the history of the executive detention power in American law, from the Founding through the detention of U.S. citizens as enemy combatants. Focusing on precedents from the Civil War, World War II, and the so-called Emergency Detention Era, the paper concludes that courts have never deferred to the concept of an inherent executive detention authority, and that, especially after the 1971 enactment of 18 U.S.C. 4001(a), the power to authorize the extra-judicial detention of U.S. citizens is emphatically the province of Congress, and Congress alone. The paper concludes with an analysis of the application of the detention power in the post-9/11 context, focusing on the detentions of Yaser Esam Hamdi and Jose Padilla. The paper concludes that neither of the statutes invoked by the government in Hamdi and Padilla, the Authorization for Use of Military Force in Afghanistan, or 10 U.S.C. 956(5), satisfies the requirements of 4001, and thus the detentions of both citizens raise serious constitutional questions.
An asymmetric-information model of litigation

Abstract This paper presents a cradle-to-grave model of tort liability, incorporating the decision to comply with the due-care standard, the decision to file suit, and the decision to settle. I use the model primarily to examine settlement rates, plaintiff win rates, and compliance with the due-care standard. The key results of the model are as follows: (1) litigation to judgment occurs only when some but not all actors comply with the due-care standard, and (2) if defendants have the information advantage at trial, plaintiff win rates generally will be less than 50 percent. I apply the model and its simulation results to several empirical issues in the litigation literature. The model simulation indicates that the British rule for allocating legal costs is superior to alternatives in terms of social welfare. In addition, the model is capable of explaining several empirical features of litigation and puzzles in the literature on trial outcomes.
Chronicling the Complexification of Negotiation Theory and Practice

The essay reviews the content of twenty-five years of Negotiation Journal, identifying themes and issues explored on its pages in the past, the current issues challenging the field's scholars and practitioners, and the issues likely to confront us in the future. It argues that while we in the field hoped for simple, elegant, and universal theories of negotiation and conflict resolution, the last twenty-five years have demonstrated the increasing complexification of negotiation theory and practice, from increased numbers of parties and issues, and dilemmas of intertemporal commitments, ethics, accountability, and relationships of private action to public responsibility.
Exclusionary Amenities in Residential Communities

This Article identifies an important mechanism by which segregation arises in new residential developments. The Fair Housing Act and other antidiscrimination laws closely regulate real estate sales, advertising, and racial steering. As a result of these laws and other factors, home purchasers often lack accurate information about the likely demographic makeup of a new neighborhood or condominium building. Yet these laws have not eroded the incentives for housing consumers to obtain this data. This Article argues that developers circumvent fair housing laws by embedding costly, demographically polarizing amenities within a new development and recording covenants mandating that all homeowners pay for those amenities. Its central claim is that developers will select common amenities not only on the basis of which amenities are inherently welfare-maximizing for the residents, but also on the basis of which amenities most effectively deter undesired residents from purchasing homes therein. The Article dubs this approach the exclusionary amenities strategy and shows how it causes sorting and focal point mechanisms to act in concert, thereby engendering substantial residential homogeneity. The inability to exclude functions as an inducement to spend. During the 1990s, the United States experienced a boom in the construction of residential developments built around costly golf courses. This occurred at a time when golf participation functioned
Hybrid Organizations in the Arts: A Cautionary View

Traditionally, the choice of organizational form facing an arts entrepreneur has been between starting a commercial enterprise or a nonprofit. Recently, state governments across the United States have passed authorizing legislation for various forms of hybrid organizations that combine elements of the commercial and nonprofit. This article presents an overview of the evolution of hybrid organizations and the associated policy and governance questions that arise. It concludes that hybrids, at least in their current state, do not present a model likely to be widely adopted in the arts.
Sales Tax Exceptionalism

There is something different about the state sales tax, or so it seems based on judicial decisions creating unique jurisdictional and apportionment standards for the tax. This article explores the concept of “sales tax exceptionalism,” and assesses whether the special treatment afforded to the sales tax is justified by the theoretical foundations of the tax. In particular, the article examines whether theoretical justifications exist for the jurisdictional standard applied to the sales tax (a “physical presence” standard), as compared to the “economic presence” standard applied to the corporate income tax. Ultimately, the article concludes that only weak theoretical justifications support the different jurisdictional standards, and that recent changes to many states’ corporate income taxes further undercut the notion of “sales tax exceptionalism.”
TIGHTER BOUNDS FOR IMPLIED VOLATILITY

We establish bounds on Black–Scholes implied volatility that improve on the uniform bounds previously derived by Tehranchi. Our upper bound is uniform, while the lower bound holds for most options likely to be encountered in practical applications. We further demonstrate the practical effectiveness of our new bounds by showing how the efficiency of the bisection algorithm is improved for a snapshot of SPX option quotes.
CHIEF COUNSEL'S SUBTLE IMPACT ON REVENUE: REGULATIONS, LITIGATION, AND ADMINISTRATIVE GUIDANCE

This essay examines the Chief Counsel’s role in the tax system and attempts to assess the ways in which it may affect revenue. The essay specifi cally discusses the revenue impacts of regulations and litigation led by the Offi ce of Chief Counsel. Ultimately, the essay calls on Congress to systematically require analyses of revenue impacts of tax legislation and the administrative response to such legislation.
Fiscal Policy Implementation in Sub-Saharan Africa

Summary This paper investigates economic and institutional constraints to fiscal policy implementation in sub-Saharan Africa. We find that planned fiscal adjustments or expansions are less likely to be implemented the larger they are, the more fragile the rule of law in the country, and the weaker the institutions framing the implementation of the annual fiscal budget. The findings support ongoing efforts in the region to enhance forecasting capacity; adopt realistic fiscal plans; and strengthen governance and public financial management procedures, with a focus on accounting, reporting, and external controls of government’s fiscal accounts.
Specialty Bars as a Site of Professionalism: The Immigration Bar Example

As the practice of law has become increasingly specialized, specialty bar associations have become more important to the work lives of many lawyers and their understanding of professional norms. This article looks at the role of a single specialty bar association — the American Immigration Lawyers Association (AILA) — in the construction of its members’ norms and values. The article draws on semi-structured interviews with 71 immigration lawyers in the New York City metropolitan area to identify the ways in which specialty bars foster lawyers’ understanding of professional norms — both formally and informally — through education, information sharing, mentoring, and other mechanisms. The article identifies some of the specific norms and values that are communicated within this bar, including what it means to be a “good lawyer.” It also considers what happens when a bar association raises lawyers’ awareness of ethical issues but fails to clearly communicate what response is required of its members. It concludes by considering the conditions that seemingly permit a specialty bar to flourish and become an important site of professional learning and socialization.
A mixed optimization technique for the generalized machine replacement problem

Abstract : A mixed optimization technique for optimal machine replacement is presented which allows much more flexibility than previous models. Optimal purchase, maintenance, and sale of a given machine between any two given points in time is treated as a sub-problem, which one may choose to solve via control theory, dynamic programming, or practical engineering considerations. (A control theory formulation is used in the paper as an illustration.) These sub-problem solutions are then incorporated into a Wagner-Whitin formulation for solution of the full problem. The technique is particularly useful for problems with such asymmetries as an existing initial machine or uneven technological change. (Author)
Endogenous City Formation with Production Externalities: Existence of Equilibrium

I show that equilibria exist in closed city-system models with production externalities if firms’ production possibilities vary continuously with the source of the externality, are constant returns to scale in own inputs, include inaction, and satisfy free disposal; if firms have to employ their own inputs to produce output; if there is a finite number of firm types; and if some standard conditions on preferences and endowments are satisfied. This is the first model to include production externalities in the fully general equilibrium framework required for endogenous city formation. Thus, this result provides formal support for the conjecture that production externalities lead to urban agglomeration.
A Model of Declining Standards

This paper presents a model in which relative standing determines standards. There are three kinds of agents in the model: candidates who wish to pass a test, members of the elite who have passed the test, and the judge who decides who passes. In order to pass, a candidate's performance must be at least as good as the performance of a representative member of the elite. Without perturbations in the underlying data, the model predicts that standards will not change. Perturbations in the preferences used to judge candidates lead to a reduction in standards.
Robust and Generalizable Visual Representation Learning via Random Convolutions

While successful for various computer vision tasks, deep neural networks have shown to be vulnerable to texture style shifts and small perturbations to which humans are robust. In this work, we show that the robustness of neural networks can be greatly improved through the use of random convolutions as data augmentation. Random convolutions are approximately shape-preserving and may distort local textures. Intuitively, randomized convolutions create an infinite number of new domains with similar global shapes but random local texture. Therefore, we explore using outputs of multi-scale random convolutions as new images or mixing them with the original images during training. When applying a network trained with our approach to unseen domains, our method consistently improves the performance on domain generalization benchmarks and is scalable to ImageNet. In particular, in the challenging scenario of generalizing to the sketch domain in PACS and to ImageNet-Sketch, our method outperforms state-of-art methods by a large margin. More interestingly, our method can benefit downstream tasks by providing a more robust pretrained visual representation.
Single-Photon Image Classification

Quantum Computing based Machine Learning mainly focuses on quantum computing hardware that is experimentally challenging to realize due to requiring quantum gates that operate at very low temperature. We demonstrate the existence of a "quantum computing toy model" that illustrates key aspects of quantum information processing while being experimentally accessible with room temperature optics. Pondering the question of the theoretical classification accuracy performance limit for MNIST (respectively "Fashion-MNIST") classifiers, subject to the constraint that a decision has to be made after detection of the very first photon that passed through an image-filter, we show that a machine learning system that is permitted to use quantum interference on the photon's state can substantially outperform any machine learning system that can not. Specifically, we prove that a "classical" MNIST (respectively "Fashion-MNIST") classifier cannot achieve an accuracy of better than 21.28% (respectively 18.28% for "Fashion-MNIST") if it must make a decision after seeing a single photon falling on one of the 28×28 image pixels of a detector array. We further demonstrate that a classifier that is permitted to employ quantum interference by optically transforming the photon state prior to detection can achieve a classification accuracy of at least 41.27% for MNIST (respectively 36.14% for "Fashion-MNIST"). We show in detail how to train the corresponding quantum state transformation with TensorFlow and also explain how this example can serve as a teaching tool for the measurement process in quantum mechanics.
The carbon state in dilute germanium carbides

Conduction and valence band states for the highly mismatched alloy (HMA) Ge:C are projected onto Ge crystal states, Ge vacancy states, and Ge/C atomic orbitals, revealing that substitutional carbon not only creates a direct bandgap but also the new conduction band is optically active. Overlap integrals of the new Ge:C conduction band state with states from unperturbed Ge show that the new band cannot be attributed to any single Ge band but is a mixture of multiple Ge states. The Ge Γ conduction band valley state plays the largest single role, but L and X valley states collectively contribute a larger share than Γ due to the multiplicity of degenerate states. C sites structurally resemble uncharged vacancies in the Ge lattice, similar to Hjalmarson's model for other HMAs. C also perturbs the entire Ge band structure even at the deepest crystal core energy levels, particularly if staggered supercells are used to mimic a disordered alloy. Projection onto atomic sites shows a relatively weak localization compared with other HMAs, but it does show a strong anisotropy in probability distribution. L-valley conduction band states in Ge contribute to the conduction band minimum in Ge:C, but the optical transition strength in Ge:C remains within a factor of 2 of the direct gap transition in Ge.
Resource Publicness Matters in Organizational Perceptions

Publicness theory deepens our understanding of differences and similarities of organizational behavior. However, in areas in which public, nonprofit, and private organizations compete to serve people, it remains unclear how the level of dimensional publicness influences individuals' perceptions and choices of organizations. In this study, we present evidence from two online experiments examining the way people's perceptions of resource publicness (operationalized as government funding, donations, and service fees) of elderly care centers in the U.S. influence their judgment of organizations and service choices. Findings suggest that people perceive donative organizations to be the warmest and most competent, followed by government-funded and commercial organizations. We conclude that individuals' perceptions of resource publicness lead to different judgment that may influence their service choice decisions.
Autonomous Spot: Long-Range Autonomous Exploration of Extreme Environments with Legged Locomotion

This paper serves as one of the rst efforts to enable large-scale and long-duration autonomy using the Boston Dynamics Spot robot. Motivated by exploring extreme environments, particularly those involved in the DARPA Subterranean Challenge, this paper pushes the boundaries of the state-of-practice in enabling legged robotic systems to accomplish real-world complex missions in relevant scenarios. In particular, we discuss the behaviors and capabilities which emerge from the integration of the autonomy architecture NeBula (Networked Belief-aware Perceptual Autonomy) with next-generation mobility systems. We will discuss the hardware and software challenges, and solutions in mobility, perception, autonomy, and very brie y, wireless networking, as well as lessons learned and future directions. We demonstrate the performance of the proposed solutions on physical systems in real-world scenarios.3 The proposed solution contributed to winning 1st-place in the 2020 DARPA Subterranean Challenge, Urban Circuit.4
Relevant Region Exploration On General Cost-maps For Sampling-Based Motion Planning

Asymptotically optimal sampling-based planners require an intelligent exploration strategy to accelerate convergence. After an initial solution is found, a necessary condition for improvement is to generate new samples in the so-called "Informed Set". However, Informed Sampling can be ineffective in focusing search if the chosen heuristic fails to provide a good estimate of the solution cost. This work proposes an algorithm to sample the "Relevant Region" instead, which is a subset of the Informed Set. The Relevant Region utilizes cost-to-come information from the planner’s tree structure, reduces dependence on the heuristic, and further focuses the search. Benchmarking tests in uniform and general cost-space settings demonstrate the efficacy of Relevant Region sampling.
Bronchopulmonary dysplasia—A historical perspective

Bronchopulmonary dysplasia (BPD) was first described by Northway et al in 1967. This article describes the evolution of our understanding of the pathophysiology of BPD and the approaches to treatments of this illness developed over the past fifty years. These interventions had their roots in the understanding of the principles of the surface tension present at air-liquid interfaces, which were developed over 150 years before BPD's initial description. Improving outcomes in neonatal care have led to greater survival of preterm and very preterm infants, and to an evolution of the pathogenesis and pathology of BPD, from an illness caused primarily by barotrauma and oxygen toxicity to one of interruption of lung development. While the incidence of BPD has remained about the same in recent decades, this is because survival of infants born at lower gestational ages is increasing. Understanding of molecular, genetic and physiologic mechanisms has led to newer treatments that have mitigated some of the harmful effects of prolonged mechanical ventilation. Recognition of BPD as a chronic multi-system disease has resulted in further improvements in care after discharge from neonatal intensive care. Since many of the origins of chronic obstructive lung disease in adults are based in childhood respiratory illnesses, improving outcomes of BPD in infancy and childhood will undoubtedly lead to improved respiratory outcomes in the adults that these children will become.
Non-perturbative geometries for planar $\mathcal{N}=4$ SYM amplitudes

There is a remarkable well-known connection between the G(4, n) cluster algebra and n-particle amplitudes in $$ \mathcal{N} $$ = 4 SYM theory. For n ≥ 8 two long-standing open questions have been to find a mathematically natural way to identify a finite list of amplitude symbol letters from among the infinitely many cluster variables, and to find an explanation for certain algebraic functions, such as the square roots of four-mass-box type, that are expected to appear in symbols but are not cluster variables. In this letter we use the notion of “stringy canonical forms” to construct polytopal realizations of certain compactifications of (the positive part of) the configuration space Confn(ℙk−1) ≅ G(k, n)/T that are manifestly finite for all k and n. Some facets of these polytopes are naturally associated to cluster variables, while others are naturally associated to algebraic functions constructed from Lusztig’s canonical basis. For (k, n) = (4, 8) the latter include precisely the expected square roots, revealing them to be related to certain “overpositive” functions of the kinematical invariants.
Macrobdella decora: Old World Leech Gut Microbial Community Structure Conserved in a New World Leech

ABSTRACT Leeches are found in terrestrial, aquatic, and marine habitats on all continents. Sanguivorous leeches have been used in medicine for millennia. Modern scientific uses include studies of neurons, anticoagulants, and gut microbial symbioses. Hirudo verbana, the European medicinal leech, maintains a gut community dominated by two bacterial symbionts, Aeromonas veronii and Mucinivorans hirudinis, which sometimes account for as much as 97% of the total crop microbiota. The highly simplified gut anatomy and microbiome of H. verbana make it an excellent model organism for studying gut microbial dynamics. The North American medicinal leech, Macrobdella decora, is a hirudinid leech native to Canada and the northern United States. In this study, we show that M. decora symbiont communities are very similar to those in H. verbana. We performed an extensive study using field-caught M. decora and purchased H. verbana from two suppliers. Deep sequencing of the V4 region of the 16S rRNA gene allowed us to determine that the core microbiome of M. decora consists of Bacteroides, Aeromonas, Proteocatella, and Butyricicoccus. The analysis revealed that the compositions of the gut microbiomes of the two leech species were significantly different at all taxonomic levels. The R2 value was highest at the genus and amplicon sequence variant (ASV) levels and much lower at the phylum, class, and order levels. The gut and bladder microbial communities were distinct. We propose that M. decora is an alternative to H. verbana for studies of wild-caught animals and provide evidence for the conservation of digestive-tract and bladder symbionts in annelid models. IMPORTANCE Building evidence implicates the gut microbiome in critical animal functions such as regulating digestion, nutrition, immune regulation, and development. Simplified, phylogenetically diverse models for hypothesis testing are necessary because of the difficulty of assigning causative relationships in complex gut microbiomes. Previous research used Hirudo verbana as a tractable animal model of digestive-tract symbioses. Our data show that Macrobdella decora may work just as well without the drawback of being an endangered organism and with the added advantage of easy access to field-caught specimens. The similarity of the microbial community structures of species from two different continents reveals the highly conserved nature of the microbial symbionts in sanguivorous leeches.
Reconstructing Maps from Text

Abstract Previous research has demonstrated that Distributional Semantic Models (DSMs) are capable of reconstructing maps from news corpora (Louwerse & Zwaan, 2009) and novels (Louwerse & Benesh, 2012). The capacity for reproducing maps is surprising since DSMs notoriously lack perceptual grounding. In this paper we investigate the statistical sources required in language to infer maps, and the resulting constraints placed on mechanisms of semantic representation. Study 1 brings word co-occurrence under experimental control to demonstrate that standard DSMs cannot reproduce maps when word co-occurrence is uniform. Specifically, standard DSMs require that direct co-occurrences between city names in a corpus mirror the proximity between the city locations in the map in order to successfully reconstruct the spatial map. Study 2 presents an instance-based DSM that is capable of reconstructing maps independent of the frequency of co-occurrence of city names.
A Benchmark Study of the Contemporary Toxicity Detectors on Software Engineering Interactions

Automated filtering of toxic conversations may help an Open-source software (OSS) community to maintain healthy interactions among the project participants. Although, several general purpose tools exist to identify toxic contents, those may incorrectly flag some words commonly used in the Software Engineering (SE) context as toxic (e.g., ‘junk’, ‘kill’, and ‘dump’) and vice versa. To encounter this challenge, an SE specific tool has been proposed by the CMU Strudel Lab (referred as the ‘STRUDEL’ hereinafter) by combining the output of the Perspective API with the output from a customized version of the Stanford's Politeness detector tool. However, since STRUDEL's evaluation was very limited with only 654 SE text, its practical applicability is unclear. Therefore, this study aims to empirically evaluate the Strudel tool as well as four state-of-the-art general purpose toxicity detectors on a large scale SE dataset. On this goal, we empirically developed a rubric to manually label toxic SE interactions. Using this rubric, we manually labeled a dataset of 6,533 code review comments and 4,140 Gitter messages. The results of our analyses suggest significant degradation of all tools' performances on our datasets. Those degradations were significantly higher on our dataset of formal SE communication such as code review than on our dataset of informal communication such as Gitter messages. Two of the models from our study showed significant performance improvements during 10-fold cross validations after we retrained those on our SE datasets. Based on our manual investigations of the incorrectly classified text, we have identified several recommendations for developing an SE specific toxicity detector.
PAIRS AutoGeo: an Automated Machine Learning Framework for Massive Geospatial Data

An automated machine learning framework for geospatial data named PAIRS AutoGeo is introduced on IBM PAIRS Geoscope big data and analytics platform. The frame-work simplifies the development of industrial machine learning solutions leveraging geospatial data to the extent that the user inputs are minimized to merely a text file containing labeled GPS coordinates. PAIRS AutoGeo automatically gathers required data at the location coordinates, assembles the training data, performs quality check, and trains multiple machine learning models for subsequent deployment. The framework is validated using a realistic industrial use case of tree species classification. Open-source tree species data are used as the input to train a random forest classifier and a modified ResNet model for 10-way tree species classification based on aerial imagery, which leads to an accuracy of 59.8% and 81.4%, respectively. This use case exemplifies how PAIRS AutoGeo enables users to leverage machine learning without extensive geospatial expertise.
Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant

We investigate the use of approximate Bayesian neural networks (BNNs) in modeling hundreds of time-delay gravitational lenses for Hubble constant ($H_0$) determination. Our BNN was trained on synthetic HST-quality images of strongly lensed active galactic nuclei (AGN) with lens galaxy light included. The BNN can accurately characterize the posterior PDFs of model parameters governing the elliptical power-law mass profile in an external shear field. We then propagate the BNN-inferred posterior PDFs into ensemble $H_0$ inference, using simulated time delay measurements from a plausible dedicated monitoring campaign. Assuming well-measured time delays and a reasonable set of priors on the environment of the lens, we achieve a median precision of $9.3$\% per lens in the inferred $H_0$. A simple combination of 200 test-set lenses results in a precision of 0.5 $\textrm{km s}^{-1} \textrm{ Mpc}^{-1}$ ($0.7\%$), with no detectable bias in this $H_0$ recovery test. The computation time for the entire pipeline -- including the training set generation, BNN training, and $H_0$ inference -- translates to 9 minutes per lens on average for 200 lenses and converges to 6 minutes per lens as the sample size is increased. Being fully automated and efficient, our pipeline is a promising tool for exploring ensemble-level systematics in lens modeling for $H_0$ inference.
A note on the squeezing function

The squeezing problem on $\mathbb{C}$ can be stated as follows. Suppose that $\Omega$ is a multiply connected domain in the unit disk $\mathbb{D}$ containing the origin $z=0$. How far can the boundary of $\Omega$ be pushed from the origin by an injective holomorphic function $f:\Omega\to \mathbb{D}$ keeping the origin fixed? In this note, we discuss recent results on this problem obtained by Ng, Tang and Tsai (Math. Anal. 2020) and by Gumenyuk and Roth (arXiv:2011.13734, 2020) and also prove few new results using a method suggested in one of our previous papers (Zapiski Nauchn. Sem. POMI 1993).
Are there good mistakes? A theoretical analysis of CEGIS

Author(s): Jha, S; Seshia, SA | Editor(s): Chatterjee, Krishnendu; Ehlers, Rudiger; Jha, Susmit | Abstract: Counterexample-guided inductive synthesis (CEGIS) is used to synthesize programs from a candidate space of programs. The technique is guaranteed to terminate and synthesize the correct program if the space of candidate programs is finite. But the technique may or may not terminate with the correct program if the candidate space of programs is infinite. In this paper, we perform a theoretical analysis of counterexample-guided inductive synthesis technique. We investigate whether the set of candidate spaces for which the correct program can be synthesized using CEGIS depends on the counterexamples used in inductive synthesis, that is, whether there are good mistakes which would increase the synthesis power. We investigate whether the use of minimal counterexamples instead of arbitrary counterexamples expands the set of candidate spaces of programs for which inductive synthesis can successfully synthesize a correct program. We consider two kinds of counterexamples: minimal counterexamples and history bounded counterexamples. The history bounded counterexample used in any iteration of CEGIS is bounded by the examples used in previous iterations of inductive synthesis. We examine the relative change in power of inductive synthesis in both cases. We show that the synthesis technique using minimal counterexamples MinCEGIS has the same synthesis power as CEGIS but the synthesis technique using history bounded counterexamples HCEGIS has different power than that of CEGIS, but none dominates the other.
Periodicity and integrability for the cube recurrence

Zamolodchikov periodicity is a property of T- and Y-systems, arising in the thermodynamic Bethe ansatz. Zamolodchikov integrability was recently considered as its affine analog in our joint work with P. Pylyavskyy. Here we prove periodicity and integrability for similar discrete dynamical systems based on the cube recurrence, also known as the discrete BKP equation. The periodicity part was conjectured by Henriques in 2007.
Phase Noise in Modular Millimeter Wave Massive MIMO

This paper investigates the effect of oscillator phase noise on a multiuser millimeter wave (mmWave) massive MIMO uplink as we scale up the number of base station antennas, fixing the load factor, defined as the ratio of the number of simultaneous users to the number of base station antennas. We consider a modular approach in which the base station employs an array of subarrays, or “tiles.” Each tile supports a fixed number of antennas, and can therefore be implemented using a separate radio frequency integrated circuit (RFIC), with synchronization across tiles accomplished by employing a phased locked loop in each tile to synthesize an on-chip oscillator at the carrier frequency by locking on to a common lower frequency reference clock. Assuming linear minimum mean squared error (LMMSE) multiuser detection, we provide an analytical framework that can be used to specify the required power spectral density (PSD) mask for phase noise for a target system performance. Our analysis for the phase noise at the output of the LMMSE receiver indicates two distinct effects: self-noise for each user which is inversely proportional to the number of tiles, and cross-talk between users which is insensitive to the number of tiles, and is proportional to the load factor. These analytical predictions, verified by simulations for a 140 GHz system targeting a per-user data rate of 10 Gbps, show that tiling is a robust approach for scaling. Numerical results for our proposed design approach yield relatively relaxed specifications for phase noise PSD masks.
The Effects of Campus Shooting on School Finance and Student Composition

Between 1999 and 2018, 210 shootings have occurred on public school campuses in the United States. Campus shootings call for increased investments in security and student support, which may crowd out instructional resources. Exposure to shootings may also cause students, especially those from socioeconomically advantaged backgrounds, to move away from those schools and neighborhoods, leading to declines in enrollment. Both changes in the budget allocation and the student composition could exert a negative impact on achievement. This study first examines the effects of public school shootings on school districts’ revenue, expenditure, debt, and staffing using a long panel of district-year data. Results from event study and difference-in-differences analyses indicate that shootings increase per-pupil spending by about $248, which is funded primarily through increased federal transfers. Most spending increases occur in noninstructional functions, such as pupil support services, and capital projects, but they do not crowd out instructional spending. Then using school-level data, we show that shootings are followed by a decline in enrollment, driven almost exclusively by the response of students who do not receive free- or reduced-price lunch. Private schools in the area also experience a drop in enrollment. In sum, despite the increased intergovernmental transfers, campus shootings reduce the desirability of the community and lead to the exit of relatively well-off families.
SAVI Objects: Sharing and Virtuality Incorporated

Direct sharing and storing of memory objects allows high-performance and low-overhead collaboration between parallel processes or application workflows with loosely coupled programs. However, sharing of objects is hindered by the inability to use subtype polymorphism which is common in object-oriented programming languages. That is because implementations of subtype polymorphism in modern compilers rely on using virtual tables stored at process-specific locations, which makes objects unusable in processes other than the creating process. In this paper, we present SAVI Objects, objects with Sharing and Virtuality Incorporated. SAVI Objects support subtype polymorphism but can still be shared across processes and stored in persistent data structures. We propose two different techniques to implement SAVI Objects and evaluate the tradeoffs between them. The first technique is virtual table duplication which adheres to the virtual-table-based implementation of subtype polymorphism, but duplicates virtual tables for shared objects to fixed memory addresses associated with each shared memory region. The second technique is hashing-based dynamic dispatch which re-implements subtype polymorphism using hashing-based look-ups to a global virtual table. Our results show that SAVI Objects enable direct sharing and storing of memory objects that use subtype polymorphism by adding modest overhead costs to object construction and dynamic dispatch time. SAVI Objects thus enable faster inter-process communication, improving the overall performance of production applications that share polymorphic objects.
Footprints to Singularity: A global population model explains late 20th century slow-down and predicts peak within ten years

Projections of future global human population are traditionally made using birth/death trend extrapolations, but these methods ignore limits. Expressing humanity as a K-selected species whose numbers are limited by the global carrying capacity produces a different outlook. Population data for the second millennium up to the year 1970 was fit to a hyper-exponential growth equation, where the rate constant for growth itself grows exponentially due to growth of life-saving technology. The discrepancies between the projected growth and the actual population data since 1970 are accounted for by a decrease in the global carrying capacity due to ecosystem degradation. A system dynamics model that best fits recent population numbers suggests that the global biocapacity may already have been reduced to one-half of its historical value and global carrying capacity may be at its 1965 level and falling. Simulations suggest that population may soon peak or may have already peaked. Population projections depend strongly on the unknown fragility or robustness of the Earth’s essential ecosystem services that affect agricultural production. Numbers for the 2020 global census were not available for this study.
The 2-Width of Embedded 3-Manifolds

We discuss a possible definition for “k-width” of a closed d-manifold $$M^d$$ , and on embedding $$M^d \overset{e}{\hookrightarrow } \mathbb {R}^n$$ , $$n > d \ge k$$ , generalizing the classical notion of width of a knot. We show that for every 3-manifold 2-width $$(M^3) \le 2$$ but that there are embeddings $$e_i: T^3 \hookrightarrow \mathbb {R}^4$$ with 2-width $$(e_i) \rightarrow \infty $$ . We explain how the divergence of 2-width of embeddings offers a tool to which might prove the Goeritz groups $$G_g$$ infinitely generated for $$g \ge 4$$ . Finally we construct a homomorphism $$\theta _g: G_g \rightarrow \mathrm {MCG}(\underset{g}{\#} S^2 \times S^2)$$ , suggesting a potential application of 2-width to 4D mapping class groups.
Learning composite constitutive laws via coupling Abaqus and deep neural network

Abstract The commercial finite element (FE) code Abaqus is coupled with the deep neural network (DNN) model, namely Abaqus-DNN mechanics system, to learn the constitutive law of the fiber-reinforced composite. The proposed system enables data communication between Abaqus and DNN model, which leverages the versatile FE analysis ability of Abaqus and the powerful machine learning using DNN. Abaqus-DNN enables DNN to learn the constitutive law in a form-free manner. The learned result automatically satisfies the equilibrium and kinematics equations, which avoids inaccuracies associated with the presumed functions in the constitutive laws and guaranteed the learned constitutive law following the laws of physics. The Abaqus-DNN mechanics system was implemented to learn the full set of engineering constants of the constituents of a fiber-reinforced composite. Furthermore, the proposed system was applied to learn the progressive damage constitutive law of a fiber-reinforced composite laminate. The backward propagation equations of the neural network were modified to track the gradient of the loss function from Abaqus to DNN. The results show that Abaqus-DNN can accurately learn constitutive laws based on structural level data. This system provides a generalized approach for learning unknown physics inside a mechanics system by coupling neural network with commercial finite element codes.
On the difficulty of unbiased alpha divergence minimization

Several approximate inference algorithms have been proposed to minimize an alpha-divergence between an approximating distribution and a target distribution. Many of these algorithms introduce bias, the magnitude of which becomes problematic in high dimensions. Other algorithms are unbiased. These often seem to suffer from high variance, but little is rigorously known. In this work we study unbiased methods for alpha-divergence minimization through the Signal-to-Noise Ratio (SNR) of the gradient estimator. We study several representative scenarios where strong analytical results are possible, such as fully-factorized or Gaussian distributions. We find that when alpha is not zero, the SNR worsens exponentially in the dimensionality of the problem. This casts doubt on the practicality of these methods. We empirically confirm these theoretical results.
Nonlinear quasi-static poroelasticity

Abstract We analyze a quasi-static Biot system of poroelasticity for both compressible and incompressible constituents. The main feature of this model is a nonlinear coupling of pressure and dilation through the system's permeability tensor. Such a model has been analyzed previously from the point of view of constructing weak solutions through a fully discretized approach. In this treatment, we consider simplified Dirichlet type boundary conditions in both the elastic displacement and pressure variables and give a full treatment of weak solutions. Our construction of weak solutions for the nonlinear problem is based on a priori estimates, a requisite feature in addressing the nonlinearity. We utilize a spatial semi-discretization and employ a multi-valued fixed point argument for a clear construction of weak solutions. We also provide regularity criteria for uniqueness of solutions.
A Representation Learning Perspective on the Importance of Train-Validation Splitting in Meta-Learning

An effective approach in meta-learning is to utilize multiple "train tasks" to learn a good initialization for model parameters that can help solve unseen "test tasks" with very few samples by fine-tuning from this initialization. Although successful in practice, theoretical understanding of such methods is limited. This work studies an important aspect of these methods: splitting the data from each task into train (support) and validation (query) sets during meta-training. Inspired by recent work (Raghu et al., 2020), we view such meta-learning methods through the lens of representation learning and argue that the train-validation split encourages the learned representation to be low-rank without compromising on expressivity, as opposed to the non-splitting variant that encourages high-rank representations. Since sample efficiency benefits from low-rankness, the splitting strategy will require very few samples to solve unseen test tasks. We present theoretical results that formalize this idea for linear representation learning on a subspace meta-learning instance, and experimentally verify this practical benefit of splitting in simulations and on standard meta-learning benchmarks.