cssi/SciSciGPT-SciSciNet huggingface dataset:
Liberalism and Freedom from the Promise Theory of Contract

If law and economics scholars represent the'utilitarian wing' of contracts scholarship,1 it is not entirely clear who represents the liberal wing. Liberalism is somewhat in vogue in contract scholarship now, with a wide variety of contract theories claiming to be in some sense liberal. One of the better known liberal theories of contract is the promise theory, most famously articulated by Charles Fried.2 Promise theorists seek to justify and/or explain contract theory by appeal to the moral obligation to keep one's promises. In a recent book Dori Kimel takes a close look at the practice of promising, and compares and contrasts it to the practice of contracting.3 By exploring the distinct benefits of each practice he looks to work, as his subtitle announces, towards his own'liberal theory of contract. Kimefs well written book can be briefly summarised as follows. Most of the book is an inquiry into the relationship between contract law and the practice of promising. Kimel concludes, roughly, that while both facilitate a form of 'reliance, co-operation, or co-ordination between people,' promise has the added benefit of enhancing personal relationships. It relies on trust, and the performance of promises openly signifies a recognition of and respect for personal obligations even absent legal obligations. Contract law, on the other hand, allows people to rely on the word of others even absent a personal relationship with them. In so doing it enhances 'personal detachment' by allowing parties to remain at arm's length. Thus, argues Kimel, when Fried practically identified contracting with promising he failed to recognise important differences between the two practices. Kimel then draws on these differences to comment on the relationship of liberalism to contract. As he points out, liberalism is often identified with a certain 'rigid' conception of freedom of contract, in which parties are to be free to order their affairs as they wish, and government intrusion is to be minimal (i.e. contracts are to be enforced according to the parties' wishes as expressed at the time of contracting). The justification for the rigid conception is normally some more or less thoroughly articulated reference to individual autonomy.
Will the Federal Insurance Office Improve Insurance Regulation

Prior to the financial crisis, insurance was the only financial service that did not have a federal regulator but relied almost exclusively on state insurance regulators. The Dodd-Frank Wall Street Reform and Consumer Protection Act (Dodd-Frank Act) began a process to address this lack of federal oversight by creating the Federal Insurance Office (FIO) within the U.S. Treasury Department. Before the crisis, state regulation of insurance was sharply criticized for its lack of uniformity, its inefficiency, and the impediments that it posed for developing international insurance norms. In the wake of the financial crisis, questions have also been raised about whether state insurance regulation was equipped to deal with the potential systemic risks posed by the insurance firms, like American Insurance Group, Inc. The Dodd-Frank Act contains provisions that begin to tackle each of these issues, primarily through the creation of FIO. This Article will look at the creation of FIO and the role that FIO is playing to address the systemic risks posed by insurance and insurance-like products and firms and the development of insurance norms. This Article will also examine the arguments raised by many within the insurance industry that greater federal oversight of insurance is unnecessary because the state regulation already provided adequate solvency protections, insurance companies do not pose the types of systemic risks posed by banks and investment firms, and market discipline is stronger in the insurance industry than in the banking industry.
Lost in Moral Space: On the Infringing/Violating Distinction and its Place in the Theory of Rights

The infringing/violating distinction, first drawn by Judith Jarvis Thomson, is central to much contemporary rights theory. According to Thomson, conduct that is in some sense opposed to a right infringes it, while conduct that is also wrong violates the right. This distinction finds a home what I call, borrowing Robert Nozick's parlance, a "moral space" conception of rights, for the infringing/violating distinction presupposes that, as Nozick puts it, "a line (or hyper-plane) circumscribes an area in moral space around an individual." In this paper, I argue against the moral space conception of rights, and more specifically, against incorporating the infringing/violating distinction into a theory of rights. There are other compelling ways to think about rights and it is my goal to stimulate their exploration.
Exchange rate pegs and foreign exchange exposure in East and South East Asia

Abstract This paper shows that many Asia-Pacific firms are significantly exposed to foreign exchange risk. Their exposure appears to be much more widespread than is typical for the large, western industrialized economies. The paper also shows that exchange rate pegs appear to do little to alleviate this widespread exposure against currencies other than the peg. The firms studied here are most exposed to fluctuations in the US dollar; the yen and the euro are important in a few countries. The extent of their exchange rate exposure has varied but not diminished over the last decade. The most widespread exchange rate sensitivity (not just the most exchange rate fluctuation) occurred during the Asian crisis period; this is evident even after accounting for the local macroeconomic conditions that affect aggregate local returns.
Dynamic crystal plasticity modeling of single crystal tantalum and validation using Taylor cylinder impact tests

Abstract In this work, we extend a dislocation-density based constitutive theory for the dynamic thermomechanical behavior of crystals to body centered cubic (BCC) tantalum. Strain rate and temperature dependence of the slip resistance in the model is incorporated via an expression for the state-dependent instantaneous saturation dislocation density and a dynamic recovery fraction that affects the rate of generation of immobile dislocation density. Crystallographic slip along 111 on the {110} or {112} planes as well as their combination are examined. The Schmid stress serves as the driving force for dislocation motion and the twinning/anti-twinning sense of slip on {112} planes is accounted for by introduction of Peierls stress that depends upon this sense. The model parameters are calibrated using a two-stage Bayesian approach against experimentally generated uniaxial stress-strain compression curves at quasi-static and high strain rates over a range of temperatures. Velocity-time histories from single crystal flyer plate impact experiments were also used to parameterize this model. The calibrated model was applied to simulate previous Taylor cylinder impact experiments. Our results show that the model must include slip on {110} and {112} planes and also account for the twinning versus anti-twinning sense of slip on {112} planes, to appropriately represent the orientation dependence of the flow stress and match the deformed geometry of the single crystal Taylor cylinders.
Muscle metabolic energy costs while modifying propulsive force generation during walking.

We pose that an age-related increase in the metabolic cost of walking arises in part from a redistribution of joint power where muscles spanning the hip compensate for insufficient ankle push-off a...
Normally hyperbolic trapping on asymptotically stationary spacetimes

We prove microlocal estimates at the trapped set of asymptotically Kerr spacetimes: these are spacetimes whose metrics decay inverse polynomially in time to a stationary subextremal Kerr metric. This combines two independent results. The first one is purely dynamical: we show that the stable and unstable manifolds of a decaying perturbation of a time-translation-invariant dynamical system with normally hyperbolic trapping are smooth and decay to their stationary counterparts. The second, independent, result provides microlocal estimates for operators whose null-bicharacteristic flow has a normally hyperbolic invariant manifold, under suitable non-degeneracy conditions on the stable and unstable manifolds; this includes operators on closed manifolds, as well as operators on spacetimes for which the invariant manifold lies at future infinity.
Cyclotomic expansion of generalized Jones polynomials

In (Compos. Math. 152(7): 1333–1384, 2016), Berest and Samuelson proposed a conjecture that the Kauffman bracket skein module of any knot in $$S^3$$ carries a natural action of a rank 1 double-affine Hecke algebra $$SH_{q,t_1, t_2}$$ depending on 3 parameters $$q, t_1, t_2$$ . As a consequence, for a knot K satisfying this conjecture, we defined a three-variable polynomial invariant $$J^K_n(q,t_1,t_2)$$ generalizing the classical coloured Jones polynomials $$J^K_n(q)$$ . In this paper, we give explicit formulas and provide a quantum group interpretation for the polynomials $$J^K_n(q,t_1,t_2)$$ . Our formulas generalize the so-called cyclotomic expansion of the classical Jones polynomials constructed by Habiro (Invent. Math. 171(1): 1–81, 2008) : as in the classical case, they imply the integrality of $$J^K_n(q,t_1,t_2)$$ and, in fact, make sense for an arbitrary knot K independent of whether or not it satisfies the conjecture of Berest and Samuelson (Compos. Math. 152(7): 1333–1384, 2016). When one of the Hecke deformation parameters is set to be 1, we show that the coefficients of the (generalized) cyclotomic expansion of $$J^K_n(q,t_1)$$ are expressed in terms of Macdonald orthogonal polynomials.
SU(3) breaking and the pseudo-scalar spectrum in multi-taste QCD

Using the Sigma model to explore the lowest order pseudo-scalar spectrum with SU(3) breaking, this talk considers an additional exact “taste” symmetry to mimic species doubling. Rooting replicas of a valid approach such as Wilson fermions reproduces the desired physical spectrum. In contrast, extra symmetries of the rooted staggered approach leave spurious states and a flavor dependent taste multiplicity.
Toward a Better Understanding and Evaluation of Tree Structures on Flash SSDs.

Solid-state drives (SSDs) are extensively used to deploy persistent data stores, as they provide low latency random access, high write throughput, high data density, and low cost. Tree-based data structures are widely used to build persistent data stores, and indeed they lie at the backbone of many of the data management systems used in production and research today. In this paper, we show that benchmarking a persistent tree-based data structure on an SSD is a complex process, which may easily incur subtle pitfalls that can lead to an inaccurate performance assessment. At a high-level, these pitfalls stem from the interaction of complex software running on complex hardware. On one hand, tree structures implement internal operations that have nontrivial effects on performance. On the other hand, SSDs employ firmware logic to deal with the idiosyncrasies of the underlying flash memory, which are well known to lead to complex performance dynamics. We identify seven benchmarking pitfalls using RocksDB and WiredTiger, two widespread implementations of an LSM-Tree and a B+Tree, respectively. We show that such pitfalls can lead to incorrect measurements of key performance indicators, hinder the reproducibility and the representativeness of the results, and lead to suboptimal deployments in production environments. We also provide guidelines on how to avoid these pitfalls to obtain more reliable performance measurements, and to perform more thorough and fair comparison among different design points.
On Interpretability of Artificial Neural Networks: A Survey

Deep learning as performed by artificial deep neural networks (DNNs) has achieved great successes recently in many important areas that deal with text, images, videos, graphs, and so on. However, the black-box nature of DNNs has become one of the primary obstacles for their wide adoption in mission-critical applications such as medical diagnosis and therapy. Because of the huge potentials of deep learning, the interpretability of DNNs has recently attracted much research attention. In this article, we propose a simple but comprehensive taxonomy for interpretability, systematically review recent studies on interpretability of neural networks, describe applications of interpretability in medicine, and discuss future research directions, such as in relation to fuzzy logic and brain science.
Do Good Citizens Need Good Laws? Economics and the Expressive Function

We explore how adding prosocial preferences to the canonical precaution model of accidents changes either the efficient damages rule or the harm from accidents. For a utilitarian lawmaker, making the potential injurer sympathetic to the victim of harm has no effect on either outcome. On the other hand, making injurers averse to harming others reduces the harm from accidents but has no effect on efficient damages. For an atomistic lawmaker — one who excludes prosocial preferences from social welfare — cultivating a taste for either harm aversion or perfect sympathy can reduce efficient damages, though neither has any effect on the amount of harm from accidents. On the other hand, causing people to act as if they are averse to harm creation, such as out of habit or moral obligation, reduces both the efficient amount of damages and total harm. In general, encouraging either a distaste for, or moral commitment against, harm creation is useful while inculcating sympathy for victims of harm is not.
1-Wasserstein Distance on the Standard Simplex

Wasserstein distances provide a metric on a space of probability measures. We consider the space $\Omega$ of all probability measures on the finite set $\chi = \{1, \dots ,n\}$ where $n$ is a positive integer. 1-Wasserstein distance, $W_1(\mu,\nu)$ is a function from $\Omega \times \Omega$ to $[0,\infty)$. This paper derives closed form expressions for the First and Second moment of $W_1$ on $\Omega \times \Omega$ assuming a uniform distribution on $\Omega \times \Omega$.
2-Group global symmetries and anomalies in six-dimensional quantum field theories

We examine six-dimensional quantum field theories through the lens of higher-form global symmetries. Every Yang-Mills gauge theory in six dimensions, with field strength f (2), naturally gives rise to a continuous 1-form global symmetry associated with the 2-form instanton current J (2) ∼ ∗Tr (f (2) ∧ f (2)). We show that suitable mixed anomalies involving the gauge field f (2) and ordinary 0-form global symmetries, such as flavor or Poincare symmetries, lead to continuous 2-group global symmetries, which allow two flavor currents or two stress tensors to fuse into the 2-form current J (2). We discuss several features of 2-group symmetry in six dimensions, many of which parallel the four-dimensional case. The majority of six-dimensional supersymmetric conformal field theories (SCFTs) and little string theories have infrared phases with non-abelian gauge fields. We show that the mixed anomalies leading to 2-group symmetries can be present in little string theories, but that they are necessarily absent in SCFTs. This allows us to establish a previously conjectured algorithm for computing the ’t Hooft anomalies of most SCFTs from the spectrum of weakly-coupled massless particles on the tensor branch of these theories. We then apply this understanding to prove that the a-type Weyl anomaly of all SCFTs with a tensor branch must be positive, a > 0.
Seasonal Stability of SARS-CoV-2 in Biological Fluids

The transmission of SARS-CoV-2 occurs by close contact with infected persons through droplets, the inhalation of infectious aerosols, and the exposure to contaminated surfaces. Previously, we determined the virus stability on different types of surfaces under indoor and seasonal climatic conditions. SARS-CoV-2 survived the longest on surfaces under winter conditions, followed by spring/fall and summer conditions, suggesting the seasonal pattern of stability on surfaces. However, under natural conditions, the virus is secreted in various biological fluids from infected humans. In this respect, it remains unclear how long the virus survives in various types of biological fluids. This study explores SARS-CoV-2 stability in virus-spiked human biological fluids under different environmental conditions by determining the virus half-life. The virus was stable for up to 21 days in nasal mucus, sputum, saliva, tear, urine, blood, and semen; it remained infectious significantly longer under winter and spring/fall conditions than under summer conditions. In contrast, the virus was only stable up to 24 h in feces and breast milk. These findings demonstrate the potential risk of infectious biological fluids in SARS-CoV-2 transmission and have implications for its seasonality.
Adaptive and Dynamically Constrained Process Noise Estimation for Orbit Determination

This article introduces two new algorithms to accurately estimate the process noise covariance of a discrete-time Kalman filter online for robust orbit determination in the presence of dynamics model uncertainties. Common orbit determination process noise techniques, such as state noise compensation and dynamic model compensation, require offline tuning and a priori knowledge of the dynamical environment. Alternatively, the process noise covariance can be estimated through adaptive filtering. However, many adaptive filtering techniques are not applicable to onboard orbit determination due to computational cost or the assumption of a linear time-invariant system. Furthermore, existing adaptive filtering techniques do not constrain the process noise covariance according to the underlying continuous-time dynamical model, and there has been limited work on adaptive filtering with colored process noise. To overcome these limitations, a novel approach is developed which optimally fuses state noise compensation and dynamic model compensation with covariance matching adaptive filtering. This yields two adaptive and dynamically constrained process noise covariance estimation techniques. Unlike many adaptive filtering approaches, the new techniques accurately extrapolate over measurement outages and do not rely on ad hoc methods to ensure the process noise covariance is positive semidefinite. The benefits of the proposed algorithms are demonstrated through two case studies: an illustrative linear system and the autonomous navigation of two spacecraft orbiting an asteroid.
Whither AutoML? Understanding the Role of Automation in Machine Learning Workflows

Efforts to make machine learning more widely accessible have led to a rapid increase in Auto-ML tools that aim to automate the process of training and deploying machine learning. To understand how Auto-ML tools are used in practice today, we performed a qualitative study with participants ranging from novice hobbyists to industry researchers who use Auto-ML tools. We present insights into the benefits and deficiencies of existing tools, as well as the respective roles of the human and automation in ML workflows. Finally, we discuss design implications for the future of Auto-ML tool development. We argue that instead of full automation being the ultimate goal of Auto-ML, designers of these tools should focus on supporting a partnership between the user and the Auto-ML tool. This means that a range of Auto-ML tools will need to be developed to support varying user goals such as simplicity, reproducibility, and reliability.
Spectral bias and task-model alignment explain generalization in kernel regression and infinitely wide neural networks.

A theoretical understanding of generalization remains an open problem for many machine learning models, including deep networks where overparameterization leads to better performance, contradicting the conventional wisdom from classical statistics. Here, we investigate generalization error for kernel regression, which, besides being a popular machine learning method, also describes certain infinitely overparameterized neural networks. We use techniques from statistical mechanics to derive an analytical expression for generalization error applicable to any kernel and data distribution. We present applications of our theory to real and synthetic datasets, and for many kernels including those that arise from training deep networks in the infinite-width limit. We elucidate an inductive bias of kernel regression to explain data with simple functions, characterize whether a kernel is compatible with a learning task, and show that more data may impair generalization when noisy or not expressible by the kernel, leading to non-monotonic learning curves with possibly many peaks. Canatar et al. propose a predictive theory of generalization in kernel regression applicable to real data. This theory explains various generalization phenomena observed in wide neural networks, which admit a kernel limit and generalize well despite being overparameterized.
Towards an Understanding of Situated AR Visualization for Basketball Free-Throw Training

We present an observational study to compare co-located and situated real-time visualizations in basketball free-throw training. Our goal is to understand the advantages and concerns of applying immersive visualization to real-world skill-based sports training and to provide insights for designing AR sports training systems. We design both a situated 3D visualization on a head-mounted display and a 2D visualization on a co-located display to provide immediate visual feedback on a player’s shot performance. Using a within-subject study design with experienced basketball shooters, we characterize user goals, report on qualitative training experiences, and compare the quantitative training results. Our results show that real-time visual feedback helps athletes refine subsequent shots. Shooters in our study achieve greater angle consistency with our visual feedback. Furthermore, AR visualization promotes an increased focus on body form in athletes. Finally, we present suggestions for the design of future sports AR studies.
Completing the Picture of Uncertain Patent Scope

Late this Term, the Supreme Court tackled the problem of rampant uncertainty in patent scope in Nautilus, Inc. v. Biosig Instruments, Inc. For over a decade, uncertain patent scope has been blamed on the Federal Circuit’s failed claim construction rules. Yet, the Supreme Court chose to tackle uncertain claim scope by reviewing the Federal Circuit’s standard for invalidating a patent as indefinite, not its claim construction rules. Surprisingly, the materials submitted to the Supreme Court almost entirely ignored the claim construction problems that have consumed academics, judges, and practitioners for years. My essay fills this gap and provides the complete picture of the intertwined relationship of claim construction, indefiniteness, and uncertain patent scope. Due to the problems created by the Federal Circuit’s claim construction rules, the Supreme Court faced two bad choices in Nautilus: either affirm the Federal Circuit’s indefiniteness standard and endorse rampant uncertainty in claim scope or tighten the indefiniteness standard and imperil large swaths of patents. The result was an opinion that did not say very much. The only logical way to address uncertain claim scope is to first fix claim construction. With improved claim construction, the Federal Circuit’s indefiniteness standard surprisingly turns out to be optimal.
Time-varying auto-regressive models for count time-series

Count-valued time series data are routinely collected in many application areas. We are particularly motivated to study the count time series of daily new cases, arising from the COVID-19 spread. First, we propose a Bayesian framework to study the time-varying semiparametric AR(p) model for the count and then extend it to a more sophisticated time-varying INGARCH model. We calculate posterior contraction rates of the proposed Bayesian methods with respect to the average Hellinger metric. Our proposed structures of the models are amenable to Hamiltonian Monte Carlo (HMC) sampling for efficient computation. We substantiate our methods by simulations that show superiority compared to some of the existing methods that closely fit this setting. Finally, we analyze the daily time series data of newly confirmed cases in NYC to study the spread of COVID for three months.
Network effects in corporate financial policies

Abstract We present a spatial econometrics framework for estimating peer effects in capital structure. This approach exploits the heterogeneous and intransitive nature of peer networks to identify economically informative structural coefficients. In models of leverage levels, we detect significant peer-effect leverage coefficients that are on the order of 0.20, indicating a moderate but substantive level of strategic complementarity in capital structure decisions. We argue that prior estimates in the literature substantially overstate the magnitude of the underlying relation. Our evidence is robust to a wide variety of model modifications and supports the hypothesis that leverage is an important strategic choice variable.
People of the Book: Empire and Social Science in the Islamic Commonwealth Period:

Social science is often described as a product of nineteenth-century Europe and as a handmaiden to its imperial and colonial projects. However, centuries prior to the Western social science enterpr...
AutoAI-TS: AutoAI for Time Series Forecasting

A large number of time series forecasting models including traditional statistical models, machine learning models and more recently deep learning have been proposed in the literature. However, choosing the right model along with good parameter values that performs well on a given data is still challenging. Automatically providing a good set of models to users for a given dataset saves both time and effort from using trial-and-error approaches with a wide variety of available models along with parameter optimization. We present AutoAI for Time Series Forecasting (AutoAI-TS) that provides users with a zero configuration (zero-conf) system to efficiently train, optimize and choose best forecasting model among various classes of models for the given dataset. With its flexible zero-conf design, AutoAI-TS automatically performs all the data preparation, model creation, parameter optimization, training and model selection for users and provides a trained model that is ready to use. For given data, AutoAI-TS utilizes a wide variety of models including classical statistical models, Machine Learning (ML) models, statistical-ML hybrid models and deep learning models along with various transformations to create forecasting pipelines. It then evaluates and ranks pipelines using the proposed T-Daub mechanism to choose the best pipeline. The paper describe in detail all the technical aspects of AutoAI-TS along with extensive benchmarking on a variety of real world data sets for various use-cases. Benchmark results show that AutoAI-TS, with no manual configuration from the user, automatically trains and selects pipelines that on average outperform existing state-of-the-art time series forecasting toolkits.
Framework for Modeling and Optimization of On-Orbit Servicing Operations Under Demand Uncertainties

This paper develops a framework that models and optimizes the operations of complex on-orbit servicing infrastructures involving one or more servicers and orbital depots to provide multiple types o...
Greedy adversarial equilibrium: an efficient alternative to nonconvex-nonconcave min-max optimization

Min-max optimization of an objective function f: ℝd × ℝd → ℝ is an important model for robustness in an adversarial setting, with applications to many areas including optimization, economics, and deep learning. In many applications f may be nonconvex-nonconcave, and finding a global min-max point may be computationally intractable. There is a long line of work that seeks computationally tractable algorithms for alternatives to the min-max optimization model. However, many of the alternative models have solution points which are only guaranteed to exist under strong assumptions on f, such as convexity, monotonicity, or special properties of the starting point. We propose an optimization model, the e-greedy adversarial equilibrium, and show that it can serve as a computationally tractable alternative to the min-max optimization model. Roughly, we say that a point (x⋆, y⋆) is an e-greedy adversarial equilibrium if y⋆ is an e-approximate local maximum for f(x⋆,·), and x⋆ is an e-approximate local minimum for a “greedy approximation” to the function maxz f(x, z) which can be efficiently estimated using second-order optimization algorithms. We prove the existence of such a point for any smooth function which is bounded and has Lipschitz Hessian. To prove existence, we introduce an algorithm that converges from any starting point to an e-greedy adversarial equilibrium in a number of evaluations of the function f, the max-player’s gradient ∇y f(x,y), and its Hessian ∇y2 f(x,y), that is polynomial in the dimension d, 1/e, and the bounds on f and its Lipschitz constant.
Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data

Treatment effect estimation from observational data is a critical research topic across many domains. The foremost challenge in treatment effect estimation is how to capture hidden confounders. Recently, the growing availability of networked observational data offers a new opportunity to deal with the issue of hidden confounders. Unlike networked data in traditional graph learning tasks, such as node classification and link detection, the networked data under the causal inference problem has its particularity, i.e., imbalanced network structure. In this paper, we propose a Graph Infomax Adversarial Learning (GIAL) model for treatment effect estimation, which makes full use of the network structure to capture more information by recognizing the imbalance in network structure. We evaluate the performance of our GIAL model on two benchmark datasets, and the results demonstrate superiority over the state-of-the-art methods.
A Novel Real-Time Energy Management Strategy for Gird-Friendly Microgrid: Harnessing Internal Fluctuation Internally

Typically, a large portion of microgrid generating capacity is from variable renewable resources that are greatly impacted by the environment and can be intermittent as well as stochastic. This would result in uncertainty of microgrid net-load, and negatively affect the grid reliability. A two-phase real-time energy management strategy for networked microgrid is proposed in this paper to address microgrid internal fluctuation internally, which enables a microgrid to become grid-friendly. The proposed strategy is based on coordination between the real-time dispatch (RTD) phase and the real-time control (RTC) phase. In the RTD phase, model predictive control (MPC) is used to optimally dispatch microgrid resources in the current time interval while considering near future situations. The RTC phase addresses microgrid internal net-load fluctuation with fast-acting batteries, which aims to maintain a constant tie-line power flow between the main grid and the microgrid for the current dispatch interval. Numerical simulations conducted on ten different net-load scenarios can demonstrate the performance of the proposed two-phase energy management strategy that will enable a microgrid to operate as a controllable asset with static electricity consumption or production in an economic dispatch interval from the perspective of the bulk grid operator.
The Great Unequalizer: Initial Health Effects of COVID-19 in the United States

We measure inequities from the COVID-19 pandemic on mortality and hospitalizations in the United States during the early months of the outbreak. We discuss challenges in measuring health outcomes and health inequality, some of which are specific to COVID-19 and others that complicate attribution during most large health shocks. As in past epidemics, pre-existing biological and social vulnerabilities profoundly influenced the distribution of disease. In addition to the elderly, Hispanic, Black and Native American communities were disproportionately affected by the virus, particularly when assessed using the years of potential life lost metric. For example, Hispanic and Black Americans in 2020 saw 39.5 and 25 percent increases in excess mortality relative to trend, compared to a less than 15 percent increase for Whites;we find losses in potential years of life three to four times larger among Hispanic and Black compared to White Americans. Individual-level data from a commercially insured population show that otherwise similar Black and Hispanic enrollees were hospitalized due to COVID-19 at a higher rate than White enrollees. We provide a conceptual framework and initial empirical analysis which seek to shed light on contributors to pandemic-related health inequality, and suggest areas for future research.
MR Slice Profile Estimation by Learning to Match Internal Patch Distributions

To super-resolve the through-plane direction of a multi-slice 2D magnetic resonance (MR) image, its slice selection profile can be used as the degeneration model from high resolution (HR) to low resolution (LR) to create paired data when training a supervised algorithm. Existing super-resolution algorithms make assumptions about the slice selection profile since it is not readily known for a given image. In this work, we estimate a slice selection profile given a specific image by learning to match its internal patch distributions. Specifically, we assume that after applying the correct slice selection profile, the image patch distribution along HR in-plane directions should match the distribution along the LR through-plane direction. Therefore, we incorporate the estimation of a slice selection profile as part of learning a generator in a generative adversarial network (GAN). In this way, the slice selection profile can be learned without any external data. Our algorithm was tested using simulations from isotropic MR images, incorporated in a through-plane super-resolution algorithm to demonstrate its benefits, and also used as a tool to measure image resolution. Our code is at https://github.com/shuohan/espreso2.
Past, present, and future capabilities of the Transonic Dynamics Tunnel from an aeroelasticity perspective

The NASA Langley Transonic Dynamics Tunnel (TDT) has provided a unique capability for aeroelastic testing for forty years. The facility has a rich history of significant contributions to the design of many United States commercial transports, military aircraft, launch vehicles, and spacecraft. The facility has many features that contribute to its uniqueness for aeroelasticity testing, perhaps the most important feature being the use of a heavy gas test medium to achieve higher test densities. Higher test medium densities substantially improve model-building requirements and therefore simplify the fabrication process for building aeroelastically scaled wind tunnel models. Aeroelastic scaling for the heavy gas results in lower model structural frequencies. Lower model frequencies tend to a make aeroelastic testing safer. This paper will describe major developments in the testing capabilities at the TDT throughout its history, the current status of the facility, and planned additions and improvements to its capabilities in the near future.
Alignment completeness for relational hoare logics

Relational Hoare logics (RHL) provide rules for reasoning about relations between programs. Several RHLs include a rule we call sequential product that infers a relational correctness judgment from judgments of ordinary Hoare logic (HL). Other rules embody sensible patterns of reasoning and have been found useful in practice, but sequential product is relatively complete on its own (with HL). As a more satisfactory way to evaluate RHLs, a notion of alignment completeness is introduced, in terms of the inductive assertion method and product automata. Alignment completeness results are given to account for several different sets of rules. The notion may serve to guide the design of RHLs and relational verifiers for richer programming languages and alignment patterns.
Theory of Solutions for an Inextensible Cantilever

Recent equations of motion for the large deflections of a cantilevered elastic beam are analyzed. In the traditional theory of beam (and plate) large deflections, nonlinear restoring forces are due to the effect of stretching on bending; for an inextensible cantilever, the enforcement of arc-length preservation leads to quasilinear stiffness effects and inertial effects that are both nonlinear and nonlocal. For this model, smooth solutions are constructed via a spectral Galerkin approach. Additional compactness is needed to pass to the limit, and this is obtained through a complex procession of higher energy estimates. Uniqueness is obtained through a non-trivial decomposition of the nonlinearity. The confounding effects of nonlinear inertia are overcome via the addition of structural (Kelvin–Voigt) damping to the equations of motion. Local well-posedness of smooth solutions is shown first in the absence of nonlinear inertial effects, and then shown with these inertial effects present, taking into account structural damping. With damping in force, global-in-time, strong well-posedness result is obtained by achieving exponential decay for small data.
A Fine-Grained Spectral Perspective on Neural Networks

Are neural networks biased toward simple functions? Does depth always help learn more complex features? Is training the last layer of a network as good as training all layers? How to set the range for learning rate tuning? These questions seem unrelated at face value, but in this work we give all of them a common treatment from the spectral perspective. We will study the spectra of the *Conjugate Kernel, CK,* (also called the *Neural Network-Gaussian Process Kernel*), and the *Neural Tangent Kernel, NTK*. Roughly, the CK and the NTK tell us respectively "what a network looks like at initialization" and "what a network looks like during and after training." Their spectra then encode valuable information about the initial distribution and the training and generalization properties of neural networks. By analyzing the eigenvalues, we lend novel insights into the questions put forth at the beginning, and we verify these insights by extensive experiments of neural networks. We derive fast algorithms for computing the spectra of CK and NTK when the data is uniformly distributed over the boolean cube, and show this spectra is the same in high dimensions when data is drawn from isotropic Gaussian or uniformly over the sphere. Code replicating our results is available at this http URL.
On the supersymmetric XXX spin chains associated to $\mathfrak{gl}_{1|1}$

We study the $$\mathfrak {gl}_{1|1}$$ supersymmetric XXX spin chains. We give an explicit description of the algebra of Hamiltonians acting on any cyclic tensor products of polynomial evaluation $$\mathfrak {gl}_{1|1}$$ Yangian modules. It follows that there exists a bijection between common eigenvectors (up to proportionality) of the algebra of Hamiltonians and monic divisors of an explicit polynomial written in terms of the Drinfeld polynomials. In particular our result implies that each common eigenspace of the algebra of Hamiltonians has dimension one. We also give dimensions of the generalized eigenspaces. We show that when the tensor product is irreducible, then all eigenvectors can be constructed using Bethe ansatz. We express the transfer matrices associated to symmetrizers and anti-symmetrizers of vector representations in terms of the first transfer matrix and the center of the Yangian.
Interval Observers for Simultaneous State and Model Estimation of Partially Known Nonlinear Systems

We consider the problem of designing interval observers for partially unknown nonlinear systems with bounded noise signals that simultaneously estimate the system states and learn a model of the unknown dynamics. Leveraging affine abstraction methods and nonlinear decomposition functions, as well as a data-driven function over-approximation/abstraction approach to over-estimate the unknown dynamic model, our proposed observer recursively computes the maximal and minimal elements of the interval estimates that are proven to frame the true augmented states. Then, using observed output/measurement signals, the observer iteratively shrinks the intervals by eliminating estimates that are not compatible with the measurements. Moreover, given new interval estimates, the observer updates the over-approximation model of the unknown dynamics. Finally, we provide sufficient conditions for uniform boundedness of the sequence of interval estimate widths, i.e., for the stability of the designed observer.
Controllable finite ultra-narrow quality-factor peak in a perturbed Dirac-cone band structure of a photonic-crystal slab

We show that by using a perturbed photonic Dirac-cone, one can realize an ultra-narrow and finite Q-factor peak in the wavevector space, with both the peak value and the width separately tunable. We also discuss a lower bound in the minimal viable width given a peak Q-value while maintaining sufficient Q differentiation among modes. The strong angular and frequency Q-selection finds applications in optical devices where strong angle- and frequency-selections are needed.
Scalar conservation laws with white noise initial data

The statistical description of the scalar conservation law of the form $$\rho _t=H(\rho )_x$$ with $$H: {\mathbb {R}} \rightarrow {\mathbb {R}}$$ a smooth convex function has been an object of interest when the initial profile $$\rho (\cdot ,0)$$ is random. The special case when $$H(\rho )=\frac{\rho ^2}{2}$$ (Burgers equation) has in particular received extensive interest in the past and is now understood for various random initial conditions. We prove in this paper a conjecture on the profile of the solution at any time $$t>0$$ for a general class of Hamiltonians H and show that it is a stationary piecewise-smooth Feller process. Along the way, we study the excursion process of the two-sided linear Brownian motion W below any strictly convex function $$\phi $$ with superlinear growth and derive a generalized Chernoff distribution of the random variable $$\text {argmax}_{z \in {\mathbb {R}}} (W(z)-\phi (z))$$ . Finally, when $$\rho (\cdot ,0)$$ is a white noise derived from an abrupt Levy process, we show that the structure of shocks of the solution is a.s discrete at any fixed time $$t>0$$ under some mild assumptions on H.
Integration of Low-Fidelity MDO and CFD-Based Redesign of Low-Boom Supersonic Transports

A mixed-fidelity low-boom multidisciplinary optimization (MDO) problem is formulated for integration of low-fidelity MDO and computational fluid dynamics (CFD)-based low-boom inverse design optimiz...
Examining normal modes as fundamental heat carriers in amorphous solids: The case of amorphous silicon

Normal mode decomposition of atomic vibrations has been used to provide microscopic understanding of thermal transport in amorphous solids for decades. In normal mode methods, it is naturally assumed that atoms vibrate around their equilibrium positions, and that individual normal modes are the fundamental vibrational excitations transporting heat. With the abundance of predictions from normal mode methods and experimental measurements now available, we carefully analyze these calculations in amorphous silicon, a model amorphous solid. We find a number of discrepancies, suggesting that treating individual normal modes as fundamental heat carriers may not be accurate in amorphous solids. Furthermore, our classical and ab initio molecular dynamics simulations of amorphous silicon demonstrate a large degree of atomic diffusion, especially at high temperatures, leading to the conclusion that thermal transport in amorphous solids could be better described starting from the perspectives of liquid physics rather than from crystalline solids.
Lower semicontinuity of ADM mass under intrinsic flat convergence

A natural question in mathematical general relativity is how the ADM mass behaves as a functional on the space of asymptotically flat 3-manifolds of nonnegative scalar curvature. In previous results, lower semicontinuity has been established by the first-named author for pointed $$C^2$$ convergence, and more generally by both authors for pointed $$C^0$$ convergence (all in the Cheeger-Gromov sense). In this paper, we show this behavior persists for the much weaker notion of pointed Sormani–Wenger intrinsic flat ( $${\mathcal {F}}$$ ) volume convergence, under natural hypotheses. We consider smooth manifolds converging to asymptotically flat local integral current spaces (a new definition), using Huisken’s isoperimetric mass as a replacement for the ADM mass. Along the way we prove results of independent interest about convergence of subregions of $${\mathcal {F}}$$ -converging sequences of integral current spaces.
Sensor failure detection for jet engines using analytical redundancy

Analytical redundant sensor failure detection, isolation and accommodation techniques for gas turbine engines are surveyed. Both the theoretical technology base and demonstrated concepts are discussed. Also included is a discussion of current technology needs and ongoing Government sponsored programs to meet those needs.
Rephine.r: a pipeline for correcting gene calls and clusters to improve phage pangenomes and phylogenies.

Background A pangenome is the collection of all genes found in a set of related genomes. For microbes, these genomes are often different strains of the same species, and the pangenome offers a means to compare gene content variation with differences in phenotypes, ecology, and phylogenetic relatedness. Though most frequently applied to bacteria, there is growing interest in adapting pangenome analysis to bacteriophages. However, working with phage genomes presents new challenges. First, most phage families are under-sampled, and homologous genes in related viruses can be difficult to identify. Second, homing endonucleases and intron-like sequences may be present, resulting in fragmented gene calls. Each of these issues can reduce the accuracy of standard pangenome analysis tools. Methods We developed an R pipeline called Rephine.r that takes as input the gene clusters produced by an initial pangenomics workflow. Rephine.r then proceeds in two primary steps. First, it identifies three common causes of fragmented gene calls: (1) indels creating early stop codons and new start codons; (2) interruption by a selfish genetic element; and (3) splitting at the ends of the reported genome. Fragmented genes are then fused to create new sequence alignments. In tandem, Rephine.r searches for distant homologs separated into different gene families using Hidden Markov Models. Significant hits are used to merge families into larger clusters. A final round of fragment identification is then run, and results may be used to infer single-copy core genomes and phylogenetic trees. Results We applied Rephine.r to three well-studied phage groups: the Tevenvirinae (e.g., T4), the Studiervirinae (e.g., T7), and the Pbunaviruses (e.g., PB1). In each case, Rephine.r recovered additional members of the single-copy core genome and increased the overall bootstrap support of the phylogeny. The Rephine.r pipeline is provided through GitHub (https://www.github.com/coevoeco/Rephine.r) as a single script for automated analysis and with utility functions to assist in building single-copy core genomes and predicting the sources of fragmented genes.
On the secondary Upsilon invariant

In this paper we construct an infinite family of knots with vanishing Upsilon invariant $\Upsilon$, although their secondary Upsilon invariants $\Upsilon^2$ show that they are linearly independent in the smooth knot concordance group. We also prove a conjecture in a paper by Allen.