cssi/SciSciGPT-SciSciNet huggingface dataset:
A new class of actuator surface models for wind turbines

Actuator line model has been widely employed in wind turbine simulations. However, the standard actuator line model does not include a model for the turbine nacelle which can significantly impact turbine wake characteristics as shown in the literature. Another disadvantage of the standard actuator line model is that more geometrical features of turbine blades cannot be resolved on a finer mesh. To alleviate these disadvantages of the standard model, we develop a new class of actuator surface models for turbine blades and nacelle to take into account more geometrical details of turbine blades and include the effect of turbine nacelle. In the actuator surface model for blade, the aerodynamic forces calculated using the blade element method are distributed from the surface formed by the foil chords at different radial locations. In the actuator surface model for nacelle, the forces are distributed from the actual nacelle surface with the normal force component computed in the same way as in the direct forcing immersed boundary method and the tangential force component computed using a friction coefficient and a reference velocity of the incoming flow. The actuator surface model for nacelle is evaluated by simulating the flow over periodically placed nacelles. Both the actuator surface simulation and the wall-resolved large-eddy simulation are carried out. The comparison shows that the actuator surface model is able to give acceptable results especially at far wake locations on a very coarse mesh. It is noted that although this model is employed for the turbine nacelle in this work, it is also applicable to other bluff bodies. The capability of the actuator surface model in predicting turbine wakes is assessed by simulating the flow over the MEXICO (Model experiments in Controlled Conditions) turbine and a hydrokinetic turbine.
The local limit theorem on nilpotent Lie groups

A local limit theorem is proven on connected, simply connected nilpotent Lie groups, for a class of generating measures satisfying a moment condition and a condition on the characteristic function of the abelianization. The result extends an earlier local limit theorem of Alexopoulos which treated absolutely continuous measures with a continuous density of compact support, and also extends local limit theorems of Breuillard and Diaconis–Hough which treated general measures on the Heisenberg group.
Anatomy of torsion in the CM case

Let $$T_{\mathrm {CM}}(d)$$ denote the maximum size of a torsion subgroup of a CM elliptic curve over a degree d number field. We initiate a systematic study of the asymptotic behavior of $$T_{\mathrm {CM}}(d)$$ as an arithmetic function. Whereas a recent result of the last two authors computes the upper order of $$T_{\mathrm {CM}}(d)$$ , here we determine the lower order, the typical order and the average order of $$T_{\mathrm {CM}}(d)$$ as well as study the number of isomorphism classes of groups G of order $$T_{\mathrm {CM}}(d)$$ which arise as the torsion subgroup of a CM elliptic curve over a degree d number field. To establish these analytic results we need to extend some prior algebraic results. Especially, if $$E_{/F}$$ is a CM elliptic curve over a degree d number field, we show that d is divisible by a certain function of $$\# E(F)[{\text {tors}}]$$ , and we give a complete characterization of all degrees d such that every torsion subgroup of a CM elliptic curve defined over a degree d number field already occurs over $$\mathbb {Q}$$ .
Splines in geometry and topology

This survey paper describes the role of splines in geometry and topology, emphasizing both similarities and differences from the classical treatment of splines. The exposition is non-technical and contains many examples, with references to more thorough treatments of the subject. Many varieties X have a graph G X that describes their essential geometric features.The splines S ∞ 0 ( G X ) form the equivariant cohomology ring and encode key data about X.We describe geometric tools to analyze splines that differ from classical tools.We give an algebraic and combinatorial generalization called generalized splines.
Walking on egg shells: colorblind ideology and race talk in teacher education

This study examined the race talk of teacher education students in the context of colorblind ideology. Analysis revealed five integrated themes: (1) racial socialization; (2) feeling and dealing wi...
Global well-posedness and scattering for the radial, defocusing, cubic wave equation with almost sharp initial data

In this article, we prove that the cubic wave equation is globally well-posed and scattering for radial initial data lying in a weighted, slightly supercritical space. This space of functions is sl...
A sharp cusp count for complex hyperbolic surfaces and related results

We derive a sharp cusp count for finite volume complex hyperbolic surfaces which admit smooth toroidal compactifications. We use this result, and the techniques developed in Di Cerbo and Di Cerbo (see [5]), to study the geometry of cusped complex hyperbolic surfaces and their compactifications.
Language Modeling by Clustering with Word Embeddings for Text Readability Assessment

We present a clustering-based language model using word embeddings for text readability prediction. Presumably, an Euclidean semantic space hypothesis holds true for word embeddings whose training is done by observing word co-occurrences. We argue that clustering with word embeddings in the metric space should yield feature representations in a higher semantic space appropriate for text regression. Also, by representing features in terms of histograms, our approach can naturally address documents of varying lengths. An empirical evaluation using the Common Core Standards corpus reveals that the features formed on our clustering-based language model significantly improve the previously known results for the same corpus in readability prediction. We also evaluate the task of sentence matching based on semantic relatedness using the Wiki-SimpleWiki corpus and find that our features lead to superior matching performance.
Combined Hypothesis Testing on Graphs with Applications to Gene Set Enrichment Analysis

Motivated by gene set enrichment analysis, we investigate the problem of combined hypothesis testing on a graph. A general framework is introduced to make effective use of the structural informatio...
Fast Matrix Completion Without the Condition Number

We give the first algorithm for Matrix Completion that achieves running time and sample complexity that is polynomial in the rank of the unknown target matrix, linear in the dimension of the matrix, and logarithmic in the condition number of the matrix. To the best of our knowledge, all previous algorithms either incurred a quadratic dependence on the condition number of the unknown matrix or a quadratic dependence on the dimension of the matrix. Our algorithm is based on a novel extension of Alternating Minimization which we show has theoretical guarantees under standard assumptions even in the presence of noise.
Balanced reduction of nonlinear control systems in reproducing kernel Hilbert space

We introduce a novel data-driven order reduction method for nonlinear control systems, drawing on recent progress in machine learning and statistical dimensionality reduction. The method rests on the assumption that the nonlinear system behaves linearly when lifted into a high (or infinite) dimensional feature space where balanced truncation may be carried out implicitly. This leads to a nonlinear reduction map which can be combined with a representation of the system belonging to a reproducing kernel Hilbert space to give a closed, reduced order dynamical system which captures the essential input-output characteristics of the original model. Empirical simulations illustrating the approach are also provided.
Random zeros on complex manifolds: conditional expectations

We study the conditional distribution K N (zjp) of zeros of a Gaussian system of random polynomials (and more generally, holomorphic sections), given that the polynomials or sections vanish at a point p (or a xed nite set of points). The conditional distribution is analogous to the pair correlation function of zeros but we show that it has quite a dierent small distance behavior. In particular, the conditional distribution does not exhibit repulsion of zeros in dimension one. To prove this, we give universal scaling asymptotics for K N k (zjp) around p. The key tool is the conditional Szeg} o kernel and its scaling asymptotics.
Unifying task specification in reinforcement learning

Reinforcement learning tasks are typically specified as Markov decision processes. This formalism has been highly successful, though specifications often couple the dynamics of the environment and the learning objective. This lack of modularity can complicate generalization of the task specification, as well as obfuscate connections between different task settings, such as episodic and continuing. In this work, we introduce the RL task formalism, that provides a unification through simple constructs including a generalization to transition-based discounting. Through a series of examples, we demonstrate the generality and utility of this formalism. Finally, we extend standard learning constructs, including Bellman operators, and extend some seminal theoretical results, including approximation errors bounds. Overall, we provide a well-understood and sound formalism on which to build theoretical results and simplify algorithm use and development.
Anti-Power Prefixes of the Thue-Morse Word

Recently, Fici , Restivo , Silva, and Zamboni defined a $k$ -anti-power to be a word of the form $w_1w_2\cdots w_k$ , where $w_1,w_2,\ldots,w_k$ are distinct words of the same length. They defined $AP(x,k)$ to be the set of all positive integers $m$ such that the prefix of length $km$ of the word $x$ is a $k$ -anti-power. Let ${\bf t}$ denote the Thue -Morse word, and let $\mathcal F(k)=AP({\bf t},k)\cap(2\mathbb Z^+-1)$ . For $k\geq 3$ , $\gamma(k)=\min(\mathcal F(k))$ and $\Gamma(k)=\max((2\mathbb Z^+-1)\setminus\mathcal F(k))$ are well-defined odd positive integers. Fici et al . speculated that $\gamma(k)$ grows linearly in $k$ . We prove that this is indeed the case by showing that $1/2\leq\displaystyle{\liminf_{k\to\infty}}(\gamma(k)/k)\leq 9/10$ and $1\leq\displaystyle{\limsup_{k\to\infty}}(\gamma(k)/k)\leq 3/2$ . In addition, we prove that $\displaystyle{\liminf_{k\to\infty}}(\Gamma(k)/k)=3/2$ and $\displaystyle{\limsup_{k\to\infty}}(\Gamma(k)/k)=3$ .
Toward predicting tensile strength of pharmaceutical tablets by ultrasound measurement in continuous manufacturing

An ultrasound measurement system was employed as a non-destructive method to evaluate its reliability in predicting the tensile strength of tablets and investigate the benefits of incorporating it in a continuous line, manufacturing solid dosage forms. Tablets containing lactose, acetaminophen, and magnesium stearate were manufactured continuously and in batches. The effect of two processing parameters, compaction force and level of shear strain were examined. Young's modulus and tensile strength of tablets were obtained by ultrasound and diametrical mechanical testing, respectively. It was found that as the blend was exposed to increasing levels of shear strain, the speed of sound in the tablets decreased and the tablets became both softer and mechanically weaker. Moreover, the results indicate that two separate tablet material properties (e.g., relative density and Young's modulus) are necessary in order to predict tensile strength. A strategy for hardness prediction is proposed that uses the existing models for Young's modulus and tensile strength of porous materials. Ultrasound testing was found to be very sensitive in differentiating tablets with similar formulation but produced under different processing conditions (e.g., different level of shear strain), thus, providing a fast, and non-destructive method for hardness prediction that could be incorporated to a continuous manufacturing process.
A linear programming approach to dynamical equivalence, linear conjugacy, and the Deficiency One Theorem

The well-known Deficiency One Theorem gives structural conditions on a chemical reaction network under which, for any set of parameter values, the steady states of the corresponding mass action system may be easily characterized. It is also known, however, that mass action systems are not uniquely associated with reaction networks and that some representations may satisfy the Deficiency One Theorem while others may not. In this paper we present a mixed-integer linear programming framework capable of determining whether a given mass action system has a dynamically equivalent or linearly conjugate representation which has an underlying network satisfying the Deficiency One Theorem. This extends recent computational work determining linearly conjugate systems which are weakly reversible and have a deficiency of zero.
Completing any low-rank matrix, provably

Matrix completion, i.e., the exact and provable recovery of a low-rank matrix from a small subset of its elements, is currently only known to be possible if the matrix satisfies a restrictive structural constraint--known as incoherence--on its row and column spaces. In these cases, the subset of elements is assumed to be sampled uniformly at random. In this paper, we show that any rank-r n-by-n matrix can be exactly recovered from as few as O(nr log2 n) randomly chosen elements, provided this random choice is made according to a specific biased distribution suitably dependent on the coherence structure of the matrix: the probability of any element being sampled should be at least a constant times the sum of the leverage scores of the corresponding row and column. Moreover, we prove that this specific form of sampling is nearly necessary, in a natural precise sense; this implies that many other perhaps more intuitive sampling schemes fail. We further establish three ways to use the above result for the setting when leverage scores are not known a priori. (a) We describe a provably-correct sampling strategy for the case when only the column space is incoherent and no assumption or knowledge of the row space is required. (b) We propose a two-phase sampling procedure for general matrices that first samples to estimate leverage scores followed by sampling for exact recovery. These two approaches assume control over the sampling procedure. (c) By using our main theorem in a reverse direction, we provide an analysis showing the advantages of the (empirically successful) weighted nuclear/trace-norm minimization approach over the vanilla un-weighted formulation given non-uniformly distributed observed elements. This approach does not require controlled sampling or knowledge of the leverage scores.
Interior estimates for generalized Forchheimer flows of slightly compressible fluids

The generalized Forchheimer ows are studied for slightly compressible uids in porous media with time-dependent Dirichlet boundary data for the pressure. No restrictions on the degree of the Forchheimer polynomial are imposed. We derive, for all time, the interior L 1 -estimates for the pressure and its partial derivatives, and the interior L 2 -estimates for its Hessian. The De Giorgi and Ladyzhenskaya-Uraltseva iteration techniques are used taking into account the special structures of the equations for both pressure and its gradient. These are combined with the uniform Gronwall-type bounds in establishing the asymptotic estimates when time tends to innity.
Learning Non-Discriminatory Predictors

We consider learning a predictor which is non-discriminatory with respect to a "protected attribute" according to the notion of "equalized odds" proposed by Hardt et al. [2016]. We study the problem of learning such a non-discriminatory predictor from a finite training set, both statistically and computationally. We show that a post-hoc correction approach, as suggested by Hardt et al, can be highly suboptimal, present a nearly-optimal statistical procedure, argue that the associated computational problem is intractable, and suggest a second moment relaxation of the non-discrimination definition for which learning is tractable.
Analytic photometric redshift estimator for Type Ia supernovae from the Large Synoptic Survey Telescope

Accurate and precise photometric redshifts (photo-zs) of Type Ia supernovae (SNe Ia) can enable the use of SNe Ia, measured only with photometry, to probe cosmology. This dramatically increases the science return of supernova surveys planned for the Large Synoptic Survey Telescope (LSST). In this paper we describe a significantly improved version of the simple analytic photo-z estimator proposed by Wang and further developed by Wang, Narayan & Wood-Vasey. We apply it to 55 422 simulated SNe Ia generated using the SNANA package with the LSST filters. We find that the estimated errors on the photo-zs, σ_(z_(phot))/(1+z_(phot)), can be used as filters to produce a set of photo-zs that have high precision, accuracy, and purity. Using SN Ia colours as well as SN Ia peak magnitude in the i band, we obtain a set of photo-zs with 2 per cent accuracy (with σ(z_(phot) − z_(spec))/(1 + z_(spec)) = 0.02), a bias in z_(phot) (the mean of z_(phot) − z_(spec)) of −9 × 10^(−5), and an outlier fraction (with |(z_(phot) − z_(spec))/(1 + z_(spec))| > 0.1) of 0.23 per cent, with the requirement that σ_(z_(phot))/(1+z_(phot))<0.01. Using the SN Ia colours only, we obtain a set of photo-zs with similar quality by requiring that σ_(z_(phot))/(1+z_(phot))<0.007; this leads to a set of photo-zs with 2 per cent accuracy, a bias in z_(phot) of 5.9 × 10^(−4), and an outlier fraction of 0.32 per cent.
Artificial intelligence and statistics

Artificial intelligence (AI) is intrinsically data-driven. It calls for the application of statistical concepts through human-machine collaboration during the generation of data, the development of algorithms, and the evaluation of results. This paper discusses how such human-machine collaboration can be approached through the statistical concepts of population, question of interest, representativeness of training data, and scrutiny of results (PQRS). The PQRS workflow provides a conceptual framework for integrating statistical ideas with human input into AI products and researches. These ideas include experimental design principles of randomization and local control as well as the principle of stability to gain reproducibility and interpretability of algorithms and data results. We discuss the use of these principles in the contexts of self-driving cars, automated medical diagnoses, and examples from the authors’ collaborative research.
Predictive Edge Computing With Hard Deadlines

Edge computing is a promising approach for localized data processing for many edge applications and systems including Internet of Things (IoT), where computationally intensive tasks in IoT devices could be divided into sub-tasks and offloaded to other IoT devices, mobile devices, and/or servers at the edge. However, existing solutions on edge computing do not address the full range of challenges, specifically heterogeneity; edge devices are highly heterogeneous and dynamic in nature. In this paper, we develop a predictive edge computing framework with hard deadlines. Our algorithm; PrComp (i) predicts the uncertain dynamics of resources of devices at the edge including energy, computing power, and mobility, and (ii) makes sub-task offloading decisions by taking into account the predicted available resources, as well as the hard deadline constraints of tasks. We evaluate Prcomp on a testbed consisting of real Android-based smartphones, and show that it significantly improves energy consumption of edge devices and task completion delay as compared to baselines.
Controlling the spectral shape of nonlinear Thomson scattering with proper laser chirping

Effects of nonlinearity in Thomson scattering of a high intensity laser pulse from electrons are analyzed. Analytic expressions for laser pulse shaping in frequency (chirping) are obtained which control spectrum broadening for high laser pulse intensities. These analytic solutions allow prediction of the spectral form and required laser parameters to avoid broadening. Results of analytical and numerical calculations agree well. The control over the scattered radiation bandwidth allows narrow bandwidth sources to be produced using high scattering intensities, which in turn greatly improves scattering yield for future x- and gamma-ray sources.
Secure Anonymous Broadcast.

In anonymous broadcast, one or more parties want to anonymously send messages to all parties. This problem is increasingly important as a black-box in many privacy-preserving applications such as anonymous communication, distributed auctions, and multi-party computation. In this paper, we design decentralized protocols for anonymous broadcast that require each party to send (and compute) a polylogarithmic number of bits (and operations) per anonymous bit delivered with $O(\log n)$ rounds of communication. Our protocol is provably secure against traffic analysis, does not require any trusted party, and is completely load-balanced. The protocol tolerates up to $n/6$ statically-scheduled Byzantine parties that are controlled by a computationally unbounded adversary. Our main strategy for achieving scalability is to perform local communications (and computations) among a logarithmic number of parties. We provide simulation results to show that our protocol improves significantly over previous work. We finally show that using a common cryptographic tool in our protocol one can achieve practical results for anonymous broadcast.
The List Distinguishing Number Equals the Distinguishing Number for Interval Graphs

A distinguishing coloring of a graph G is a coloring of the vertices so that every nontrivial automorphism of G maps some vertex to a vertex with a different color. The distinguishing number of G is the minimum k such that G has a distinguishing coloring where each vertex is assigned a color from {1, . . . , k}. A list assignment to G is an assignment L = {L(v)}v∈V (G) of lists of colors to the vertices of G. A distinguishing L-coloring of G is a distinguishing coloring of G where the color of each vertex v comes from L(v). The list distinguishing number of G is the minimum k such that every list assignment to G in which |L(v)| = k for all v ∈ V (G) yields a distinguishing L-coloring of G. We prove that if G is an interval graph, then its distinguishing number and list distinguishing number are equal.
Contact loci in arc spaces

We give a geometric description of the loci in the arc space defined by order of contact with a given subscheme, using the resolution of singularities. This induces an identification of the valuations defined by cylinders in the arc space with divisorial valuations. In particular, we recover the description of invariants coming from the resolution of singularities in terms of arcs and jets.
A dataset of daily interactive manipulation

Robots that succeed in factories may struggle to complete even the simplest daily task that humans take for granted, because the change of environment makes the task exceedingly difficult. Aiming t...
Some Results on Surfaces with $p_g=q=1$ and $K^2=2$

Following an idea of Ishida, we develop polynomial equations for certain unramified double covers of surfaces with p_g=q=1 and K^2=2. Our first main result provides an explicit surface surface X with these invariants defined over Q that has Picard number 2, which is the smallest possible for these surfaces. This is done by giving equations for the double cover Y of X, calculating the zeta function of the reduction of Y to F_3, and extracting from this the zeta function of the reduction of X to F_3; the basic idea used in this process may also be of independent interest. Our second main result is a big monodromy theorem for a family that contains all surfaces with p_g=q=1, K^2=2, and K is ample. It follows from this that a certain Hodge correspondence of Kuga and Satake, between such a surface and an abelian variety, is motivated (and hence absolute Hodge). This allows us to deduce our third main result, which is that the Tate Conjecture in characteristic zero holds for all surfaces with p_g=q=1, K^2=2, and K ample.
Dual rate control for security in cyber-physical systems

We consider malicious attacks on actuators and sensors of a feedback system which can be modeled as additive, possibly unbounded, disturbances at the digital (cyber) part of the feedback loop. We precisely characterize the role of the unstable poles and zeros of the system in the ability to detect stealthy attacks in the context of the sampled data implementation of the controller in feedback with the continuous (physical) plant. We show that, if there is a single sensor that is guaranteed to be secure and the plant is observable from that sensor, then there exist a class of multirate sampled data controllers that ensure that all attacks remain detectable. These dual rate controllers are sampling the output faster than the zero order hold rate that operates on the control input and as such, they can even provide better nominal performance than single rate, at the price of higher sampling of the continuous output.
Sparse Solutions to Nonnegative Linear Systems and Applications

We give an efficient algorithm for finding sparse approximate solutions to linear systems of equations with nonnegative coefficients. Unlike most known results for sparse recovery, we do not require any assumption on the matrix other than non-negativity. Our algorithm is combinatorial in nature, inspired by techniques for the set cover problem, as well as the multiplicative weight update method. We then present a natural application to learning mixture models in the PAC framework. For learning a mixture of k axisaligned Gaussians in d dimensions, we give an algorithm that outputs a mixture of O(k/� 3 ) Gaussians that is �-close in statistical distance to the true distribution, without any separation assumptions. The time and sample complexity is roughly O(kd/� 3 ) d . This is polynomial when d is constant – precisely the regime in which known methods fail to identify the components efficiently. Given that non-negativity is a natural assumption, we believe that our result may find use in other settings in which we wish to approximately explain data using a small number of a (large) candidate set of components.
Choosability with union separation

Abstract List coloring generalizes graph coloring by requiring the color of a vertex to be selected from a list of colors specific to that vertex. One refinement of list coloring, called choosability with separation, requires that the intersection of adjacent lists is sufficiently small. We introduce a new refinement, called choosability with union separation, where we require that the union of adjacent lists is sufficiently large. For t ≥ k , a ( k , t ) -list assignment is a list assignment L where | L ( v ) | ≥ k for all vertices v and | L ( u ) ∪ L ( v ) | ≥ t for all edges u v . A graph is ( k , t ) -choosable if there is a proper coloring for every ( k , t ) -list assignment. We explore this concept through examples of graphs that are not ( k , t ) -choosable, demonstrating sparsity conditions that imply a graph is ( k , t ) -choosable, and proving that all planar graphs are ( 3 , 11 ) -choosable and ( 4 , 9 ) -choosable.
Inequivalent Lefschetz fibrations and surgery equivalence of symplectic 4-manifolds

We prove that any symplectic 4-manifold which is not a rational or ruled surface, after sufficiently many blow-ups, admits an arbitrary number of nonisomorphic Lefschetz fibrations of the same genus which cannot be obtained from one another via Luttinger surgeries. This generalizes results of Park and Yun who constructed pairs of nonisomorphic Lefschetz fibrations on knot surgered elliptic surfaces. In turn, we prove that there are monodromy factorizations of Lefschetz pencils which have the same characteristic numbers but cannot be obtained from each other via partial conjugations by Dehn twists, answering a problem posed by Auorux.
Nested dictionary learning for hierarchical organization of imagery and text

A tree-based dictionary learning model is developed for joint analysis of imagery and associated text. The dictionary learning may be applied directly to the imagery from patches, or to general feature vectors extracted from patches or superpixels (using any existing method for image feature extraction). Each image is associated with a path through the tree (from root to a leaf), and each of the multiple patches in a given image is associated with one node in that path. Nodes near the tree root are shared between multiple paths, representing image characteristics that are common among different types of images. Moving toward the leaves, nodes become specialized, representing details in image classes. If available, words (text) are also jointly modeled, with a path-dependent probability over words. The tree structure is inferred via a nested Dirichlet process, and a retrospective stick-breaking sampler is used to infer the tree depth and width.
Monodromy and vanishing cycles in toric surfaces

Given an ample line bundle on a toric surface, a question of Donaldson asks which simple closed curves can be vanishing cycles for nodal degenerations of smooth curves in the complete linear system. This paper provides a complete answer. This is accomplished by reformulating the problem in terms of the mapping class group-valued monodromy of the linear system, and giving a precise determination of this monodromy group.
Impact of sex, androgens, and prostate size on C57BL/6J mouse urinary physiology: functional assessment

Laboratory mice are used to identify causes of urinary dysfunction including prostate-related mechanisms of lower urinary tract symptoms. Effective use of mice for this purpose requires a clear und...
Partial hyperplane activation for generalized intersection cuts

The generalized intersection cut paradigm is a recent framework for generating cutting planes in mixed integer programming with attractive theoretical properties. We investigate this computationally unexplored paradigm and observe that a key hyperplane activation procedure embedded in it is not computationally viable. To overcome this issue, we develop a novel replacement to this procedure called partial hyperplane activation (PHA), introduce a variant of PHA based on a notion of hyperplane tilting, and prove the validity of both algorithms. We propose several implementation strategies and parameter choices for our PHA algorithms and provide supporting theoretical results. We computationally evaluate these ideas in the COIN-OR framework on MIPLIB instances. Our findings shed light on the the strengths of the PHA approach as well as suggest properties related to strong cuts that can be targeted in the future.
The Equity Differential Factor in Currency Markets

We show that the differential in trailing equity market performance across countries strongly predicts the cross-section of currency returns. Specifically, exchange rates tend to appreciate for cou...
Antitrust and Economic History: The Historic Failure of the Chicago School of Antitrust:

This article presents an historical analysis of the antitrust laws. Its central contention is that the history of antitrust can only be understood in light of U.S. economic history and the successi...
Barriers to education: Policies and perceptions of individuals with histories of sexual offenses in higher education

AbstractWhile education has been demonstrated to promote desistance, a person with a sex offense conviction faces numerous obstacles when pursuing a postsecondary education. In the present study, w...
Power plant heat-rate efficiency as a regulatory mechanism: Implications for emission rates and levels

Abstract In August 2018, the U.S. Environmental Protection Agency (EPA) proposed a new policy – the Affordable Clean Energy rule – to reduce greenhouse gas (GHG) emissions from existing coal-fired electric generating units and power plants. The new rule establishes emissions guidelines, including heat-rate efficiency improvements, for states when developing plans to limit GHG emissions. Past studies have indicated that heat-rate efficiency improvements can increase electricity output, leading to a reduction in emissions rates and an increase in emissions levels – a rebound effect that can temper the emissions-reduction benefits of plant-level heat-rate efficiency. This study adds to the literature by examining data on the relationship of plant-level heat-rate efficiency on the rate and level of GHG emissions. We explored three different types of GHGs – carbon dioxide, methane, and nitrous oxide. Controlling for variation across operators, our results suggest that gains in heat-rate efficiency are associated with higher levels of all three pollutants. Specifically, we found that a ten percent increase in heat-rate efficiency led to an average seven-to-nine percent increase in the level of GHG emissions. Our analysis highlights the need to further study the full effects of heat-rate efficiency policies before such rules are enacted.
FiPIt: A simple, fast global method for solving models with two endogenous states & occasionally binding constraints

Abstract We propose a simple and fast fixed-point iteration algorithm (FiPIt) to obtain the global, non-linear solution of macro models with two endogenous state variables and occasionally binding constraints. This method uses fixed-point iteration on Euler equations to avoid solving two simultaneous non-linear equations (as with the time iteration method) or creating modified state variables requiring irregular interpolation (as with the endogenous grids method). In the small-open-economy RBC and Sudden Stops models provided as examples, FiPIt is much faster than time iteration and various hybrid methods.
Li Evolution and the Open Cluster NGC 6819: A Correlation between Li Depletion and Spindown in Dwarfs More Massive Than the F-Dwarf Li-Dip

Spectroscopy of 333 NGC 6819 stars and Gaia astrometry are used to map Li evolution from the giant branch tip to 0.5 mag below the Li dip. Isochrone comparison with [Fe/H] = -0.04, based upon neural network spectroscopic analysis, produces an age of 2.25 (2.4) Gyr for E(B-V) = 0.16 (0.14) and (m-M) = 12.40 (12.29). Despite originating outside the Li dip, only 10% of single subgiants/giants have measurable Li. Above the Li dip, the limiting A(Li) for single stars is 3.2 +/- 0.1 but the lower range is comparable to that found within the dip. The F-dwarf Li dip profile agrees with the Hyades/Praesepe, evolved forward. The Li level among stars populating the plateau fainter than the Li dip is A(Li) = 2.83 +/- 0.16; the dispersion is larger than expected from spectroscopic error alone. Comparison of Li and V_ROT distributions among turnoff stars in NGC 7789, NGC 2506, NGC 3680, and NGC 6819 indicates that rotational spindown from the main sequence is critical in defining the boundaries of the Li dip. For higher mass dwarfs, spindown is likewise correlated with Li depletion, creating a second dip, but at higher mass and on a longer time scale. The Li distribution among evolved stars of NGC 6819 is more representative of the older M67, where subgiant and giant stars emerge from within the Li dip, than the younger NGC 7789, where a broad range in V_ROT among the turnoff stars likely produces a range in mass among the giants.
Initial Clinical Experience with Swiss LithoClast Trilogy During Percutaneous Nephrolithotomy.

Introduction and Objective: Current available lithotrites have clinical stone clearance rates averaging 24 to 32 mm2/minute. The objective of this study was to critically evaluate the initial exper...
A discrete Hopf interpolant and stability of the finite element method for natural convection

The temperature in natural convection problems is, under mild data assumptions, uniformly bounded in time. This property has not yet been proven for the standard finite element method (FEM) approximation of natural convection problems with nonhomogeneous partitioned Dirichlet boundary conditions, e.g., the differentially heated vertical wall and Rayleigh-Benard problems. For these problems, only stability in time, allowing for possible exponential growth of $\| T^{n}_{h} \| $, has been proven using Gronwall's inequality. Herein, we prove that the temperature approximation can grow at most linearly in time provided that the first mesh line in the finite element mesh is within $\mathcal{O} (Ra^{-1})$ of the nonhomogeneous Dirichlet boundary.
Integral operators, bispectrality and growth of Fourier algebras

In the mid 80's it was conjectured that every bispectral meromorphic function $\psi(x,y)$ gives rise to an integral operator $K_{\psi}(x,y)$ which possesses a commuting differential operator. This has been verified by a direct computation for several families of functions $\psi(x,y)$ where the commuting differential operator is of order $\leq 6$. We prove a general version of this conjecture for all self-adjoint bispectral functions of rank 1 and all self-adjoint bispectral Darboux transformations of the rank 2 Bessel and Airy functions. The method is based on a theorem giving an exact estimate of the second and first order terms of the growth of the Fourier algebra of each such bispectral function. From it we obtain a sharp upper bound on the order of the commuting differential operator for the integral kernel $K_{\psi}(x,y)$ and a fast algorithmic procedure for constructing the differential operator; unlike the previous examples its order is arbitrarily high. We prove that the above classes of bispectral functions are parametrized by infinite dimensional Grassmannians which are the Lagrangian loci of the Wilson adelic Grassmannian and its analogs in rank 2.
Least action principles for incompressible flows and geodesics between shapes

As V. I. Arnold observed in the 1960s, the Euler equations of incompressible fluid flow correspond formally to geodesic equations in a group of volume-preserving diffeomorphisms. Working in an Eulerian framework, we study incompressible flows of shapes as critical paths for action (kinetic energy) along transport paths constrained to have characteristic-function densities. The formal geodesic equations for this problem are Euler equations for incompressible, inviscid potential flow of fluid with zero pressure and surface tension on the free boundary. The problem of minimizing this action exhibits an instability associated with microdroplet formation, with the following outcomes: any two shapes of equal volume can be approximately connected by an Euler spray—a countable superposition of ellipsoidal geodesics. The infimum of the action is the Wasserstein distance squared, and is almost never attained except in dimension 1. Every Wasserstein geodesic between bounded densities of compact support provides a solution of the (compressible) pressureless Euler system that is a weak limit of (incompressible) Euler sprays.
Cryo electron microscopy of mixed ensembles: simultaneous pattern recognition and 3-D reconstruction

Summary form only given. In the study of biological processes like virus maturation, experimental situations arise where the sample is a mixture of virus particles in which each particle is from one of a few classes of identical particle. In order to use cryo electron microscopy to compute a 3-D reconstruction of each class of particle, a pattern recognition problem must be solved. A model-based statistical approach using the maximum likelihood criteria in which the unknown class labels are treated as nuisance parameters is described. An expectation-maximization algorithm is used to solve the maximum likelihood problem where, in order to compute reconstructions at biologically interesting spatial resolutions, a high-performance computing implementation has been developed on a cluster computer.
Model-Based Recommendations for Optimal Surgical Placement of Epiretinal Implants

A major limitation of current electronic retinal implants is that in addition to stimulating the intended retinal ganglion cells, they also stimulate passing axon fibers, producing perceptual ‘streaks’ that limit the quality of the generated visual experience. Recent evidence suggests a dependence between the shape of the elicited visual percept and the retinal location of the stimulating electrode. However, this knowledge has yet to be incorporated into the surgical placement of retinal implants. Here we systematically explored the space of possible implant configurations to make recommendations for optimal intraocular positioning of the electrode array. Using a psychophysically validated computational model, we demonstrate that better implant placement has the potential to reduce the spatial extent of axonal activation in existing implant users by up to \(\sim \)55%. Importantly, the best implant location, as inferred from a population of simulated virtual patients, is both surgically feasible and is relatively stable across individuals. This study is a first step towards the use of computer simulations in patient-specific planning of retinal implant surgery.
Assessing Oil and Non-Oil GDP Growth from Space: An Application to Yemen 2012-17

This paper uses an untapped source of satellite-recorded nightlights and gas flaring data to characterize the contraction of economic activity in Yemen throughout the ongoing conflict that erupted in 2015. Using estimated nightlights elasticities on a sample of 72 countries for real GDP and 28 countries for oil GDP over 6 years, I derive oil and non-oil GDP growth for Yemen. I show that real GDP contracted by a cumulative 24 percent over 2015-17 against 50 percent according to official figures. I also find that the impact of the conflict has been geographically uneven with economic activity contracting more in some governorates than in others.
Bounded contractions for affine buildings

We consider affine buildings with refined chamber structure. For each vertex in the refined chamber structure we construct a contraction, based at the vertex, that is used to prove exactness of Schneider-Stuhler resolutions of arbitrary depth.
Accuracy, uncertainty, and adaptability of automatic myocardial ASL segmentation using deep CNN.

PURPOSE To apply deep convolution neural network to the segmentation task in myocardial arterial spin labeled perfusion imaging and to develop methods that measure uncertainty and that adapt the convolution neural network model to a specific false-positive versus false-negative tradeoff. METHODS The Monte Carlo dropout U-Net was trained on data from 22 subjects and tested on data from 6 heart transplant recipients. Manual segmentation and regional myocardial blood flow were available for comparison. We consider 2 global uncertainty measures, named "Dice uncertainty" and "Monte Carlo dropout uncertainty," which were calculated with and without the use of manual segmentation, respectively. Tversky loss function with a hyperparameter β was used to adapt the model to a specific false-positive versus false-negative tradeoff. RESULTS The Monte Carlo dropout U-Net achieved a Dice coefficient of 0.91 ± 0.04 on the test set. Myocardial blood flow measured using automatic segmentations was highly correlated to that measured using the manual segmentation (R2 = 0.96). Dice uncertainty and Monte Carlo dropout uncertainty were in good agreement (R2 = 0.64). As β increased, the false-positive rate systematically decreased and false-negative rate systematically increased. CONCLUSION We demonstrate the feasibility of deep convolution neural network for automatic segmentation of myocardial arterial spin labeling, with good accuracy. We also introduce 2 simple methods for assessing model uncertainty. Finally, we demonstrate the ability to adapt the convolution neural network model to a specific false-positive versus false-negative tradeoff. These findings are directly relevant to automatic segmentation in quantitative cardiac MRI and are broadly applicable to automatic segmentation problems in diagnostic imaging.
Transfer in Deep Reinforcement Learning Using Knowledge Graphs.

Text adventure games, in which players must make sense of the world through text descriptions and declare actions through text descriptions, provide a stepping stone toward grounding action in language. Prior work has demonstrated that using a knowledge graph as a state representation and question-answering to pre-train a deep Q-network facilitates faster control policy learning. In this paper, we explore the use of knowledge graphs as a representation for domain knowledge transfer for training text-adventure playing reinforcement learning agents. Our methods are tested across multiple computer generated and human authored games, varying in domain and complexity, and demonstrate that our transfer learning methods let us learn a higher-quality control policy faster.
Pre–Liver Transplant Cardiac Catheterization Is Associated With Low Rate of Myocardial Infarction and Cardiac Mortality

BACKGROUND AND AIMS A study at Indiana University demonstrated a reduction in myocardial infarction (MI) incidence with increased frequency of cardiac catheterization (CATH) in liver transplant (LT) candidates. A strict protocol for performing CATH based upon predefined risk factors, rather than noninvasive testing alone, was applied to a subgroup (2009-2010) from that study. CATH was followed by percutaneous coronary intervention (PCI) in cases of significant coronary artery disease (CAD; ≥50% stenosis). The current study applies this screening protocol to a larger cohort (2010-2016) to assess post-LT clinical outcomes. APPROACH AND RESULTS Among 811 LT patients, 766 underwent stress testing (94%) and 559 underwent CATH (69%), of whom 10% had CAD requiring PCI. The sensitivity of stress echocardiography in detecting significant CAD was 37%. Predictors of PCI included increasing age, male gender, and personal history of CAD (P < 0.05 for all). Compared to patients who had no CATH, patients who underwent CATH had higher mortality (P = 0.07), and the hazard rates (HR) for mortality increased with CAD severity (normal CATH, HR, 1.35; 95% confidence interval [CI], 0.79-2.33; P = 0.298; nonobstructive CAD, HR, 1.53; 95% CI, 0.84-2.77; P = 0.161; and significant CAD, HR, 1.96; 95% CI, 0.93-4.15; P = 0.080). Post-LT outcomes were compared to the 2009-2010 subgroup from the previous study and showed similar 1-year overall mortality (8% and 6%, P = 0.48), 1-year MI incidence (<1% and <1%, P = 0.8), and MI deaths as a portion of all deaths (3% and 9%, P = 0.35). CONCLUSIONS Stress echocardiography alone is not reliable in screening LT patients for CAD. Aggressive CAD screening with CATH is associated with low rate of MI and cardiac mortality and validates the previously published protocol when extrapolated over a larger sample and longer follow-up period.
Interesting examples in $\mathbb{C}^2$ of maps tangent to the identity without domains of attraction

We give an interesting example of a map in $\mathbb{C}^2$ that is tangent to the identity, but that does not have a domain of attraction along any of its characteristic direction. This map has three characteristic directions, two of which are not attracting while the third attracts points to that direction, but not to the origin. In addition, we show that if we add higher degree terms to this map, sometimes a domain of attraction along one of its characteristic directions will exist and sometimes one will not.
Approximation algorithms and an integer program for multi-level graph spanners

Given a weighted graph G(V, E) and \(t \ge 1\), a subgraph H is a t–spanner of G if the lengths of shortest paths in G are preserved in H up to a multiplicative factor of t. The subsetwise spanner problem aims to preserve distances in G for only a subset of the vertices. We generalize the minimum-cost subsetwise spanner problem to one where vertices appear on multiple levels, which we call the multi-level graph spanner (MLGS) problem, and describe two simple heuristics. Applications of this problem include road/network building and multi-level graph visualization, especially where vertices may require different grades of service.