cssi/SciSciGPT-SciSciNet huggingface dataset:
OPTICAL CROSS-CORRELATION FILTERS: AN ECONOMICAL APPROACH FOR IDENTIFYING SNe Ia AND ESTIMATING THEIR REDSHIFTS

Large photometric surveys of transient phenomena, such as Panoramic Survey Telescope & Rapid Response System and Large Synoptic Survey Telescope, will locate thousands to millions of Type Ia supernova (SN Ia) candidates per year, a rate prohibitive for acquiring spectroscopy to determine each candidate's type and redshift. In response, we have developed an economical approach to identifying SNe Ia and their redshifts using an uncommon type of optical filter which has multiple, discontinuous passbands on a single substrate. Observation of a supernova through a specially designed pair of these "cross-correlation filters" measures the approximate amplitude and phase of the cross-correlation between the spectrum and a SN Ia template, a quantity typically used to determine the redshift and type of a high-redshift SN Ia. Simulating the use of these filters, we obtain a sample of SNe Ia which is ~98% pure with individual redshifts measured to σ z = 0.01 precision. The advantages of this approach over standard broadband photometric methods are that it is insensitive to reddening, independent of the color data used for subsequent distance determinations which reduce selection or interpretation bias, and because it makes use of the spectral features its reliability is greater. A great advantage over long-slit spectroscopy comes from increased throughput, enhanced multiplexing, and reduced setup time resulting in a net gain in speed of up to ~30 times. This approach is also insensitive to host galaxy contamination. Prototype filters were built and successfully used on Magellan with LDSS-3 to characterize three SuperNova Legacy Survey candidates. We discuss how these filters can provide critical information for the upcoming photometric supernova surveys.
Conductivity of strongly coupled striped superconductor

We study the conductivity of a strongly coupled striped superconductor using gauge/gravity duality (holography). The study is done analytically, in the large modulation regime. We show that at low temperatures, the optical conductivity is inhomogeneous and anisotropic, but with the anisotropy vanishing when the scaling dimension of the superconducting order parameter is unity. Near but below the critical temperature, we calculate the conductivity analytically at small frequency ω and find it to be both inhomogeneous and anisotropic. The anisotropy is imaginary and scales like 1/ω. We also calculate analytically the speed of the second sound and the thermodynamic susceptibility.
Accelerating Large-Scale Data Analysis by Offloading to High-Performance Computing Libraries using Alchemist

Apache Spark is a popular system aimed at the analysis of large data sets, but recent studies have shown that certain computations---in particular, many linear algebra computations that are the basis for solving common machine learning problems---are significantly slower in Spark than when done using libraries written in a high-performance computing framework such as the Message-Passing Interface (MPI). To remedy this, we introduce Alchemist, a system designed to call MPI-based libraries from Apache Spark. Using Alchemist with Spark helps accelerate linear algebra, machine learning, and related computations, while still retaining the benefits of working within the Spark environment. We discuss the motivation behind the development of Alchemist, and we provide a brief overview of its design and implementation. We also compare the performances of pure Spark implementations with those of Spark implementations that leverage MPI-based codes via Alchemist. To do so, we use data science case studies: a large-scale application of the conjugate gradient method to solve very large linear systems arising in a speech classification problem, where we see an improvement of an order of magnitude; and the truncated singular value decomposition (SVD) of a 400GB three-dimensional ocean temperature data set, where we see a speedup of up to 7.9x. We also illustrate that the truncated SVD computation is easily scalable to terabyte-sized data by applying it to data sets of sizes up to 17.6TB.
Optical Variability of the Dwarf AGN NGC 4395 from the Transiting Exoplanet Survey Satellite

We present optical light curves from the Transiting Exoplanet Survey Satellite (TESS) for the archetypical dwarf active galactic nucleus (AGN) in the nearby galaxy NGC 4395 hosting a ~105 M⊙ supermassive black hole (SMBH). Significant variability is detected on timescales from weeks to hours before reaching the background noise level. The ~month-long, 30 minute-cadence, high-precision TESS light curve can be well fit by a simple damped random walk (DRW) model, with the damping timescale τDRW constrained to be ${2.3}_{-0.7}^{+1.8}$ days (1σ). NGC 4395 lies almost exactly on the extrapolation of the ${\tau }_{\mathrm{DRW}}-{M}_{\mathrm{BH}}$ relation measured for AGNs with BH masses that are more than three orders of magnitude larger. The optical variability periodogram can be well fit by a broken power law with the high-frequency slope (−1.88 ± 0.15) and the characteristic timescale (${\tau }_{\mathrm{br}}\equiv 1/(2\pi {f}_{\mathrm{br}})={1.4}_{-0.5}^{+1.9}$ days) consistent with the DRW model within 1σ. This work demonstrates the power of TESS light curves in identifying low-mass accreting SMBHs with optical variability, and a potential global ${\tau }_{\mathrm{DRW}}-{M}_{\mathrm{BH}}$ relation that can be used to estimate SMBH masses with optical variability measurements.
The RAPTOR experiment: a system for monitoring the optical sky in real time

The Rapid Telescopes for Optical Response (RAPTOR) experiment is a spatially distributed system of autonomous robotic telescopes that is designed to monitor the sky for optical transients. The core of the ystem is composed of two telescope arrays, separated by 38 kilometers, that stereoscopically view the same 1500 square-degree field with a wide-field imaging array and a central 4 square-degree field with a more sensitive narrow-field ``fovea" imager. Coupled to each telescope array is a real-time data analysis pipeline that is designed to identify interesting transients on timescales of seconds and, when a celestial transient is identified, to command the rapidly slewing robotic mounts to point the narrow-field ``fovea'' imagers at the transient. The two narrow-field telescopes then image the transient with higher spatial resolution and at a faster cadence to gather light curve information. Each ``fovea" camera also images the transient through a different filter to provide color information. This stereoscopic monitoring array is supplemented by a rapidly slewing telescope with a low resolution spectrograph for follow-up observations of transients and a sky patrol telescope that nightly monitors about 10,000 square-degrees for variations, with timescales of a day or longer, to a depth about 100 times fainter. In addition to searching for fast transients, we will use the data stream from RAPTOR as a real-time sentinel for recognizing important variations in known sources. All of the data will be publically released through a virtual observatory called SkyDOT (Sky Database for Objects in the Time Domain) that we are developing for studying variability of the optical sky. Altogether, the RAPTOR project aims to construct a new type of system for discovery in optical astronomy---one that explores the time domain by "mining the sky in real time".© (2002) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Are Electric Currents Heating the Magnetic Chromosphere

This Letter presents an analysis of three-dimensional vector currents and temperatures observed in a sunspot from the photosphere to the chromosphere, spanning a range of heights of approximately 1500 km. With this unique data set, based on novel spectropolarimetric observations of the 850 nm spectral region, it is possible to conduct for the first time an empirical study of the relation between currents and chromospheric heating. It is shown that while resistive current dissipation contributes to heat the sunspot chromosphere, it is not the dominant factor. The heating effect of current dissipation is more important in the penumbra of the sunspot, but even there it is still a relatively modest contribution.
A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions

We propose an adaptively weighted stochastic gradient Langevin dynamics algorithm (SGLD), so-called contour stochastic gradient Langevin dynamics (CSGLD), for Bayesian learning in big data statistics. The proposed algorithm is essentially a scalable dynamic importance sampler, which automatically flattens the target distribution such that the simulation for a multi-modal distribution can be greatly facilitated. Theoretically, we prove a stability condition and establish the asymptotic convergence of the self-adapting parameter to a unique fixed-point, regardless of the non-convexity of the original energy function; we also present an error analysis for the weighted averaging estimators. Empirically, the CSGLD algorithm is tested on multiple benchmark datasets including CIFAR10 and CIFAR100. The numerical results indicate its superiority over the existing state-of-the-art algorithms in training deep neural networks.
Monte Carlo Simulations of Globular Cluster Evolution. II. Mass Spectra, Stellar Evolution, and Lifetimes in the Galaxy

We study the dynamical evolution of globular clusters using our new two-dimensional Monte Carlo code, and we calculate the lifetimes of clusters in the Galactic environment. We include the effects of a mass spectrum, mass loss in the Galactic tidal field, and stellar evolution. We consider initial King models containing N = 105-3 × 105 stars, with the dimensionless central potential W0 = 1, 3, and 7, and with power-law mass functions m-α, with α = 1.5, 2.5, and 3.5. The evolution is followed up to core collapse or disruption, whichever occurs first. We compare our results with those from similar calculations using Fokker-Planck methods. The disruption and core collapse times of our models are significantly longer than those of one-dimensional Fokker-Planck models. This is consistent with recent comparisons with direct N-body simulations, which have also shown that the one-dimensional Fokker-Planck models can significantly overestimate the escape rate from tidally truncated clusters. However, we find that our results are in very good agreement with recent two-dimensional Fokker-Planck calculations, for a wide range of initial conditions, although our Monte Carlo models have a slightly lower mass-loss rate. We find even closer agreement of our results with modified Fokker-Planck calculations that take into account the finite nature of the system. In agreement with previous studies, our results show that the direct mass loss due to stellar evolution can significantly accelerate the mass-loss rate through the tidal boundary, by reducing the binding energy of the cluster and making it expand. This effect causes most clusters with a low initial central concentration (W0 3) to disrupt quickly in the Galactic tidal field. The disruption is particularly rapid in clusters with a relatively flat mass spectrum. Only clusters born with high central concentrations (W0 7) or with very steep initial mass functions (α 3.5) are likely to survive to the present and undergo core collapse. We identify the mechanism by which clusters disrupt as a dynamical instability in which the rate of mass loss increases catastrophically as the tidal boundary moves inward on the crossing timescale. To understand the various processes that lead to the escape of stars, we study the velocity distribution and orbital characteristics of escaping stars. We also compute the lifetime of a cluster on an eccentric orbit in the Galaxy, such that it fills its Roche lobe only at perigalacticon. We find that such an orbit can extend the disruption time by at most a factor of a few compared to a circular orbit in which the cluster fills its Roche lobe at all times.
CLIFFORD AND WEYL SUPERALGEBRAS AND SPINOR REPRESENTATIONS

We construct a family of twisted generalized Weyl algebras which includes Weyl–Clifford superalgebras and quotients of the enveloping algebras of $$ \mathfrak{gl}\left(m|n\right) $$ and $$ \mathfrak{osp}\left(m|2n\right) $$ . We give a condition for when a canonical representation by differential operators is faithful. Lastly, we give a description of the graded support of these algebras in terms of pattern-avoiding vector compositions.
Wilson Loops, D-Branes, and Reparametrization Path-Integrals

We study path integrals over reparametrizations of the world-sheet boundary. Such integrals arise when string propagates between fixed space-time contours. In gauge/string duality they are needed to describe gauge theory Wilson loops. We show that (1) in AdS/CFT, the integral is well defined and gives a finite 1-loop correction to the Wilson loop; (2) in critical string theory, the integral is UV divergent, and fixed contour amplitudes are off shell. In the second case, we show that the divergences can be removed by renormalizing the contour. We calculate the 2-loop contour β-function and explain how it is related to the D0-brane effective action. We also apply this method to compute the first α' correction to the effective action of higher dimensional branes.
The Quantum Orbifold Cohomology of Toric Stack Bundles

We study Givental’s Lagrangian cone for the quantum orbifold cohomology of toric stack bundles. Using Gromov–Witten invariants of the base and combinatorics of the toric stack fibers, we construct an explicit slice of the Lagrangian cone defined by the genus 0 Gromov–Witten theory of a toric stack bundle.
A MAGNETOHYDRODYNAMIC MODEL OF THE M87 JET. I. SUPERLUMINAL KNOT EJECTIONS FROM HST-1 AS TRAILS OF QUAD RELATIVISTIC MHD SHOCKS

This is the first in a series of papers that introduces a new paradigm for understanding the jet in M87: a collimated relativistic flow in which strong magnetic fields play a dominant dynamical role. Here, we focus on the flow downstream of HST-1—an essentially stationary flaring feature that ejects trails of superluminal components. We propose that these components are quad relativistic magnetohydrodynamic shock fronts (forward/reverse fast and slow modes) in a narrow jet with a helically twisted magnetic structure. And we demonstrate the properties of such shocks with simple one-dimensional numerical simulations. Quasi-periodic ejections of similar component trails may be responsible for the M87 jet substructures observed further downstream on 102-103 pc scales. This new paradigm requires the assimilation of some new concepts into the astrophysical jet community, particularly the behavior of slow/fast-mode waves/shocks and of current-driven helical kink instabilities. However, the prospects of these ideas applying to a large number of other jet systems may make this worth the effort.
A method to derive concentration of measure bounds on Markov chains

We explore a method introduced by Chatterjee and Ledoux in a paper on eigenvalues of principle submatrices. The method provides a tool to prove concentration of measure in cases where there is a Markov chain meeting certain conditions, and where the spectral gap of the chain is known. We provide several additional applications of this method. These applications include results on operator compressions using the Kac walk on $SO(n)$ and a Kac walk coupled to a thermostat, and a concentration of measure result for the length of the longest increasing subsequence of a random walk distributed under the invariant measure for the asymmetric exclusion process.
On the Local Extension of Killing Vector Fields in Electrovacuum Spacetimes

We revisit the problem of extension of a Killing vector field in a spacetime which is solution to the Einstein–Maxwell equation. This extension has been proved to be unique in the case of a Killing vector field which is normal to a bifurcate horizon in Yu (Ann Henri Poincare 11(1–2):1–21, 2010). Here we generalize the extension of the vector field to a strong null convex domain in an electrovacuum spacetime, inspired by the same technique used in Ionescu and Klainerman (J Am Math Soc 26(2):563–593, 2013) in the setting of Ricci flat manifolds. We also prove a result concerning non-extendibility: we show that one can find local, stationary electrovacuum extension of a Kerr–Newman solution in a full neighborhood of a point of the horizon (that is not on the bifurcation sphere) which admits no extension of the Hawking vector field. This generalizes the construction in [5] to the electrovacuum case.
Advances in modeling nuclear matrix elements of neutrinoless double beta decay

Accurate nuclear matrix elements (NMEs) for neutrinoless double beta decays of candidate nuclei are important for the design and interpretation of future experiments. Significant progress has been made in the modeling of these NMEs from first principles. The NME for 48Ca shows a good agreement among three different ab initio calculations starting from the same nuclear interaction constructed within the chiral EFT and the same decay operator. These studies open the door to ab initio calculations of the matrix elements for the decay of heavier nuclei such as 76Ge, 130Te, and 136Xe. The ultimate goal is the computation of NMEs in many-body calculations with controllable approximations, using nuclear interactions and weak transition operators derived consistently from chiral EFT. We are expecting more progress towards this goal in the near future.
Orbifolds, the A, D, E Family of Caustic Singularities, and Gravitational Lensing

We provide a geometric explanation for the existence of magnification relations for the An(n≥2),Dn(n≥4),E6,E7,E8 family of caustic singularities, which were established in recent work. In particular, it was shown that for families of general mappings between planes exhibiting any of these caustic singularities, and for any noncaustic target point, the total signed magnification of the corresponding preimages vanishes. As an application to gravitational lensing, it was also shown that, independent of the choice of a lens model, the total signed magnification vanishes for a light source anywhere in the four-image region close to elliptic and hyperbolic umbilic caustics. This is a more global and higher order analog of the well-known fold and cusp magnification relations. We now extend each of these mappings to weighted projective space, which is a compact orbifold, and show that magnification relations translate into a statement about the behavior of these extended mappings at infinity. This generalizes mul...
A Dynamical Systems Perspective on Chimeric Antigen Receptor T-Cell Dosing

Chimeric antigen receptor T cells (CAR T cells) are dosed similarly to donor lymphocyte infusions following hematopoietic cell transplantation. However, the mechanism driving proliferation in CAR T cells is distinct from conventional T cells. As such there are quantitative differences in the antigen response of these engineered cells when compared with conventional T cells. In this perspective paper the logistic equation of growth is used to develop a mathematical basis for understanding the difference between CAR T cell and conventional T cell response to antigen burden.
On Vorticity Gradient Growth for the Axisymmetric 3D Euler Equations Without Swirl

We consider solutions of the 3D axisymmetric Euler equations without swirl. In this setting, well-posedness is well-known due to the essentially 2D geometry. The quantity $$\omega ^\theta /r$$ plays an analogous role as vorticity in 2D. For our first result, we prove that the gradient of $$\omega ^\theta /r$$ can grow with at most double exponential rate with an improving a priori bound close to the axis of symmetry. Next, on the unit ball, we show that at the boundary, one can achieve double exponential growth of the gradient of $$\omega ^\theta /r$$ .
Solving the Rubik's Cube Optimally is NP-complete

In this paper, we prove that optimally solving an n x n x n Rubik's Cube is NP-complete by reducing from the Hamiltonian Cycle problem in square grid graphs. This improves the previous result that optimally solving an n x n x n Rubik's Cube with missing stickers is NP-complete. We prove this result first for the simpler case of the Rubik's Square--an n x n x 1 generalization of the Rubik's Cube--and then proceed with a similar but more complicated proof for the Rubik's Cube case. Our results hold both when the goal is make the sides monochromatic and when the goal is to put each sticker into a specific location.
PARTICLE ACCELERATION BY COLLISIONLESS SHOCKS CONTAINING LARGE-SCALE MAGNETIC-FIELD VARIATIONS

Diffusive shock acceleration at collisionless shocks is thought to be the source of many of the energetic particles observed in space. Large-scale spatial variations of the magnetic field have been shown to be important in understanding observations. The effects are complex, so here we consider a simple, illustrative model. Here we solve numerically the Parker transport equation for a shock in the presence of large-scale sinusoidal magnetic-field variations. We demonstrate that the familiar planar-shock results can be significantly altered as a consequence of large-scale, meandering magnetic lines of force. Because the perpendicular diffusion coefficient κ⊥ is generally much smaller than the parallel diffusion coefficient κ� , the energetic charged particles are trapped and preferentially accelerated along the shock front in the regions where the connection points of magnetic field lines intersecting the shock surface converge, and thus create the “hot spots” of the accelerated particles. For the regions where the connection points separate from each other, the acceleration to high energies will be suppressed. Further, the particles diffuse away from the “hot spot” regions and modify the spectra of downstream particle distribution. These features are qualitatively similar to the recent Voyager observations in the Heliosheath. These results are potentially important for particle acceleration at shocks propagating in turbulent magnetized plasmas as well as those which contain large-scale nonplanar structures. Examples include anomalous cosmic rays accelerated by the solar wind termination shock, energetic particles observed in propagating heliospheric shocks, galactic cosmic rays accelerated by supernova blast waves, etc.
A simplified proof of the relation between scaling exponents in first-passage percolation

In a recent breakthrough work, Chatterjee [Ann. of Math. (2) 177 (2013) 663–697] proved a long standing conjecture that relates the transversal exponent $\xi$ and the fluctuation exponent $\chi$ in first-passage percolation on $\mathbb{Z}^{d}$. The purpose of this paper is to replace the main argument of Chatterjee (2013) and give an alternative proof of this relation. Specifically, we show that under the assumption that exponents defined in Chatterjee (2013) exist, one has the relation $\chi\leq2\xi-1$. One advantage of our argument is that it does not require the “nearly Gamma” assumption of Chatterjee (2013).
No-regret Learning in Price Competitions under Consumer Reference Effects

We study long-run market stability for repeated price competitions between two firms, where consumer demand depends on firms' posted prices and consumers' price expectations called reference prices. Consumers' reference prices vary over time according to a memory-based dynamic, which is a weighted average of all historical prices. We focus on the setting where firms are not aware of demand functions and how reference prices are formed but have access to an oracle that provides a measure of consumers' responsiveness to the current posted prices. We show that if the firms run no-regret algorithms, in particular, online mirror descent(OMD), with decreasing step sizes, the market stabilizes in the sense that firms' prices and reference prices converge to a stable Nash Equilibrium (SNE). Interestingly, we also show that there exist constant step sizesunder which the market stabilizes. We further characterize the rate of convergence to the SNE for both decreasing and constant OMD step sizes.
Angular momentum exchange between light and material media deduced from the Doppler shift

Electromagnetic waves carry energy as well as linear and angular momenta. When a light pulse is reflected from, transmitted through, or absorbed by a material medium, energy and momentum (both linear and angular) are generally exchanged, while the total amount of each entity remains intact. The extent of such exchanges between light and matter can be deduced, among other methods, with the aid of the Doppler shift phenomenon. The main focus of the present paper is on the transfer of angular momentum from a monochromatic light pulse to spinning objects such as a mirror, an absorptive dielectric, or a birefringent plate. The fact that individual photons of frequency ω o carry energy in the amount of ħω o , where ħ is Planck’s reduced constant, enables one to relate the Doppler shift to the amount of energy exchanged. Under certain circumstances, the knowledge of exchanged energy leads directly to a determination of the momentum transferred from the photon to the material body, or vice versa.
Hamiltonian and Algebraic Theories of Gapped Boundaries in Topological Phases of Matter

We present an exactly solvable lattice Hamiltonian to realize gapped boundaries of Kitaev’s quantum double models for Dijkgraaf-Witten theories. We classify the elementary excitations on the boundary, and systematically describe the bulk-to-boundary condensation procedure. We also present the parallel algebraic/categorical structure of gapped boundaries.
Spontaneous formation of stable capillary bridges for firming compact colloidal microstructures in phase separating liquids: A computational study

Computer modeling and simulations are performed to investigate capillary bridges spontaneously formed between closely packed colloidal particles in phase separating liquids. The simulations reveal a self-stabilization mechanism that operates through diffusive equilibrium of two-phase liquid morphologies. Such mechanism renders desired microstructural stability and uniformity to the capillary bridges that are spontaneously formed during liquid solution phase separation. This self-stabilization behavior is in contrast to conventional coarsening processes during phase separation. The volume fraction limit of the separated liquid phases as well as the adhesion strength and thermodynamic stability of the capillary bridges are discussed. Capillary bridge formations in various compact colloid assemblies are considered. The study sheds light on a promising route to in situ (in-liquid) firming of fragile colloidal crystals and other compact colloidal microstructures via capillary bridges.
Hermitian Symmetric Polynomials and CR Complexity

Properties of Hermitian forms are used to investigate several natural questions from CR geometry. To each Hermitian symmetric polynomial we assign a Hermitian form. We study how the signature pairs of two Hermitian forms behave under the polynomial product. We show, except for three trivial cases, that every signature pair can be obtained from the product of two indefinite forms. We provide several new applications to the complexity theory of rational mappings between hyperquadrics, including a stability result about the existence of non-trivial rational mappings from a sphere to a hyperquadric with a given signature pair.
An aggregate and iterative disaggregate algorithm with proven optimality in machine learning

We propose a clustering-based iterative algorithm to solve certain optimization problems in machine learning, where we start the algorithm by aggregating the original data, solving the problem on aggregated data, and then in subsequent steps gradually disaggregate the aggregated data. We apply the algorithm to common machine learning problems such as the least absolute deviation regression problem, support vector machines, and semi-supervised support vector machines. We derive model-specific data aggregation and disaggregation procedures. We also show optimality, convergence, and the optimality gap of the approximated solution in each iteration. A computational study is provided.
CAPABILITIES OF EARTH-BASED RADAR FACILITIES FOR NEAR-EARTH ASTEROID OBSERVATIONS

We evaluated the planetary radar capabilities at Arecibo, the Goldstone 70 m DSS-14 and 34 m DSS-13 antennas, the 70 m DSS-43 antenna at Canberra, the Green Bank Telescope (GBT), and the Parkes Radio Telescope in terms of their relative sensitivities and the number of known near-Earth asteroids (NEAs) detectable per year in monostatic and bistatic configurations. In the 2015 calendar year, monostatic observations with Arecibo and DSS-14 were capable of detecting 253 and 131 NEAs respectively, with signal-to-noise ratios (SNRs) greater than 30/track. Combined, the two observatories were capable of detecting 276 NEAs. Of these, Arecibo detected 77 and Goldstone detected 32, or 30% and 24% of the numbers that were possible. The two observatories detected an additional 18 and 7 NEAs respectively, with SNRs of less than 30/track. This indicates that a substantial number of potential targets are not being observed. The bistatic configuration with DSS-14 transmitting and the GBT receiving was capable of detecting about 195 NEAs, or ~50% more than with monostatic observations at DSS-14. Most of the detectable asteroids were targets of opportunity that were discovered less than 15 days before the end of their observing windows. About 50% of the detectable asteroids have absolute magnitudes , which corresponds to diameters m.
The look-elsewhere effect from a unified Bayesian and frequentist perspective

When searching over a large parameter space for anomalies such as events, peaks, objects, or particles, there is a large probability that spurious signals with seemingly high significance will be found. This is known as the look-elsewhere effect and is prevalent throughout cosmology, (astro)particle physics, and beyond. To avoid making false claims of detection, one must account for this effect when assigning the statistical significance of an anomaly. This is typically accomplished by considering the trials factor, which is generally computed numerically via potentially expensive simulations. In this paper we develop a continuous generalization of the Bonferroni and Sidak corrections by applying the Laplace approximation to evaluate the Bayes factor, and in turn relating the trials factor to the prior-to-posterior volume ratio. We use this to define a test statistic whose frequentist properties have a simple interpretation in terms of the global p-value, or statistical significance. We apply this method to various physics-based examples and show it to work well for the full range of p-values, i.e. in both the asymptotic and non-asymptotic regimes. We also show that this method naturally accounts for other model complexities such as additional degrees of freedom, generalizing Wilks' theorem. This provides a fast way to quantify statistical significance in light of the look-elsewhere effect, without resorting to expensive simulations.
An Analysis of Crystal Cleavage in the Passage from Atomistic Models to Continuum Theory

We study the behavior of brittle atomistic models in general dimensions under uniaxial tension and investigate the system for critical fracture loads. We rigorously prove that in the discrete-to-continuum limit the minimal energy satisfies a particular cleavage law with quadratic response to small boundary displacements followed by a sharp constant cut-off beyond some critical value. Moreover, we show that the minimal energy is attained by homogeneous elastic configurations in the subcritical case and that beyond critical loading cleavage along specific crystallographic hyperplanes is energetically favorable. In particular, our results apply to mass spring models with full nearest and next-to-nearest pair interactions and provide the limiting minimal energy and minimal configurations.
Management of heavy menstrual bleeding on anticoagulation

Heavy menstrual bleeding (HMB) is a common complication of anticoagulation, affecting ∼70% of menstruating women receiving oral anticoagulants. The risk of HMB is lower with apixaban and/or dabigatran than with rivaroxaban. HMB can result in iron deficiency with or without anemia, increased need for medical interventions, decreased quality of life, and missed school/work. Mainstays of treatment include hormone therapies such as the levonorgestrel intrauterine system, subdermal implant, and other progesterone-based therapies, which can result in decreased blood loss and, in some cases, amenorrhea. Combined hormone therapies can be used while patients continue receiving anticoagulation and are also highly effective for decreasing menstrual blood loss. Rarely, procedure-based interventions such as endometrial ablation may be required. Patients should be evaluated for iron deficiency and anemia and offered supportive therapies as needed. Abbreviating the course of anticoagulation or skipping doses can increase the risk of recurrent venous thromboembolism by as much as fivefold, but switching oral anticoagulants may be considered. Awareness of HMB and careful history taking at each visit are crucial to avoid a missed diagnosis.
Electrostatic equilibria on the unit circle via Jacobi polynomials

We use classical Jacobi polynomials to identify the equilibrium configurations of charged particles confined to the unit circle. Our main result unifies two theorems from a 1986 paper of Forrester and Rogers.
A Permutation-Based Model for Crowd Labeling: Optimal Estimation and Robustness

The task of aggregating and denoising crowd-labeled data has gained increased significance with the advent of crowdsourcing platforms and massive datasets. We propose a permutation-based model for crowd labeled data that is a significant generalization of the classical Dawid-Skene model, and introduce a new error metric by which to compare different estimators. We derive global minimax rates for the permutation-based model that are sharp up to logarithmic factors, and match the minimax lower bounds derived under the simpler Dawid-Skene model. We then design two computationally-efficient estimators: the WAN estimator for the setting where the ordering of workers in terms of their abilities is approximately known, and the OBI- WAN estimator where that is not known. For each of these estimators, we provide non-asymptotic bounds on their performance. We conduct synthetic simulations and experiments on real-world crowdsourcing data, and the experimental results corroborate our theoretical findings.
Aligning with Heterogeneous Preferences for Kidney Exchange

AI algorithms increasingly make decisions that impact entire groups of humans. Since humans tend to hold varying and even conflicting preferences, AI algorithms responsible for making decisions on behalf of such groups encounter the problem of preference aggregation: combining inconsistent and sometimes contradictory individual preferences into a representative aggregate. In this paper, we address this problem in a real-world public health context: kidney exchange. The algorithms that allocate kidneys from living donors to patients needing transplants in kidney exchange matching markets should prioritize patients in a way that aligns with the values of the community they serve, but allocation preferences vary widely across individuals. In this paper, we propose, implement and evaluate a methodology for prioritizing patients based on such heterogeneous moral preferences. Instead of selecting a single static set of patient weights, we learn a distribution over preference functions based on human subject responses to allocation dilemmas, then sample from this distribution to dynamically determine patient weights during matching. We find that this methodology increases the average rank of matched patients in the sampled preference ordering, indicating better satisfaction of group preferences. We hope that this work will suggest a roadmap for future automated moral decision making on behalf of heterogeneous groups.
Wigner–Smith Time Delay Matrix for Electromagnetics: Computational Aspects for Radiation and Scattering Analysis

The Wigner–Smith (WS) time-delay matrix relates a lossless and reciprocal system’s scattering matrix to its frequency derivative and enables the synthesis of modes that experience well-defined group delays when interacting with the system. The elements of the WS time-delay matrix for surface scatterers and antennas comprise renormalized energy-like volume integrals involving electric and magnetic fields that arise when exciting the system via its ports. Here, direct and indirect methods for computing the WS time-delay matrix are presented. The direct method evaluates the energy-like volume integrals using surface integral operators that act on the incident electric fields and current densities for all excitations characterizing the scattering matrix. The indirect method accomplishes the same task by computing scattering parameters and their frequency derivatives. Both methods are computationally efficient and readily integrated into the existing surface integral equation codes. The proposed techniques facilitate the evaluation of frequency derivatives of antenna impedances, antenna patterns, and scatterer radar cross sections in terms of renormalized field energies derived from a single-frequency characterization of the system.
The Racialization of International Trade

Despite their less vulnerable economic status, white individuals’ attitudes toward overseas trade in the U.S. may have become more protectionist than those of economically disadvantaged minorities. We present results from five different studies examining two different ways in which trade may have become racialized. First, we examine the extent to which a person’s racial identity is associated with levels of trade support. Second, we examine whether the predominant racial identity of a potential trading partner country influences people’s willingness to trade with that country. Using various surveys and multiple survey-experiments conducted over the past twelve years, we find that white individuals have become less supportive of trade than minorities, and that whites are more likely than minorities to favor trade with highly similar countries. We suggest that minority support for trade is due to four well-documented differences in the psychological predispositions of whites and minorities in the United States. Minorities have lower levels of racial prejudice, are lower in social dominance, and express less nationalism than whites. At the same time, there is evidence of rising ingroup racial consciousness among whites. Each of these characteristics has been independently linked to trade support in a direction encouraging greater support for trade among minorities. As the U.S. grows ever closer to becoming a “majority minority” nation, the racialization of trade attitudes may stimulate shifts in the likely future of America’s trade relationships.
Deep FPF: Gain function approximation in high-dimensional setting

In this paper, we present a novel approach to approximate the gain function of the feedback particle filter (FPF). The exact gain function is the solution of a Poisson equation involving a probability-weighted Laplacian. The numerical problem is to approximate the exact gain function using only finitely many particles sampled from the probability distribution.Inspired by the recent success of the deep learning methods, we represent the gain function as a gradient of the output of a neural network. Thereupon considering a certain variational formulation of the Poisson equation, an optimization problem is posed for learning the weights of the neural network. A stochastic gradient algorithm is described for this purpose.The proposed approach has two significant properties/advantages: (i) The stochastic optimization algorithm allows one to process, in parallel, only a batch of samples (particles) ensuring good scaling properties with the number of particles; (ii) The remarkable representation power of neural networks means that the algorithm is potentially applicable and useful to solve high-dimensional problems. We numerically establish these two properties and provide extensive comparison to the existing approaches.
A Skellam regression model for quantifying positional value in soccer

Soccer is undeniably the most popular sport world-wide and everyone from general managers and coaching staff to fans and media are interested in evaluating players' performance. Metrics applied successfully in other sports, such as the (adjusted) +/- that allows for division of credit among a basketball team's players, exhibit several challenges when applied to soccer due to severe co-linearities. Recently, a number of player evaluation metrics have been developed utilizing optical tracking data, but they are based on proprietary data. In this work, our objective is to develop an open framework that can estimate the expected contribution of a soccer player to his team's winning chances using publicly available data. In particular, using data from (i) approximately 20,000 games from 11 European leagues over 8 seasons, and, (ii) player ratings from the FIFA video game, we estimate through a Skellam regression model the importance of every line (attackers, midfielders, defenders and goalkeeping) in winning a soccer game. We consequently translate the model to expected league points added above a replacement player (eLPAR). This model can further be used as a guide for allocating a team's salary budget to players based on their expected contributions on the pitch. We showcase similar applications using annual salary data from the English Premier League and identify evidence that in our dataset the market appears to under-value defensive line players relative to goalkeepers.
Attention-Gated Graph Convolutions for Extracting Drug Interaction Information from Drug Labels

Preventable adverse events as a result of medical errors present a growing concern in the healthcare system. As drug-drug interactions (DDIs) may lead to preventable adverse events, being able to extract DDIs from drug labels into a machine-processable form is an important step toward effective dissemination of drug safety information. Herein, we tackle the problem of jointly extracting mentions of drugs and their interactions, including interaction outcome, from drug labels. Our deep learning approach entails composing various intermediate representations, including graph-based context derived using graph convolutions (GCs) with a novel attention-based gating mechanism (holistically called GCA), which are combined in meaningful ways to predict on all subtasks jointly. Our model is trained and evaluated on the 2018 TAC DDI corpus. Our GCA model in conjunction with transfer learning performs at 39.20% F1 and 26.09% F1 on entity recognition (ER) and relation extraction (RE), respectively, on the first official test set and at 45.30% F1 and 27.87% F1 on ER and RE, respectively, on the second official test set. These updated results lead to improvements over our prior best by up to 6 absolute F1 points. After controlling for available training data, the proposed model exhibits state-of-the-art performance for this task.
Thermodynamic Discrimination between Energy Sources for Chemical Reactions

Summary Chemical transformations traverse large energy differences, yet the comparison of energy sources to drive a reaction is often done on a case-by-case basis; there is no fundamentally driven, universal framework with which to analyze and compare driving forces for chemical reactions. In this work, we present a reaction-independent expression for the equilibrium constant as a function of temperature, pressure, and voltage. With a specific set of axes, all reactions are represented by a single ( x , y ) point, and a quantitative divide between electrochemically and thermochemically driven reactions is visually evident. Additionally, we show that our expression has a strong physical basis in work and energy fluxes to the system, although specific data about operating conditions are necessary to provide a quantitative energy analysis. Overall, this universal equation and facile visualization of chemical reactions provides a consistent thermodynamic framework for comparing electrochemical versus thermochemical energy sources without knowledge of detailed process parameters.
Asymmetric foreign exchange cash flow exposure: A firm-level analysis

Abstract This study analyzes foreign exchange (FX) cash flow and equity exposures of a sample of U.S. multinational firms. Focusing on asymmetry in FX cash flow exposures to direction and magnitude of FX shocks, the study finds that asymmetry is pervasive in several alternative measures of FX cash flow exposure. Also, after decomposing FX equity exposures into discount rate and cash flow components, the study documents significant asymmetries in FX discount rate exposures. The latter finding implies that market-related factors in addition to cash flow–based arguments need to be considered when further exploring FX equity exposure. This study also highlights the importance of model specification: models with asymmetric specifications detect more firms with significant FX exposures.
Electronic Discovery and the Adoption of Information Technology

After firms adopt electronic information and communication technologies, their decision-making leaves a trail of electronic information that may be more extensive and accessible than a paper trail. We ask how the expected costs of litigation affect decisions to adopt technologies, such as electronic medical records (EMRs), which leave more of an electronic trail. EMRs allow hospitals to document electronically both patient symptoms and health providers’ reactions to those symptoms and may improve the quality of care that makes the net impact of their adoption on expected litigation costs ambiguous. This article studies the impact of state rules that facilitate the use of electronic records in court. We find evidence that hospitals are one-third less likely to adopt EMRs after these rules are enacted. (JEL K41, O33, I12).