cssi/SciSciGPT-SciSciNet huggingface dataset:
Identification of Risk Significant Automotive Scenarios Under Hardware Failures

The level of autonomous functions in vehicular control systems has been on a steady rise. This rise makes it more challenging for control system engineers to ensure a high level of safety, especially against unexpected failures such as stochastic hardware failures. A generic Backtracking Process Algorithm (BPA) based on a deductive implementation of the Markov/Cell-to-Cell Mapping technique is proposed for the identification of critical scenarios leading to the violation of safety goals. A discretized state-space representation of the system allows tracing of fault propagation throughout the system, and the quantification of probabilistic system evolution in time. A case study of a Hybrid State Control System for an autonomous vehicle prone to a brake-by-wire failure is constructed. The hazard of interest is collision with a stationary vehicle. The BPA is implemented to identify the risk significant scenarios leading to the hazard of interest.
How old are SN Ia progenitor systems? New observational constraints on the distribution of time delays from GALEX

The time delay between the formation of the progenitor systems of Type Ia supernovae (SNe Ia) and their detonation is a vital discriminant between the various progenitor scenarios that have been proposed for them. We use Sloan Digital Sky Survey optical and Galaxy Evolution Explorer (GALEX) ultraviolet observations of the early-type host galaxies of 21 nearby SNe Ia and quantify the presence or absence of any young stellar population to constrain the minimum time delay for each supernova. We find that early-type host galaxies lack ‘prompt’ SNe Ia with time delays of ≲100 Myr and that ∼70 per cent SNe Ia have minimum time delays of 275 Myr–1.25 Gyr, with a median of 650 Myr, while at least 20 per cent SNe Ia have minimum time delays of at least 1 Gyr at 95 per cent confidence and two of these four SNe Ia are likely older than 2 Gyr. The distribution of minimum time delays observed matches most closely the expectation for the single-degenerate channel with a main sequence donor. Furthermore, we do not find any evidence that subluminous SNe Ia are associated with long time delays.
The Aggregate Implications of Mergers and Acquisitions

This article develops a search and matching model of mergers and acquisitions (M&A) and uses it to evaluate the implications of merger activity for aggregate economic outcomes. The theory is consistent with a rich set of facts on U.S. M&A, including sorting among merging firms, a substantial merger premium and serial acquisition. It provides a sharp link between these facts and the nature of merger gains. At the micro-level, both complementarities between merging firms and productivity improvements of target firms are important in generating gains. At the macro-level, the model suggests a significant beneficial impact of M&A on aggregate outcomes—the contribution to steady state output is 14% and 4% for consumption—which occurs through the reallocation of resources across firms and equilibrium effects on firm selection and new entrepreneurship. Nevertheless, the economy is not efficient, suggesting a scope for policy improvements—a simple flat tax on M&A can raise steady state consumption as much as 2% relative to the laissez-faire equilibrium. In short, the boundaries of the firm can matter for macroeconomic outcomes.
EXPLOITING LOW-DIMENSIONAL STRUCTURE IN ASTRONOMICAL SPECTRA

Dimension-reduction techniques can greatly improve statistical inference in astronomy. A standard approach is to use Principal Components Analysis (PCA). In this work, we apply a recently developed technique, diffusion maps, to astronomical spectra for data parameterization and dimensionality reduction, and develop a robust, eigenmode-based framework for regression. We show how our framework provides a computationally efficient means by which to predict redshifts of galaxies, and thus could inform more expensive redshift estimators such as template cross-correlation. It also provides a natural means by which to identify outliers (e.g., misclassified spectra, spectra with anomalous features). We analyze 3835 Sloan Digital Sky Survey spectra and show how our framework yields a more than 95% reduction in dimensionality. Finally, we show that the prediction error of the diffusion-map-based regression approach is markedly smaller than that of a similar approach based on PCA, clearly demonstrating the superiority of diffusion maps over PCA for this regression task.
Simplicial volume of links from link diagrams

The hyperbolic volume of a link complement is known to be unchanged when a half-twist is added to a link diagram, and a suitable 3-punctured sphere is present in the complement. We generalize this to the simplicial volume of link complements by analyzing the corresponding toroidal decompositions. We then use it to prove a refined upper bound for the volume in terms of twists of various lengths for links.
Polarized gravitational waves from gamma-ray bursts

Significant gravitational-wave emission is expected from gamma-ray bursts arising from compact stellar mergers and possibly also from bursts associated with fast-rotating massive stellar core collapses. These models have in common a high angular rotation rate, and observations provide evidence for jet collimation of the photon emission, with properties depending on the polar angle. Here we consider the gravitational-wave emission and its polarization as a function of angle that is expected from such sources. We discuss possible correlations between the burst photon luminosity or the delay between gravitational-wave bursts and X-ray flashes and the polarization degree of the gravitational waves.
ARE SOME MILKY WAY GLOBULAR CLUSTERS HOSTED BY UNDISCOVERED GALAXIES

The confirmation of a globular cluster (GC) in the recently discovered ultrafaint galaxy Eridanus II (Eri II) motivated us to examine the question posed in the title. After estimating the halo mass of Eri II using a published stellar mass—halo mass relation, the one GC in this galaxy supports extending the relationship between the number of GCs hosted by a galaxy and the galaxy's total mass about two orders of magnitude in stellar mass below the previous limit. For this empirically determined specific frequency of between 0.06 and 0.39 GCs per 109 M ⊙ of total mass, the surviving Milky Way (MW) subhalos with masses smaller than 1010 M ⊙ could host as many as 5–31 GCs, broadly consistent with the actual population of outer halo MW GCs, although matching the radial distribution in detail remains a challenge. Using a subhalo mass function from published high-resolution numerical simulations and a Poissonian model for populating those halos with the aforementioned empirically constrained frequency, we find that about 90% of these GCs lie in lower-mass subhalos than that of Eri II. From what we know about the stellar mass–halo mass function, the subhalo mass function, and the mass-normalized GC specific frequency, we conclude that some of the MW's outer halo GCs are likely to be hosted by undetected subhalos with extremely modest stellar populations.
Matter from geometry without resolution

We utilize the deformation theory of algebraic singularities to study charged matter in compactifications of M-theory, F-theory, and type IIa string theory on ellipti- cally fibered Calabi-Yau manifolds. In F-theory, this description is more physical than that of resolution. We describe how two-cycles can be identified and systematically studied after deformation. For ADE singularities, we realize non-trivial ADE representations as sublattices of $ {{\mathbb{Z}}^N} $ , where N is the multiplicity of the codimension one singularity be- fore deformation. We give a method for the determination of Picard-Lefschetz vanishing cycles in this context and utilize this method for one-parameter smooth deformations of ADE singularities. We give a general map from junctions to weights and demonstrate that Freudenthal’s recursion formula applied to junctions correctly reproduces the structure of high-dimensional ADE representations, including the 126 of SO(10) and the 43,758 of E 6. We identify the Weyl group action in some examples, and verify its order in others. We describe the codimension two localization of matter in F-theory in the case of heterotic duality or simple normal crossing and demonstrate the branching of adjoint representations. Finally, we demonstrate geometrically that deformations correctly reproduce the appearance of non-simply-laced algebras induced by monodromy around codimension two singularities, showing the reduction of D 4 to G 2 in an example. A companion mathematical paper will follow.
A Parabolic Problem with a Fractional Time Derivative

We study regularity for a parabolic problem with fractional diffusion in space and a fractional time derivative. Our main result is a De Giorgi–Nash–Moser Holder regularity theorem for solutions in a divergence form equation. We also prove results regarding existence, uniqueness, and higher regularity in time.
The Brauer-Manin obstruction for subvarieties of abelian varieties over function fields

We prove that for a large class of subvarieties of abelian varieties over global function fields, the Brauer-Manin condition on adelic points cuts out exactly the rational points. This result is obtained from more general results concerning the intersection of the adelic points of a subvariety with the adelic closure of the group of rational points of the abelian variety.
Statistical structure of the trial-to-trial timing variability in synfire chains.

Timing and its variability are crucial for behavior. Consequently, neural circuits that take part in the control of timing and in the measurement of temporal intervals have been the subject of much research. Here we provide an analytical and computational account of the temporal variability in what is perhaps the most basic model of a timing circuit---the synfire chain. First we study the statistical structure of trial-to-trial timing variability in a reduced but analytically tractable model: a chain of single integrate-and-fire neurons. We show that this circuit's variability is well described by a generative model consisting of local, global, and jitter components. We relate each of these components to distinct neural mechanisms in the model. Next we establish in simulations that these results carry over to a noisy homogeneous synfire chain. Finally, motivated by the fact that a synfire chain is thought to underlie the circuit that takes part in the control and timing of the zebra finch song, we present simulations of a biologically realistic synfire chain model of the zebra finch timekeeping circuit. We find the structure of trial-to-trial timing variability to be consistent with our previous findings and to agree with experimental observations of the song's temporal variability. Our study therefore provides a possible neuronal account of behavioral variability in zebra finches.
Discovering State-Parameter Mappings in Subsurface Models Using Generative Adversarial Networks

A fundamental problem in geophysical modeling is related to the identification and approximation of causal structures among physical processes. However, resolving the bidirectional mappings between physical parameters and model state variables (i.e., solving the forward and inverse problems) is challenging, especially when parameter dimensionality is high. Deep learning has opened a new door toward knowledge representation and complex pattern identification. In particular, the recently introduced generative adversarial networks (GANs) hold strong promises in learning cross-domain mappings for image translation. This study presents a state-parameter identification GAN (SPID-GAN) for simultaneously learning bidirectional mappings between a high-dimensional parameter space and the corresponding model state space. SPID-GAN is demonstrated using a series of representative problems from subsurface flow modeling. Results show that SPID-GAN achieves satisfactory performance in identifying the bidirectional state-parameter mappings, providing a new deep-learning-based, knowledge representation paradigm for a wide array of complex geophysical problems.
New insights on an old problem: resummation of the D -parameter

The D-parameter is one of the oldest and most experimentally well-studied hadronic observables for e+e− collisions. Nevertheless, unlike other classic observables like the C-parameter or thrust, the D-parameter has never been resummed throughout its entire singular phase space. Using insights and techniques motivated by modern multi-differential jet substructure calculations, we are able to predict the D-parameter distribution with no additional phase space cuts. Our approach is to measure both the C- and D-parameters on hadronic final states in e+e− collisions. We can tune the value of the C-parameter with respect to the D-parameter to specify simple, physical configurations of final state particles in which to perform calculations. There are three parametric regions that exist: D ≪C2 ∼1, D ≪C2 ≪1, and D ∼C2 ≪1, and we calculate the D-parameter in each region separately. In the first two of these three regions, we present all-orders factorization theorems and explicitly demonstrate resummation to next-to-leading logarithmic accuracy. The region in which D ∼ C2 ≪ 1 corresponds to the dijet limit and where the D-parameter loses the property of additivity. In this region we introduce a systematically-improvable procedure exploiting properties of conditional probabilities and resum to approximate next-to-leading logarithmic accuracy. The contributions from these regions can be consistently combined, and the value of the C-parameter integrated over to produce the cross section for the D-parameter. With these results, we match to leading fixed order as proof of principle and compare our resummed and matched prediction to data from LEP.
Squark flavor violation at the LHC

We consider the prospects for measuring squark flavor violation through the signal of single top production at the LHC. We study this signal in the context of R-symmetric supersymmetry, which allows for large flavor violation in the squark sector, however the results can also be generalized to the MSSM. The single top signal arises from squark pair production in which one squark decays to a top and gaugino, whereas the other squark decays to a non-top quark and gaugino. We study three decay patterns: (I) squark decay into a quark and neutralino LSP; (II) squark decay into a quark and neutralino NLSP, with subsequent decay of the NLSP to a photon and gravitino; (III) squark decay into a quark and chargino NLSP, with subsequent decay of the NLSP to a H±/W± and gravitino. Case II is the most promising, when the NLSP decay is prompt, since every event contains two hard photons that can be used to tag the events, reducing the background to a negligible level. Case I is promising if the neutralino LSP is bino-like. We carefully consider large SM backgrounds and identify a series of cuts to isolate the signal. Case III can occur in the minimal R-symmetric supersymmetric standard model (MRSSM) with Higgsino-like lightest gauginos. Due to the large Higgs coupling, squarks preferentially decay to top quarks, substantially reducing the potential flavor violating signal. Nevertheless, the flavor violating signal might still be identifiable if the chargino NLSP is long-lived.
Intermittent turbulence in a many-body system

This paper shows that stochastic driving and localized disorder are sufficient to give rise to intermittent cascades between distinct energy scales. The dynamics closely resemble transitional turbulence in a pipe flow and can be modeled using stochastic predator-prey equations.
The Scikit-HEP Project

The Scikit-HEP project is a community-driven and community-oriented effort with the aim of providing Particle Physics at large with a Python scientific toolset containing core and common tools. The project builds on five pillars that embrace the major topics involved in a physicist’s analysis work: datasets, data aggregations, modelling, simulation and visualisation. The vision is to build a user and developer community engaging collaboration across experiments, to emulate scikit-learn’s unified interface with Astropy’s embrace of third-party packages, and to improve discoverability of relevant tools.
Early Detection of Sepsis Using Ensemblers

This paper describes a methodology to detect sepsis ahead of time by analyzing hourly patient records. The Physionet 2019 challenge consists of medical records of over 40,000 patients. Using imputation and weak ensem- bler technique to analyze these medical records and 3-fold validation, a model is created and validated internally. On a hidden test data set maintained by the organizers, the model obtained a utility score of 0.192. The utility score as defined by the organizers takes into account true positives, negatives and false alarms. Our team was Team Tesseract and our overall ranking was 49 out of 79 officially ranked entries.
Reconstructing an icosahedral virus from single-particle diffraction experiments

The first experimental data from single-particle scattering experiments from free electron lasers (FELs) are now becoming available. The first such experiments are being performed on relatively large objects such as viruses, which produce relatively low-resolution, low-noise diffraction patterns in so-called "diffract-and-destroy" experiments. We describe a very simple test on the angular correlations of measured diffraction data to determine if the scattering is from an icosahedral particle. If this is confirmed, the efficient algorithm proposed can then combine diffraction data from multiple shots of particles in random unknown orientations to generate a full 3D image of the icosahedral particle. We demonstrate this with a simulation for the satellite tobacco necrosis virus (STNV), the atomic coordinates of whose asymmetric unit is given in Protein Data Bank entry 2BUK.
Initial Characteristics of Kepler Long Cadence Data for Detecting Transiting Planets

The Kepler Mission seeks to detect Earth-size planets transiting solar-like stars in its ~115?deg2 field of view over the course of its 3.5 year primary mission by monitoring the brightness of each of ~156,000 Long Cadence stellar targets with a time resolution of 29.4 minutes. We discuss the photometric precision achieved on timescales relevant to transit detection for data obtained in the 33.5 day long Quarter 1 (Q1) observations that ended 2009 June 15. The lower envelope of the photometric precision obtained at various timescales is consistent with expected random noise sources, indicating that Kepler has the capability to fulfill its mission. The Kepler light curves exhibit high precision over a large dynamic range, which will surely permit their use for a large variety of investigations in addition to finding and characterizing planets. We discuss the temporal characteristics of both the raw flux time series and the systematic error-corrected flux time series produced by the Kepler Science Pipeline, and give examples illustrating Kepler's large dynamic range and the variety of light curves obtained from the Q1 observations.
Symbolic dynamics on amenable groups: the entropy of generic shifts

Let G be a finitely generated amenable group. We study the space of shifts on G over a given finite alphabet A. We show that the zero entropy shifts are generic in this space, and that more generally the shifts of entropy c are generic in the space of shifts with entropy at least c. The same is shown to hold for the space of transitive shifts and for the space of weakly mixing shifts. As applications of this result, we show that for every entropy value c∈[0,log|A|] there is a weakly mixing subshift of A^G with entropy c. We also show that the set of strongly irreducible shifts does not form a G_δ in the space of shifts, and that all non-trivial, strongly irreducible shifts are non-isolated points in this space.
The moduli and gravitino (non)-problems in models with strongly stabilized moduli

In gravity mediated models and in particular in models with strongly stabilized moduli, there is a natural hierarchy between gaugino masses, the gravitino mass and moduli masses: m1/2 << m3/2 << m. Given this hierarchy, we show that 1) moduli problems associated with excess entropy production from moduli decay and 2) problems associated with moduli/gravitino decays to neutralinos are non-existent. Placed in an inflationary context, we show that the amplitude of moduli oscillations are severely limited by strong stabilization. Moduli oscillations may then never come to dominate the energy density of the Universe. As a consequence, moduli decay to gravitinos and their subsequent decay to neutralinos need not overpopulate the cold dark matter density.
Numerical Relativity and the Discovery of Gravitational Waves

Solving Einstein's equations precisely for strong‐field gravitational systems is essential to determining the full physics content of gravitational wave detections. Without these solutions it is not possible to infer precise values for initial and final‐state system parameters. Obtaining these solutions requires extensive numerical simulations, as Einstein's equations governing these systems are much too difficult to solve analytically. These difficulties arise principally from the curved, non‐linear nature of spacetime in general relativity. Developing the numerical capabilities needed to produce reliable, efficient calculations has required a Herculean 50‐year effort involving hundreds of researchers using sophisticated physical insight, algorithm development, computational technique, and computers that are a billion times more capable than they were in 1964 when computations were first attempted. The purpose of this review is to give an accessible overview for non‐experts of the major developments that have made such dramatic progress possible.
Social bots for online public health interventions

According to the Center for Disease Control and Prevention, hundreds of thousands initiate smoking each year, and millions live with smoking-related diseases in the United States. Many tobacco users discuss their opinions, habits and preferences on social media. This work conceptualizes a framework for targeted health interventions to inform tobacco users about the consequences of tobacco use. We designed a Twitter bot named Notobot (short for No-Tobacco Bot) that leverages machine learning to identify users posting pro-tobacco tweets and select individualized interventions to curb their tobacco use. We searched the Twitter feed for tobacco-related keywords and phrases, and trained a convolutional neural network using over 4,000 tweets manually labeled as either pro-tobacco or not pro-tobacco. This model achieved a 90% accuracy rate on the training set and 74% on test data. Users posting pro-tobacco tweets were matched with former smokers with similar interests who posted anti-tobacco tweets. Algorithmic matching, leveraging the power of peer influence, allows for the systematic delivery of personalized interventions based on real anti-tobacco tweets from former smokers. Experimental evaluation suggested that our system would perform well if deployed.
Limits to Poisson's ratio in isotropic materials—general result for arbitrary deformation

The lower bound customarily cited for Poisson's ratio ?, ?1, is derived from the relationship between ? and the bulk and shear moduli in the classical theory of linear elasticity. However, experimental verification of the theory has been limited to materials having ????0.2. From consideration of the longitudinal and biaxial moduli, we recently determined that the lower bound on ? for isotropic materials from this theory is actually . Herein we generalize this result, first by analyzing expressions for ? in terms of six common elastic constants, and then by considering arbitrary strains. The results corroborate that for classical linear elasticity to be applicable. Of course, a few materials exist for which ??<?0.2, thus deviating from this bound; accurate analysis of their mechanical behavior requires more sophisticated elasticity models.
Binary Black Holes in Dense Star Clusters: Exploring the Theoretical Uncertainties

United States. National Aeronautics and Space Administration. Astrophysics Theory Program (Grant NNX14AP92G)
Topology of closed hypersurfaces of small entropy

We use a weak mean curvature flow together with a surgery procedure to show that all closed hypersurfaces in ℝ4 with entropy less than or equal to that of S2×ℝ, the round cylinder in ℝ4, are diffeomorphic to S3.
CLIMATE INSTABILITY ON TIDALLY LOCKED EXOPLANETS

Feedbacks that can destabilize the climates of synchronously rotating rocky planets may arise on planets with strong day-night surface temperature contrasts. Earth-like habitable planets maintain stable surface liquid water over geologic time. This requires equilibrium between the temperature-dependent rate of greenhouse-gas consumption by weathering, and greenhouse-gas resupply by other processes. Detected small-radius exoplanets, and anticipated M-dwarf habitable-zone rocky planets, are expected to be in synchronous rotation (tidally locked). In this paper, we investigate two hypothetical feedbacks that can destabilize climate on planets in synchronous rotation. (1) If small changes in pressure alter the temperature distribution across a planet's surface such that the weathering rate goes up when the pressure goes down, a runaway positive feedback occurs involving increasing weathering rate near the substellar point, decreasing pressure, and increasing substellar surface temperature. We call this feedback enhanced substellar weathering instability (ESWI). (2) When decreases in pressure increase the fraction of surface area above the melting point (through reduced advective cooling of the substellar point), and the corresponding increase in volume of liquid causes net dissolution of the atmosphere, a further decrease in pressure will occur. This substellar dissolution feedback can also cause a runaway climate shift. We use an idealized energy balance model to map out the conditions under which these instabilities may occur. In this simplified model, the weathering runaway can shrink the habitable zone and cause geologically rapid 103-fold atmospheric pressure shifts within the habitable zone. Mars may have undergone a weathering runaway in the past. Substellar dissolution is usually a negative feedback or weak positive feedback on changes in atmospheric pressure. It can only cause runaway changes for small, deep oceans and highly soluble atmospheric gases. Both instabilities are suppressed if the atmosphere has a high radiative efficiency. Our results are most relevant for atmospheres that are thin, have low greenhouse-gas radiative efficiency, and have a principal greenhouse gas that is also the main constituent of the atmosphere. ESWI also requires land near the substellar point, and tectonic resurfacing (volcanism, mountain-building) is needed for large jumps in pressure. These results identify a new pathway by which habitable-zone planets can undergo rapid climate shifts and become uninhabitable.
Remainder terms for some quantum entropy inequalities

We consider three von Neumann entropy inequalities: subadditivity; Pinsker's inequality for relative entropy; and the monotonicity of relative entropy. For these we state conditions for equality, and we prove some new error bounds away from equality, including an improved version of Pinsker's inequality.
Local Arboreal Representations

Let $K$ be a field complete with respect to a discrete valuation $v$ of residue characteristic $p$. Let $f(z) \in K[z]$ be a separable polynomial of the form $z^\ell-c.$ Given $a \in K$, we examine the Galois groups and ramification groups of the extensions of $K$ generated by the solutions to $f^n(z)=a$. The behavior depends upon $v(c)$, and we find that it shifts dramatically as $v(c)$ crosses a certain value: $0$ in the case $p \nmid \ell$, and $-p/(p-1)$ in the case $p=\ell$.
On partially wrapped Fukaya categories

We define a new class of symplectic spaces called ``pumpkin domains'', which roughly speaking comprise a Liouville domain and a Liouville hypersurface of its boundary. To such an object we assign an $A_\infty$-category called its partially wrapped Fukaya category. An exact Landau-Ginzburg model gives rise to a pumpkin domain, and the partially wrapped Fukaya category of this pumpkin domain is meant to agree with the Fukaya category one is supposed to assign to the Landau-Ginzburg model. As evidence, we prove a formula that relates the partially wrapped Fukaya category of a pumpkin domain to the wrapped Fukaya category of its underlying Liouville domain. This operation is mirror to removing a divisor.
Building Disease Detection Algorithms with Very Small Numbers of Positive Samples

Although deep learning can provide promising results in medical image analysis, the lack of very large annotated datasets confines its full potential. Furthermore, limited positive samples also create unbalanced datasets which limit the true positive rates of trained models. As unbalanced datasets are mostly unavoidable, it is greatly beneficial if we can extract useful knowledge from negative samples to improve classification accuracy on limited positive samples. To this end, we propose a new strategy for building medical image analysis pipelines that target disease detection. We train a discriminative segmentation model only on normal images to provide a source of knowledge to be transferred to a disease detection classifier. We show that using the feature maps of a trained segmentation network, deviations from normal anatomy can be learned by a two-class classification network on an extremely unbalanced training dataset with as little as one positive for 17 negative samples. We demonstrate that even though the segmentation network is only trained on normal cardiac computed tomography images, the resulting feature maps can be used to detect pericardial effusion and cardiac septal defects with two-class convolutional classification networks.
Removal energies and final state interaction in lepton nucleus scattering

We investigate the binding energy parameters that should be used in modeling electron and neutrino scattering from nucleons bound in a nucleus within the framework of the impulse approximation. We discuss the relation between binding energy, missing energy, removal energy ( $$\epsilon $$ ), spectral functions and shell model energy levels and extract updated removal energy parameters from $$\hbox {ee}^{\prime }$$ p spectral function data. We address the difference in parameters for scattering from bound protons and neutrons. We also use inclusive e-A data to extract an empirical parameter $$U_{FSI}( (\varvec{q}_3+\varvec{k})^2)$$ to account for the interaction of final state nucleons (FSI) with the optical potential of the nucleus. Similarly we use $$V_{eff}$$ to account for the Coulomb potential of the nucleus. With three parameters $$\epsilon $$ , $$U_{FSI}( (\varvec{q}_3+\varvec{k})^2)$$ and $$V_{eff}$$ we can describe the energy of final state electrons for all available electron QE scattering data. The use of the updated parameters in neutrino Monte Carlo generators reduces the systematic uncertainty in the combined removal energy (with FSI corrections) from ± 20 to ± 5 MeV.
Resonance-enhanced waveguide-coupled silicon-germanium detector

A photodiode with 0.55 ± 0.1 A/W responsivity at a wavelength of 1176.9 nm has been fabricated in a 45 nm microelectronics silicon-on-insulator foundry process. The resonant waveguide photodetector exploits carrier generation in silicon-germanium within a microring which is compatible with high-performance electronics. A 3 dB bandwidth of 5 GHz at −4 V bias is obtained with a dark current of less than 20 pA.
Stabilization of dayside surface liquid water via tropopause cold trapping on arid slowly rotating tidally locked planets

Terrestrial-type exoplanets orbiting nearby red dwarf stars (M-dwarfs) are among the best targets for atmospheric characterization and biosignature searches in the near future. Recent evolutionary studies have suggested that terrestrial planets in the habitable zone of M-dwarfs are probably tidally locked and have limited surface water inventories as a result of their host stars' high early luminosities. Several previous climate simulations of such planets have indicated that their remaining water would be transported to the planet's permanent nightside and become trapped as surface ice, leaving the dayside devoid of water. Here we use a three-dimensional general circulation model with a water cycle and accurate radiative transfer scheme to investigate the surface water evolution on slowly rotating tidally locked terrestrial planets with limited surface water inventories. We show that there is a competition for water trapping between the nightside surface and the substellar tropopause in this type of climate system. Although under some conditions the surface water remains trapped on the nightside as an ice sheet, in other cases liquid water stabilizes in a circular area in the substellar region as a wetland. Planets with 1 bar N$_2$ and atmospheric CO$_2$ levels greater than 0.1 bar retain stable dayside liquid water, even with very small surface water inventories. Our results reveal the diversity of possible climate states on terrestrial-type exoplanets and highlight the importance of surface liquid water detection techniques for future characterization efforts.
Holomorphic Line Bundles on Projective Toric Manifolds from Lagrangian Sections of their Mirrors by SYZ Transformations

The mirror of a projective toric manifold is given by a Landau-Ginzburg model (Y, W). We introduce a class of Lagrangian submanifolds in (Y, W) and show that, under the SYZ mirror transformation, they can be transformed to torus-invariant Hermitian metrics on holomorphic line bundles over . Through this geometric correspondence, we also identify the mirrors of Hermitian-Einstein metrics, which are given by distinguished Lagrangian sections whose potentials satisfy certain Laplace-type equations.
Redox Evolution via Gravitational Differentiation on Low-mass Planets: Implications for Abiotic Oxygen, Water Loss, and Habitability

The oxidation of rocky planet surfaces and atmospheres, which arises from the twin forces of stellar nucleosynthesis and gravitational differentiation, is a universal process of key importance to habitability and exoplanet biosignature detection. Here we take a generalized approach to this phenomenon. Using a single parameter to describe redox state, we model the evolution of terrestrial planets around nearby M-stars and the Sun. Our model includes atmospheric photochemistry, diffusion and escape, line-by-line climate calculations and interior thermodynamics and chemistry. In most cases we find abiotic atmospheric O2 buildup around M-stars during the pre-main sequence phase to be much less than calculated previously, because the planet's magma ocean absorbs most oxygen liberated from H2O photolysis. However, loss of non-condensing atmospheric gases after the mantle solidifies remains a significant potential route to abiotic atmospheric O2 subsequently. In all cases, we predict that exoplanets that receive lower stellar fluxes, such as LHS1140b and TRAPPIST-1f and g, have the lowest probability of abiotic O2 buildup and hence may be the most interesting targets for future searches for biogenic O2. Key remaining uncertainties can be minimized in future by comparing our predictions for the atmospheres of hot, sterile exoplanets such as GJ1132b and TRAPPIST-1b and --c with observations.
The WYSIWYG compactification

We show that the partial compactification of a stratum of Abelian differentials previously considered by Mirzakhani and Wright is not an algebraic variety. Despite this, we use a combination of algebro-geometric and other methods to provide a short, unconditional proof of Mirzakhani and Wright's formula for the tangent space to the boundary of a GL(2,R) orbit closure, and give new results on the structure of the boundary.
Cylinder deformations in orbit closures of translation surfaces

The Siegel–Veech formula establishes such a connection asymptotically in counting problems; see Veech [26] and Eskin and Masur [5]. The Veech dichotomy shows in particular that for translation surfaces in closed orbits, a very strong form of complete periodicity holds [25]. Work of Calta [4] and also very recent work of Lanneau and Nguyen [13] show that in certain special orbit closures in low genus, every translation surface is completely periodic; see also McMullen [17].
Altarelli-parisi equation in non-equilibrium QCD

The Q2 evolution of fragmentation function in non-equilibrium QCD by using DGLAP evolution equation may be necessary to study hadron formation from quark-gluon plasma at RHIC and LHC. In this paper we study splitting functions in non-equilibrium QCD by using Schwinger-Keldysh closed-time path integral formalism. For quarks and gluons with arbitrary non-equilibrium distribution functions fq(\(\vec p\)) and fg(\(\vec p\)), we derive expressions for quark and gluon splitting functions in non-equilibrium QCD at leading order in αs. We make a comparison of these splitting functions with that obtained by Altarelli and Parisi in vacuum.
Resonance Production in Heavy-Ion Collisions

Hadronic resonances are unique probes that allow the properties of heavyion collisions to be studied. Topics that can be studied include modification of spectral shapes, in-medium energy loss of parsons, vector-meson spin alignment, hydrodynamic flow, recombination, strangeness production, and the properties of the hadronic phase. Measurements of resonances in p+p , p+A , and d+A collisions serve as baselines for heavy-ion studies and also permit searches for possible collective effects in these smaller systems. These proceedings present a selection of results related to these topics from experiments at RHIC, LHC, and other facilities, as well as comparisons to theoretical models.
The Naming Game in social networks: community formation and consensus engineering

We study the dynamics of the Naming Game (Baronchelli et al. in J Stat Mech Theory Exp P06014, 2006b) in empirical social networks. This stylized agent-based model captures essential features of agreement dynamics in a network of autonomous agents, corresponding to the development of shared classification schemes in a network of artificial agents or opinion spreading and social dynamics in social networks. Our study focuses on the impact that communities in the underlying social graphs have on the outcome of the agreement process. We find that networks with strong community structure hinder the system from reaching global agreement; the evolution of the Naming Game in these networks maintains clusters of coexisting opinions indefinitely. Further, we investigate agent-based network strategies to facilitate convergence to global consensus.
$O(n)$ Connections are Expressive Enough: Universal Approximability of Sparse Transformers

Recently, Transformer networks have redefined the state of the art in many NLP tasks. However, these models suffer from quadratic computational cost in the input sequence length $n$ to compute pairwise attention in each layer. This has prompted recent research into sparse Transformers that sparsify the connections in the attention layers. While empirically promising for long sequences, fundamental questions remain unanswered: Can sparse Transformers approximate any arbitrary sequence-to-sequence function, similar to their dense counterparts? How does the sparsity pattern and the sparsity level affect their performance? In this paper, we address these questions and provide a unifying framework that captures existing sparse attention models. We propose sufficient conditions under which we prove that a sparse attention model can universally approximate any sequence-to-sequence function. Surprisingly, our results show that sparse Transformers with only $O(n)$ connections per attention layer can approximate the same function class as the dense model with $n^2$ connections. Lastly, we present experiments comparing different patterns/levels of sparsity on standard NLP tasks.
On the dynamic homogenization of periodic media: Willis’ approach versus two-scale paradigm

When considering an effective, i.e. homogenized description of waves in periodic media that transcends the usual quasi-static approximation, there are generally two schools of thought: (i) the two-...
Recent advances in computational methods for the power flow equations

The power flow equations are at the core of most of the computations for designing and operating electric grids. This system of multivariate nonlinear equations relate the power injections and voltages in an electric power system. A plethora of methods have been devised to solve these equations, from Newton-based methods to homotopy continuation and other optimization-based methods. Although many of these methods often efficiently find a high-voltage, stable solution, challenges remain for finding low-voltage solutions, which play significant roles in certain stability-related computations. While we do not claim to have exhausted the existing literature on all related methods, this tutorial paper introduces some of the recent advances in power flow solution methods to the wider power systems community as well as bringing attention from the computational mathematics and optimization communities to power systems problems. After briefly reviewing some of the traditional computational methods used to solve the power flow equations, we focus on three emerging methods: the numerical polynomial homotopy continuation method, Grobner basis techniques, and moment/sum-of-squares relaxations using semidefinite programming. In passing, we also emphasize the importance of an upper bound on the number of solutions of the power flow equations and review the current status of research in this direction.
Unstable magnetic fluxes in heterotic string theory

We study exact string backgrounds representing a constant magnetic field background in heterotic string theory. These backgrounds are obtained by Kaluza-Klein reduction of a special class of plane wave solutions. For small values of the magnetic field they possess localized closed string tachyons analogous to the Nielsen-Olesen instability of a constant magnetic field in SO(3) Yang-Mills theory. When the magnetic field is embedded in the SO(32) gauge group of the heterotic string it is possible to study the lowest level tachyon in supergravity. We identify the closed string tachyons as fluctuations of the supergravity fields about the background. We argue that the tachyon signals the decay of the background to flat space. Our evidence rests on the study of the closed string tachyon potential, world sheet renormalization group equations in the supergravity approximation and the S-dual of this system in type-I theory. In the S-dual description, the closed string tachyons in heterotic string theory correspond to open string tachyons in type-I theory. Finally we analyze the non-perturbative stability of these models representing constant magnetic field backgrounds and show that it is stable to pair production of particles.
Computer-automated tuning procedures for semiconductor quantum dot arrays

As with any quantum computing platform, semiconductor quantum dot devices require sophisticated hardware and controls for operation. The increasing complexity of quantum dot devices necessitates the advancement of automated control software and image recognition techniques for rapidly evaluating charge stability diagrams. We use an image analysis toolbox developed in Python to automate the calibration of virtual gates, a process that previously involved a large amount of user intervention. Moreover, we show that straightforward feedback protocols can be used to simultaneously tune multiple tunnel couplings in a triple quantum dot in a computer automated fashion. Finally, we adopt the use of a “tunnel coupling lever arm” to model the interdot barrier gate response and discuss how it can be used to more rapidly tune interdot tunnel couplings to the gigahertz values that are compatible with exchange gates.
rTop- k : A Statistical Estimation Approach to Distributed SGD

The large communication cost for exchanging gradients between different nodes significantly limits the scalability of distributed training for large-scale learning models. Motivated by this observation, there has been significant recent interest in techniques that reduce the communication cost of distributed Stochastic Gradient Descent (SGD), with gradient sparsification techniques such as top- $k$ and random- $k$ shown to be particularly effective. The same observation has also motivated a separate line of work in distributed statistical estimation theory focusing on the impact of communication constraints on the estimation efficiency of different statistical models. The primary goal of this paper is to connect these two research lines and demonstrate how statistical estimation models and their analysis can lead to new insights in the design of communication-efficient training techniques. We propose a simple statistical estimation model for the stochastic gradients which captures the sparsity and skewness of their distribution. The statistically optimal communication scheme arising from the analysis of this model leads to a new sparsification technique for SGD, which concatenates random- $k$ and top- $k$ , considered separately in the prior literature. We show through extensive experiments on both image and language domains with CIFAR-10, ImageNet, and Penn Treebank datasets that the concatenated application of these two sparsification methods consistently and significantly outperforms either method applied alone.
Differential-henselianity and maximality of asymptotic valued differential fields

We show that asymptotic (valued differential) fields have unique maximal immediate extensions. Connecting this to differential-henselianity, we prove that any differential-henselian asymptotic field is differential-algebraically maximal, removing the assumption of monotonicity from a theorem of Aschenbrenner, van den Dries, and van der Hoeven (arXiv:1509.02588, Theorem 7.0.3). Finally, we use this result to show the existence and uniqueness of differential-henselizations of asymptotic fields.
Deep spectral CNN for laser induced breakdown spectroscopy

Abstract This work proposes a spectral convolutional neural network (CNN) operating on laser induced breakdown spectroscopy (LIBS) signals to learn to (1) disentangle spectral signals from the sources of sensor uncertainty (i.e., pre-process) and (2) get qualitative and quantitative measures of chemical content of a sample given a spectral signal (i.e., calibrate). Once the spectral CNN is trained, it can accomplish either task through a single feed-forward pass, with real-time benefits and without any additional side information requirements including dark current, system response, temperature and detector-to-target range. Our experiments demonstrate that the proposed method outperforms the existing approaches used by the Mars Science Lab for pre-processing and calibration for remote sensing observations from the Mars rover, ‘Curiosity’.
Measuring Real Activity Using a Weekly Economic Index

This paper describes a weekly economic index (WEI) developed to track the rapid economic developments associated with the onset of and policy response to the novel coronavirus in the United States. The WEI is a weekly composite index of real economic activity, with eight of 10 series available the Thursday after the end of the reference week. In addition to being a weekly real activity index, the WEI has strong predictive power for output measures and provided an accurate nowcast of current-quarter GDP growth in the first half of 2020, with weaker performance in the second half. We document how the WEI responded to key events and data releases during the first 10 months of the pandemic.