nils-herrmann/clean_scirepeval huggingface dataset:
Order g2 susceptibilities in the symmetric phase of the Standard Model

Susceptibilities of conserved charges such as baryon minus lepton number enter baryogenesis computations, since they provide the relationship between conserved charges and chemical potentials. Their next-to-leading order corrections are of order g, where g is a generic Standard Model coupling. They are due to soft Higgs boson exchange, and have been calculated recently, together with some order g2 corrections. Here we compute the complete g2 contributions. Close to the electroweak crossover the soft Higgs contribution is of order g2, and is determined by the non-perturbative physics at the magnetic screening scale.
EARLY-TYPE ECLIPSING BINARIES WITH INTERMEDIATE ORBITAL PERIODS

We analyze 221 eclipsing binaries (EBs) in the Large Magellanic Cloud with B-type main-sequence (MS) primaries (M1 ≈ 4–14 M ⊙ ?> ) and orbital periods P = 20–50 days that were photometrically monitored by the Optical Gravitational Lensing Experiment. We utilize our three-stage automated pipeline to (1) classify all 221 EBs, (2) fit physical models to the light curves of 130 detached well-defined EBs from which unique parameters can be determined, and (3) recover the intrinsic binary statistics by correcting for selection effects. We uncover two statistically significant trends with age. First, younger EBs tend to reside in dustier environments with larger photometric extinctions, an empirical relation that can be implemented when modeling stellar populations. Second, younger EBs generally have large eccentricities. This demonstrates that massive binaries at moderate orbital periods are born with a Maxwellian “thermal” orbital velocity distribution, which indicates they formed via dynamical interactions. In addition, the age-eccentricity anticorrelation provides a direct constraint for tidal evolution in highly eccentric binaries containing hot MS stars with radiative envelopes. The intrinsic fraction of B-type MS stars with stellar companions q = M2/M1 > 0.2 and orbital periods P = 20–50 days is (7 ± 2)%. We find early-type binaries at P = 20–50 days are weighted significantly toward small mass ratios q ≈ 0.2–0.3, which is different than the results from previous observations of closer binaries with P < 20 days. This indicates that early-type binaries at slightly wider orbital separations have experienced substantially less competitive accretion and coevolution during their formation in the circumbinary disk.
Many-body instabilities and mass generation in slow Dirac materials

Some Kondo insulators are expected to possess topologically protected surface states with linear Dirac spectrum: the topological Kondo insulators. Because the bulk states of these systems typically have heavy effective electron masses, the surface states may exhibit extraordinarily small Fermi velocities that could force the effective fine structure constant of the surface states into the strong coupling regime. Using a tight-binding model, we study the many-body instabilities of these systems and identify regions of parameter space in which the system exhibits spin density wave and charge density wave order.
Transverse diffeomorphism and Weyl invariant massive spin 2: Linear theory

We use Kaluza-Klein reduction to construct flat-space massive spin-2 Lagrangians based on a kinetic term that has local Weyl and transverse-diffeomorphism gauge symmetries in the massless limit. This yields Lagrangians describing a free massive spin 2 which differ from the usual Fierz-Pauli theory, but are physically equivalent to it. These Lagrangians require the use of auxiliary fields, which appear naturally from the higher-dimensional construction. We discuss how these Lagrangians are related to each other and to the Fierz-Pauli theory through Stuckelberg transformations, gauge fixings, and field replacements, and we use this to generalize the construction to spin-2 fields on Einstein manifolds.
Neutrino-Nucleus Interactions at the LBNF Near Detector

The reaction mechanisms for neutrino interactions with an 40Ar nucleus with the LBNF flux are calculated with the Giessen-Boltzmann-Uehling-Uhlenbeck (GiBUU) transport-theoretical implementation of these interactions. Quasielastic scattering, many-body effects, pion production and absorption and Deep Inelastic Scattering are discussed; they all play a role at the LBNF energies and are experimentally entangled with each other. Quasielastic scattering makes up for only about 1/3 of the total cross section whereas pion production channels make up about 2/3 of the total. This underlines the need for a consistent description of the neutrino-nucleus reaction that treats all channels on an equal, consistent footing. The results discussed here can also serve as useful guideposts for the Intermediate Neutrino Program.
Scaling theory of the cuprate strange metals

We show that the anomalous temperature scaling of five distinct transport quantities in the strange metal regime of the cuprate superconductors can be reproduced with only two nontrivial critical exponents. The quantities are: (i) the electrical resistivity, (ii) the Hall angle, (iii) the Hall Lorenz ratio, (iv) the magnetoresistance and (v) the thermopower. The exponents are the dynamical critical exponent z = 4/3 and an anomalous scaling dimension Phi = -2/3 for the charge density operator.
Propagation of Input Uncertainty in Presence of Model-Form Uncertainty: A Multifidelity Approach for Computational Fluid Dynamics Applications

Proper quantification and propagation of uncertainties in computational simulations are of critical importance. This issue is especially challenging for CFD applications. A particular obstacle for uncertainty quantifications in CFD problems is the large model discrepancies associated with the CFD models used for uncertainty propagation. Neglecting or improperly representing the model discrepancies leads to inaccurate and distorted uncertainty distribution for the Quantities of Interest. High-fidelity models, being accurate yet expensive, can accommodate only a small ensemble of simulations and thus lead to large interpolation errors and/or sampling errors; low-fidelity models can propagate a large ensemble, but can introduce large modeling errors. In this work, we propose a multi-model strategy to account for the influences of model discrepancies in uncertainty propagation and to reduce their impact on the predictions. Specifically, we take advantage of CFD models of multiple fidelities to estimate the model discrepancies associated with the lower-fidelity model in the parameter space. A Gaussian process is adopted to construct the model discrepancy function, and a Bayesian approach is used to infer the discrepancies and corresponding uncertainties in the regions of the parameter space where the high-fidelity simulations are not performed. The proposed multi-model strategy combines information from models with different fidelities and computational costs, and is of particular relevance for CFD applications, where a hierarchy of models with a wide range of complexities exists. Several examples of relevance to CFD applications are performed to demonstrate the merits of the proposed strategy. Simulation results suggest that, by combining low- and high-fidelity models, the proposed approach produces better results than what either model can achieve individually.
Optical tractor beam with chiral light

We suggest a novel mechanism to induce the motion of a chiral material body towards an optical source. Our solution is based on the interference between a chiral light beam and its reflection on an opaque mirror. Surprisingly, it is theoretically shown that the electromagnetic response of the material may be tailored in such a way that independent of the specific body location with the respect to the mirror, it is always pushed upstream against the photon flow associated with the incoming wave. Moreover, it is proven that by controlling the handedness of the incoming light it may be possible to harness the sign of the optical force, switching from a pulling force to a pushing force.
Low-energy capture of asteroids onto KAM tori

We present a new method for engineering the artificial capture of asteroids. Based on theories of the chaos-assisted capture of natural satellites of the giant planets, we show how an unbound asteroid that passes close to a regular region of phase space can be easily moved onto the nearby KAM tori and essentially permanently captured with the Earth's Hill sphere without closing the zero velocity curves. The method has the advantages of a relatively low delta-v requirement and no need for control strategies. An illustration of the method is given for an example asteroid trajectory, demonstrating that it is a viable strategy for the final capture stage of asteroids in the Earth's neighbourhood.
Wave Function Collapse and the No-Superluminal-Signaling Principle

The assumption that wave function collapse is a real occurrence has very interesting consequences - both experimental and theoretical. Besides predicting observable deviations from linear evolution, it implies that these deviations must originate in nondeterministic effects at the elementary level in order to prevent superluminal signaling, as demonstrated by Gisin. This lack of determinism implies that information cannot be instantiated in a reproducible form in isolated microsystems (as illustrated by the No-cloning theorem). By stipulating that information is a reproducible and referential property of physical systems, one can formulate the no-signaling principle in strictly physical terms as a prohibition of the acquisition of information about spacelike-separated occurrences. This formulation provides a new perspective on the relationship between relativity and spacetime structure, and it imposes tight constraints on the way in which collapse effects are induced. These constraints indicate that wave function collapse results from (presumably small) nondeterministic deviations from linear evolution associated with nonlocally entangling interactions. This hypothesis can be formalized in a stochastic collapse equation and used to assess the feasibility of testing for collapse effects.
No return to classical reality

At a fundamental level, the classical picture of the world is dead, and has been dead now for almost a century. Pinning down exactly which quantum phenomena are responsible for this has proved to be a tricky and controversial question, but a lot of progress has been made in the past few decades. We now have a range of precise statements showing that whatever the ultimate laws of nature are, they cannot be classical. In this article, we review results on the fundamental phenomena of quantum theory that cannot be understood in classical terms. We proceed by first granting quite a broad notion of classicality, describe a range of quantum phenomena (such as randomness, discreteness, the indistinguishability of states, measurement-uncertainty, measurement-disturbance, complementarity, non-commutativity, interference, the no-cloning theorem and the collapse of the wave-packet) that do fall under its liberal scope, and then finally describe some aspects of quantum physics that can never admit a classical understanding – the intrinsically quantum mechanical aspects of nature. The most famous of these is Bell’s theorem, but we also review two more recent results in this area. Firstly, Hardy’s theorem shows that even a finite-dimensional quantum system must contain an infinite amount of information, and secondly, the Pusey–Barrett–Rudolph theorem shows that the wave function must be an objective property of an individual quantum system. Besides being of foundational interest, results of this sort now find surprising practical applications in areas such as quantum information science and the simulation of quantum systems.
Quantifying Prosodic Variability in Middle English Alliterative Poetry

Interest in the mathematical structure of poetry dates back to at least the 19th century: after retiring from his mathematics position,  Sylvester wrote a book on prosody called $\textit{The Laws of Verse}$. Today there is interest in the computer analysis of poems, and this paper discusses how a statistical approach can be applied to this task. Starting with the definition of what Middle English alliteration is, $\textit{Sir Gawain and the Green Knight}$ and William Langland's $\textit{Piers Plowman}$ are used to illustrate the methodology. Theory first developed for analyzing data from a Riemannian manifold turns out to be applicable to strings allowing one to compute a generalized mean and variance for textual data, which is applied to the poems above. The ratio of these two variances produces the analogue of the F test, and resampling allows p-values to be estimated. Consequently, this methodology provides a way to compare prosodic variability between two texts.
Using Riemannian geometry for SSVEP-based Brain Computer Interface

Challenges for the next generation of Brain Computer Interfaces (BCI) are to mitigate the common sources of variability (electronic, electrical, biological) and to develop online and adaptive systems following the evolution of the subject's brain waves. Studying electroencephalographic (EEG) signals from their associated covariance matrices allows the construction of a representation which is invariant to extrinsic perturbations. As covariance matrices should be estimated, this paper first presents a thorough study of all estimators conducted on real EEG recording. Working in Euclidean space with covariance matrices is known to be error-prone, one might take advantage of algorithmic advances in Riemannian geometry and matrix manifold to implement methods for Symmetric Positive-Definite (SPD) matrices. Nonetheless, existing classification algorithms in Riemannian spaces are designed for offline analysis. We propose a novel algorithm for online and asynchronous processing of brain signals, borrowing principles from semi-unsupervised approaches and following a dynamic stopping scheme to provide a prediction as soon as possible. The assessment is conducted on real EEG recording: this is the first study on Steady-State Visually Evoked Potential (SSVEP) experimentations to exploit online classification based on Riemannian geometry. The proposed online algorithm is evaluated and compared with state-of-the-art SSVEP methods, which are based on Canonical Correlation Analysis (CCA). It is shown to improve both the classification accuracy and the information transfer rate in the online and asynchronous setup.
Muonless events in ICAL at INO

The primary physics signal events in the Iron Calorimeter at India-based Neutrino Observatory are the νμ charged current (CC) interactions with a well defined muon track. Apart from these events, the Iron Calorimeter can also detect other types of neutrino interactions, i.e. the electron neutrino charged current interactions and the neutral current events. It is possible to have a dataset containing mostly νeCC events, by imposing appropriate selection cuts on the events. The νμCC and the neutral current events form the background to these events. This study uses Monte Carlo generated neutrino events, to design the necessary selection cuts to obtain a νeCC rich dataset. An optimized set of constraints are developed which balance the need for improving the purity of the sample and having a large enough event sample. Depending on the constraints used, one can obtain a neutrino data sample with the purity of νe events varying between 55% to 70%.
Electric Field and Humidity Trigger Contact Electrification

Here, we study the old problem of why identical insulators can charge one another on contact. We perform several experiments showing that, if driven by a preexisting electric field, charge is transferred between contacting insulators. This happens because the insulator surfaces adsorb small amounts of water from a humid atmosphere. We believe the electric field then separates positively from negatively charged ions prevailing within the water, which we believe to be hydronium and hydroxide ions, such that at the point of contact, positive ions of one insulator neutralize negative ions of the other one, charging both of them. This mechanism can explain for the first time the observation made four decades ago that wind-blown sand discharges in sparks if and only if a thunderstorm is nearby.
Classification with Low Rank and Missing Data

We consider classification and regression tasks where we have missing data and assume that the (clean) data resides in a low rank subspace. Finding a hidden subspace is known to be computationally hard. Nevertheless, using a nonproper formulation we give an efficient agnostic algorithm that classifies as good as the best linear classifier coupled with the best low-dimensional subspace in which the data resides. A direct implication is that our algorithm can linearly (and non-linearly through kernels) classify provably as well as the best classifier that has access to the full data.
Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models

Our paper deals with inferring simulator-based statistical models given some observed data. A simulator-based model is a parametrized mechanism which specifies how data are generated. It is thus also referred to as generative model. We assume that only a finite number of parameters are of interest and allow the generative process to be very general; it may be a noisy nonlinear dynamical system with an unrestricted number of hidden variables. This weak assumption is useful for devising realistic models but it renders statistical inference very difficult. The main challenge is the intractability of the likelihood function. Several likelihood-free inference methods have been proposed which share the basic idea of identifying the parameters by finding values for which the discrepancy between simulated and observed data is small. A major obstacle to using these methods is their computational cost. The cost is largely due to the need to repeatedly simulate data sets and the lack of knowledge about how the parameters affect the discrepancy. We propose a strategy which combines probabilistic modeling of the discrepancy with optimization to facilitate likelihood-free inference. The strategy is implemented using Bayesian optimization and is shown to accelerate the inference through a reduction in the number of required simulations by several orders of magnitude.
Hard to Cheat: A Turing Test based on Answering Questions about Images

Progress in language and image understanding by machines has sparkled the interest of the research community in more open-ended, holistic tasks, and refueled an old AI dream of building intelligent machines. We discuss a few prominent challenges that characterize such holistic tasks and argue for "question answering about images" as a particular appealing instance of such a holistic task. In particular, we point out that it is a version of a Turing Test that is likely to be more robust to over-interpretations and contrast it with tasks like grounding and generation of descriptions. Finally, we discuss tools to measure progress in this field.
Autonomous movement of a chemically powered vesicle.

We investigate the diffusio-phoretic motion of a deformable vesicle. A vesicle is built from the linked catalytic and noncatalytic vertices that consumes fuel in the environment and utilize the resulting self-generated concentration gradient to exhibit propulsive motion. Under nonequilibrium conditions it is found that the self-propulsion velocity of the vesicle depends on its shape, which in turn is controlled by the bending rigidity of the membrane and solvent density around it. The self-propulsion velocity of the vesicle for different shapes has been calculated and the factors which affect the velocity are identified.
Entanglement dynamics for uniformly accelerated two-level atoms

We study, in the paradigm of open quantum systems, the entanglement dynamics of two uniformly accelerated atoms with the same acceleration perpendicular to the separation. The two-atom system is treated as an open system coupled with a bath of fluctuating massless scalar fields in the Minkowski vacuum, and the master equation that governs its evolution is derived. It has been found that, for accelerated atoms with a nonvanishing separation, entanglement sudden death is a general feature when the initial state is entangled, while for those in a separable initial state, entanglement sudden birth only happens for atoms with an appropriate interatomic separation and sufficiently small acceleration. Remarkably, accelerated atoms can get entangled in certain circumstances while the inertial ones in the Minkowski vacuum can not. A comparison between the results of accelerated atoms and those of static ones in a thermal bath shows that, uniformly accelerated atoms exhibit distinct features from those immersed in a thermal bath at the Unruh temperature in terms of entanglement dynamics.
Unbiased Bayes for Big Data: Paths of Partial Posteriors

A key quantity of interest in Bayesian inference are expectations of functions with respect to a posterior distribution. Markov Chain Monte Carlo is a fundamental tool to consistently compute these expectations via averaging samples drawn from an approximate posterior. However, its feasibility is being challenged in the era of so called Big Data as all data needs to be processed in every iteration. Realising that such simulation is an unnecessarily hard problem if the goal is estimation, we construct a computationally scalable methodology that allows unbiased estimation of the required expectations -- without explicit simulation from the full posterior. The scheme's variance is finite by construction and straightforward to control, leading to algorithms that are provably unbiased and naturally arrive at a desired error tolerance. This is achieved at an average computational complexity that is sub-linear in the size of the dataset and its free parameters are easy to tune. We demonstrate the utility and generality of the methodology on a range of common statistical models applied to large-scale benchmark and real-world datasets.
Research Data Explored: Citations versus Altmetrics

The study explores the citedness of research data, its distribution over time and how it is related to the availability of a DOI (Digital Object Identifier) in Thomson Reuters' DCI (Data Citation Index). We investigate if cited research data "impact" the (social) web, reflected by altmetrics scores, and if there is any relationship between the number of citations and the sum of altmetrics scores from various social media-platforms. Three tools are used to collect and compare altmetrics scores, i.e. PlumX, ImpactStory, and Altmetric.com. In terms of coverage, PlumX is the most helpful altmetrics tool. While research data remain mostly uncited (about 85%), there has been a growing trend in citing data sets published since 2007. Surprisingly, the percentage of the number of cited research data with a DOI in DCI has decreased in the last years. Only nine repositories account for research data with DOIs and two or more citations. The number of cited research data with altmetrics scores is even lower (4 to 9%) but shows a higher coverage of research data from the last decade. However, no correlation between the number of citations and the total number of altmetrics scores is observable. Certain data types (i.e. survey, aggregate data, and sequence data) are more often cited and receive higher altmetrics scores.