nils-herrmann/clean_scirepeval huggingface dataset:
Chameleon f(R) gravity on the Virgo cluster scale

Models of modified gravity offer promising alternatives to the concordance Λ cold dark matter (ΛCDM) cosmology to explain the late-time acceleration of the universe. A popular such model is f(R) gravity, in which the Ricci scalar in the Einstein–Hilbert action is replaced by a general function of it. We study the f(R) model of Hu & Sawicki, which recovers standard general relativity in high-density regimes, while reproducing the desired late time acceleration at cosmological scales. We run a suite of high-resolution zoom simulations using the ECOSMOG code to examine the effect of f(R) gravity on the properties of a halo that is analogous to the Virgo cluster. We show that the velocity dispersion profiles can potentially discriminate between f(R) models and ΛCDM, and provide complementary analysis of lensing signal profiles to explore the possibility to further distinguish the different f(R) models. Our results confirm the techniques explored by Cabre et al. to quantify the effect of environment in the behaviour of f(R) gravity, and we extend them to study halo satellites at various redshifts. We find that the modified gravity effects in our models are most observable at low redshifts, and that effects are generally stronger for satellites far from the centre of the main halo. We show that the screening properties of halo satellites trace very well that of dark matter particles, which means that low-resolution simulations in which subhaloes are not very well resolved can in principle be used to study satellite properties. We discuss observables, particularly for halo satellites, that can potentially be used to constrain the observational viability of f(R) gravity.
Realization Utility with Reference-Dependent Preferences

We develop a tractable model of realization utility that studies the role of reference-dependent S-shaped preferences in a dynamic investment setting with reinvestment. Our model generates both voluntarily realized gains and losses. It makes specific predictions about the volume of gains and losses, the holding periods, and the sizes of both realized and paper gains and losses that can be calibrated to a variety of statistics, including Odean's measure of the disposition effect. Our model also predicts several anomalies, including, among others, the flattening of the capital market line and a negative price for idiosyncratic risk.
Cluster based RBF Kernel for Support Vector Machines

In the classical Gaussian SVM classification we use the feature space projection transforming points to normal distributions with fixed covariance matrices (identity in the standard RBF and the covariance of the whole dataset in Mahalanobis RBF). In this paper we add additional information to Gaussian SVM by considering local geometry-dependent feature space projection. We emphasize that our approach is in fact an algorithm for a construction of the new Gaussian-type kernel. 
We show that better (compared to standard RBF and Mahalanobis RBF) classification results are obtained in the simple case when the space is preliminary divided by k-means into two sets and points are represented as normal distributions with a covariances calculated according to the dataset partitioning. 
We call the constructed method C$_k$RBF, where $k$ stands for the amount of clusters used in k-means. We show empirically on nine datasets from UCI repository that C$_2$RBF increases the stability of the grid search (measured as the probability of finding good parameters).
Self-force on a scalar charge in Kerr spacetime: Inclined circular orbits

Accurately modeling astrophysical extreme-mass-ratio-insprials requires calculating the gravitational self-force for orbits in Kerr spacetime. The necessary calculation techniques are typically very complex and, consequently, toy scalar-field models are often developed in order to establish a particular calculational approach. To that end, I present a calculation of the scalar-field self-force for a particle moving on a (fixed) inclined circular geodesic of a background Kerr black hole. I make the calculation in the frequency-domain and demonstrate how to apply the mode-sum regularization procedure to all four components of the self-force. I present results for a number of strong-field orbits which can be used as benchmarks for emerging self-force calculation techniques in Kerr spacetime.
Sailing under the Magellanic Clouds: a DECam view of the Carina dwarf

We present deep optical photometry from the DECam imager on the 4m Blanco telescope of over 12 deg$^2$ around the Carina dwarf spheroidal, with complete coverage out to 1 degree and partial coverage extending out to 2.6 degrees. Using a Poisson-based matched filter analysis to identify stars from each of the three main stellar populations, old, intermediate, and young, we confirm the previously identified radial age gradient, distance, tidal radius, stellar radial profiles, relative stellar population sizes, ellipticity, and position angle. We find an angular offset between the three main elliptical populations of Carina, and find only tentative evidence for tidal debris, suggesting that past tidal interactions could not have significantly influenced the Carina dwarf. We detect stars in the vicinity of, but distinct to, the Carina dwarf, and measure their distance to be 46$\pm$2 kpc. We determine this population to be part of the halo of the Large Magellanic Cloud at an angular radius of over 20 degrees. Due to overlap in colour-magnitude space with Magellanic stars, previously detected tidal features in the old population of Carina are likely weaker than previously thought.
Asymptotic and finite-sample properties of estimators based on stochastic gradients

Stochastic gradient descent procedures have gained popularity for parameter estimation from large data sets. However, their statistical properties are not well understood, in theory. And in practice, avoiding numerical instability requires careful tuning of key parameters. Here, we introduce implicit stochastic gradient descent procedures, which involve parameter updates that are implicitly defined. Intuitively, implicit updates shrink standard stochastic gradient descent updates. The amount of shrinkage depends on the observed Fisher information matrix, which does not need to be explicitly computed; thus, implicit procedures increase stability without increasing the computational burden. Our theoretical analysis provides the first full characterization of the asymptotic behavior of both standard and implicit stochastic gradient descent-based estimators, including finite-sample error bounds. Importantly, analytical expressions for the variances of these stochastic gradient-based estimators reveal their exact loss of efficiency. We also develop new algorithms to compute implicit stochastic gradient descent-based estimators for generalized linear models, Cox proportional hazards, M-estimators, in practice, and perform extensive experiments. Our results suggest that implicit stochastic gradient descent procedures are poised to become a workhorse for approximate inference from large data sets
Charged Black Holes in a Five-dimensional Kaluza-Klein Universe

We examine an exact solution which represents a charged black hole in a Kaluza-Klein universe in the five-dimensional Einstein-Maxwell theory. The spacetime approaches to the five-dimensional Kasner solution that describes expanding three dimensions and shrinking an extra dimension in the far region. The metric is continuous but not smooth at the black hole horizon. There appears a mild curvature singularity that a free-fall observer can traverse the horizon. The horizon is a squashed three-sphere with a constant size, and the metric is approximately static near the horizon.
Gravitational entropy of cosmic expansion

We apply a recent proposal to define “gravitational entropy” to the expansion of cosmic voids within the framework of nonperturbative General Relativity. By considering CDM void configurations compatible with basic observational constraints, we show that this entropy grows from post-inflationary conditions towards a final asymptotic value in a late time fully nonlinear regime described by the Lemaitre-Tolman-Bondi (LTB) dust models. A qualitatively analogous behavior occurs if we assume a positive cosmological constant consistent with a Λ-CDM background model. However, the Λ term introduces a significant suppression of entropy growth with the terminal equilibrium value reached at a much faster rate.
Centrality dependence of high energy jets in p+Pb collisions at the LHC

Recently measured centrality dependence of high energy jets in proton-lead collisions at the LHC is investigated. We hypothesize that events with jets of very high energy (a few hundred GeV) are characterized by a suppressed number of soft particles thus shifting these events into more peripheral bins. This naturally results in the suppression (enhancement) of the nuclear modification factor, RpA, in central (peripheral) collisions. Our calculations suggest that a moderate suppression of the order of 20% can quantitatively reproduce the experimental data. We further propose an independent experimental test to verify this hypothesis.
The Value of Using Big Data Technologies in Computational Social Science

The discovery of phenomena in social networks has prompted renewed interests in the field. Data in social networks however can be massive, requiring scalable Big Data architecture. Conversely, research in Big Data needs the volume and velocity of social media data for testing its scalability. Not only so, appropriate data processing and mining of acquired datasets involve complex issues in the variety, veracity, and variability of the data, after which visualisation must occur before we can see fruition in our efforts. This extended abstract presents topical, multimodal, and longitudinal social media datasets from the integration of various scalable open source technologies. The full article details the process that led to the discovery of social information landscapes within the Twitter social network, highlighting the experience of dealing with social media datasets, using a funneling approach so that data becomes manageable.
JIDT: An Information-Theoretic Toolkit for Studying the Dynamics of Complex Systems

Complex systems are increasingly being viewed as distributed information processing systems, particularly in the domains of computational neuroscience, bioinformatics and Artificial Life. This trend has resulted in a strong uptake in the use of (Shannon) information-theoretic measures to analyse the dynamics of complex systems in these fields. We introduce the Java Information Dynamics Toolkit (JIDT): a Google code project which provides a standalone, (GNU GPL v3 licensed) open-source code implementation for empirical estimation of information-theoretic measures from time-series data. While the toolkit provides classic information-theoretic measures (e.g. entropy, mutual information, conditional mutual information), it ultimately focusses on implementing higher-level measures for information dynamics. That is, JIDT focusses on quantifying information storage, transfer and modification, and the dynamics of these operations in space and time. For this purpose, it includes implementations of the transfer entropy and active information storage, their multivariate extensions and local or pointwise variants. JIDT provides implementations for both discrete and continuous-valued data for each measure, including various types of estimator for continuous data (e.g. Gaussian, box-kernel and Kraskov-Stoegbauer-Grassberger) which can be swapped at run-time due to Java's object-oriented polymorphism. Furthermore, while written in Java, the toolkit can be used directly in MATLAB, GNU Octave, Python and other environments. We present the principles behind the code design, and provide several examples to guide users.
N-body methods for relativistic cosmology

We present a framework for general relativistic N-body simulations in the regime of weak gravitational fields. In this approach, Einsteinʼs equations are expanded in terms of metric perturbations about a Friedmann–Lemaître background, which are assumed to remain small. The metric perturbations themselves are only kept to linear order, but we keep their first spatial derivatives to second order and treat their second spatial derivatives as well as sources of stress–energy fully non-perturbatively. The evolution of matter is modelled by an N-body ensemble which can consist of free-streaming nonrelativistic (e.g. cold dark matter) or relativistic particle species (e.g. cosmic neutrinos), but the framework is fully general and also allows for other sources of stress–energy, in particular additional relativistic sources like modified-gravity models or topological defects. We compare our method with the traditional Newtonian approach and argue that relativistic methods are conceptually more robust and flexible, at the cost of a moderate increase of numerical difficulty. However, for a Λ CDM ?> cosmology, where nonrelativistic matter is the only source of perturbations, the relativistic corrections are expected to be small. We quantify this statement by extracting post-Newtonian estimates from Newtonian N-body simulations.
Bonnsai: a Bayesian tool for comparing stars with stellar evolution models

Powerful telescopes equipped with multi-fibre or integral field spectrographs combined with detailed models of stellar atmospheres and automated fitting techniques allow for the analysis of large number of stars. These datasets contain a wealth of information that require new analysis techniques to bridge the gap between observations and stellar evolution models. To that end, we develop Bonnsai (BONN Stellar Astrophysics Interface), a Bayesian statistical method, that is capable of comparing all available observables simultaneously to stellar models while taking observed uncertainties and prior knowledge such as initial mass functions and distributions of stellar rotational velocities into account. Bonnsai can be used to (1) determine probability distributions of fundamental stellar parameters such as initial masses and stellar ages from complex datasets, (2) predict stellar parameters that were not yet observationally determined and (3) test stellar models to further advance our understanding of stellar evolution. An important aspect of Bonnsai is that it singles out stars that cannot be reproduced by stellar models through 2 hypothesis tests and posterior predictive checks. Bonnsai can be used with any set of stellar models and currently supports massive main-sequence single star models of Milky Way and Large and Small Magellanic Cloud composition. We apply our new method to mock stars to demonstrate its functionality and capabilities. In a first application, we use Bonnsai to test the stellar models of Brott et al. (2011a) by comparing the stellar ages inferred for the primary and secondary stars of eclipsing Milky Way binaries of which the components range in mass between 4:5 and 28 M . Ages are determined from dynamical masses and radii that are known to better than 3%. We show that the stellar models must include rotation because stellar radii can be increased by several percent via centrifugal forces. We find that the average age di erence between the primary and secondary stars of the binaries is 0:9 2:3 Myr (95% CI), i.e. that the stellar models reproduce the Milky Way binaries well. The predicted e ective temperatures are in agreement for observed e ective temperatures for stars cooler than 25; 000 K. In hotter stars, i.e. stars earlier than B1‐2V and more massive than about 10 M , we find that the observed e ective temperatures are on average hotter by 1:1 0:3 kK (95% CI) than predicted by the stellar models. The hotter temperatures consequently result in bolometric luminosities that are larger by 0:06 0:02 dex (95% CI) than those predicted by the models.
Entanglement in curved spacetimes and cosmology

We review recent results regarding entanglement in quantum fields in cosmological spacetimes and related phenomena in flat spacetime such as the Unruh effect. We begin with a summary of important results about field entanglement and the mathematics of Bogoliubov transformations that is very often used to describe it. We then discuss the Unruh–DeWitt detector model, which is a useful model of a generic local particle detector. This detector model has been successfully used as a tool to obtain many important results. In this context we discuss two specific types of these detectors: a qubit and a harmonic oscillator. The latter has recently been shown to have important applications when one wants to probe nonperturbative physics of detectors interacting with quantum fields. We then detail several recent advances in the study and application of these ideas, including echoes of the early universe, entanglement harvesting, and a nascent proposal for quantum seismology.
On the genetic architecture of intelligence and other quantitative traits

How do genes affect cognitive ability or other human quantitative traits such as height or disease risk? Progress on this challenging question is likely to be significant in the near future. I begin with a brief review of psychometric measurements of intelligence, introducing the idea of a "general factor" or g score. The main results concern the stability, validity (predictive power), and heritability of adult g. The largest component of genetic variance for both height and intelligence is additive (linear), leading to important simplifications in predictive modeling and statistical estimation. Due mainly to the rapidly decreasing cost of genotyping, it is possible that within the coming decade researchers will identify loci which account for a significant fraction of total g variation. In the case of height analogous efforts are well under way. I describe some unpublished results concerning the genetic architecture of height and cognitive ability, which suggest that roughly 10k moderately rare causal variants of mostly negative effect are responsible for normal population variation. Using results from Compressed Sensing (L1-penalized regression), I estimate the statistical power required to characterize both linear and nonlinear models for quantitative traits. The main unknown parameter s (sparsity) is the number of loci which account for the bulk of the genetic variation. The required sample size is of order 100s, or roughly a million in the case of cognitive ability.
Fractional quantum Hall effect in Hofstadter butterflies of Dirac fermions

We report on the influence of a periodic potential on the fractional quantum Hall effect (FQHE) states in monolayer graphene. We have shown that for two values of the magnetic flux per unit cell (one-half and one-third flux quantum) an increase of the periodic potential strength results in a closure of the FQHE gap and appearance of gaps due to the periodic potential. In the case of one-half flux quantum this causes a change of the ground state and consequently the change of the momentum of the system in the ground state. While there is also crossing between low-lying energy levels for one-third flux quantum, the ground state does not change with the increase of the periodic potential strength and is always characterized by the same momentum. Finally, it is shown that for one-half flux quantum the emergent gaps are due entirely to the electron–electron interaction, whereas for the one-third flux quantum per unit cell these are due to both non-interacting electrons (Hofstadter butterfly pattern) and the electron–electron interaction.
Collective credit allocation in science

The increasing dominance of multiauthor papers is straining the credit system of science: although for single-author papers, the credit is obvious and undivided, for multiauthor papers, credit assignment varies from discipline to discipline. Consequently, each research field runs its own informal credit allocation system, which is hard to decode for outsiders. Here we develop a discipline-independent algorithm to decipher the collective credit allocation process within science, capturing each coauthor’s perceived contribution to a publication. The proposed method provides scientists and policy-makers an effective tool to quantify and compare the scientific contribution of each researcher without requiring familiarity with the credit allocation system of the specific discipline. Collaboration among researchers is an essential component of the modern scientific enterprise, playing a particularly important role in multidisciplinary research. However, we continue to wrestle with allocating credit to the coauthors of publications with multiple authors, because the relative contribution of each author is difficult to determine. At the same time, the scientific community runs an informal field-dependent credit allocation process that assigns credit in a collective fashion to each work. Here we develop a credit allocation algorithm that captures the coauthors’ contribution to a publication as perceived by the scientific community, reproducing the informal collective credit allocation of science. We validate the method by identifying the authors of Nobel-winning papers that are credited for the discovery, independent of their positions in the author list. The method can also compare the relative impact of researchers working in the same field, even if they did not publish together. The ability to accurately measure the relative credit of researchers could affect many aspects of credit allocation in science, potentially impacting hiring, funding, and promotion decisions.
Robust Statistical Ranking: Theory and Algorithms

Deeply rooted in classical social choice and voting theory, statistical ranking with paired comparison data experienced its renaissance with the wide spread of crowdsourcing technique. As the data quality might be significantly damaged in an uncontrolled crowdsourcing environment, outlier detection and robust ranking have become a hot topic in such data analysis. In this paper, we propose a robust ranking framework based on the principle of Huber's robust statistics, which formulates outlier detection as a LASSO problem to find sparse approximations of the cyclic ranking projection in Hodge decomposition. Moreover, simple yet scalable algorithms are developed based on Linearized Bregman Iteration to achieve an even less biased estimator than LASSO. Statistical consistency of outlier detection is established in both cases which states that when the outliers are strong enough and in Erdos-Renyi random graph sampling settings, outliers can be faithfully detected. Our studies are supported by experiments with both simulated examples and real-world data. The proposed framework provides us a promising tool for robust ranking with large scale crowdsourcing data arising from computer vision, multimedia, machine learning, sociology, etc.
Estimating the Reproduction Number of Ebola Virus (EBOV) During the 2014 Outbreak in West Africa

The 2014 Ebola virus (EBOV) outbreak in West Africa is the largest outbreak of the genus Ebolavirus to date. To better understand the spread of infection in the affected countries, it is crucial to know the number of secondary cases generated by an infected index case in the absence and presence of control measures, i.e., the basic and effective reproduction number. In this study, I describe the EBOV epidemic using an SEIR (susceptible-exposed-infectious-recovered) model and fit the model to the most recent reported data of infected cases and deaths in Guinea, Sierra Leone and Liberia. The maximum likelihood estimates of the basic reproduction number are 1.51 (95% confidence interval [CI]: 1.50-1.52) for Guinea, 2.53 (95% CI: 2.41-2.67) for Sierra Leone and 1.59 (95% CI: 1.57-1.60) for Liberia. The model indicates that in Guinea and Sierra Leone the effective reproduction number might have dropped to around unity by the end of May and July 2014, respectively. In Liberia, however, the model estimates no decline in the effective reproduction number by end-August 2014. This suggests that control efforts in Liberia need to be improved substantially in order to stop the current outbreak.