nils-herrmann/clean_scirepeval huggingface dataset:
Charged black holes in expanding Einstein–de Sitter universes

Inspired by a previous work by McClure and Dyer (2006) (), we analyze some solutions of the Einstein–Maxwell equations that were originally written to describe charged black holes in cosmological backgrounds. A detailed analysis of the electromagnetic sources for a sufficiently general metric is performed, and we then focus on deriving the electromagnetic four-current as well as the conserved electric charge of each metric. The charged McVittie solution is revisited, and a brief study of its causal structure is performed, showing that it may represent a charged black hole in an expanding universe, with the black hole horizon being formed at infinite late times. Charged versions of solutions originally put forward by Vaidya (Vd) and Sultana and Dyer (SD) are also analyzed. It is shown that the charged SD metric requires a global electric current, besides a central (spherically symmetric) electric charge. With the aim of comparing to the charged McVittie metric, new charged solutions of Vd and SD types are considered. In these cases, the original mass and charge parameters are replaced by particular functions of the cosmological time. In the new generalized charged Vaidya metric, the black hole horizon never forms, whereas in the new generalized SD case, both the Cauchy and the black hole horizons develop at infinite late times. A charged version of the Thakurta metric is also studied here. It is also a new solution. As in the charged SD case, the natural source of the electromagnetic field is a central electric charge with an additional global electric current. The global structure is briefly studied, and it is verified that the corresponding spacetime may represent a charged black hole in a cosmological background. All the solutions present initial singularities as found in the McVittie metric.
Coupling human mobility and social ties

Studies using massive, passively collected data from communication technologies have revealed many ubiquitous aspects of social networks, helping us understand and model social media, information diffusion and organizational dynamics. More recently, these data have come tagged with geographical information, enabling studies of human mobility patterns and the science of cities. We combine these two pursuits and uncover reproducible mobility patterns among social contacts. First, we introduce measures of mobility similarity and predictability and measure them for populations of users in three large urban areas. We find individuals' visitations patterns are far more similar to and predictable by social contacts than strangers and that these measures are positively correlated with tie strength. Unsupervised clustering of hourly variations in mobility similarity identifies three categories of social ties and suggests geography is an important feature to contextualize social relationships. We find that the composition of a user's ego network in terms of the type of contacts they keep is correlated with mobility behaviour. Finally, we extend a popular mobility model to include movement choices based on social contacts and compare its ability to reproduce empirical measurements with two additional models of mobility.
Study of in-medium mass modification at J-PARC

Study of in-medium mass modification has attracted interest in terms of the restoration of the spontaneously broken chiral symmetry, which is responsible for the generation of hadron mass. Many experiments were performed to measure in-medium property of hadrons but there is no consensus yet. J-PARC E16 has been proposed to study in-medium property of vector mesons via dilepton decay channel. The status of spectrometer R \& D is explained. Other related experiments planned at J-PARC are also introduced.
Cheaper and Better: Selecting Good Workers for Crowdsourcing

Crowdsourcing provides a popular paradigm for data collection at scale. We study the problem of selecting subsets of workers from a given worker pool to maximize the accuracy under a budget constraint. One natural question is whether we should hire as many workers as the budget allows, or restrict on a small number of top-quality workers. By theoretically analyzing the error rate of a typical setting in crowdsourcing, we frame the worker selection problem into a combinatorial optimization problem and propose an algorithm to solve it efficiently. Empirical results on both simulated and real-world datasets show that our algorithm is able to select a small number of high-quality workers, and performs as good as, sometimes even better than, the much larger crowds as the budget allows.
Learning Planar Ising Models

Inference and learning of graphical models are both well-studied problems in statistics and machine learning that have found many applications in science and engineering. However, exact inference is intractable in general graphical models, which suggests the problem of seeking the best approximation to a collection of random variables within some tractable family of graphical models. In this paper, we focus on the class of planar Ising models, for which exact inference is tractable using techniques of statistical physics. Based on these techniques and recent methods for planarity testing and planar embedding, we propose a simple greedy algorithm for learning the best planar Ising model to approximate an arbitrary collection of binary random variables (possibly from sample data). Given the set of all pairwise correlations among variables, we select a planar graph and optimal planar Ising model defined on this graph to best approximate that set of correlations. We demonstrate our method in simulations and for the application of modeling senate voting records.
GIANT MOLECULAR CLOUDS IN THE EARLY-TYPE GALAXY NGC 4526

We present a high spatial resolution (≈20 pc) of 12CO(2 −1) observations of the lenticular galaxy NGC 4526. We identify 103 resolved giant molecular clouds (GMCs) and measure their properties: size R, velocity dispersion σv, and luminosity L. This is the first GMC catalog of an early-type galaxy. We find that the GMC population in NGC 4526 is gravitationally bound, with a virial parameter α ∼ 1. The mass distribution, dN/dM ∝ M−2.39 ± 0.03, is steeper than that for GMCs in the inner Milky Way, but comparable to that found in some late-type galaxies. We find no size–line width correlation for the NGC 4526 clouds, in contradiction to the expectation from Larson’s relation. In general, the GMCs in NGC 4526 are more luminous, denser, and have a higher velocity dispersion than equal-size GMCs in the Milky Way and other galaxies in the Local Group. These may be due to higher interstellar radiation field than in the Milky Way disk and weaker external pressure than in the Galactic center. In addition, a kinematic measurement of cloud rotation shows that the rotation is driven by the galactic shear. For the vast majority of the clouds, the rotational energy is less than the turbulent and gravitational energy, while the four innermost clouds are unbound and will likely be torn apart by the strong shear at the galactic center. We combine our data with the archival data of other galaxies to show that the surface density Σ of GMCs is not approximately constant, as previously believed, but varies by ∼3 orders of magnitude. We also show that the size and velocity dispersion of the GMC population across galaxies are related to the surface density, as expected from the gravitational and pressure equilibrium, i.e., σv R−1/2 ∝ Σ1/2.
‘Firewall’ phenomenology with astrophysical neutrinos

One of the most fundamental features of a black hole in general relativity is its event horizon: a boundary from which nothing can escape. There has been a recent surge of interest in the nature of these event horizons and their local neighbourhoods. In an attempt to resolve black hole information paradox(es), and more generally, to better understand the path towards quantum gravity, ‘firewalls’ have been proposed as an alternative to black hole event horizons. In this paper, we explore the phenomenological implications of black holes possessing a surface or ‘firewall’, and predict a potentially detectable signature of these firewalls in the form of a high energy astrophysical neutrino flux. We compute the spectrum of this neutrino flux in different models and show that it is a possible candidate for the source of the PeV neutrinos recently detected by IceCube. This opens up a new area of research, bridging the non-perturbative physics of quantum gravity with the observational black hole and high energy astrophysics.
Black holes in higher derivative gravity.

Extensions of Einstein gravity with higher-order derivative terms arise in string theory and other effective theories, as well as being of interest in their own right. In this Letter we study static black-hole solutions in the example of Einstein gravity with additional quadratic curvature terms. A Lichnerowicz-type theorem simplifies the analysis by establishing that they must have vanishing Ricci scalar curvature. By numerical methods we then demonstrate the existence of further black-hole solutions over and above the Schwarzschild solution. We discuss some of their thermodynamic properties, and show that they obey the first law of thermodynamics.
Theoretical analysis on x-ray cylindrical grating interferometer

Grating interferometer is a state of art x-ray imaging approach, which can simultaneously acquire information of x-ray attenuation, phase shift, and small angle scattering. This approach is very sensitive to micro-structural variation and offers superior contrast resolution for biological soft tissues. The present grating interferometer often uses flat gratings, with serious limitations in the field of view and the flux of photons. The use of curved gratings allows perpendicular incidence of x-rays on the gratings, and gives higher visibility over a larger field of view than a conventional interferometer with flat gratings. In the study, we present a rigorous theoretical analysis of the self-imaging of curved transmission gratings based on Rayleigh-Sommerfeld diffraction. Numerical simulations have demonstrated the self-imaging phenomenon of cylindrical grating interferometer. The theoretical results are in agreement with the results of numerical simulations.
Personalized Web Search

Personalization is important for search engines to improve user experience. Most of the existing work do pure feature engineering and extract a lot of session-style features and then train a ranking model. Here we proposed a novel way to model both long term and short term user behavior using Multi-armed bandit algorithm. Our algorithm can generalize session information across users well, and as an Explore-Exploit style algorithm, it can generalize to new urls and new users well. Experiments show that our algorithm can improve performance over the default ranking and outperforms several popular Multi-armed bandit algorithms.
A high precision TDC based on a multi-phase clock

The design of a high-precision time-to-digital converter (TDC) based on a multiphase clock implemented using a single field-programmable gate array is discussed in this paper. The TDC can increase the resolution of the measurement by using time interpolation. A phase-locked loop is used to generate four multiphase clocks whose frequencies are the same and whose phases are 0{\deg}, 45{\deg}, 90{\deg}, and 135{\deg}. In addition, the duty ratios of the four clocks are 50%. By utilizing four multiphase clocks to make up the interpolation clock, one clock period can be divided into eight uniform parts. The resolution of the TDC can be improved to 1/8 of a clock period. Furthermore, we have also designed a discriminator circuit for identifying the start and stop signals. On the basis of this circuit, the TDC can still measure the time interval of two signals when the start and stop signals are uncertain. The experimental results indicate that the time resolution of the TDC can achieve the theoretical value, and the linearity is very good. The architecture consumes fewer logic cells and is more stable.
Robotics for Learning

Teaching Robotics is about empowering students to create and configure robotics devices and program computers to nurture in students the skill sets necessary to play an active role in society. The robot in Figure 1 focuses on the design of scaffolds and physical assembly methods, coupled with a computer logic program to make that makes it move or behave in a very precise (remote controlled or autonomous) manner. This enables students to investigate, explore and refine the program to affect the robots. The Robotics approach takes into account the increasing popularity of Computer Science and the learning by doing (Schank, Berman, & Macpherson, 1999) approach to solve complex problems and use computers meaningfully in learning (Barron & Darling-Hammond, 2008; Jonassen, Howland, Marra, & Crismond, 2008). In Singapore, teachers and students in Woodlands Ring Secondary and Rulang Primary have incorporated robotics to varying extents into formal and informal curricula. In addition, other less expensive robotics tools can be used, depending on the curriculum or different requirements at international competitions. The two main pedagogical learning approaches that have taken root in Robotics are learning- by-doing and constructionism (Papert & Harel, 1991).
Multimedia-Video for Learning

Multimedia engages an audience through a combination of text, audio, still images, animation, video, or interactivity-based content formats. Along this vein, free platforms have been seen to allow budding enthusiasts to create multimedia content. For example, Google sites (Wee, 2012b) offer creative opportunities in website development that enable text insertion, still image, video and animation embedding, along with audio and hyper-interactive links to simulations (Christian & Esquembre, 2012; Wee, 2013; Wee, Goh, & Chew, 2013; Wee, Goh, & Lim, 2013; Wee, Lee, Chew, Wong, & Tan, 2015). This chapter focuses on the video aspect of multimedia, which can be positioned as a component to any effective self-paced on-line lesson that would be available anytime, anywhere via computer or mobile devices. The multimedia video approach aims to help users overcome barriers in creating engaging, effective and meaningful content (Barron & Darling-Hammond, 2008) for teaching and learning in an online environment.
P-wave charmed baryons from QCD sum rules

We study the P-wave charmed baryons using the method of QCD sum rule in the framework of heavy quark effective theory. We consider systematically all possible baryon currents with a derivative for internal ρ- and λ-mode excitations. We have found a good working window for the currents corresponding to the ρ-mode excitations for Λc(2595), Λc(2625), Ξc(2790), and Ξc(2815) that complete two SU(3) 3¯F multiplets of JP=1/2- and 3/2-, while the currents corresponding to the λ-mode excitations seem also consistent with the data. Our results also suggest that there are two Σc(2800) states of JP=1/2- and 3/2- whose mass splitting is 14±7  MeV, and two Ξc(2980) states whose mass splitting is 12±7  MeV. They have two Ωc partners of JP=1/2- and 3/2-, whose masses are around 3.25±0.20  GeV with mass splitting 10±6  MeV. All of them together complete two SU(3) 6F multiplets of JP=1/2- and 3/2-. They may also have JP=5/2- partners. Ξc(3080) may be one of them, and the other two are Σc(5/2-) and Ωc(5/2-), whose masses are 85±23  MeV and 50±27  MeV larger.
Novel microwave near-field sensors for material characterization, biology, and nanotechnology

The wide range of interesting electromagnetic behavior of contemporary materials requires that experimentalists working in this field master many diverse measurement techniques and have a broad understanding of condensed matter physics and biophysics. Measurement of the electromagnetic response of materials at microwave frequencies is important for both fundamental and practical reasons. In this paper, we propose a novel near-field microwave sensor with application to material characterization, biology, and nanotechnology. The sensor is based on a subwavelength ferrite-disk resonator with magnetic-dipolar-mode (MDM) oscillations. Strong energy concentration and unique topological structures of the near fields originated from the MDM resonators allow effective measuring material parameters in microwaves, both for ordinary structures and objects with chiral properties.
Primordial black holes as biased tracers

Primordial black holes (PBHs) are theoretical black holes which may be formed during the radiation dominant era and, basically, caused by the gravitational collapse of radiational overdensities. It has been well known that in the context of the structure formation in our Universe such collapsed objects, e.g., halos/galaxies, could be considered as bias tracers of underlying matter fluctuations and the halo/galaxy bias has been studied well. Employing a peak-background split picture which is known to be a useful tool to discuss the halo bias, we consider the large scale clustering behavior of the PBH and propose an almost mass-independent constraint to the scenario that dark matters (DMs) consist of PBHs. We consider the case where the statistics of the primordial curvature perturbations is almost Gaussian, but with small local-type non-Gaussianity. If PBHs account for the DM abundance, such a large scale clustering of PBHs behaves as nothing but the matter isocurvature perturbation and constrained strictly by the observations of cosmic microwave backgrounds (CMB). From this constraint, we show that, in the case a certain single field causes both CMB temperature perturbations and PBH formations, the PBH-DM scenario is excluded even with quite small local-type non-Gaussianity, $|f_\mathrm{NL}|\sim\mathcal{O}(0.01)$, while we give the constraints to parameters in the case where the source field of PBHs is different from CMB perturbations.
Plasmon-Enhanced Nonlinear Wave Mixing in Nanostructured Graphene

Localized plasmons in metallic nanostructures have been widely used to enhance nonlinear optical effects due to their ability to concentrate and enhance light down to extreme-subwavelength scales. As alternatives to noble metal nanoparticles, graphene nanostructures can host long-lived plasmons that efficiently couple to light and are actively tunable via electrical doping. Here we show that doped graphene nanoislands present unique opportunities for enhancing nonlinear optical wave-mixing processes between two externally applied optical fields at the nanoscale. These small islands can support pronounced plasmons at multiple frequencies, resulting in extraordinarily high wave-mixing susceptibilities when one or more of the input or output frequencies coincide with a plasmon resonance. By varying the doping charge density in a nanoisland with a fixed geometry, enhanced wave mixing can be realized over a wide spectral range in the visible and near-infrared. We concentrate, in particular, on second- and thir...
Learning Local Invariant Mahalanobis Distances

For many tasks and data types, there are natural transformations to which the data should be invariant or insensitive. For instance, in visual recognition, natural images should be insensitive to rotation and translation. This requirement and its implications have been important in many machine learning applications, and tolerance for image transformations was primarily achieved by using robust feature vectors. In this paper we propose a novel and computationally efficient way to learn a local Mahalanobis metric per datum, and show how we can learn a local invariant metric to any transformation in order to improve performance.
Signal Partitioning Algorithm for Highly Efficient Gaussian Mixture Modeling in Mass Spectrometry

Mixture - modeling of mass spectra is an approach with many potential applications including peak detection and quantification, smoothing, de-noising, feature extraction and spectral signal compression. However, existing algorithms do not allow for automated analyses of whole spectra. Therefore, despite highlighting potential advantages of mixture modeling of mass spectra of peptide/protein mixtures and some preliminary results presented in several papers, the mixture modeling approach was so far not developed to the stage enabling systematic comparisons with existing software packages for proteomic mass spectra analyses. In this paper we present an efficient algorithm for Gaussian mixture modeling of proteomic mass spectra of different types (e.g., MALDI-ToF profiling, MALDI-IMS). The main idea is automated partitioning of protein mass spectral signal into fragments. The obtained fragments are separately decomposed into Gaussian mixture models. The parameters of the mixture models of fragments are then aggregated to form the mixture model of the whole spectrum. We compare the elaborated algorithm to existing algorithms for peak detection and we demonstrate improvements of peak detection efficiency obtained by using Gaussian mixture modeling. We also show applications of the elaborated algorithm to real proteomic datasets of low and high resolution.
Effective-one-body Hamiltonian with next-to-leading order spin-spin coupling

We propose a way of including the next-to-leading (NLO) order spin-spin coupling into an effective-one-body (EOB) Hamiltonian. This work extends [S. Balmelli and P. Jetzer,  (2013)], which is restricted to the case of equatorial orbits and aligned spins, to general orbits with arbitrary spin orientations. This is done applying appropriate canonical phase-space transformations to the NLO spin-spin Hamiltonian in Arnowitt-Deser-Misner (ADM) coordinates, and systematically adding "effectiv" quantities at NLO to all spin-squared terms appearing in the EOB Hamiltonian. As required by consistency, the introduced quantities reduce to zero in the test- mass limit. We expose the result both in a general gauge and in a gauge-fixed form. The last is chosen such as to minimize the number of new coefficients that have to be inserted into the effective spin squared. As a result, the 25 parameters that describe the ADM NLO spin-spin dynamics get condensed into only 12 EOB terms.