nils-herrmann/clean_scirepeval huggingface dataset:
PHOTON RINGS AROUND KERR AND KERR-LIKE BLACK HOLES

Very long baseline interferometric observations have resolved structure on scales of only a few Schwarzschild radii around the supermassive black holes at the centers of our Galaxy and M87. In the near future, such observations are expected to image the shadows of these black holes together with a bright and narrow ring surrounding their shadows. For a Kerr black hole, the shape of this photon ring is nearly circular unless the black hole spins very rapidly. Whether or not, however, astrophysical black holes are truly described by the Kerr metric as encapsulated in the no-hair theorem still remains an untested assumption. For black holes that differ from Kerr black holes, photon rings have been shown numerically to be asymmetric for small to intermediate spins. In this paper, I calculate semi-analytic expressions of the shapes of photon rings around black holes described by a new Kerr-like metric which is valid for all spins. I show that photon rings in this spacetime are affected by two types of deviations from the Kerr metric which can cause the ring shape to be highly asymmetric. I argue that the ring asymmetry is a direct measure of a potential violation of the no-hair theorem and that both types of deviations can be detected independently if the mass and distance of the black hole are known. In addition, I obtain approximate expressions of the diameters, displacements, and asymmetries of photon rings around Kerr and Kerr-like black holes.
X-ray Probes of Black Hole Accretion Disks for Testing the No-Hair Theorem

The spins of a number of supermassive and stellar-mass black holes have been measured based on detections of thermal continuum emission and relativistically broadened iron lines in their x-ray spectra. Likewise, quasiperiodic variability has been observed in several sources. Such measurements commonly make the assumption that black holes are described by the Kerr metric, which according to the no-hair theorem characterizes black holes uniquely in terms of their masses and spins. This fundamental property of black holes can be tested observationally by measuring potential deviations from the Kerr metric introduced by a parametrically deformed Kerr-like spacetime. Thermal spectra, iron lines, and variability have already been studied extensively in several such metrics, which usually depend on only one particular type of deviation or contain unphysical regions outside of the compact object. In this paper, I study these x-ray probes in the background of a new Kerr-like metric which depends on four independent deviation functions and is free of pathological regions outside of the event horizon. I show that the observed signals depend significantly on primarily two types of deviations and that the strong correlation between the spin and the deviation parameters found previously in other Kerr-like metrics is partially broken for rapidly spinning black holes. This suggests that high-spin sources are the best candidates for tests of the no-hair theorem with x-rays and I obtain first constraints on such deviations from the stellar-mass black hole Cygnus X-1.
The entropy of the six-vertex model with a variety of different boundary conditions

We study the dependence of entropy [per lattice site] of six-vertex model on boundary conditions. We start with lattices of finite size and then proceed to thermodynamic limit. We argue that the six-vertex model with periodic, anti-periodic and mixed boundary conditions produce the same free-energy in the thermodynamic limit. We have found fixed boundary conditions such that the entropy varies continuously from zero to its value for periodic boundary condition. We have also shown that the physical quantities of the six-vertex model at the isotropic point does not change in the case of singular toroidal boundary.
A Survey on Recent Advances of Computer Vision Algorithms for Egocentric Video

Recent technological advances have made lightweight, head mounted cameras both practical and affordable and products like Google Glass show first approaches to introduce the idea of egocentric (first-person) video to the mainstream. Interestingly, the computer vision community has only recently started to explore this new domain of egocentric vision, where research can roughly be categorized into three areas: Object recognition, activity detection/recognition, video summarization. In this paper, we try to give a broad overview about the different problems that have been addressed and collect and compare evaluation results. Moreover, along with the emergence of this new domain came the introduction of numerous new and versatile benchmark datasets, which we summarize and compare as well.
First Order Models of Human Crowds with Behavioral-Social Dynamics

This paper presents a new approach to behavioral-social dynamics of human crowds. First order models are derived based on mass conservation at the macroscopic scale, while methods of the kinetic theory are used to model the decisional process by which walking individuals select their velocity direction. Crowd heterogeneity is modeled by dividing the whole system into subsystems identified by different features. The passage from one subsystem to the other is induced by interactions. It is shown how heterogeneous individual behaviors can modify the collective dynamics, as well as how local unusual behaviors can propagate in the crowd. The paper also proposes a system approach to the modeling of the dynamics in complex venues, where individuals move through areas with different features.
Acoustic metamaterial with negative modulus and a double negative structure

An acoustic negative bulk modulus metamaterial based on the concept of expansion chambers is proposed. It is shown that addition of a neck region to an ordinary expansion chamber improves its transmission loss characteristics at low frequencies and the resulting structure displays a negative bulk modulus behavior. Additionally, membrane based metamaterials are analyzed. Using FEM, the negative density behavior of a membrane carrying a center mass and of a tensioned membrane array is analyzed and the inherent similarity of the two designs is discussed. Further, the modified expansion chamber is combined with an array of stretched membranes and the resulting structure is analyzed for double negative behavior.
Persistence of strain in motor-filament assemblies

Crosslinked semi-flexible and flexible filaments that are actively deformed by molecular motors occur in various natural settings, such as the ordered eukaryotic flagellum, and the disordered cytoskeleton. The deformation of these composite systems is driven by active motor forces and resisted by passive filament elasticity, and structural constraints due to permanent cross-links. Using a mean field theory for a one-dimensional ordered system, we show that the combination of motor activity and finite filament extensibility yields a characteristic persistence length scale over which active strain decays. This decay length is set by the ability of motors to respond to combination of the weak extensional elasticity, passive shear resistance and the viscoelastic properties of the motor assembly, and generalizes the notion of persistence in purely thermal filaments to active systems.
SPRITE: A Response Model For Multiple Choice Testing

Item response theory (IRT) models for categorical response data are widely used in the analysis of educational data, computerized adaptive testing, and psychological surveys. However, most IRT models rely on both the assumption that categories are strictly ordered and the assumption that this ordering is known a priori. These assumptions are impractical in many real-world scenarios, such as multiple-choice exams where the levels of incorrectness for the distractor categories are often unknown. While a number of results exist on IRT models for unordered categorical data, they tend to have restrictive modeling assumptions that lead to poor data fitting performance in practice. Furthermore, existing unordered categorical models have parameters that are difficult to interpret. In this work, we propose a novel methodology for unordered categorical IRT that we call SPRITE (short for stochastic polytomous response item model) that: (i) analyzes both ordered and unordered categories, (ii) offers interpretable outputs, and (iii) provides improved data fitting compared to existing models. We compare SPRITE to existing item response models and demonstrate its efficacy on both synthetic and real-world educational datasets.
Some measure-theoretic properties of generalized means

If $\Lambda $ is a measure space, $u:\Lambda ^{m}\rightarrow \Bbb{R}$ is a given function and $N\geq m,$ the function $U(x_{1},...,x_{N})=\left( \begin{array}{l} N \\ m \end{array} \right) ^{-1}\sum_{1\leq i_{1}<\cdots <i_{m}\leq N}u(x_{i_{1}},...,x_{i_{m}}) $ is called the generalized $N$-mean with kernel $u,$ a terminology borrowed from $U$-statistics. Physical potentials for systems of particles are also defined by generalized means. 
This paper investigates whether various measure-theoretic concepts for generalized $N$-means are equivalent to the analogous concepts for their kernels: a.e. convergence of sequences, measurability, essential boundedness and integrability with respect to absolutely continuous probability measures. The answer is often, but not always, positive. This information is crucial in some problems addressing the existence of generalized means satisfying given conditions, such as the classical Inverse Problem of statistical physics (in the canonical ensemble).
Using Tracker to understand ‘toss up’ and free fall motion: a case study

This paper reports the use of Tracker as a computer-based learning tool to support effective learning and teaching of ‘toss up’ and free fall motion for beginning secondary three (15 year-old) students. The case study involved (N = 123) students from express pure physics classes at a mainstream school in Singapore. We used eight multiple-choice questions pre- and post-test to gauge the impact on learning. The experimental group showed learning gains of d = 0.79  ±  0.23 (large effect) for Cohen’s d effect size analysis, and gains with a gradient of  total = 0.42  ±  0.08 (medium gain) above the traditional baseline value of  non interactive = 0.23 for Hake’s normalized gain regression analysis. This applied to all of the teachers and students who participated in this study. Our initial research findings suggest that allowing learners to relate abstract physics concepts to real life through coupling traditional video analysis with video modelling might be an innovative and effective method for teaching and learning about free fall motion.
$\ell_{0}$ Sparsifying Transform Learning With Efficient Optimal Updates and Convergence Guarantees

Many applications in signal processing benefit from the sparsity of signals in a certain transform domain or dictionary. Synthesis sparsifying dictionaries that are directly adapted to data have been popular in applications such as image denoising, inpainting, and medical image reconstruction. In this paper, we focus instead on the sparsifying transform model, and study the learning of well-conditioned square sparsifying transforms. The proposed algorithms alternate between a l0 “norm”-based sparse coding step, and a non-convex transform update step. We derive the exact analytical solution for each of these steps. The proposed solution for the transform update step achieves the global minimum in that step, and also provides speedups over iterative solutions involving conjugate gradients. We establish that our alternating algorithms are globally convergent to the set of local minimizers of the nonconvex transform learning problems. In practice, the algorithms are insensitive to initialization. We present results illustrating the promising performance and significant speed-ups of transform learning over synthesis K-SVD in image denoising.
Deep Image: Scaling up Image Recognition

We present a state-of-the-art image recognition system, Deep Image, developed using end-to-end deep learning. The key components are a custom-built supercomputer dedicated to deep learning, a highly optimized parallel algorithm using new strategies for data partitioning and communication, larger deep neural network models, novel data augmentation approaches, and usage of multi-scale high-resolution images. Our method achieves excellent results on multiple challenging computer vision benchmarks.
Patch-recovery filters for curvature in discontinuous Galerkin-based level-set methods

In two-phase flow simulations, a difficult issue is usually the treatment of surface tension effects. These cause a pressure jump that is proportional to the curvature of the interface separating the two fluids. Since the evaluation of the curvature incorporates second derivatives, it is prone to numerical instabilities. Within this work, the interface is described by a level-set method based on a discontinuous Galerkin discretization. In order to stabilize the evaluation of the curvature, a patch-recovery operation is employed. There are numerous ways in which this filtering operation can be applied in the whole process of curvature computation. Therefore, an extensive numerical study is performed to identify optimal settings for the patch-recovery operations with respect to computational cost and accuracy.
Sampling online social networks via heterogeneous statistics

Most sampling techniques for online social networks (OSNs) are based on a particular sampling method on a single graph, which is referred to as a statistic. However, various realizing methods on different graphs could possibly be used in the same OSN, and they may lead to different sampling efficiencies, i.e., asymptotic variances. To utilize multiple statistics for accurate measurements, we formulate a mixture sampling problem, through which we construct a mixture unbiased estimator which minimizes the asymptotic variance. Given fixed sampling budgets for different statistics, we derive the optimal weights to combine the individual estimators; given a fixed total budget, we show that a greedy allocation towards the most efficient statistic is optimal. In practice, the sampling efficiencies of statistics can be quite different for various targets and are unknown before sampling. To solve this problem, we design a two-stage framework which adaptively spends a partial budget to test different statistics and allocates the remaining budget to the inferred best statistic. We show that our two-stage framework is a generalization of 1) randomly choosing a statistic and 2) evenly allocating the total budget among all available statistics, and our adaptive algorithm achieves higher efficiency than these benchmark strategies in theory and experiment.
Efficient Blind Compressed Sensing Using Sparsifying Transforms with Convergence Guarantees and Application to MRI

Natural signals and images are well-known to be approximately sparse in transform domains such as Wavelets and DCT. This property has been heavily exploited in various applications in image processing and medical imaging. Compressed sensing exploits the sparsity of images or image patches in a transform domain or synthesis dictionary to reconstruct images from undersampled measurements. In this work, we focus on blind compressed sensing, where the underlying sparsifying transform is a priori unknown, and propose a framework to simultaneously reconstruct the underlying image as well as the sparsifying transform from highly undersampled measurements. The proposed block coordinate descent type algorithms involve highly efficient optimal updates. Importantly, we prove that although the proposed blind compressed sensing formulations are highly nonconvex, our algorithms are globally convergent (i.e., they converge from any initialization) to the set of critical points of the objectives defining the formulations. These critical points are guaranteed to be at least partial global and partial local minimizers. The exact point(s) of convergence may depend on initialization. We illustrate the usefulness of the proposed framework for magnetic resonance image reconstruction from highly undersampled k-space measurements. As compared to previous methods involving the synthesis dictionary model, our approach is much faster, while also providing promising reconstruction quality.
Intense femtosecond photoexcitation of bulk and monolayer MoS2

The effect of femtosecond laser irradiation on bulk and single-layer MoS2 on silicon oxide is studied. Optical, Field Emission Scanning Electron Microscopy (FESEM) and Raman microscopies were used to quantify the damage. The intensity of A1g and E2g1 vibrational modes was recorded as a function of the number of irradiation pulses. The observed behavior was attributed to laser-induced bond breaking and subsequent atoms removal due to electronic excitations. The single-pulse optical damage threshold was determined for the monolayer and bulk under 800nm and 1030nm pulsed laser irradiation and the role of two-photon versus one photon absorption effects is discussed.
Quantum Geometry and Black Holes

We present an overall picture of the advances in the description of black hole physics from the perspective of loop quantum gravity. After an introduction that discusses the main conceptual issues we present some details about the classical and quantum geometry of isolated horizons and their quantum geometry and then use this scheme to give a natural definition of the entropy of black holes. The entropy computations can be neatly expressed in the form of combinatorial problems solvable with the help of methods based on number theory and the use of generating functions. The recovery of the Bekenstein-Hawking law and corrections to it is explained in some detail. After this, due attention is paid to the discussion of semiclassical issues. An important point in this respect is the proper interpretation of the horizon area as the energy that should appear in the statistical-mechanical treatment of the black hole model presented here. The chapter ends with a comparison between the microscopic and semiclassical approaches to the computation of the entropy and discusses a number of issues regarding the relation between entanglement and statistical entropy and the possibility of comparing the subdominant (logarithmic) corrections to the entropy obtained with the help of the Euclidean path integral with the ones obtained in the present framework.
Fundamental oscillation modes of neutron stars: validity of universal relations

We study the $f$-mode frequencies and damping times of nonrotating neutron stars (NS) in general relativity (GR) by solving the linearized perturbation equations, with the aim to establish "universal" relations that depend only weakly on the equations of state (EOS). Using a more comprehensive set of EOSs, we re-examine some proposed linearizations that describe the $f$-mode parameters in terms of mass and radius of the neutron star (NS), and we test a more recent proposal for expressing the $f$-mode parameters as quadratic functions of the effective compactness. Our extensive results for each equation of state considered allow us to study the accuracy of each proposal. In particular, we find that the damping time deviates quite considerably from the proposed linearization. We introduce a new universal relation for the product of the $f$-mode frequency and damping time as a function of the (ordinary) compactness, which proved to be more accurate. The relations using the effective compactness on the other hand also fit our data accurately. Our results show that the maximum oscillation frequency depends strongly on the EOS, such that the measurement of a high oscillation frequency would rule out several EOSs. Lastly, we compare the exact mode frequencies to those obtained in the Cowling approximation, and also to results obtained with a nonlinear evolution code, validating the implementations of the different approaches.
An Improvement to the Domain Adaptation Bound in a PAC-Bayesian context

This paper provides a theoretical analysis of domain adaptation based on the PAC-Bayesian theory. We propose an improvement of the previous domain adaptation bound obtained by Germain et al. in two ways. We first give another generalization bound tighter and easier to interpret. Moreover, we provide a new analysis of the constant term appearing in the bound that can be of high interest for developing new algorithmic solutions.
Fluctuations of Conserved Quantities in High Energy Nuclear Collisions at RHIC

Fluctuations of conserved quantities in heavy-ion collisions are used to probe the phase transition and the QCD critical point for the strongly interacting hot and dense nuclear matter. The STAR experiment has carried out moment analysis of net-proton (proxy for net- baryon (B)), net-kaon (proxy for net-strangeness (S)), and net-charge (Q). These measurements are important for understanding the quantum chromodynamics phase diagram. We present the analysis techniques used in the moment analysis by the STAR experiment and discuss the moments of net-proton and net-charge distributions from the first phase of the Beam Energy Scan program at the Relativistic Heavy Ion Collider.
Metals Production Requirements for Rapid Photovoltaics Deployment

As global photovoltaics (PV) deployment grows, the required input materials need to be supplied at an increasing rate. In this paper, we quantify the effect of PV deployment levels on the scale of metal production. For example, we find that if cadmium telluride {copper indium gallium diselenide} PV accounts for more than 3% {10%} of electricity generation by 2030, the required growth rates for the production of indium and tellurium would exceed historically-observed production growth rates for a large set of metals. In contrast, even if crystalline silicon PV supplies all electricity in 2030, the required silicon production growth rate would fall within the historical range. More generally, this paper highlights possible constraints to the rate of scaling up of metal production for some PV technologies, and outlines an approach to assess projected metal growth requirements against an ensemble of past growth rates from across the metal production sector. The framework developed in this paper may be useful for evaluating the scalability of a wide range of materials and devices, to inform technology development in the laboratory, as well as public and private research investment.
Future Supply of Medical Radioisotopes for the UK Report 2014

The UK has no research nuclear reactors and relies on the importation of 99Mo and other medical radioisotopes (e.g. Iodine-131) from overseas (excluding PET radioisotopes). The UK is therefore vulnerable not only to global shortages, but to problems with shipping and importation of the products. In this context Professor Erika Denton UK national Clinical Director for Diagnostics requested that the British Nuclear Medicine Society lead a working group with stakeholders including representatives from the Science & Technology Facilities Council (STFC) to prepare a report. The group had a first meeting on 10 April 2013 followed by a working group meeting with presentations on 9th September 2013 where the scope of the work required to produce a report was agreed. 
The objectives of the report are: to describe the status of the use of medical radioisotopes in the UK; to anticipate the potential impact of shortages for the UK; to assess potential alternative avenues of medical radioisotope production for the UK market; and to explore ways of mitigating the impact of medical radioisotopes on patient care pathways. The report incorporates details of a visit to the Cyclotron Facilities at Edmonton, Alberta and at TRIUMF, Vancouver BC in Canada by members of the report team.