nils-herrmann/clean_scirepeval huggingface dataset:
Using Virtual Observatory with Python: querying remote astronomical databases

This tutorial is devoted to extending an existing catalogue with data taken elsewhere, either from CDS Vizier or Simbad database. As an example, we used the so-called 'Spectroscopic Survey of Stars in the Solar Neighborhood' (aka. S4N, Allende Prieto et al. 2004) in order to retrieve all objects with available data for the set of fundamental stellar parameters effective temperature, surface gravity and metallicity. Then for each object in this dataset we query Simbad database to retrieve the projected rotational velocity. This combines Vizier and Simbad queries made using Python astroquery module. The tutorial covers remote database access, filtering tables with arbitrary criteria, creating and writing your own tables, and basics of plotting in Python.
Alternative to particle dark matter

We propose an alternative to particle dark matter that borrows ingredients of MOdified Newtonian Dynamics (MOND) while adding new key components. The first new feature is a dark matter fluid, in the form of a scalar field with small equation of state and sound speed. This component is critical in reproducing the success of cold dark matter for the expansion history and the growth of linear perturbations, but does not cluster significantly on non-linear scales. Instead, the missing mass problem on non-linear scales is addressed by a modification of the gravitational force law. The force law approximates MOND at large and intermediate accelerations, and therefore reproduces the empirical success of MOND at fitting galactic rotation curves. At ultra-low accelerations, the force law reverts to an inverse-square-law, albeit with a larger Newton's constant. This latter regime is important in galaxy clusters and is consistent with their observed isothermal profiles, provided the characteristic acceleration scale of MOND is mildly varying with scale or mass, such that it is ~12 times higher in clusters than in galaxies. We present an explicit relativistic theory in terms of two scalar fields. The first scalar field is governed by a Dirac-Born-Infeld action and behaves as a dark matter fluid on large scales. The second scalar field also has single-derivative interactions and mediates a fifth force that modifies gravity on non-linear scales. Both scalars are coupled to matter via an effective metric that depends locally on the fields. The form of this effective metric implies the equality of the two scalar gravitational potentials, which ensures that lensing and dynamical mass estimates agree. Further work is needed in order to make both the acceleration scale of MOND and the fraction at which gravity reverts to an inverse-square law explicitly dynamical quantities, varying with scale or mass.
Computing classic closeness centrality, at scale

Closeness centrality, first considered by Bavelas (1948), is an importance measure of a node in a network which is based on the distances from the node to all other nodes. The classic definition, proposed by Bavelas (1950), Beauchamp (1965), and Sabidussi (1966), is (the inverse of) the average distance to all other nodes.
 We propose the first highly scalable (near linear-time processing and linear space overhead) algorithm for estimating, within a small relative error, the classic closeness centralities of all nodes in the graph. Our algorithm applies to undirected graphs, as well as for centrality computed with respect to round-trip distances in directed graphs.
 For directed graphs, we also propose an efficient algorithm that approximates generalizations of classic closeness centrality to outbound and inbound centralities. Although it does not provide worst-case theoretical approximation guarantees, it is designed to perform well on real networks.
 We perform extensive experiments on large networks, demonstrating high scalability and accuracy.
A New Proton CT Scanner

The design, construction, and preliminary testing of a second generation proton CT scanner is presented. All current treatment planning systems at proton therapy centers use X-ray CT as the primary imaging modality for treatment planning to calculate doses to tumor and healthy tissues. One of the limitations of X-ray CT is in the conversion of X-ray attenuation coefficients to relative (proton) stopping powers, or RSP. This results in more proton range uncertainty, larger target volumes and therefore, more dose to healthy tissues. To help improve this, we present a novel scanner capable of high dose rates, up to 2~MHz, and large area coverage, 20~x~24~cm$^2$, for imaging an adult head phantom and reconstructing more accurate RSP values.
First Calorimetric Measurement of OI-line in the Electron Capture Spectrum of $^{163}$Ho

The isotope $^{163}$Ho undergoes an electron capture process with a recommended value for the energy available to the decay, $Q_{\rm EC}$, of about 2.5 keV. According to the present knowledge, this is the lowest $Q_{\rm EC}$ value for electron capture processes. Because of that, $^{163}$Ho is the best candidate to perform experiments to investigate the value of the electron neutrino mass based on the analysis of the calorimetrically measured spectrum. 
We present for the first time the calorimetric measurement of the atomic de-excitation of the $^{163}$Dy daughter atom upon the capture of an electron from the 5s shell in $^{163}$Ho, OI-line. The measured peak energy is 48 eV. This measurement was performed using low temperature metallic magnetic calorimeters with the $^{163}$Ho ion implanted in the absorber. 
We demonstrate that the calorimetric spectrum of $^{163}$Ho can be measured with high precision and that the parameters describing the spectrum can be learned from the analysis of the data. Finally, we discuss the implications of this result for the Electron Capture $^{163}$Ho experiment, ECHo, aiming to reach sub-eV sensitivity on the electron neutrino mass by a high precision and high statistics calorimetric measurement of the $^{163}$Ho spectrum.
Searching for a Unique Style in Soccer

Is it possible to have a unique, recognizable style in soccer nowadays? We address this question by proposing a method to quantify the motif characteristics of soccer teams based on their pass networks. We introduce the the concept of "flow motifs" to characterize the statistically significant pass sequence patterns. It extends the idea of the network motifs, highly significant subgraphs that usually consists of three or four nodes. The analysis of the motifs in the pass networks allows us to compare and differentiate the styles of different teams. Although most teams tend to apply homogenous style, surprisingly, a unique strategy of soccer exists. Specifically, FC Barcelona's famous tiki-taka does not consist of uncountable random passes but rather has a precise, finely constructed structure.
Google matrix analysis of directed networks

In past ten years, modern societies developed enormous communication and social networks. Their classification and information retrieval processing become a formidable task for the society. Due to the rapid growth of World Wide Web, social and communication networks, new mathematical methods have been invented to characterize the properties of these networks on a more detailed and precise level. Various search engines are essentially using such methods. It is highly important to develop new tools to classify and rank enormous amount of network information in a way adapted to internal network structures and characteristics. This review describes the Google matrix analysis of directed complex networks demonstrating its efficiency on various examples including World Wide Web, Wikipedia, software architecture, world trade, social and citation networks, brain neural networks, DNA sequences and Ulam networks. The analytical and numerical matrix methods used in this analysis originate from the fields of Markov chains, quantum chaos and Random Matrix theory.
DISCOVERY OF SUBSTRUCTURE IN THE SCATTER-BROADENED IMAGE OF SGR A*

We have detected substructure within the smooth scattering disk of the celebrated Galactic center radio source Sagittarius A* (Sgr A*). We observed this structure at 1.3 cm wavelength with the Very Long Baseline Array together with the Green Bank Telescope, on baselines of up to 3000 km, long enough to completely resolve the average scattering disk. Such structure is predicted theoretically as a consequence of refraction by large-scale plasma fluctuations in the interstellar medium. Along with the much-studied θd∝λ2 scaling of angular broadening θd with observing wavelength λ, our observations indicate that the spectrum of interstellar turbulence is shallow with an inner scale larger than 300 km. The substructure is consistent with an intrinsic size of about 1 mas at 1.3 cm wavelength, as inferred from deconvolution of the average scattering. Further observations of the substructure can set stronger constraints on the properties of scattering material and on the intrinsic size of Sgr A*. These constraints will guide our understanding of the effects of scatter broadening and the emission physics near the black hole in images with the Event Horizon Telescope at millimeter wavelengths.
Understanding the assembly of Kepler's compact planetary systems

The Kepler mission has recently discovered a number of exoplanetary systems, such as Kepler-11 and Kepler-32, in which ensembles of several planets are found in very closely packed orbits (often within a few percent of an AU of one another). These compact configurations present a challenge for traditional planet formation and migration scenarios. We present a dynamical study of the assembly of these systems, using an N-body method which incorporates a parametrized model of planet migration in a turbulent protoplanetary disc. We explore a wide parameter space, and find that under suitable conditions it is possible to form compact, close-packed planetary systems via traditional disc-driven migration. We find that simultaneous migration of multiple planets is a viable mechanism for the assembly of tightly-packed planetary systems, as long as the disc provides significant eccentricity damping and the level of turbulence in the disc is modest. We discuss the implications of our preferred parameters for the protoplanetary discs in which these systems formed, and comment on the occurrence and significance of mean-motion resonances in our simulations.
Study of the nonlinear instability of confined geometries

The discovery of a "weakly-turbulent" instability of anti-de Sitter spacetime supports the idea that confined fluctuations eventually collapse to black holes and suggests that similar phenomena might be possible in asymptotically-flat spacetime, for example in the context of spherically symmetric oscillations of stars or nonradial pulsations of ultracompact objects. Here we present a detailed study of the evolution of the Einstein-Klein-Gordon system in a cavity, with different types of deformations of the spectrum, including a mass term for the scalar and Neumann conditions at the boundary. We provide numerical evidence that gravitational collapse always occurs, at least for amplitudes that are three orders of magnitude smaller than Choptuik's critical value and corresponding to more than $10^5$ reflections before collapse. The collapse time scales as the inverse square of the initial amplitude in the small-amplitude limit. In addition, we find that fields with nonresonant spectrum collapse earlier than in the fully-resonant case, a result that is at odds with the current understanding of the process. Energy is transferred through a direct cascade to high frequencies when the spectrum is resonant, but we observe both direct- and inverse-cascade effects for nonresonant spectra. Our results indicate that a fully-resonant spectrum might not be a crucial ingredient of the conjectured turbulent instability and that other mechanisms might be relevant. We discuss how a definitive answer to this problem is essentially impossible within the present framework.
Floquet engineering of long-range p -wave superconductivity

M.B., , and G.P. acknowledge the Spanish Ministry of Economy and Competitiveness through Grant No. MAT2011-24331 and the associated FPI scholarship (M.B.). V.M.B. and T.B. wish to acknowledge financial support by the Deutsche Forschungsgemeinschaft (DFG) via GRK 1558, Grants No. BRA 1528/7, No. BRA 1528/8, and No. SFB 910.
Manipulating Decision Making of Typical Agents

We investigate how the choice of decision makers can be varied under the presence of risk and uncertainty. Our analysis is based on the approach we have previously applied to individual decision makers, which we now generalize to the case of decision makers that are members of a society. The approach employs the mathematical techniques that are common in quantum theory, justifying our naming as quantum decision theory. However, we do not assume that decision makers are quantum objects. The techniques of quantum theory are needed only for defining the prospect probabilities taking into account such hidden variables as behavioral biases and other subconscious feelings. The approach describes an agent's choice as a probabilistic event occurring with a probability that is the sum of a utility factor and of an attraction factor. The attraction factor embodies subjective and unconscious dimensions in the mind of the decision maker. We show that the typical aggregate amplitude of the attraction factor is 1/4, and it can be either positive or negative depending on the relative attraction of the competing choices. The most efficient way of varying the decision makers choice is realized by influencing the attraction factor. This can be done in two ways. One method is to arrange in a special manner the payoff weights, which induces the required changes of the values of attraction factors. We show that a slight variation of the payoff weights can invert the sign of the attraction factors and reverse the decision preferences, even when the prospect utilities remain unchanged. The second method of influencing the decision makers choice is by providing information to decision makers. The methods of influencing decision making are illustrated by several experiments, whose outcomes are compared quantitatively with the predictions of our approach.
On computable abstractions (a conceptual introduction)

This paper introduces abstractions that are meaningful for computers and that can be built and used according to computers' own criteria, i.e., computable abstractions. It is analyzed how abstractions can be seen to serve as the building blocks for the creation of one own's understanding of things, which is essential in performing intellectual tasks. Thus, abstractional machines are defined, which following a mechanical process can, based on computable abstractions, build and use their own understanding of things. Abstractional machines are illustrated through an example that outlines their application to the task of natural language processing.
An Algorithm for Constructing Principal Geodesics in Phylogenetic Treespace

Most phylogenetic analyses result in a sample of trees, but summarizing and visualizing these samples can be challenging. Consensus trees often provide limited information about a sample, and so methods such as consensus networks, clustering and multidimensional scaling have been developed and applied to tree samples. This paper describes a stochastic algorithm for constructing a principal geodesic or line through treespace which is analogous to the first principal component in standard principal components analysis. A principal geodesic summarizes the most variable features of a sample of trees, in terms of both tree topology and branch lengths, and it can be visualized as an animation of smoothly changing trees. The algorithm performs a stochastic search through parameter space for a geodesic which minimizes the sum of squared projected distances of the data points. This procedure aims to identify the globally optimal principal geodesic, though convergence to locally optimal geodesics is possible. The methodology is illustrated by constructing principal geodesics for experimental and simulated data sets, demonstrating the insight into samples of trees that can be gained and how the method improves on a previously published approach. A java package called GeoPhytter for constructing and visualizing principal geodesics is freely available from www.ncl.ac.uk/ ntmwn/geophytter.
Friendly Artificial Intelligence: The Physics Challenge

Relentless progress in artificial intelligence (AI) is increasingly raising concerns that machines will replace humans on the job market, and perhaps altogether. Eliezer Yudkowski and others have explored the possibility that a promising future for humankind could be guaranteed by a superintelligent "Friendly AI", designed to safeguard humanity and its values. I argue that, from a physics perspective where everything is simply an arrangement of elementary particles, this might be even harder than it appears. Indeed, it may require thinking rigorously about the meaning of life: What is "meaning" in a particle arrangement? What is "life"? What is the ultimate ethical imperative, i.e., how should we strive to rearrange the particles of our Universe and shape its future? If we fail to answer the last question rigorously, this future is unlikely to contain humans.
Structure Growth and the CMB in Modified Gravity (MOG)

An important piece of evidence for dark matter is the need to explain the growth of structure from the time of horizon entry and radiation-matter equality to the formation of stars and galaxies. This cannot be explained by using general relativity without dark matter. So far, dark matter particles have not been detected in laboratory measurements or at the LHC. We demonstrate that enhanced structure growth can happen in a modified gravity theory (MOG). The vector field and particle introduced in the theory to explain galaxy and cluster dynamics plays an important role in generating the required structure growth. The particle called the phion (a light hidden photon) is neutral and is a dominant, pressureless component in the MOG Friedmann equations, before the time of decoupling. The dominant energy density of the phion particle in the early universe, generates an explanation for the growth of density perturbations. The angular acoustical power spectrum due to baryon-photon pressure waves is in agreement with the Planck 2013 data. As the universe expands and large scale structures are formed, the density of baryons dominates and the rotation curves of galaxies and the dynamics of clusters are explained in MOG, when the phion particle in the present universe is ultra-light. The matter power spectrum determined by the theory is in agreement with current galaxy redshift surveys.
Quasar ionization front Lyα emission in an inhomogeneous intergalactic medium

The conditions within the ionization front of a quasar during reionization (T � 30,000 K, neutral hydrogen fraction xH I � 0.5) are ideal for producing Lyα emission via collisional ex- citation of hydrogen atoms. Observations of this emission, which could subtend > 10 arcmin 2 on the sky, would definitively demonstrate the presence of a n eutral intergalactic medium at the observed epoch, placing valuable constraints on the progress of reionization. We find that the expected Lyα surface brightness is significantly weaker than previously determined and may be impossible to observe with current and near-future instruments. Past work calculated the Lyα emission from a quasar ionization front in a homogeneous medium with a clump- ing factor approximation to account for inhomogeneities. We find using 1D radiative transfer calculations that this approximation overestimates the emission by a factor of > 3. Our calcu- lations model the propagation of ionizing photons and compute the Lyα emission from quasar ionization fronts on sightlines from a hydrodynamic cosmological simulation at z = 7.1. To better understand the physical properties of the emission, we also develop an analytic model that accurately describes the results of the full radiative transfer calculation.
THE DYNAMICAL EVOLUTION OF STELLAR BLACK HOLES IN GLOBULAR CLUSTERS

Our current understanding of the stellar initial mass function and massive star evolution suggests that young globular clusters (GCs) may have formed hundreds to thousands of stellar-mass black holes (BHs), the remnants of stars with initial masses from ∼20–100 M☉. Birth kicks from supernova explosions may eject some BHs from their birth clusters, but most should be retained. Using a Monte Carlo method we investigate the long-term dynamical evolution of GCs containing large numbers of stellar BHs. We describe numerical results for 42 models, covering a broad range of realistic initial conditions, including up to 1.6 × 106 stars. In almost all models we find that significant numbers of BHs (up to ∼103) are retained all the way to the present. This is in contrast to previous theoretical expectations that most BHs should be ejected dynamically within a few gigayears The main reason for this difference is that core collapse driven by BHs (through the Spitzer “mass segregation instability”) is easily reverted through three-body processes, and involves only a small number of the most massive BHs, while lower-mass BHs remain well-mixed with ordinary stars far from the central cusp. Thus the rapid segregation of stellar BHs does not lead to a long-term physical separation of most BHs into a dynamically decoupled inner core, as often assumed previously. Combined with the recent detections of several BH X-ray binary candidates in Galactic GCs, our results suggest that stellar BHs could still be present in large numbers in many GCs today, and that they may play a significant role in shaping the long-term dynamical evolution and the present-day dynamical structure of many clusters.
High-Performance Kernel Machines With Implicit Distributed Optimization and Randomization

We propose a framework for massive-scale training of kernel-based statistical models, based on combining distributed convex optimization with randomization techniques. Our approach is based on a block-splitting variant of the alternating directions method of multipliers, carefully reconfigured to handle very large random feature matrices under memory constraints, while exploiting hybrid parallelism typically found in modern clusters of multicore machines. Our high-performance implementation supports a variety of statistical learning tasks by enabling several loss functions, regularization schemes, kernels, and layers of randomized approximations for both dense and sparse datasets, in an extensible framework. We evaluate our implementation on large-scale model construction tasks and provide a comparison against existing sequential and parallel libraries.
Electronic Structure of Large-Scale Graphene Nanoflakes

Wei Hu, ∗ Lin Lin, 1, † Chao Yang, ‡ and Jinlong Yang 4, § Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA Department of Mathematics, University of California, Berkeley, CA 94720, USA Hefei National Laboratory for Physical Sciences at Microscale, and Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui 230026, China Synergetic Innovation Center of Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei, Anhui 230026, China ( 2014)