nils-herrmann/clean_scirepeval huggingface dataset:
Internal gravity wave excitation by turbulent convection

We calculate the flux of internal gravity waves (IGWs) generated by turbulent convection in stars. We solve for the IGW eigenfunctions analytically near the radiative-convective interface in a local, Boussinesq, and cartesian domain. We consider both discontinuous and smooth transitions between the radiative and convective regions and derive Green's functions to solve for the IGWs in the radiative region. We find that if the radiative-convective transition is smooth, the IGW flux depends on the exact form of the buoyancy frequency near the interface. IGW excitation is most efficient for very smooth interfaces, which gives an upper bound on the IGW flux of ~ F_conv (d/H), where F_conv is the flux carried by the convective motions, d is the width of the transition region, and H is the pressure scale height. This can be much larger than the standard result in the literature for a discontinuous radiative-convective transition, which gives a wave flux ~ F_conv M$, where M is the convective Mach number. However, in the smooth transition case, the most efficiently excited perturbations will break in the radiative zone. The flux of IGWs which do not break and are able to propagate in the radiative region is at most F_conv M^(5/8) (d/H)^(3/8), larger than the discontinuous transition result by (MH/d)^(-3/8). The transition region in the Sun is smooth for the energy-bearing waves; as a result, we predict that the IGW flux is a few to five times larger than previous estimates. We discuss the implications of our results for several astrophysical applications, including IGW driven mass loss and the detectability of convectively excited IGWs in main sequence stars.
2011 HM102: DISCOVERY OF A HIGH-INCLINATION L5 NEPTUNE TROJAN IN THE SEARCH FOR A POST-PLUTO NEW HORIZONS TARGET

We present the discovery of a long-term stable L5 (trailing) Neptune Trojan in data acquired to search for candidate trans-Neptunian objects for the New Horizons spacecraft to fly by during an extended post-Pluto mission. This Neptune Trojan, 2011 HM102, has the highest inclination (29.°4) of any known member of this population. It is intrinsically brighter than any single L5 Jupiter Trojan at HV ∼ 8.18. We have determined its gri colors (a first for any L5 Neptune Trojan), which we find to be similar to the moderately red colors of the L4 Neptune Trojans, suggesting similar surface properties for members of both Trojan clouds. We also present colors derived from archival data for two L4 Neptune Trojans (2006 RJ103 and 2007 VL305), better refining the overall color distribution of the population. In this document we describe the discovery circumstances, our physical characterization of 2011 HM102, and this object's implications for the Neptune Trojan population overall. Finally, we discuss the prospects for detecting 2011 HM102 from the New Horizons spacecraft during its close approach in mid- to late-2013.
Gender identity and lexical variation in social media

We present a study of the relationship between gender, linguistic style, and social networks, using a novel corpus of 14,000 Twitter users. Prior quantitative work on gender often treats this social variable as a female/male binary; we argue for a more nuanced approach. By clustering Twitter users, we find a natural decomposition of the dataset into various styles and topical interests. Many clusters have strong gender orientations, but their use of linguistic resources sometimes directly conflicts with the population-level language statistics. We view these clusters as a more accurate reflection of the multifaceted nature of gendered language styles. Previous corpus-based work has also had little to say about individuals whose linguistic styles defy population-level gender patterns. To identify such individuals, we train a statistical classifier, and measure the classifier confidence for each individual in the dataset. Examining individuals whose language does not match the classifier's model for their gender, we find that they have social networks that include significantly fewer same-gender social connections and that, in general, social network homophily is correlated with the use of same-gender language markers. Pairing computational methods and social theory thus offers a new perspective on how gender emerges as individuals position themselves relative to audiences, topics, and mainstream gender norms.
PHOTOMETRICALLY DERIVED MASSES AND RADII OF THE PLANET AND STAR IN THE TrES-2 SYSTEM

We measure the mass and radius of the star and planet in the TrES-2 system using 2.7 years of observations by the Kepler spacecraft. The light curve shows evidence for ellipsoidal variations and Doppler beaming on a period consistent with the orbital period of the planet with amplitudes of 2.79+0.44−0.62 and 3.44+0.32−0.37 parts per million (ppm), respectively, and a difference between the dayside and the nightside planetary flux of 3.41+0.55−0.82 ppm. We present an asteroseismic analysis of solar-like oscillations on TrES-2A which we use to calculate the stellar mass of 0.94 ± 0.05 M☉ and radius of 0.95 ± 0.02 R☉. Using these stellar parameters, a transit model fit and the phase-curve variations, we determine the planetary radius of 1.162+0.020−0.024 RJup and derive a mass for TrES-2b from the photometry of 1.44 ± 0.21 MJup. The ratio of the ellipsoidal variation to the Doppler beaming amplitudes agrees to better than 2σ with theoretical predications, while our measured planet mass and radius agree within 2σ of previously published values based on spectroscopic radial velocity measurements. We measure a geometric albedo of 0.0136+0.0022−0.0033 and an occultation (secondary eclipse) depth of 6.5+1.7−1.8 ppm which we combined with the day/night planetary flux ratio to model the atmosphere of TrES-2b. We find that an atmosphere model that contains a temperature inversion is strongly preferred. We hypothesize that the Kepler bandpass probes a significantly greater atmospheric depth on the night side relative to the day side.
Econoinformatics meets Data-Centric Social Sciences

Our society has been computerised and globalised due to emergence and spread of information and communication technology (ICT). This enables us to investigate our own socio-economic systems based on large amounts of data on human activities. In this article, methods of treating complexity arising from a vast amount of data, and linking data from different sources, are discussed. Furthermore, several examples are given of studies into the applications of econoinformatics for the Japanese stock exchange, foreign exchange markets, domestic hotel booking data and international flight booking data are shown. It is the main message that spatio-temporal information is a key element to synthesise data from different data sources.
Discrete Signal Processing on Graphs

In social settings, individuals interact through webs of relationships. Each individual is a node in a complex network (or graph) of interdependencies and generates data, lots of data. We label the data by its source, or formally stated, we index the data by the nodes of the graph. The resulting signals (data indexed by the nodes) are far removed from time or image signals indexed by well ordered time samples or pixels. DSP, discrete signal processing, provides a comprehensive, elegant, and efficient methodology to describe, represent, transform, analyze, process, or synthesize these well ordered time or image signals. This paper extends to signals on graphs DSP and its basic tenets, including filters, convolution, z-transform, impulse response, spectral representation, Fourier transform, frequency response, and illustrates DSP on graphs by classifying blogs, linear predicting and compressing data from irregularly located weather stations, or predicting behavior of customers of a mobile service provider.
Designing Informative Securities

We create a formal framework for the design of informative securities in prediction markets. These securities allow a market organizer to infer the likelihood of events of interest as well as if he knew all of the traders' private signals. We consider the design of markets that are always informative, markets that are informative for a particular signal structure of the participants, and informative markets constructed from a restricted selection of securities. We find that to achieve informativeness, it can be necessary to allow participants to express information that may not be directly of interest to the market organizer, and that understanding the participants' signal structure is important for designing informative prediction markets.
Spectral Estimation of Conditional Random Graph Models for Large-Scale Network Data

Generative models for graphs have been typically committed to strong prior assumptions concerning the form of the modeled distributions. Moreover, the vast majority of currently available models are either only suitable for characterizing some particular network properties (such as degree distribution or clustering coefficient), or they are aimed at estimating joint probability distributions, which is often intractable in large-scale networks. In this paper, we first propose a novel network statistic, based on the Laplacian spectrum of graphs, which allows to dispense with any parametric assumption concerning the modeled network properties. Second, we use the defined statistic to develop the Fiedler random graph model, switching the focus from the estimation of joint probability distributions to a more tractable conditional estimation setting. After analyzing the dependence structure characterizing Fiedler random graphs, we evaluate them experimentally in edge prediction over several real-world networks, showing that they allow to reach a much higher prediction accuracy than various alternative statistical models.
Graph-Coupled HMMs for Modeling the Spread of Infection

We develop Graph-Coupled Hidden Markov Models (GCHMMs) for modeling the spread of infectious disease locally within a social network. Unlike most previous research in epidemiology, which typically models the spread of infection at the level of entire populations, we successfully leverage mobile phone data collected from 84 people over an extended period of time to model the spread of infection on an individual level. Our model, the GCHMM, is an extension of widely-used Coupled Hidden Markov Models (CHMMs), which allow dependencies between state transitions across multiple Hidden Markov Models (HMMs), to situations in which those dependencies are captured through the structure of a graph, or to social networks that may change over time. The benefit of making infection predictions on an individual level is enormous, as it allows people to receive more personalized and relevant health advice.
Colloquium: Perspectives on core-collapse supernova theory

Core-collapse theory brings together many facets of high-energy and nuclear astrophysics and the numerical arts to present theorists with one of the most important, yet frustrating, astronomical questions: "What is the mechanism of core-collapse supernova explosions?" A review of all the physics and the fifty-year history involved would soon bury the reader in minutiae that could easily obscure the essential elements of the phenomenon, as we understand it today. Moreover, much remains to be discovered and explained, and a complicated review of an unresolved subject in flux could grow stale fast. Therefore, in this paper I describe what I think are various important facts and perspectives that may have escaped the attention of those interested in this puzzle. Furthermore, I attempt to describe the modern theory's physical underpinnings and briefly summarize the current state of play. In the process, I identify a few myths (as I see them) that have crept into modern discourse. However, there is much more to do and humility in the face of this age-old challenge is clearly the most prudent stance as we seek its eventual resolution.
Accretion disk parameters in HLX-1

We estimate the outer radius of the accretion disk in HLX-1 from its optical brightness and from the exponential timescale of the decline in the X-ray lightcurve after an outburst. We find that the disk is an order of magnitude smaller than the semimajor axis of the orbit. If the disk size is determined by the circularization radius near periastron, the eccentricity of the binary system must be ≳ 0.95. We report on the discovery of Hα emission during the 2012 outburst, with a single-peaked, narrow profile (consistent with a nearly face-on view), and a central velocity displaced by ≈ 490 km s−1 from that of the host galaxy.
Bayesian clustering of replicated time-course gene expression data with weak signals

To identify novel dynamic patterns of gene expression, we develop a statistical method to cluster noisy measurements of gene expression collected from multiple replicates at multiple time points, with an unknown number of clusters. We propose a random-effects mixture model coupled with a Dirichlet-process prior for clustering. The mixture model formulation allows for probabilistic cluster assignments. The random-effects formulation allows for attributing the total variability in the data to the sources that are consistent with the experimental design, particularly when the noise level is high and the temporal dependence is not strong. The Dirichlet-process prior induces a prior distribution on partitions and helps to estimate the number of clusters (or mixture components) from the data. We further tackle two challenges associated with Dirichlet-process prior-based methods. One is efficient sampling. We develop a novel Metropolis-Hastings Markov Chain Monte Carlo (MCMC) procedure to sample the partitions. The other is efficient use of the MCMC samples in forming clusters. We propose a two-step procedure for posterior inference, which involves resampling and relabeling, to estimate the posterior allocation probability matrix. This matrix can be directly used in cluster assignments, while describing the uncertainty in clustering. We demonstrate the effectiveness of our model and sampling procedure through simulated data. Applying our method to a real data set collected from Drosophila adult muscle cells after five-minute Notch activation, we identify 14 clusters of different transcriptional responses among 163 differentially expressed genes, which provides novel insights into underlying transcriptional mechanisms in the Notch signaling pathway. The algorithm developed here is implemented in the R package DIRECT, available on CRAN.
The scaling of human interactions with city size

The size of cities is known to play a fundamental role in social and economic life. Yet, its relation to the structure of the underlying network of human interactions has not been investigated empirically in detail. In this paper, we map society-wide communication networks to the urban areas of two European countries. We show that both the total number of contacts and the total communication activity grow superlinearly with city population size, according to well-defined scaling relations and resulting from a multiplicative increase that affects most citizens. Perhaps surprisingly, however, the probability that an individual's contacts are also connected with each other remains largely unaffected. These empirical results predict a systematic and scale-invariant acceleration of interaction-based spreading phenomena as cities get bigger, which is numerically confirmed by applying epidemiological models to the studied networks. Our findings should provide a microscopic basis towards understanding the superlinear increase of different socioeconomic quantities with city size, that applies to almost all urban systems and includes, for instance, the creation of new inventions or the prevalence of certain contagious diseases.
Collisionless stellar hydrodynamics as an efficient alternative to N-body methods

The dominant constituents of the Universe’s matter are believed to be collisionless in nature and thus their modelling in any self-consistent simulation is extremely important. For simulations that deal only with dark matter or stellar systems, the conventional N-body technique is fast, memory efficient, and relatively simple to implement. However when extending simulations to include the effects of gas physics, mesh codes are at a distinct disadvantage compared to SPH codes. Whereas implementing the N-body approach into SPH codes is fairly trivial, the particle-mesh technique used in mesh codes to couple collisionless stars and dark matter to the gas on the mesh, has a series of significant scientific and technical limitations. These include spurious entropy generation resulting from discreteness effects, poor load balancing and increased communication overhead which spoil the excellent scaling in massively parallel grid codes. In this paper we propose the use of the collisionless Boltzmann moment equations as a means to model the collisionless material as a fluid on the mesh, implementing it into the massively parallel FLASH AMR code. This approach which we term “collisionless stellar hydrodynamics” enables us to do away with the particle-mesh approach and since the parallelisation scheme is identical to that used for the hydrodynamics, it preserves the excellent scaling of the FLASH code already demonstrated on peta-flop machines. We find that the classic hydrodynamic equations and the Boltzmann moment equations can be reconciled under specific conditions, allowing us to generate analytic solutions for collisionless systems using conventional test problems. We confirm the validity of our approach using a suite of demanding test problems, including the use of a modified Sod shock test. By deriving the relevant eigenvalues and eigenvectors of the Boltzmann moment equations, we are able to use high order accurate characteristic tracing methods with Riemann solvers to generate numerical solutions which show excellent agreement with our analytic solutions. We conclude by demonstrating the ability of our code to model complex phenomena by simulating the evolution of a two armed spiral galaxy whose properties agree with those predicted by the swing amplification theory.
Diffusion of Lexical Change in Social Media

Computer-mediated communication is driving fundamental changes in the nature of written language. We investigate these changes by statistical analysis of a dataset comprising 107 million Twitter messages (authored by 2.7 million unique user accounts). Using a latent vector autoregressive model to aggregate across thousands of words, we identify high-level patterns in diffusion of linguistic change over the United States. Our model is robust to unpredictable changes in Twitter's sampling rate, and provides a probabilistic characterization of the relationship of macro-scale linguistic influence to a set of demographic and geographic predictors. The results of this analysis offer support for prior arguments that focus on geographical proximity and population size. However, demographic similarity – especially with regard to race – plays an even more central role, as cities with similar racial demographics are far more likely to share linguistic influence. Rather than moving towards a single unified “netspeak” dialect, language evolution in computer-mediated communication reproduces existing fault lines in spoken American English.
Pairwise MRF Calibration by Perturbation of the Bethe Reference Point

We investigate different ways of generating approximate solutions to the inverse problem of pairwise Markov random field (MRF) model learning. We focus mainly on the inverse Ising problem, but discuss also the somewhat related inverse Gaussian problem. In both cases, the belief propagation algorithm can be used in closed form to perform inference tasks. Our approach consists in taking the Bethe mean-field solution as a reference point for further perturbation procedures. We remark indeed that both the natural gradient and the best link to be added to a maximum spanning tree (MST) solution can be computed analytically. These observations open the way to many possible algorithms, able to find approximate sparse solutions compatible with belief propagation inference procedures. Experimental tests on various datasets with refined $L_0$ or $L_1$ regularization procedures indicate that this approach may be a competitive and useful alternative to existing ones.
Ethics and Finance: the role of mathematics

This paper presents the contemporary Fundamental Theorem of Asset Pricing as being equivalent to approaches to pricing that emerged before 1700 in the context of Virtue Ethics. This is done by considering the history of science and mathematics in the thirteenth and seventeenth century. An explanation as to why these approaches to pricing were forgotten between 1700 and 2000 is given, along with some of the implications on economics of viewing the Fundamental Theorem as a product of Virtue Ethics. The Fundamental Theorem was developed in mathematics to establish a `theory' that underpinned the Black-Scholes-Merton approach to pricing derivatives. In doing this, the Fundamental Theorem unified a number of different approaches in financial economics, this strengthened the status of neo-classical economics based on Consequentialist Ethics. We present an alternative to this narrative.
Simple arbitrage

We characterize absence of arbitrage with simple trading strategies in a discounted market with a constant bond and several risky assets. We show that if there is a simple arbitrage, then there is a 0admissible one or an obvious one, that is, a simple arbitrage which promises a minimal riskless gain of ε, if the investor trades at all. For continuous stock models, we provide an equivalent condition for absence of 0-admissible simple arbitrage in terms of a property of the fine structure of the paths, which we call “two-way crossing.” This property can be verified for many models by the law of the iterated logarithm. As an application we show that the mixed fractional Black–Scholes model, with Hurst parameter bigger than a half, is free of simple arbitrage on a compact time horizon. More generally, we discuss the absence of simple arbitrage for stochastic volatility models and local volatility models which are perturbed by an independent 1/2-Hölder continuous process.
Circumstellar habitable zones of binary-star systems in the solar neighbourhood

Binary and multiple systems constitute more than half of the total stellar population in the Solar neighborhood (Kiseleva-Eggleton & Eggleton 2001). Their frequent occurrence as well as the fact that more than 70 (Schneider et al. 2011) planets have already been discovered in such configurations - most noteably the telluric companion of α Cen B (Dumusque et al. 2012) - make them interesting targets in the search for habitable worlds. Recent studies (Eggl et al. 2012b; Forgan 2012) have shown, that despite the variations in gravitational and radiative environment, there are indeed circumstellar regions where planets can stay within habitable insolation limits on secular dynamical timescales. In this article we provide habitable zones for 19 near S-Type binary systems from the Hipparchos and WDS catalogues with semimajor axes between 1 and 100 AU. Hereby, we accounted for the combined dynamical and radiative influence of the second star on the Earth-like planet. Out of the 19 systems presented, 17 offer dynamically stable habitable zones around at least one component. The 17 potentially habitable systems contain 5 F, 3 G, 7 K and 16 M class stars. As their proximity to the Solar System (d < 31 pc) makes the selected binary stars exquisite targets for observational campaigns, we offer estimates on radial velocity, astrometric and transit signatures produced by habitable Earth-like planets in eccentric circumstellar orbits.