nils-herrmann/clean_scirepeval huggingface dataset:
The velocity dispersion and mass function of the outer halo globular cluster Palomar 4

We obtained precise line-of-sight radial velocities of 23 member stars of the remote halo globular cluster Palomar 4 (Pal 4) using the High Resolution Echelle Spectrograph (HIRES) at the Keck I telescope. We also measured the mass function of the cluster down to a limiting magnitude of V 28 mag using archival HST /WFPC2 imaging. We derived the cluster’s surface brightness prole based on the WFPC2 data and on broad-band imaging with the Low-Resolution Imaging Spectrometer (LRIS) at the Keck II telescope. We nd a mean cluster velocity of 72 :55 0:22 km s 1 and a velocity dispersion of 0:87 0:18 km s 1 . The global mass function of the cluster, in the mass range 0:55 6 M 6 0:85 M , is shallower than a Kroupa mass function and the cluster is signicantly depleted in low-mass stars in its center compared to its outskirts. Since the relaxation time of Pal 4 is of the order of a Hubble time, this points to primordial mass segregation in this cluster. Extrapolating the measured mass function towards lower-mass stars and including the contribution of compact remnants, we derive a total cluster mass of 29,800 M . For this mass, the measured velocity dispersion is consistent with the expectations of Newtonian dynamics and below the prediction of MOND. Pal 4 adds to the growing body of evidence that the dynamics of star clusters in the outer Galactic halo can hardly be explained by MOND.
Spectral functions of the Higgs mode near two-dimensional quantum critical points

We study the Higgs excitation in the Goldstone phase of the relativistic O(N ) model in two spatial dimensions at zero temperature. The response functions of the order parameter, and its magnitude squared, become universal functions of frequency in the vicinity of the quantum critical point described by the Wilson-Fisher fixed point, and we compute them to next-to-leading order in 1/N . The Higgs particle has an infrared singular decay to gapless Goldstone excitations, and its response functions are characterized by a pole in the lower half of the complex frequency plane. The pole acquires a nonzero real part only at next-to-leading order in 1/N , demonstrating that the Higgs excitation has an oscillatory component even in the scaling limit. Both the real and imaginary parts of the pole position vanish with the correlation length exponent ν upon approaching the critical point. We present evidence that the spectral density of the O(N )-invariant amplitude-squared of the order parameter has a peak at a nonzero frequency in the scaling limit. We connect our results to recent experimental studies of the superfluid-insulator quantum phase transition of ultracold bosonic atoms in optical lattices.
Real-time quantum evolution in the classical approximation and beyond

We acknowledge financial support from the MCINN Grants No. FPA2009-08785 and No. FPA2009-09017, the Comunidad Autonoma de Madrid under the Program No. HEPHACOS S2009/ESP- 1473, and the European Union under Grant Agreement No. PITN-GA-2009-238353 (ITN STRONGnet). The authors participate in the Consolider-Ingenio 2010 CPAN (CSD2007-00042). We acknowledge the use of the IFT clusters for part of our numerical results
How Visibility and Divided Attention Constrain Social Contagion

How far and how fast does information spread in social media? Researchers have recently examined a number of factors that affect information diffusion in online social networks, including: the novelty of information, users' activity levels, who they pay attention to, and how they respond to friends' recommendations. Using URLs as markers of information, we carry out a detailed study of retweeting, the primary mechanism by which information spreads on the Twitter follower graph. Our empirical study examines how users respond to an incoming stimulus, i.e., a tweet (message) from a friend, and reveals that dynamically decaying visibility, which is the increasing cognitive effort required for discovering and acting upon a tweet, combined with limited attention play dominant roles in retweeting behavior. Specifically, we observe that users retweet information when it is most visible, such as when it near the top of their Twitter feed. Moreover, our measurements quantify how a user's limited attention is divided among incoming tweets, providing novel evidence that highly connected individuals are less likely to propagate an arbitrary tweet. Our study indicates that the finite ability to process incoming information constrains social contagion, and we conclude that rapid decay of visibility is the primary barrier to information propagation online.
Clustering of random scale-free networks

We derive the finite-size dependence of the clustering coefficient of scale-free random graphs generated by the configuration model with degree distribution exponent 2<γ<3. Degree heterogeneity increases the presence of triangles in the network up to levels that compare to those found in many real networks even for extremely large nets. We also find that for values of γ≈2, clustering is virtually size independent and, at the same time, becomes a de facto non-self-averaging topological property. This implies that a single-instance network is not representative of the ensemble even for very large network sizes.
Current Practice in Software Development for Computational Neuroscience and How to Improve It

Almost all research work in computational neuroscience involves software. As researchers try to understand ever more complex systems, there is a continual need for software with new capabilities. Because of the wide range of questions being investigated, new software is often developed rapidly by individuals or small groups. In these cases, it can be hard to demonstrate that the software gives the right results. Software developers are often open about the code they produce and willing to share it, but there is little appreciation among potential users of the great diversity of software development practices and end results, and how this affects the suitability of software tools for use in research projects. To help clarify these issues, we have reviewed a range of software tools and asked how the culture and practice of software development affects their validity and trustworthiness. We identified four key questions that can be used to categorize software projects and correlate them with the type of product that results. The first question addresses what is being produced. The other three concern why, how, and by whom the work is done. The answers to these questions show strong correlations with the nature of the software being produced, and its suitability for particular purposes. Based on our findings, we suggest ways in which current software development practice in computational neuroscience can be improved and propose checklists to help developers, reviewers, and scientists to assess the quality of software and whether particular pieces of software are ready for use in research.
A Comparative Study of Collaborative Filtering Algorithms

Collaborative ltering is a rapidly advancing research area. Every year several new techniques are proposed and yet it is not clear which of the techniques work best and under what conditions. In this paper we conduct a study comparing several collaborative ltering techniques { both classic and recent state-of-the-art { in a variety of experimental contexts. Specically, we report conclusions controlling for number of items, number of users, sparsity level, performance criteria, and computational complexity. Our conclusions identify what algorithms work well and in what conditions, and contribute to both industrial deployment collaborative ltering algorithms and to the research community.
SportSense: Real-Time Detection of NFL Game Events from Twitter

We report our experience in building a working system, SportSense (this http URL), which exploits Twitter users as human sensors of the physical world to detect events in real-time. Using the US National Football League (NFL) games as a case study, we report in-depth measurement studies of the delay and post rate of tweets, and their dependence on other properties. We subsequently develop a novel event detection method based on these findings, and demonstrate that it can effectively and accurately extract game events using open access Twitter data. SportSense has been evolving during the 2010-11 and 2011-12 NFL seasons and is able to recognize NFL game big plays in 30 to 90 seconds with 98% true positive, and 9% false positive rates. Using a smart electronic TV program guide, we show that SportSense can utilize human sensors to empower novel services.
Chiral symmetry restoration and deconfinement in QCD at finite temperature

The light-quark correlator in the axial-vector channel is used, in conjunction with finite energy QCD sum rules at finite temperature, in order to (a) establish a relation between chiral-symmetry restoration and deconfinement, and (b) determine the temperature behavior of the $a_1(1260)$ width and coupling. Results indicate that deconfinement takes place at a slightly lower temperature than chiral-symmetry restoration, although this difference is not significant given the accuracy of the method. The behaviour of the $a_1(1260)$ parameters is consistent with quark-gluon deconfinement, as the width grows and the coupling decreases with increasing temperature.
Optimal starting times, stopping times and risk measures for algorithmic trading

We derive explicit recursive formulas for Target Close (TC) and Implementation Shortfall (IS) in the Almgren-Chriss framework. We explain how to compute the optimal starting and stopping times for IS and TC, respectively, given a minimum trading size. We also show how to add a minimum participation rate constraint (Percentage of Volume, PVol) for both TC and IS. We also study an alternative set of risk measures for the optimisation of algorithmic trading curves. We assume a self-similar process (e.g. Levy process, fractional Brownian motion or fractal process) and define a new risk measure, the p-variation, which reduces to the variance if the process is a Brownian motion. We deduce the explicit formula for the TC and IS algorithms under a self-similar process. We show that there is an equivalence between self-similar models and a family of risk measures called p-variations: assuming a self-similar process and calibrating empirically the parameter p for the p-variation yields the same result as assuming a Brownian motion and using the p-variation as risk measure instead of the variance. We also show that p can be seen as a measure of the aggressiveness: p increases if and only if the TC algorithm starts later and executes faster. From the explicit expression of the TC algorithm one can compute the sensitivities of the curve with respect to the parameters up to any order. As an example, we compute the first order sensitivity with respect to both a local and a global surge of volatility. Finally, we show how the parameter p of the p-variation can be implied from the optimal starting time of TC, and that under this framework p can be viewed as a measure of the joint impact of market impact (i.e. liquidity) and volatility.
THE OPTICALLY UNBIASED GRB HOST (TOUGH) SURVEY. III. REDSHIFT DISTRIBUTION

We present 10 new gamma-ray burst (GRB) redshifts and another five redshift limits based on host galaxy spectroscopy obtained as part of a large program conducted at the Very Large Telescope (VLT). The redshifts span the range 0.345  ⩽  z  ≲  2.54. Three of our measurements revise incorrect values from the literature. The homogeneous host sample researched here consists of 69 hosts that originally had a redshift completeness of 55% (with 38 out of 69 hosts having redshifts considered secure). Our project, including VLT/X-shooter observations reported elsewhere, increases this fraction to 77% (53/69), making the survey the most comprehensive in terms of redshift completeness of any sample to the full Swift depth, analyzed to date. We present the cumulative redshift distribution and derive a conservative, yet small, associated uncertainty. We constrain the fraction of Swift GRBs at high redshift to a maximum of 14% (5%) for z  >  6 (z  >  7). The mean redshift of the host sample is assessed to be 〈z〉  ≳  2.2, with the 10 new redshifts reducing it significantly. Using this more complete sample, we confirm previous findings that the GRB rate at high redshift (z  ≳  3) appears to be in excess of predictions based on assumptions that it should follow conventional determinations of the star formation history of the universe, combined with an estimate of its likely metallicity dependence. This suggests that either star formation at high redshifts has been significantly underestimated, for example, due to a dominant contribution from faint, undetected galaxies, or that GRB production is enhanced in the conditions of early star formation, beyond that usually ascribed to lower metallicity.
Extending BEAMS to incorporate correlated systematic uncertainties

New supernova surveys such as the Dark Energy Survey, Pan-STARRS and the LSST will produce an unprecedented number of photometric supernova candidates, most with no spectroscopic data. Avoiding biases in cosmological parameters due to the resulting inevitable contamination from non-Ia supernovae can be achieved with the BEAMS formalism, allowing for fully photometric supernova cosmology studies. Here we extend BEAMS to deal with the case in which the supernovae are correlated by systematic uncertainties. The analytical form of the full BEAMS posterior requires evaluating 2N terms, where N is the number of supernova candidates. This `exponential catastrophe' is computationally unfeasible even for N of order 100. We circumvent the exponential catastrophe by marginalising numerically instead of analytically over the possible supernova types: we augment the cosmological parameters with nuisance parameters describing the covariance matrix and the types of all the supernovae, τi, that we include in our MCMC analysis. We show that this method deals well even with large, unknown systematic uncertainties without a major increase in computational time, whereas ignoring the correlations can lead to significant biases and incorrect credible contours. We then compare the numerical marginalisation technique with a perturbative expansion of the posterior based on the insight that future surveys will have exquisite light curves and hence the probability that a given candidate is a Type Ia will be close to unity or zero, for most objects. Although this perturbative approach changes computation of the posterior from a 2N problem into an N2 or N3 one, we show that it leads to biases in general through a small number of misclassifications, implying that numerical marginalisation is superior.
Approximating Stochastic Volatility by Recombinant Trees

A general method to construct recombinant tree approximations for stochastic volatility models is developed and applied to the Heston model for stock price dynamics. In this application, the resulting approximation is a four tuple Markov process. The first two components are related to the stock and volatility processes and take values in a two dimensional Binomial tree. The other two components of the Markov process are the increments of random walks with simple values in \{âˆ’1, +1\}. The resulting efficient option pricing equations are numerically implemented for general American and European options including the standard put and calls, barrier, lookback and Asian type pay-offs. The weak and extended weak convergences are also proved.
MICROSCOPIC BLACK HOLE AND UNCERTAINTY PRINCIPLE WITH MINIMAL LENGTH AND MOMENTUM

We investigate a microscopic black hole in the case of modified generalized uncertainty principle with a minimal uncertainty in position as well as in momentum. We calculate thermodynamical functions of a Schwarzschild black hole such as temperature, entropy and heat capacity. It is shown that the incorporation of minimal uncertainty in momentum leads to minimal temperature of a black hole. Minimal temperature gives rise to appearance of a phase transition. Emission rate equation and black hole's evaporation time are also obtained.
A k-shell decomposition method for weighted networks

We present a generalized method for calculating the k-shell structure of weighted networks. The method takes into account both the weight and the degree of a network, in such a way that in the absence of weights we resume the shell structure obtained by the classic k-shell decomposition. In the presence of weights, we show that the method is able to partition the network in a more refined way, without the need of any arbitrary threshold on the weight values. Furthermore, by simulating spreading processes using the susceptible-infectious-recovered model in four different weighted real-world networks, we show that the weighted k-shell decomposition method ranks the nodes more accurately, by placing nodes with higher spreading potential into shells closer to the core. In addition, we demonstrate our new method on a real economic network and show that the core calculated using the weighted k-shell method is more meaningful from an economic perspective when compared with the unweighted one.
New Measurements with Photons at the Tevatron

With the recent completion of Run II at the Fermilab Tevatron, the CDF and D0 experiments are publishing results based on challenging measurements that probe quantum chromodynamics (QCD) and are sensitive to next-to-leading-order (NLO) and next-to-next-to-leading-order (NNLO) effects and non-perturbative physics. A superior understanding of parton distribution functions and QCD backgrounds will improve the sensitivity of searches for new phenomena at the LHC and reduce uncertainties in a multitude of future measurements. We present three recent photon analyses from data collected at the Fermilab Tevatron: measurements of the direct photon pair production cross section at CDF and D0, measurements of azimuthal decorrelations and multiple parton interactions in {gamma} + 2 jet and {gamma} + 3 jet events at D0, and an observation of exclusive diphoton production at CDF.
Social Turing Tests: Crowdsourcing Sybil Detection

As popular tools for spreading spam and malware, Sybils (or fake accounts) pose a serious threat to online communities such as Online Social Networks (OSNs). Today, sophisticated attackers are creating realistic Sybils that effectively befriend legitimate users, rendering most automated Sybil detection techniques ineffective. In this paper, we explore the feasibility of a crowdsourced Sybil detection system for OSNs. We conduct a large user study on the ability of humans to detect today's Sybil accounts, using a large corpus of ground-truth Sybil accounts from the Facebook and Renren networks. We analyze detection accuracy by both "experts" and "turkers" under a variety of conditions, and find that while turkers vary significantly in their effectiveness, experts consistently produce near-optimal results. We use these results to drive the design of a multi-tier crowdsourcing Sybil detection system. Using our user study data, we show that this system is scalable, and can be highly effective either as a standalone system or as a complementary technique to current tools.
Price manipulation in a market impact model with dark pool

For a market impact model, price manipulation and related notions play a role that is similar to the role of arbitrage in a derivatives pricing model. Here, we give a systematic investigation into such regularity issues when orders can be executed both at a traditional exchange and in a dark pool. To this end, we focus on a class of dark-pool models whose market impact at the exchange is described by an Almgren–Chriss model. Conditions for the absence of price manipulation for all Almgren–Chriss models include the absence of temporary cross-venue impact, the presence of full permanent cross-venue impact and the additional penalization of orders executed in the dark pool. When a particular Almgren–Chriss model has been fixed, we show by a number of examples that the regularity of the dark-pool model hinges in a subtle way on the interplay of all model parameters and on the liquidation time constraint. The paper can also be seen as a case study for the regularity of market impact models in general.
Multi-scale dynamics in a massive online social network

Data confidentiality policies at major social network providers have severely limited researchers' access to large-scale datasets. The biggest impact has been on the study of network dynamics, where researchers have studied citation graphs and content-sharing networks, but few have analyzed detailed dynamics in the massive social networks that dominate the web today. In this paper, we present results of analyzing detailed dynamics in a large Chinese social network, covering a period of 2 years when the network grew from its first user to 19 million users and 199 million edges. Rather than validate a single model of network dynamics, we analyze dynamics at different granularities (per-user, per-community, and network-wide) to determine how much, if any, users are influenced by dynamics processes at different scales. We observe independent predictable processes at each level, and find that the growth of communities has moderate and sustained impact on users. In contrast, we find that significant events such as network merge events have a strong but short-lived impact on users, and they are quickly eclipsed by the continuous arrival of new users.
ON THE LOCAL DARK MATTER DENSITY

An analysis of the kinematics of 412 stars at 1–4 kpc from the Galactic midplane by Moni Bidin et al. has claimed to derive a local density of dark matter that is an order of magnitude below standard expectations. We show that this result is incorrect and that it arises from the assumption that the mean azimuthal velocity of the stellar tracers is independent of Galactocentric radius at all heights. We substitute the assumption, supported by data, that the circular speed is independent of radius in the midplane. We demonstrate that the assumption of constant mean azimuthal velocity is implausible by showing that it requires the circular velocity to drop more steeply than allowed by any plausible mass model, with or without dark matter, at large heights above the midplane. Using the approximation that the circular-velocity curve is flat in the midplane, we find that the data imply a local dark matter density of 0.008 ± 0.003 M☉ pc−3 = 0.3 ± 0.1 GeV  cm−3, fully consistent with standard estimates of this quantity. This is the most robust direct measurement of the local dark matter density to date.
The darkness that shaped the void: dark energy and cosmic voids

We assess the sensitivity of void shapes to the nature of dark energy that was pointed out in recent studies and also investigate whether or not void shapes are useable as an observational probe in galaxy redshift surveys. Our focus is on the evolution of the mean void ellipticity and its underlying physical cause. To this end, we analyse the morphological properties of voids in five sets of cosmological N-body simulations, each with a different nature of dark energy. To address the question of whether galaxy redshift surveys yield sufficiently accurate void morphologies, voids in the dark matter distribution are compared to those in the halo population. Voids are identified using the parameter-free Watershed Void Finder. The effect of redshift distortions is investigated as well. The main conclusions of this study are as follows: (i) the statistically significant sensitivity of voids in the dark matter distribution is confirmed; (ii) the level of clustering as measured by s8(z) is identified as the main cause of differences in the mean void shape ; and (iii) in the halo and/or galaxy distribution, it is practically unfeasible to distinguish at a statistically significant level between the various cosmologies due to the sparsity and spatial bias of the sample.