nils-herrmann/clean_scirepeval huggingface dataset:
The Evolution of Complex Networks: A New Framework

We introduce a new framework for the analysis of the dynamics of networks, based on randomly reinforced urn (RRU) processes, in which the weight of the edges is determined by a reinforcement mechanism. We rigorously explain the empirical evidence that in many real networks there is a subset of "dominant edges" that control a major share of the total weight of the network. Furthermore, we introduce a new statistical procedure to study the evolution of networks over time, assessing if a given instance of the nework is taken at its steady state or not. Our results are quite general, since they are not based on a particular probability distribution or functional form of the weights. We test our model in the context of the International Trade Network, showing the existence of a core of dominant links and determining its size.
Exploring complex networks by means of adaptive walkers

Finding efficient algorithms to explore large networks with the aim of recovering information about their structure is an open problem. Here, we investigate this challenge by proposing a model in which random walkers with previously assigned home nodes navigate through the network during a fixed amount of time. We consider that the exploration is successful if the walker gets the information gathered back home, otherwise no data are retrieved. Consequently, at each time step, the walkers, with some probability, have the choice to either go backward approaching their home or go farther away. We show that there is an optimal solution to this problem in terms of the average information retrieved and the degree of the home nodes and design an adaptive strategy based on the behavior of the random walker. Finally, we compare different strategies that emerge from the model in the context of network reconstruction. Our results could be useful for the discovery of unknown connections in large-scale networks.
STRONG EVOLUTION OF X-RAY ABSORPTION IN THE TYPE IIn SUPERNOVA SN 2010jl

We report two epochs of Chandra-ACIS X-ray imaging spectroscopy of the nearby bright Type IIn supernova SN 2010jl, taken around two months and then a year after the explosion. The majority of the X-ray emission in both spectra is characterized by a high temperature (≳ 10 keV) and is likely to be from the forward shocked region resulting from circumstellar interaction. The absorption column density in the first spectrum is high (∼1024 cm−2), more than three orders of magnitude higher than the Galactic absorption column, and we attribute it to absorption by circumstellar matter. In the second epoch observation, the column density has decreased by a factor of three, as expected for shock propagation in the circumstellar medium. The unabsorbed 0.2–10 keV luminosity at both epochs is ∼7 × 1041 erg s−1. The 6.4 keV Fe line clearly present in the first spectrum is not detected in the second spectrum. The strength of the fluorescent line is roughly that expected for the column density of circumstellar gas, provided the Fe is not highly ionized. There is also evidence for an absorbed power-law component in both spectra, which we attribute to a background ultraluminous X-ray source.
The exoplanet eccentricity distribution from Kepler planet candidates

The eccentricity distribution of exoplanets is known from radial velocity surveys to be divergent from circular orbits beyond 0.1 au. This is particularly the case for large planets where the radial velocity technique is most sensitive. The eccentricity of planetary orbits can have a large effect on the transit probability and subsequently the planet yield of transit surveys. The Kepler mission is the first transit survey that probes deep enough into period space to allow this effect to be seen via the variation in transit durations. We use the Kepler planet candidates to show that the eccentricity distribution is consistent with that found from radial velocity surveys to a high degree of confidence. We further show that the mean eccentricity of the Kepler candidates decreases with decreasing planet size indicating that smaller planets are preferentially found in low-eccentricity orbits.
Velocity-enhanced cooperation of moving agents playing public goods games.

In this paper we study the evolutionary dynamics of the public goods game in a population of mobile agents embedded in a two-dimensional space. In this framework, the backbone of interactions between agents changes in time, allowing us to study the impact that mobility has on the emergence of cooperation in structured populations. Our results point out that a low degree of mobility enhances cooperation in the system. In addition, we study the impact of the size of the groups in which games are played on cooperation. Again we find a rise and fall of cooperation related to the percolation point of the instant interaction networks created by the set of mobile agents.
Broadcasters and Hidden Influentials in Online Protest Diffusion

This article explores the growth of online mobilizations using data from the indignados (outraged) movement in Spain, which emerged under the influence of the revolution in Egypt and as a precursor to the global Occupy mobilizations. The data track Twitter activity around the protests that took place in May 2011, which led to the formation of camp sites in dozens of cities all over the country and massive daily demonstrations during the week prior to the elections of May 22. We reconstruct the network of tens of thousands of users and monitor their message activity for a month (April 25, 2011, to May 25, 2011). Using both the structure of the network and levels of activity in message exchange, we identify four types of users and analyze their role in the growth of the protest. Drawing from theories of online activism and research on information diffusion in networks, this article centers on the following two questions: How does protest information spread in online networks? And how do different actors contribute to the growth of activity? The article aims to inform the theoretical debate on whether digital technologies are changing the logic of collective action and to provide evidence of how new media facilitates the emergence of massive offline mobilizations.
Low-Frequency Waves and the Medium to Long-Term US Stock Market Outlook

In this paper we provide compelling evidence of cyclical mean reversion and multiperiod stock return predictability over horizons of about 30 years with a half-life of about 15 years. This implies that the US stock market follows a long-term rhythm where a period of above average returns tends to be followed by a period of below average returns. We demonstrate that this long-term stock market rhythm moves in lockstep with corresponding long-term economic, social, and political rhythms in the US. Assuming that the past relationship between these rhythms will hold unaltered in the future, we provide the medium to long-term stock market outlook.
Counterparty Risk Valuation: A Marked Branching Diffusion Approach

The purpose of this paper is to design an algorithm for the computation of the counterparty risk which is competitive in regards of a brute force ''Monte-Carlo of Monte-Carlo" method (with nested simulations). This is achieved using marked branching diffusions describing a Galton-Watson random tree. Such an algorithm leads at the same time to a computation of the (bilateral) counterparty risk when we use the default-risky or counterparty-riskless option values as mark-to-market. Our method is illustrated by various numerical examples.
Control Centrality and Hierarchical Structure in Complex Networks

We introduce the concept of control centrality to quantify the ability of a single node to control a directed weighted network. We calculate the distribution of control centrality for several real networks and find that it is mainly determined by the network’s degree distribution. We show that in a directed network without loops the control centrality of a node is uniquely determined by its layer index or topological position in the underlying hierarchical structure of the network. Inspired by the deep relation between control centrality and hierarchical structure in a general directed network, we design an efficient attack strategy against the controllability of malicious networks.
THE CENTAURUS A ULTRAHIGH-ENERGY COSMIC-RAY EXCESS AND THE LOCAL EXTRAGALACTIC MAGNETIC FIELD

The ultrahigh-energy cosmic-ray (UHECR) anisotropies discovered by the Pierre Auger Observatory provide the potential to finally address both the particle origins and properties of the nearby extragalactic magnetic field (EGMF). We examine the implications of the excess of ∼1020 eV events around the nearby radio galaxy Centaurus A. We find that, if Cen A is the source of these cosmic rays, the angular distribution of events constrains the EGMF strength within several Mpc of the Milky Way to ≳ 20 nG for an assumed primary proton composition. Our conclusions suggest that either the observed excess is a statistical anomaly or the local EGMF is stronger than conventionally thought. We discuss several implications, including UHECR scattering from more distant sources, time delays from transient sources, and the possibility of using magnetic lensing signatures to attain tighter constraints.
THE MORPHOLOGY OF HADRONIC EMISSION MODELS FOR THE GAMMA-RAY SOURCE AT THE GALACTIC CENTER

Recently, detections of a high-energy γ-ray source at the position of the Galactic center have been reported by multiple γ-ray telescopes, spanning the energy range between 100 MeV and 100 TeV. Analysis of these signals strongly suggests the TeV emission to have a morphology consistent with a point source up to the angular resolution of the HESS telescope (approximately 3 pc), while the point-source nature of the GeV emission is currently unsettled, with indications that it may be spatially extended. In the case that the emission is hadronic and in a steady state, we show that the expected γ-ray morphology is dominated by the distribution of target gas, rather than by details of cosmic-ray injection and propagation. Specifically, we expect a significant portion of hadronic emission to coincide with the position of the circumnuclear ring, which resides between 1 and 3 pc from the Galactic center. We note that the upcoming Cherenkov Telescope Array (CTA) will be able to observe conclusive correlations between the morphology of the TeV γ-ray source and the observed gas density, convincingly confirming or ruling out a hadronic origin for the γ-ray emission.
UNIDENTIFIED γ-RAY SOURCES: HUNTING γ-RAY BLAZARS

One of the main scientific objectives of the ongoing Fermi mission is unveiling the nature of unidentified γ-ray sources (UGSs). Despite the major improvements of Fermi in the localization of γ-ray sources with respect to the past γ-ray missions, about one-third of the Fermi-detected objects are still not associated with low-energy counterparts. Recently, using the Wide-field Infrared Survey Explorer survey, we discovered that blazars, the rarest class of active galactic nuclei and the largest population of γ-ray sources, can be recognized and separated from other extragalactic sources on the basis of their infrared (IR) colors. Based on this result, we designed an association method for the γ-ray sources to recognize if there is a blazar candidate within the positional uncertainty region of a generic γ-ray source. With this new IR diagnostic tool, we searched for γ-ray blazar candidates associated with the UGS sample of the second Fermi γ-ray LAT catalog (2FGL). We found that our method associates at least one γ-ray blazar candidate as a counterpart to each of 156 out of 313 UGSs analyzed. These new low-energy candidates have the same IR properties as the blazars associated with γ-ray sources in the 2FGL catalog.
Learning Loopy Graphical Models with Latent Variables: Efficient Methods and Guarantees

The problem of structure estimation in graphical models with latent variables is considered. We characterize conditions for tractable graph estimation and develop efficient methods with provable guarantees. We consider models where the underlying Markov graph is locally tree-like, and the model is in the regime of correlation decay. For the special case of the Ising model, the number of samples n required for structural consistency of our method scales as n=Ω(θ^(−δη(η+1)−2)_(min)log p), where p is the number of variables, θ_(min) is the minimum edge potential, δ is the depth (i.e., distance from a hidden node to the nearest observed nodes), and η is a parameter which depends on the bounds on node and edge potentials in the Ising model. Necessary conditions for structural consistency under any algorithm are derived and our method nearly matches the lower bound on sample requirements. Further, the proposed method is practical to implement and provides flexibility to control the number of latent variables and the cycle lengths in the output graph.
Do Linguistic Style and Readability of Scientific Abstracts Affect their Virality?

Reactions to textual content posted in an online social network show different dynamics depending on the linguistic style and readability of the submitted content. Do similar dynamics exist for responses to scientific articles? Our intuition, supported by previous research, suggests that the success of a scientific article depends on its content, rather than on its linguistic style. In this article, we examine a corpus of scientific abstracts and three forms of associated reactions: article downloads, citations, and bookmarks. Through a class-based psycholinguistic analysis and readability indices tests, we show that certain stylistic and readability features of abstracts clearly concur in determining the success and viral capability of a scientific article.
Cosmology when living near the Great Attractor

If we live in the vicinity of the hypothesized Great Attractor, the age of the universe as inferred from the local expansion rate can be off by 3 per cent. We study the effect that living inside or near a massive overdensity has on cosmological parameters induced from observations of supernovae, the Hubble parameter and the cosmic microwave background. We compare the results to those for an observer in a perfectly homogeneous Λ cold dark matter universe. We find that for instance the inferred value for the global Hubble parameter changes by around three per cent if we happen to live inside a massive overdensity such as the hypothesized Great Attractor. Taking into account the effect of such structures on our perception of the universe makes cosmology perhaps less precise, but more accurate.
Overpopulated gauge fields on the lattice

We study nonequilibrium dynamics of SU(2) pure gauge theory starting from initial over-population, where intense classical gauge fields are characterized by a single momentum scale Q_s. Classical-statistical lattice simulations indicate a quick evolution towards an approximate scaling behavior with exponent 3/2 at intermediate times. Remarkably, the value for the scaling exponent may be understood as arising from the leading O(g^2) contribution in the presence of a time-dependent background field. The phenomenon is associated to weak wave turbulence describing an energy cascade towards higher momenta. This particular aspect is very similar to what is observed for scalar theories, where an effective cubic interaction arises because of the presence of a time-dependent Bose condensate.
A Local Approach for Identifying Clusters in Networks

Graph clustering is a fundamental problem that has been extensively studied both in theory and practice. The problem has been defined in several ways in literature and most of them have been proven to be NP-Hard. Due to their high practical relevancy, several heuristics for graph clustering have been introduced which constitute a central tool for coping with NP-completeness, and are used in applications of clustering ranging from computer vision, to data analysis, to learning. There exist many methodologies for this problem, however most of them are global in nature and are unlikely to scale well for very large networks. In this paper, we propose two scalable local approaches for identifying the clusters in any network. We further extend one of these approaches for discovering the overlapping clusters in these networks. Some experimentation results obtained for the proposed approaches are also presented.
Altmetrics in the wild: Using social media to explore scholarly impact

In growing numbers, scholars are integrating social media tools like blogs, Twitter, and Mendeley into their professional communications. The online, public nature of these tools exposes and reifies scholarly processes once hidden and ephemeral. Metrics based on this activities could inform broader, faster measures of impact, complementing traditional citation metrics. This study explores the properties of these social media-based metrics or "altmetrics", sampling 24,331 articles published by the Public Library of Science. 
We find that that different indicators vary greatly in activity. Around 5% of sampled articles are cited in Wikipedia, while close to 80% have been included in at least one Mendeley library. There is, however, an encouraging diversity; a quarter of articles have nonzero data from five or more different sources. Correlation and factor analysis suggest citation and altmetrics indicators track related but distinct impacts, with neither able to describe the complete picture of scholarly use alone. There are moderate correlations between Mendeley and Web of Science citation, but many altmetric indicators seem to measure impact mostly orthogonal to citation. Articles cluster in ways that suggest five different impact "flavors", capturing impacts of different types on different audiences; for instance, some articles may be heavily read and saved by scholars but seldom cited. Together, these findings encourage more research into altmetrics as complements to traditional citation measures.
A Flexible Matrix Libor Model with Smiles

We present a flexible approach for the valuation of interest rate derivatives based on affine processes. We extend the methodology proposed in Keller-Ressel et al. (in press) by changing the choice of the state space. We provide semi-closed-form solutions for the pricing of caps and floors. We then show that it is possible to price swaptions in this multifactor setting with a good degree of analytical tractability. This is done via the Edgeworth expansion approach developed in Collin-Dufresne and Goldstein (2002). A numerical exercise illustrates the flexibility of Wishart Libor model in describing the movements of the implied volatility surface.
Variational Bayesian Algorithm for Quantized Compressed Sensing

Compressed sensing (CS) is on recovery of high dimensional signals from their low dimensional linear measurements under a sparsity prior and digital quantization of the measurement data is inevitable in practical implementation of CS algorithms. In the existing literature, the quantization error is modeled typically as additive noise and the multi-bit and 1-bit quantized CS problems are dealt with separately using different treatments and procedures. In this paper, a novel variational Bayesian inference based CS algorithm is presented, which unifies the multi- and 1-bit CS processing and is applicable to various cases of noiseless/noisy environment and unsaturated/saturated quantizer. By decoupling the quantization error from the measurement noise, the quantization error is modeled as a random variable and estimated jointly with the signal being recovered. Such a novel characterization of the quantization error results in superior performance of the algorithm which is demonstrated by extensive simulations in comparison with state-of-the-art methods for both multi-bit and 1-bit CS problems.
"Selfish" algorithm for optimizing the network survivability analysis

In Nature, the primary goal of any network is to survive. This is less obvious for engineering networks (electric power, gas, water, transportation systems etc.) that are expected to operate under normal conditions most of time. As a result, the ability of a network to withstand massive sudden damage caused by adverse events (or survivability) has not been among traditional goals in the network design. Reality, however, calls for the adjustment of design priorities. As modern networks develop toward increasing their size, complexity, and integration, the likelihood of adverse events increases too due to technological development, climate change, and activities in the political arena among other factors. Under such circumstances, a network failure has an unprecedented effect on lives and economy. To mitigate the impact of adverse events on the network operability, the survivability analysis must be conducted at the early stage of the network design. Such analysis requires the development of new analytical and computational tools. Computational analysis of the network survivability is the exponential time problem at least. The current paper describes a new algorithm, in which the reduction of the computational complexity is achieved by mapping an initial network topology with multiple sources and sinks onto a set of simpler smaller topologies with multiple sources and a single sink. Steps for further reducing the time and space expenses of computations are also discussed.
Entropy-based Tuning of Musical Instruments

The human sense of hearing perceives a combination of sounds 'in tune' if the corresponding harmonic spectra are correlated, meaning that the neuronal excitation pattern in the inner ear exhibits some kind of order. Based on this observation it is suggested that musical instruments such as pianos can be tuned by minimizing the Shannon entropy of suitably preprocessed Fourier spectra. This method reproduces not only the correct stretch curve but also similar pitch fluctuations as in the case of high-quality aural tuning.
Dynamical fluctuations in a simple housing market model

We consider a simple stochastic model of an urban rental housing market, in which the interaction of tenants and landlords induces rent fluctuations. We simulate the model numerically and measure the equilibrium rent distribution, which is found to be close to a lognormal law. We also study the influence of the density of agents (or equivalently, the vacancy rate) on the rent distribution. A simplified version of the model, amenable to analytical treatment, is studied and leads to a lognormal distribution of rents. The predicted equilibrium value agrees quantitatively with numerical simulations, while a qualitative agreement is obtained for the standard deviation. The connection with non-equilibrium statistical physics models such as ratchets is also emphasized.
Incremental Collaborative Filtering Considering Temporal Effects

Recommender systems require their recommendation algorithms to be accurate, scalable and should handle very sparse training data which keep changing over time. Inspired by ant colony optimization, we propose a novel collaborative filtering scheme: Ant Collaborative Filtering that enjoys those favorable characteristics above mentioned. With the mechanism of pheromone transmission between users and items, our method can pinpoint most relative users and items even in face of the sparsity problem. By virtue of the evaporation of existing pheromone, we capture the evolution of user preference over time. Meanwhile, the computation complexity is comparatively small and the incremental update can be done online. We design three experiments on three typical recommender systems, namely movie recommendation, book recommendation and music recommendation, which cover both explicit and implicit rating data. The results show that the proposed algorithm is well suited for real-world recommendation scenarios which have a high throughput and are time sensitive.