nils-herrmann/clean_scirepeval huggingface dataset:
Consumers behavior of Portuguese wine

There are few papers about the consumption pattern of the Portuguese wine, using econometrics techniques. This work, pretend to analyze the consumers behavior of the wine produced in Portugal, determining the demand equation with panel data methods. There were used statistical data available in the Alentejo Regional Winegrowing Commission (CVRA) website. These data were obtained from a study about the market analysis, made, in 2009, by the A.C. Nielsen. The data are disaggregated by region and type of wine (Doc Verde+Regional Minho, Doc Regiao do Douro+Regional Terras Durienses, Doc Regiao da Bairrada+Regional Beiras, Doc Regiao do Dao+Regional Beiras, Doc da Regiao de Lisboa+Regional de Lisboa, Doc da Regiao do Tejo+Regional do Tejo, Doc da Regiao de Setubal+Regional Terras do Sado, Doc Alentejo+Regional Alentejano e Doc da Regiao do Algarve+Regional do Algarve), year (2008 and 2009) and by form of consumption (take home, direct consumption and discount). This work found some linear regularity in the consumers behavior of the Portuguese wine.
Evolutionary dynamics of group interactions on structured populations: a review

Interactions among living organisms, from bacteria colonies to human societies, are inherently more complex than interactions among particles and non-living matter. Group interactions are a particularly important and widespread class, representative of which is the public goods game. In addition, methods of statistical physics have proved valuable for studying pattern formation, equilibrium selection and self-organization in evolutionary games. Here, we review recent advances in the study of evolutionary dynamics of group interactions on top of structured populations, including lattices, complex networks and coevolutionary models. We also compare these results with those obtained on well-mixed populations. The review particularly highlights that the study of the dynamics of group interactions, like several other important equilibrium and non-equilibrium dynamical processes in biological, economical and social sciences, benefits from the synergy between statistical physics, network science and evolutionary game theory.
Feynman’s clock, a new variational principle, and parallel-in-time quantum dynamics

Methodology for studying quantum ground-state problems is currently more mature than what is available for quantum dynamical problems. Moreover, modern computing architectures demand that new algorithms be able to make use of parallel processing. We introduce a construction inspired by quantum computation that allows one to use virtually any model for a ground-state wavefunction to model quantum many-body dynamics and formulate it in a way that naturally leads to a parallel-in-time algorithm. We show how the elementary ground-state variational principle maps onto a new time-dependent variational principle and demonstrate practical examples of its use. We introduce a discrete-time variational principle inspired by the quantum clock originally proposed by Feynman and use it to write down quantum evolution as a ground-state eigenvalue problem. The construction allows one to apply ground-state quantum many-body theory to quantum dynamics, extending the reach of many highly developed tools from this fertile research area. Moreover, this formalism naturally leads to an algorithm to parallelize quantum simulation over time. We draw an explicit connection between previously known time-dependent variational principles and the time-embedded variational principle presented. Sample calculations are presented, applying the idea to a hydrogen molecule and the spin degrees of freedom of a model inorganic compound, demonstrating the parallel speedup of our method as well as its flexibility in applying ground-state methodologies. Finally, we take advantage of the unique perspective of this variational principle to examine the error of basis approximations in quantum dynamics.
The Community Structure of the Global Corporate Network

We investigate the community structure of the global ownership network of transnational corporations. We find a pronounced organization in communities that cannot be explained by randomness. Despite the global character of this network, communities reflect first of all the geographical location of firms, while the industrial sector plays only a marginal role. We also analyze the meta-network in which the nodes are the communities and the links are obtained by aggregating the links among firms belonging to pairs of communities. We analyze the network centrality of the top 50 communities and we provide a quantitative assessment of the financial sector role in connecting the global economy.
Dating medieval English charters

Deeds, or charters, dealing with property rights, provide a continuous documentation which can be used by historians to study the evolution of social, economic and political changes. This study is concerned with charters (written in Latin) dating from the tenth through early fourteenth centuries in England. Of these, at least one million were left undated, largely due to administrative changes introduced by William the Conqueror in 1066. Correctly dating such charters is of vital importance in the study of English medieval history. This paper is concerned with computer-automated statistical methods for dating such document collections, with the goal of reducing the considerable efforts required to date them manually and of improving the accuracy of assigned dates. Proposed methods are based on such data as the variation over time of word and phrase usage, and on measures of distance between documents. The extensive (and dated) Documents of Early England Data Set (DEEDS) maintained at the University of Toronto was used for this purpose.
Tensor correlation, pairing interaction, and deformation in Ne isotopes and Ne hypernuclei

We study tensor and pairing effects on the quadruple deformation of neon isotopes based on a deformed Skyrme-Hartree-Fock model with BCS approximation for the pairing channel. We extend the SkyrmeHartree-Fock formalism for the description of single- and d ouble-lambda hypernuclei adopting two different hyperon-nucleon interactions. It is found that the inte rplay of pairing and tensor interactions is crucial to derive the deformations in several neon isotopes. Especi ally, the shapes of 26,30 Ne are studied in details in comparisons with experimentally observed shapes. Furthermore the deformations of the hypernuclei are compared with the corresponding neon isotopic cores in the presence of tensor force. We find the same shapes with somewhat smaller deformations for single Λ-hypernuclei compared with their core deformations. It is also pointed out that the latest version of hyper on interaction, the ESC08b model, having a deeper Λ potential makes smaller deformations for hypernuclei than those of another NSC97f model.
Gauge fields and inflation: Chiral gravitational waves, fluctuations, and the Lyth bound

Models of inflation involving non-Abelian gauge field backgrounds can produce gravitational waves at an observable level with a preferred handedness. This asymmetry comes about because the non-Abelian background generates parity-violation in the action for perturbations. In the specific model we study, Chromo-Natural Inflation, these gravitational waves can be produced at observable levels even when no field makes a super-Planckian field excursion, thus evading a common formulation of the Lyth bound. Unfortunately, when considered in concert with the scalar fluctuations, this chiral enhancement of the gravitational waves makes the model observationally inviable.
BliStr: The Blind Strategymaker

BliStr is a system that automatically develops strategies for E prover on a large set of problems. The main idea is to interleave (i) iterated low-timelimit local search for new strategies on small sets of similar easy problems with (ii) higher-timelimit evaluation of the new strategies on all problems. The accumulated results of the global higher-timelimit runs are used to define and evolve the notion of "similar easy problems", and to control the selection of the next strategy to be improved. The technique was used to significantly strengthen the set of E strategies used by the MaLARea, PS-E, E-MaLeS, and E systems in the CASC@Turing 2012 competition, particularly in the Mizar division. Similar improvement was obtained on the problems created from the Flyspeck corpus.
Crowd computing: a survey

Crowd computing empowers computer systems by utilizing humans' perception, and their ability to solve non-algorithmic problems. In this approach, a group of humans are asked to contributively solve a problem that cannot be solved easily by individuals, or perfectly by computers. However, there are complexities in using humans to solve problems. Lack of generative models, complex cost models, lower speed in comparison to computers, limitation of knowledge and skills, noise, bias and error are examples of such complexities. An optimized crowd computing system should overcome these complexities, and improve the quality of solutions. 
This paper includes answers to three main questions: What is crowd computing? Why should one use crowd computing? And, how to use crowd computing? We will briefly answer the two former questions, while we will focus more on the latter one, specially on solving classification problems using multiple checking scenario. In addition, we will compare the current methods of crowed computing, and provide some guidelines for future works based on the current open issues in this field.
Lévy information and the aggregation of risk aversion

When investors have heterogeneous attitudes towards risk, it is reasonable to assume that each investor has a pricing kernel, and that these individual pricing kernels are aggregated to form a market pricing kernel. The various investors are then buyers or sellers depending on how their individual pricing kernels compare with that of the market. In Brownian-based models, we can represent such heterogeneous attitudes by letting the market price of risk be a random variable, the distribution of which corresponds to the variability of attitude across the market. If the flow of market information is determined by the movements of prices, then neither the Brownian driver nor the market price of risk are directly visible: the filtration is generated by an ‘information process’ given by a combination of the two. We show that the market pricing kernel is then given by the harmonic mean of the individual pricing kernels associated with the various market participants. Remarkably, with an appropriate definition of Lévy information one draws the same conclusion in the case when asset prices can jump. As a consequence, we are led to a rather general scheme for the management of investments in heterogeneous markets subject to jump risk.
Measuring Cultural Dynamics through the Eurovision Song Contest

Measuring culture and its dynamics through surveys has important limitations, but the emerging field of computational social science allows us to overcome them by analyzing large-scale datasets. In this paper, we study cultural dynamics through the votes in the Eurovision song contest, which are decided by a crowd-based scheme in which viewers vote through mobile phone messages. Taking into account asymmetries and imperfect perception of culture, we measure cultural relations among European countries in terms of cultural affinity. We propose the Friend-or-Foe coefficient, a metric to measure voting biases among participants of a Eurovision contest. We validate how this metric represents cultural affinity through its relation with known cultural distances, and through numerical analysis of biased Eurovision contests. We apply this metric to the historical set of Eurovision contests from 1975 to 2012, finding new patterns of stronger modularity than using votes alone. Furthermore, we define a measure of polarization that, when applied to empirical data, shows a sharp increase within EU countries during 2010 and 2011. We empirically validate the relation between this polarization and economic indicators in the EU, showing how political decisions influence both the economy and the way citizens relate to the culture of other EU members.
On Bankruptcy Game Theoretic Interval Rules

Interval bankruptcy problems arise in situations where an estate has to be liquidated among a fixed number of creditors and uncertainty about the amounts of the claims is modeled by intervals. We extend in the interval setting the classical results by Curiel, Maschler and Tijs (1987) that characterize division rules which correspond to solutions of the cooperative bankruptcy game. Finally, we analyze the difficulties with incorporating the uncertainty about the estate.
Chaotic cold accretion on to black holes

Using 3D AMR simulations, linking the 50 kpc to the sub-pc scales over the course of 40 Myr, we systematically relax the classic Bondi assumptions in a typical galaxy hosting a SMBH. In the realistic scenario, where the hot gas is cooling, while heated and stirred on large scales, the accretion rate is boosted up to two orders of magnitude compared with the Bondi prediction. The cause is the nonlinear growth of thermal instabilities, leading to the condensation of cold clouds and filaments when t_cool/t_ff 0.2) induces the formation of thermal instabilities, even in the absence of heating, while in the transonic regime turbulent dissipation inhibits their growth (t_turb/t_cool < 1). When heating restores global thermodynamic balance, the formation of the multiphase medium is violent, and the mode of accretion is fully cold and chaotic. The recurrent collisions and tidal forces between clouds, filaments and the central clumpy torus promote angular momentum cancellation, hence boosting accretion. On sub-pc scales the clouds are channelled to the very centre via a funnel. A good approximation to the accretion rate is the cooling rate, which can be used as subgrid model, physically reproducing the boost factor of 100 required by cosmological simulations, while accounting for fluctuations. Chaotic cold accretion may be common in many systems, such as hot galactic halos, groups, and clusters, generating high-velocity clouds and strong variations of the AGN luminosity and jet orientation. In this mode, the black hole can quickly react to the state of the entire host galaxy, leading to efficient self-regulated AGN feedback and the symbiotic Magorrian relation. During phases of overheating, the hot mode becomes the single channel of accretion (with a different cuspy temperature profile), though strongly suppressed by turbulence.
Deconfined criticality flow in the Heisenberg model with ring-exchange interactions.

Quantum transition points in the J-Q model--the test bed of the deconfined critical point theory--and the SU(2)-symmetric discrete noncompact CP(1) representation of the deconfined critical action are directly compared by the flowgram method. We find that the flows of two systems coincide in a broad region of linear system sizes (10 < L < 50 for the J-Q model), implying that the deconfined critical point theory correctly captures the mesoscopic physics of competition between the antiferromagnetic and valence-bond orders in quantum spin systems. At larger sizes, however, we observe significant deviations between the two flows which both demonstrate strong violations of scale invariance. This reliably rules out the second-order transition scenario in at least one of the two models and suggests the most likely explanation for the nature of the transition in the J-Q model.
Universal properties of the Higgs resonance in (2+1)-dimensional U(1) critical systems.

We present spectral functions for the magnitude squared of the order parameter in the scaling limit of the two-dimensional superfluid to Mott insulator quantum phase transition at constant density, which has emergent particle-hole symmetry and Lorentz invariance. The universal functions for the superfluid, Mott insulator, and normal liquid phases reveal a low-frequency resonance which is relatively sharp and is followed by a damped oscillation (in the first two phases only) before saturating to the quantum critical plateau. The counterintuitive resonance feature in the insulating and normal phases calls for deeper understanding of collective modes in the strongly coupled (2+1)-dimensional relativistic field theory. Our results are derived from analytically continued correlation functions obtained from path-integral Monte Carlo simulations of the Bose-Hubbard model.
DID THE INFANT R136 AND NGC 3603 CLUSTERS UNDERGO RESIDUAL GAS EXPULSION?

Based on kinematic data observed for very young, massive clusters that appear to be in dynamical equilibrium, it has recently been argued that such young systems are examples of where the early residual gas expulsion did not happen or had no dynamical effect. The intriguing scenario of a star cluster forming through a single starburst has thereby been challenged. Choosing the case of the R136 cluster of the Large Magellanic Cloud, the most cited one in this context, we perform direct N-body computations that mimic the early evolution of this cluster including the gas-removal phase (on a thermal timescale). Our calculations show that under plausible initial conditions which are consistent with observational data, a large fraction (>60%) of a gas-expelled, expanding R136-like cluster is bound to regain dynamical equilibrium by its current age. Therefore, the recent measurements of velocity dispersion in the inner regions of R136, which indicate that the cluster is in dynamical equilibrium, are consistent with an earlier substantial gas expulsion of R136 followed by a rapid re-virialization (in ≈1 Myr). Additionally, we find that the less massive Galactic NGC 3603 Young Cluster (NYC), with a substantially longer re-virialization time, is likely to be found to have deviated from dynamical equilibrium at its present age (≈1 Myr). The recently obtained stellar proper motions in the central part of the NYC indeed suggest this and are consistent with the computed models. This work significantly extends previous models of the Orion Nebula Cluster which already demonstrated that the re-virialization time of young post-gas-expulsion clusters decreases with increasing pre-expulsion density.
Bottom Mass from Nonrelativistic Sum Rules at NNLL

We report on a recent determination of the bottom quark mass from nonrelativistic (large-n) Upsilon sum rules with renormalization group improvement (RGI) at next-to-next-to-leading logarithmic (NNLL) order. The comparison to previous fixed-order analyses shows that the RGI computed in the vNRQCD framework leads to a substantial stabilization of the theoretical sum rule moments with respect to scale variations. A single moment fit (n=10) to the available experimental data yields M_b^{1S}=4.755 +- 0.057(pert) +- 0.009(alpha_s) +- 0.003(exp) GeV for the bottom 1S mass and m_b(m_b)= 4.235 +- 0.055(pert) +- 0.003(exp) GeV for the bottom MSbar mass. The quoted uncertainties refer to the perturbative error and the uncertainties associated with the strong coupling and the experimental input.
Deep Predictive Coding Networks

The quality of data representation in deep learning methods is directly related to the prior model imposed on the representations; however, generally used fixed priors are not capable of adjusting to the context in the data. To address this issue, we propose deep predictive coding networks, a hierarchical generative model that empirically alters priors on the latent representations in a dynamic and context-sensitive manner. This model captures the temporal dependencies in time-varying signals and uses top-down information to modulate the representation in lower layers. The centerpiece of our model is a novel procedure to infer sparse states of a dynamic model which is used for feature extraction. We also extend this feature extraction block to introduce a pooling function that captures locally invariant representations. When applied on a natural video data, we show that our method is able to learn high-level visual features. We also demonstrate the role of the top-down connections by showing the robustness of the proposed model to structured noise.
On sampling and modeling complex systems

The study of complex systems is limited by the fact that only a few variables are accessible for modeling and sampling, which are not necessarily the most relevant ones to explain the system behavior. In addition, empirical data typically undersample the space of possible states. We study a generic framework where a complex system is seen as a system of many interacting degrees of freedom, which are known only in part, that optimize a given function. We show that the underlying distribution with respect to the known variables has the Boltzmann form, with a temperature that depends on the number of unknown variables. In particular, when the influence of the unknown degrees of freedom on the known variables is not too irregular, the temperature decreases as the number of variables increases. This suggests that models can be predictable only when the number of relevant variables is less than a critical threshold. Concerning sampling, we argue that the information that a sample contains on the behavior of the system is quantified by the entropy of the frequency with which different states occur. This allows us to characterize the properties of maximally informative samples: within a simple approximation, the most informative frequency size distributions have power law behavior and Zipf’s law emerges at the crossover between the under sampled regime and the regime where the sample contains enough statistics to make inferences on the behavior of the system. These ideas are illustrated in some applications, showing that they can be used to identify relevant variables or to select the most informative representations of data, e.g. in data clustering.
Modeling human dynamics of face-to-face interaction networks

Face-to-face interaction networks describe social interactions in human gatherings, and are the substrate for processes such as epidemic spreading and gossip propagation. The bursty nature of human behavior characterizes many aspects of empirical data, such as the distribution of conversation lengths, of conversations per person, or of interconversation times. Despite several recent attempts, a general theoretical understanding of the global picture emerging from data is still lacking. Here we present a simple model that reproduces quantitatively most of the relevant features of empirical face-to-face interaction networks. The model describes agents that perform a random walk in a two-dimensional space and are characterized by an attractiveness whose effect is to slow down the motion of people around them. The proposed framework sheds light on the dynamics of human interactions and can improve the modeling of dynamical processes taking place on the ensuing dynamical social networks.