nils-herrmann/clean_scirepeval huggingface dataset:
CUTE solutions for two-point correlation functions from large cosmological datasets

In the advent of new large galaxy surveys, which will produce enormous datasets with hundreds of millions of objects, new computational techniques are necessary in order to extract from them any two-point statistic, the computational time of which grows with the square of the number of objects to be correlated. Fortunately technology now provides multiple means to massively parallelize this problem. Here we present a free-source code specifically designed for this kind of calculations. Two implementations are provided: one for execution on shared-memory machines using OpenMP and one that runs on graphical processing units (GPUs) using CUDA. The code is available at this http URL
Three-state herding model of the financial markets

We propose a Markov jump process with the three-state herding interaction. We see our approach as an agent-based model for the financial markets. Under certain assumptions this agent-based model can be related to the stochastic description exhibiting sophisticated statistical features. Along with power-law probability density function of the absolute returns we are able to reproduce the fractured power spectral density, which is observed in the high-frequency financial market data. The given example of consistent agent-based and stochastic modeling will provide a background for further developments in the research of complex social systems.
Conserved actions, maximum entropy and dark matter haloes

We use maximum entropy arguments to derive the phase space distribution of a virializeddark matter halo. Our distribution function gives an improved representation of the end prod-uct of violent relaxation. This is achieved by incorporating physically motivated dynamicalconstraints (speciﬁcally on orbital actions) which prevent arbitrary redistribution of energy.We compare the predictions with three high-resolution dark matter simulations of widelyvarying mass. The numerical distribution function is accurately predicted by our argument,producing an excellent match for the vast majority of particles.The remaining particles constitute the central cusp of the halo (˘<4% of the dark mat-ter). They can be accounted for within the presented framework once the short dynamicaltimescales of the centre are taken into account. 1 INTRODUCTIONFor over two decades it has been possible to use numerical meth-ods to model systems of cold, collisionless dark matter particlescollapsing under gravity to form stable, virialized ‘halos’ (Frenket al. 1985; Dubinski & Carlberg 1991; for a review see Frenk &White 2012). The density of these halos declines with radius fol-lowing a slowly changing power law dependence, roughly r ˘r
Jets and photons.

This Letter applies the concept of "jets," as constructed from calorimeter cell four-vectors, to jets composed (primarily) of photons (or leptons). Thus jets become a superset of both traditional objects such as QCD jets, photons, and electrons, and more unconventional objects such as photon jets and electron jets, defined as collinear photons and electrons, respectively. Since standard objects such as single photons become a subset of jets in this approach, standard jet substructure techniques are incorporated into the photon finder toolbox. Using a (reasonably) realistic calorimeter model we demonstrate that, for a single photon identification efficiency of 80% or above, the use of jet substructure techniques reduces the number of QCD jets faking photons by factors of 2.5 to 4. Depending on the topology of the photon jets, the substructure variables reduce the number of photon jets faking single photons by factors of 10 to 10(3) at a single photon identification efficiency of 80%.
Information fusion in multi-task Gaussian process models

This paper evaluates heterogeneous information fusion using multi-task Gaussian processes in the context of geological resource modeling. Specifically, it empirically demonstrates that information integration across heterogeneous information sources leads to superior estimates of all the quantities being modeled, compared to modeling them individually. Multi-task Gaussian processes provide a powerful approach for simultaneous modeling of multiple quantities of interest while taking correlations between these quantities into consideration. Experiments are performed on large scale real sensor data.
Connect and win: The role of social networks in political elections

Many real systems are made of strongly interacting networks, with profound consequences on their dynamics. Here, we consider the case of two interacting social networks and, in the context of a simple model, we address the case of political elections. Each network represents a competing party and every agent, on the election day, can choose to be either active in one of the two networks (vote for the corresponding party) or to be inactive in both (not vote). The opinion dynamics during the election campaign is described through a simulated annealing algorithm. We find that for a large region of the parameter space the result of the competition between the two parties allows for the existence of pluralism in the society, where both parties have a finite share of the votes. The central result is that a densely connected social network is key for the final victory of a party. However, small committed minorities can play a crucial role, and even reverse the election outcome.
A POSSIBLE CARBON-RICH INTERIOR IN SUPER-EARTH 55 Cancri e

Terrestrial planets in the solar system, such as the Earth, are oxygen-rich, with silicates and iron being the most common minerals in their interiors. However, the true chemical diversity of rocky planets orbiting other stars is yet unknown. Mass and radius measurements are used to constrain the interior compositions of super-Earths (exoplanets with masses of 1–10 M⊕), and are typically interpreted with planetary interior models that assume Earth-centric oxygen-rich compositions. Using such models, the super-Earth 55 Cancri e (mass 8 M⊕, radius 2 R⊕) has been suggested to bear an interior composition consisting of Fe, silicates, and an envelope (≳ 10% by mass) of supercritical water. We report that the mass and radius of 55 Cancri e can also be explained by a carbon-rich solid interior made of Fe, C, SiC, and/or silicates and without a volatile envelope. While the data allow Fe mass fractions of up to 40%, a wide range of C, SiC, and/or silicate mass fractions are possible. A carbon-rich 55 Cancri e is also plausible if its protoplanetary disk bore the same composition as its host star, which has been reported to be carbon-rich. However, more precise estimates of the stellar elemental abundances and observations of the planetary atmosphere are required to further constrain its interior composition. The possibility of a C-rich interior in 55 Cancri e opens a new regime of geochemistry and geophysics in extraterrestrial rocky planets, compared to terrestrial planets in the solar system.
Statistical properties of inter-arrival times distribution in social tagging systems

Folksonomies provide a rich source of data to study social patterns taking place on the World Wide Web. Here we study the temporal patterns of users' tagging activity. We show that the statistical properties of inter-arrival times between subsequent tagging events cannot be explained without taking into account correlation in users' behaviors. This shows that social interaction in collaborative tagging communities shapes the evolution of folksonomies. A consensus formation process involving the usage of a small number of tags for a given resources is observed through a numerical and theoretical analysis of some well-known folksonomy datasets.
Contagion dynamics in time-varying metapopulation networks

The metapopulation framework is adopted in a wide array of disciplines to describe systems of well separated yet connected subpopulations. The subgroups or patches are often represented as nodes in a network whose links represent the migration routes among them. The connections have been so far mostly considered as static, but in general evolve in time. Here we address this case by investigating simple contagion processes on time-varying metapopulation networks. We focus on the SIR process and determine analytically the mobility threshold for the onset of an epidemic spreading in the framework of activity-driven network models. We find profound differences from the case of static networks. The threshold is entirely described by the dynamical parameters defining the average number of instantaneously migrating individuals and does not depend on the properties of the static network representation. Remarkably, the diffusion and contagion processes are slower in time-varying graphs than in their aggregated static counterparts, the mobility threshold being even two orders of magnitude larger in the first case. The presented results confirm the importance of considering the time-varying nature of complex networks.
Experimental investigation of the stability of the floating water bridge.

When a high voltage is applied between two beakers filled with deionized water, a floating bridge of water is formed in between exceeding the length of 2 cm when the beakers are pulled apart. Currently two theories regarding the stability of the floating water bridge exist, one suggesting that the tension caused by electric field in the dielectric medium is holding the bridge and the other suggesting surface tension to be responsible for the vertical equilibrium. We construct experiments in which the electric field and the geometry of the bridge are measured and compared with predictions of theories of the floating water bridge stability. We use a numerical simulation for estimation of the electric field. Our results indicate that the two forces of dielectric and surface tensions hold the bridge against gravity simultaneously and, having the same order of magnitude, neither of the two forces are negligible. In bridges with larger diameters, the effect of dielectric tension is slightly more in the vertical equilibrium than surface tension. Results show that the stability can be explained by macroscopic forces, regardless of the microscopic changes in the water structure.
Consequences of Strong Compression in Tidal Disruption Events

The dynamics of stellar tidal disruption depend strongly on how deeply the victim star’s orbit plunges into the tidal sphere. In this Chapter, we show that most previous estimates of the spread in debris energy, \(\Updelta \epsilon\), are inaccurate when \(\beta=R_{\rm t}/R_{\rm p}> 1\); the traditional formula for \(\Updelta \epsilon\) can be incorrect by orders of magnitude for high β events. After presenting a revised formula that is independent of β and correct to leading order, we develop a new analytic model for tidal disruption events (TDEs) and employ this to test our conclusion that \(\Updelta\epsilon\) does not depend on β.
A class where qualitative discussions, coming weeks before computationally complicated practice, helps students' problem solving abilities

Psychologists have long known that an expert in a field not only knows significantly more individual facts/skills than a novice but also has these facts/skills organized into a mental hierarchy that links the individual facts (at the bottom of the hierarchy) together with larger more-encompassing ideas (at the top of the hierarchy). In the Spring quarter of 2012, UC Davis offered 4 sections (about 180 students each) of the first quarter of introductory physics, Physics 9A, covering Newtonian mechanics. One of these sections is a "treatment" group and had the entire 10-week quarter's set of ideas introduced, largely qualitatively, in the first 6 weeks followed by the 4 weeks where students learn to use those ideas to solve the algebraically complicated problems that physicists prize. The other three sections were organized as usual. The treatment group and one of the other sections were taught by the author and were identical (same homework, discussion, lecture, and lab) except for the organization of the content. After controlling for GPA as well as Force Concept Inventory pretest scores, the treatment group was found, with better than 99% confidence, to score higher on the final exam. Some curricular details of the treatment class are discussed in this paper.
Bayesian Analysis for miRNA and mRNA Interactions Using Expression Data

MicroRNAs (miRNAs) are small RNA molecules composed of 19-22 nt playing important regulatory roles in post-transcriptional gene regulation by inhibiting the translation of the mRNA into proteins or otherwise cleaving the target mRNA. Inferring miRNA targets provides useful information for understanding the roles of miRNA involving in biological processes which may result in diagnosing complex diseases. Statistical methodologies of point estimates such as the LASSO algorithm have been proposed to identify the interactions of miRNA and mRNA based on sequence and expression data. In this paper, we propose Bayesian LASSO and non-negative Bayesian LASSO to analyze the interactions between miRNA and mRNA using the expression data. The proposed Bayesian methods explore the posterior distributions for those parameters required in the model depicting the miRNA-mRNA interactions. For comparison purposes, we applied the Least Square Regression (LSR), Ridge Regression (RR), LASSO, non-negative LASSO (nLASSO), and the Bayesian approaches to four public data sets which have the known interaction pairs of miRNA and mRNA. Comparing to the point estimate algorithms, the Bayesian methods are able to infer more known interactions and are more meaningful to provide credible intervals to take into account the uncertainty of the interactions of miRNA and mRNA. The Bayesian approaches are useful for graphing the inferred eects of the miRNAs on the targets by plotting the posterior distributions of those parameters, and while the point estimate algorithm only provides a single estimate for those parameters.
Evidence of Long Range Order in the Riemann Zeta Function

We have done a statistical analysis of some properties of the contour lines Im$(\zeta (s))$ = 0 of the Riemann zeta function. We find that this function is broken up into strips whose average width on the critical line does not appear to vary with height. We also compute the position of the primary zero for the lowest 200 strips, and find that this probability distribution also appears to be scale invariant.
A Correlation between the Higgs Mass and Dark Matter

Depending on the value of the Higgs mass, the Standard Model acquires an unstable region at large Higgs field values due to RG running of couplings, which we evaluate at 2-loop order. For currently favored values of the Higgs mass, this renders the electroweak vacuum only metastable with a long lifetime. We argue on statistical grounds that the Higgs field would be highly unlikely to begin in the small field metastable region in the early universe, and thus some new physics should enter in the energy range of order of, or lower than, the instability scale to remove the large field unstable region. We assume that Peccei-Quinn (PQ) dynamics enters to solve the strong CP problem and, for a PQ-scale in this energy range, may also remove the unstable region. We allow the PQ-scale to scan and argue, again on statistical grounds, that its value in our universe should be of order of the instability scale, rather than (significantly) lower. Since the Higgs mass determines the instability scale, which is argued to set the PQ-scale, and since the PQ-scale determines the axion properties, including its dark matter abundance, we are led to a correlation between the Higgs mass and the abundance of dark matter. We find the correlation to be in good agreement with current data.
Critical Brain Dynamics at Large Scale

Highly correlated brain dynamics produces synchronized states with no behavioral value, while weakly correlated dynamics prevent information flow. In between these states, the unique dynamical features of the critical state endow the brain with properties which are fundamental for adaptive behavior. We discuss the idea put forward two decades ago by Per Bak that the working brain stays at an intermediate (critical) regime characterized by power-law correlations. This proposal is now supported by a wide body of empirical evidence at different scales demonstrating that the spatiotemporal brain dynamics exhibit key signatures of critical dynamics, previously recognized in other complex systems. The rationale behind this program is discussed in these notes, followed by an account of the most recent results.
Redistribution Spurs Growth by Using a Portfolio Effect on Risky Human Capital

We demonstrate by mathematical analysis and systematic computer simulations that redistribution can lead to sustainable growth in a society. In accordance with economic models of risky human capital, we assume that dynamics of human capital is modeled as a multiplicative stochastic process which, in the long run, leads to the destruction of individual human capital. When agents are linked by fully redistributive taxation the situation might turn to individual growth in the long run. We consider that a government collects a proportion of income and reduces it by a fraction as costs for administration (efficiency losses). The remaining public good is equally redistributed to all agents. Sustainable growth is induced by redistribution despite the losses from the random growth process and despite administrative costs. Growth results from a portfolio effect. The findings are verified for three different tax schemes: proportional tax, taking proportionally more from the rich, and proportionally more from the poor. We discuss which of these tax schemes performs better with respect to maximize growth under a fixed rate of administrative costs, and the governmental income. This leads us to general conclusions about governmental decisions, the relation to public good games with free riding, and the function of taxation in a risk-taking society.
Single molecule thermodynamics of ATP synthesis by F1-ATPase

FoF1-ATP synthase is a factory for synthesizing ATP in virtually all cells. Its core machinery is the subcomplex F1-motor (F1-ATPase) and performs the reversible mechanochemical coupling. The isolated F1-motor hydrolyzes ATP, which is accompanied by unidirectional rotation of its central γ - shaft ?> . When a strong opposing torque is imposed, the γ ?> -shaft rotates in the opposite direction and drives the F1-motor to synthesize ATP. This mechanical-to-chemical free-energy transduction is the final and central step of the multistep cellular ATP-synthetic pathway. Here, we determined the amount of mechanical work exploited by the F1-motor to synthesize an ATP molecule during forced rotations using a methodology combining a nonequilibrium theory and single molecule measurements of responses to external torque. We found that the internal dissipation of the motor is negligible even during rotations far from a quasistatic process.
STELLAR TRANSITS IN ACTIVE GALACTIC NUCLEI

Supermassive black holes (SMBHs) are typically surrounded by a dense stellar population in galactic nuclei. Stars crossing the line of site in active galactic nuclei (AGNs) produce a characteristic transit light curve, just like extrasolar planets do when they transit their host star. We examine the possibility of finding such AGN transits in deep optical, UV, and X-ray surveys. We calculate transit light curves using the Novikov–Thorne thin accretion disk model, including general relativistic effects. Based on the expected properties of stellar cusps, we find that around 106 solar mass SMBHs, transits of red giants are most common for stars on close orbits with transit durations of a few weeks and orbital periods of a few years. We find that detecting AGN transits requires repeated observations of thousands of low-mass AGNs to 1% photometric accuracy in optical, or ∼10% in UV bands or soft X-ray. It may be possible to identify stellar transits in the Pan-STARRS and LSST optical and the eROSITA X-ray surveys. Such observations could be used to constrain black hole mass, spin, inclination, and accretion rate. Transit rates and durations could give valuable information on the circumnuclear stellar clusters as well. Transit light curves could be used to image accretion disks with unprecedented resolution, allowing us to resolve the SMBH silhouette in distant AGNs.
Semisupervised Classification Through the Bag-of-Paths Group Betweenness

This paper introduces a novel and well-founded betweenness measure, called the bag-of-paths (BoP) betweenness, as well as its extension, the BoP group betweenness, to tackle semisupervised classification problems on weighted directed graphs. The objective of semisupervised classification is to assign a label to unlabeled nodes using the whole topology of the graph and the labeled nodes at our disposal. The BoP betweenness relies on a BoP framework, assigning a Boltzmann distribution on the set of all possible paths through the network such that long (high-cost) paths have a low probability of being picked from the bag, while short (low-cost) paths have a high probability of being picked. Within that context, the BoP betweenness of node j is defined as the sum of the a posteriori probabilities that node j lies in between two arbitrary nodes (i, k) when picking a path starting in i and ending in k. Intuitively, a node typically receives a high betweenness if it has a large probability of appearing on paths connecting two arbitrary nodes of the network. This quantity can be computed in closed form by inverting an n×n matrix where n is the number of nodes. For the group betweenness, the paths are constrained to start and end in nodes within the same class, thereby defining a within-class group betweenness for each class. Unlabeled nodes are then classified according to the class showing the highest group betweenness. Experiments on various real-world datasets show that the BoP group betweenness performs competitively compared to all the tested state-of-the-art methods. The benefit of the BoP betweenness is particularly noticeable when only a few labeled nodes are available.
Order statistics of observed network degrees

This article discusses the properties of extremes of degree sequences calculated from network data. We introduce the notion of a normalized degree, in order to permit a comparison of degree sequences between networks with differing numbers of nodes. We model each normalized degree as a bounded continuous random variable, and determine the properties of the ordered k-maxima and minima of the normalized network degrees when they comprise a random sample from a Beta distribution. In this setting, their means and variances take a simplified form given by their ordering, and we discuss the relation of these quantities to other prescribed decays such as power laws. We verify the derived properties from simulated sets of normalized degrees, and discuss possible extensions to more flexible classes of distributions.
Modeling Spatial Equilibrium in Cities: the Isobenefit Lines

I propose and briefly define the concept of Urban Isobenefit Lines by using functions as easy as efficient, whose results can offer a rich tool to use into spatial equilibrium analysis involving cities. They are line joining urban points with equal level of positional advantage from city amenities. The results which one obtain by implementing a chosen function, gave specific scenarios: numerically described by indicators and graphically visualized by efficient city matrix views. This is also a theoretical concept for the Urban Economics theory and Spatial Equilibrium analysis in cities.