nils-herrmann/clean_scirepeval huggingface dataset:
Topological properties of a time-integrated activity-driven network.

Here we consider the topological properties of the integrated networks emerging from the activity-driven model [N. Perra et al.,  (2012)], a temporal network model recently proposed to explain the power-law degree distribution empirically observed in many real social networks. By means of a mapping to a hidden-variable network model, we provide analytical expressions for the main topological properties of the integrated network, depending on the integration time and the distribution of activity potential characterizing the model. The expressions obtained, exacts in some cases, the results of controlled asymptotic expansions in others, are confirmed by means of extensive numerical simulations. Our analytical approach, which highlights the differences of the model with respect to the empirical observations made in real social networks, can be easily extended to deal with improved, more realistic modifications of the activity-driven network paradigm.
An Inequality Paradigm for Probabilistic Knowledge

We propose an inequality paradigm for probabilistic reasoning based on a logic of upper and lower bounds on conditional probabilities. We investigate a family of probabilistic logics, generalizing the work of Nilsson . We develop a variety of logical notions for probabilistic reasoning, including soundness, completeness justification; and convergence: reduction of a theory to a simpler logical class. We argue that a bound view is especially useful for describing the semantics of probabilistic knowledge representation and for describing intermediate states of probabilistic inference and updating. We show that the Dempster-Shafer theory of evidence is formally identical to a special case of our generalized probabilistic logic. Our paradigm thus incorporates both Bayesian "rule-based" approaches and avowedly non-Bayesian "evidential" approaches such as MYCIN and DempsterShafer. We suggest how to integrate the two "schools", and explore some possibilities for novel synthesis of a variety of ideas in probabilistic reasoning.
A Framework for Non-Monotonic Reasoning About Probabilistic Assumptions

Attempts to replicate probabilistic reasoning in expert systems have typically overlooked a critical ingredient of that process. Probabilistic analysis typically requires extensive judgments regarding interdependencies among hypotheses and data, and regarding the appropriateness of various alternative models. The application of such models is often an iterative process, in which the plausibility of the results confirms or disconfirms the validity of assumptions made in building the model. In current expert systems, by contrast, probabilistic information is encapsulated within modular rules (involving, for example, "certainty factors"), and there is no mechanism for reviewing the overall form of the probability argument or the validity of the judgments entering into it.
Probability Judgement in Artificial Intelligence

This paper is concerned with two theories of probability judgment: the Bayesian theory and the theory of belief functions. It illustrates these theories with some simple examples and discusses some of the issues that arise when we try to implement them in expert systems. The Bayesian theory is well known; its main ideas go back to the work of Thomas Bayes (1702-1761). The theory of belief functions, often called the Dempster-Shafer theory in the artificial intelligence community, is less well known, but it has even older antecedents; belief-function arguments appear in the work of George Hooper (16401723) and James Bernoulli (1654-1705). For elementary expositions of the theory of belief functions, see Shafer (1976, 1985).
A Framework for Control Strategies in Uncertain Inference Networks

Control Strategies for hierarchical tree-like probabilistic inference networks are formulated and investigated. Strategies that utilize staged look-ahead and temporary focus on subgoals are formalized and refined using the Depth Vector concept that serves as a tool for defining the 'virtual tree' regarded by the control strategy. The concept is illustrated by four types of control strategies for three-level trees that are characterized according to their Depth Vector, and according to the way they consider intermediate nodes and the role that they let these nodes play. INFERENTI is a computerized inference system written in Prolog, which provides tools for exercising a variety of control strategies. The system also provides tools for simulating test data and for comparing the relative average performance under different strategies.
LSST's DC Bias Against Planets and Galactic-Plane Science

An LSST-like survey of the Galactic plane (deep images every 3-4 days) could probe the Galactic distribution of planets by two distinct methods: gravitational microlensing of planets beyond the snow line and transits by planets very close to their hosts. The survey would identify over 250 disk-lens/disk-source microlensing events per year that peak at r 100 that makes them especially sensitive to planets. Intensive followup of these events would be required to find planets, similar to what is done presently for Galactic bulge microlensing. The same data would enable a wealth of other science, including detection of isolated black holes, systematic study of brown-dwarf binaries, a pre-explosion lightcurve of the next Galactic supernova, pre-explosion lightcurves of stellar mergers, early nova lightcurves, proper motions of many more stars than can be reached by GAIA, and probably much more. As usual, the most exciting discoveries from probing the huge parameter space encompassed by Galactic-plane stellar populations might well be serendipitous. Unfortunately, the LSST collaboration plans to exclude the first and fourth quadrants of the Galactic plane from their "synoptic" observations because the DC image that resulted from repeated observations would be limited by crowding. I demonstrate that the majority of this science can be recovered by employing well-developed image subtraction analysis methods, and that the cost to other (high Galactic latitude) science would be negligible.
Constraints on New Neutrino Interactions via Light Abelian Vector Bosons

We calculate new constraints on extra neutrino interactions via light Abelian vector bosons, where the boson mass arises from Stuckelberg mechanism. We use the requirement that Z, W , and kaon decays, as well as electron-neutrino scattering, are not altered by the new interactions beyond what is allowed by experimental uncertainties. These constraints are strong and apply to neutrinophilic dark matter, where interactions of neutrinos and dark matter via a new gauge boson are important. In particular, we show that models where neutrino interactions are needed to solve the small-scale structure problems in the CDM cosmology are constrained.
Dark matter detectors as dark photon helioscopes.

Light new particles with masses below 10 keV, often considered as a plausible extension of the standard model, will be emitted from the solar interior and can be detected on Earth with a variety of experimental tools. Here, we analyze the new "dark" vector state V, a massive vector boson mixed with the photon via an angle κ, that in the limit of the small mass mV has its emission spectrum strongly peaked at low energies. Thus, we utilize the constraints on the atomic ionization rate imposed by the results of the XENON10 experiment to set the limit on the parameters of this model: κ×mV<3×10(-12)  eV. This makes low-threshold dark matter experiments the most sensitive dark vector helioscopes, as our result not only improves current experimental bounds from other searches by several orders of magnitude but also surpasses even the most stringent astrophysical and cosmological limits in a seven-decade-wide interval of mV. We generalize this approach to other light exotic particles and set the most stringent direct constraints on "minicharged" particles.
Gravitational-Wave Tests of General Relativity with Ground-Based Detectors and Pulsar-Timing Arrays

This review is focused on tests of Einstein’s theory of general relativity with gravitational waves that are detectable by ground-based interferometers and pulsar-timing experiments. Einstein’s theory has been greatly constrained in the quasi-linear, quasi-stationary regime, where gravity is weak and velocities are small. Gravitational waves will allow us to probe a complimentary, yet previously unexplored regime: the non-linear and dynamical strong-field regime. Such a regime is, for example, applicable to compact binaries coalescing, where characteristic velocities can reach fifty percent the speed of light and gravitational fields are large and dynamical. This review begins with the theoretical basis and the predicted gravitational-wave observables of modified gravity theories. The review continues with a brief description of the detectors, including both gravitational-wave interferometers and pulsar-timing arrays, leading to a discussion of the data analysis formalism that is applicable for such tests. The review ends with a discussion of gravitational-wave tests for compact binary systems.
Friendship Paradox Redux: Your Friends Are More Interesting Than You

Feld's friendship paradox states that "your friends have more friends than you, on average." This paradox arises because extremely popular people, despite being rare, are overrepresented when averaging over friends. Using a sample of the Twitter firehose, we confirm that the friendship paradox holds for >98% of Twitter users. Because of the directed nature of the follower graph on Twitter, we are further able to confirm more detailed forms of the friendship paradox: everyone you follow or who follows you has more friends and followers than you. This is likely caused by a correlation we demonstrate between Twitter activity, number of friends, and number of followers. In addition, we discover two new paradoxes: the virality paradox that states "your friends receive more viral content than you, on average," and the activity paradox, which states "your friends are more active than you, on average." The latter paradox is important in regulating online communication. It may result in users having difficulty maintaining optimal incoming information rates, because following additional users causes the volume of incoming tweets to increase super-linearly. While users may compensate for increased information flow by increasing their own activity, users become information overloaded when they receive more information than they are able or willing to process. We compare the average size of cascades that are sent and received by overloaded and underloaded users. And we show that overloaded users post and receive larger cascades and they are poor detector of small cascades.
Crowdsourcing Dilemma

Crowdsourcing offers unprecedented potential for solving tasks efficiently by tapping into the skills of large groups of people. A salient feature of crowdsourcing—its openness of entry—makes it vulnerable to malicious behavior. Such behavior took place in a number of recent popular crowdsourcing competitions. We provide game-theoretic analysis of a fundamental tradeoff between the potential for increased productivity and the possibility of being set back by malicious behavior. Our results show that in crowdsourcing competitions malicious behavior is the norm, not the anomaly—a result contrary to the conventional wisdom in the area. Counterintuitively, making the attacks more costly does not deter them but leads to a less desirable outcome. These findings have cautionary implications for the design of crowdsourcing competitions.
Hedging of Game Options under Model Uncertainty in Discrete Time

We introduce a setup of model uncertaintyin discrete time. In this setup wederive dual expressions for the super-replication prices of game options with upper semicontinuous payoffs. We show that the super-replication price is equal to the supremum over a special (non dominated) set of martingale measures, of the corresponding Dynkin games values. This type of results is also new for American options.
The Rise and Fall of R&D Networks

Drawing on a large database of publicly announced R&D alliances, we track the evolutionof R&D networks in a large number of economic sectors over a long time period (1986-2009). Our main goal is to evaluate temporal and sectoral robustness of the main statisticalproperties of empirical R&D networks. By studying a large set of indicators, we providea more complete description of these networks with respect to the existing literature. Wefind that most network properties are invariant across sectors. In addition, they do notchange when alliances are considered independently of the sectorsto which partners belong.Moreover, we find that many properties of R&D networks are characterized by a rise-and-fall dynamics with a peak in the mid-nineties. Finally, we show that suchproperties of empirical R&D networks support predictions of the recent theoretical literature on R&D network formation.
Triply heavy baryon mass estimated within pNRQCD

Potential-NRQCD offers an effective-theory based approach to heavy-quark physics. While meson Q-anti_Q computations are tractable in pure alpha_s-perturbation theory, more complex many-body quark systems transcend it. A possibility inherited from nuclear physics is to employ the perturbative static potentials in a numerical diagonalization, eventually obtaining the exact lowest eigenvalue in each channel for a given-order perturbative potential. The power counting is manifest in the potential instead of the spectrum. The NNLO-potential for the 3-body problem is already available, so we have addressed triply-heavy baryons in this initial work with a computer-aided 2-parameter variational treatment.
Integrating influenza antigenic dynamics with molecular evolution

Influenza viruses undergo continual antigenic evolution allowing mutant viruses to evade host immunity acquired to previous virus strains. Antigenic phenotype is often assessed through pairwise measurement of cross-reactivity between influenza strains using the hemagglutination inhibition (HI) assay. Here, we extend previous approaches to antigenic cartography, and simultaneously characterize antigenic and genetic evolution by modeling the diffusion of antigenic phenotype over a shared virus phylogeny. Using HI data from influenza lineages A/H3N2, A/H1N1, B/Victoria and B/Yamagata, we determine patterns of antigenic drift across viral lineages, showing that A/H3N2 evolves faster and in a more punctuated fashion than other influenza lineages. We also show that year-to-year antigenic drift appears to drive incidence patterns within each influenza lineage. This work makes possible substantial future advances in investigating the dynamics of influenza and other antigenically-variable pathogens by providing a model that intimately combines molecular and antigenic evolution.
An exactly solvable three-dimensional nonlinear quantum oscillator

Exact analytical, closed-form solutions, expressed in terms of special functions, are presented for the case of a three-dimensional nonlinear quantum oscillator with a position dependent mass. This system is the generalization of the corresponding one-dimensional system, which has been the focus of recent attention. In contrast to other approaches, we are able to obtain solutions in terms of special functions, without a reliance upon a Rodrigues-type of formula. The wave functions of the quantum oscillator have the familiar spherical harmonic solutions for the angular part. For the s-states of the system, the radial equation accepts solutions that have been recently found for the one-dimensional nonlinear quantum oscillator, given in terms of associated Legendre functions, along with a constant shift in the energy eigenvalues. Radial solutions are obtained for all angular momentum states, along with the complete energy spectrum of the bound states.
The Social Maintenance of Cooperation through Hypocrisy

Cooperation is widespread in human societies, but its maintenance at the group level remains puzzling if individuals benefit from not cooperating. Explanations of the maintenance of cooperation generally assume that cooperative and non-cooperative behavior in others can be assessed and copied accurately. However, humans have a well known capacity to deceive and thus to manipulate how others assess their behavior. Here, we show that hypocrisy - claiming to be acting cooperatively while acting selfishly - can maintain social cooperation because it prevents the spread of selfish behavior. We demonstrate this effect both theoretically and experimentally. Hypocrisy allows the cooperative strategy to spread by taking credit for the success of the non-cooperative strategy
Identification of biologically relevant subtypes via preweighted sparse clustering

Cluster analysis methods are used to identify homogeneous subgroups in a data set. Frequently one applies cluster analysis in order to identify biologically interesting subgroups. In particular, one may wish to identify subgroups that are associated with a particular outcome of interest. Conventional clustering methods often fail to identify such subgroups, particularly when there are a large number of high-variance features in the data set. Conventional methods may identify clusters associated with these high-variance features when one wishes to obtain secondary clusters that are more interesting biologically or more strongly associated with a particular outcome of interest. We describe a modification of the sparse clustering method of Witten and Tibshirani (2010) that can be used to identify such secondary clusters or clusters associated with an outcome of interest. We show that this method can correctly identify such clusters of interest in several simulation scenarios. The method is also applied to a large case-control study of TMD and a leukemia microarray data set.
Pricing and Valuation under the Real-World Measure

In general it is not clear which kind of information is supposed to be used for calculating the fair value of a contingent claim. Even if the information is specified, it is not guaranteed that the fair value is uniquely determined by the given information. A further problem is that asset prices are typically expressed in terms of a risk-neutral measure. This makes it difficult to transfer the fundamental results of financial mathematics to econometrics. I show that the aforementioned problems evaporate if the financial market is complete and sensitive. In this case, after an appropriate choice of the numeraire, the discounted price processes turn out to be uniformly integrable martingales under the real-world measure. This leads to a Law of One Price and a simple real-world valuation formula in a model-independent framework where the number of assets as well as the lifetime of the market can be finite or infinite.
Pions: Experimental Tests of Chiral Symmetry Breaking

Based on the spontaneous breaking of chiral symmetry, chiral perturbation theory (ChPT) is believed to approximate confinement scale QCD. Dedicated and increasingly accurate experiments and improving lattice calculations are confirming this belief, and we are entering a new era in which we can test confinement scale QCD in some well chosen reactions. This is demonstrated with an overview of low energy experimental tests of ChPT predictions of $\pi\pi$ scattering, pion properties, $\pi$N scattering and electromagnetic pion production. These predictions have been shown to be consistent with QCD in the meson sector by increasingly accurate lattice calculations. At present there is good agreement between experiment and ChPT calculations, including the $\pi\pi$ and $\pi$N s wave scattering lengths and the $\pi^{0}$ lifetime. Recent, accurate pionic atom data are in agreement with chiral calculations once isospin breaking effects due to the mass difference of the up and down quarks are taken into account, as was required to extract the $\pi\pi$ scattering lengths. In addition to tests of the theory, comparisons between $\pi\pi$ and $\pi$N interactions based on general chiral principles are discussed. Lattice calculations are now providing results for the fundamental, long and inconclusively studied, $\pi$N $\sigma$ term and the contribution of the strange quark to the mass of the proton. Increasingly accurate experiments in electromagnetic pion production experiments from the proton which test ChPT calculations (and their energy region of validity) are presented. These experiments are also beginning to measure the final state $\pi$N interaction. This paper is based on the concluding remarks made at the Chiral Dynamics Workshop CD12 held at Jefferson Lab in Aug. 2012.
Silicon detector dark matter results from the final exposure of CDMS II.

We report results of a search for weakly interacting massive particles (WIMPS) with the silicon detectors of the CDMS II experiment. This blind analysis of 140.2 kg day of data taken between July 2007 and September 2008 revealed three WIMP-candidate events with a surface-event background estimate of 0.41(-0.08)(+0.20)(stat)(-0.24)(+0.28)(syst). Other known backgrounds from neutrons and 206Pb are limited to <0.13 and <0.08 events at the 90% confidence level, respectively. The exposure of this analysis is equivalent to 23.4 kg day for a recoil energy range of 7-100 keV for a WIMP of mass 10  GeV/c2. The probability that the known backgrounds would produce three or more events in the signal region is 5.4%. A profile likelihood ratio test of the three events that includes the measured recoil energies gives a 0.19% probability for the known-background-only hypothesis when tested against the alternative WIMP+background hypothesis. The highest likelihood occurs for a WIMP mass of 8.6  GeV/c2 and WIMP-nucleon cross section of 1.9×10(-41)  cm2.
Epidemic fronts in complex networks with metapopulation structure.

Infection dynamics have been studied extensively on complex networks, yielding insight into the effects of heterogeneity in contact patterns on disease spread. Somewhat separately, metapopulations have provided a paradigm for modeling systems with spatially extended and "patchy" organization. In this paper we expand on the use of multitype networks for combining these paradigms, such that simple contagion models can include complexity in the agent interactions and multiscale structure. Using a generalization of the Miller-Volz mean-field approximation for susceptible-infected-recovered (SIR) dynamics on multitype networks, we study the special case of epidemic fronts propagating on a one-dimensional lattice of interconnected networks-representing a simple chain of coupled population centers-as a necessary first step in understanding how macroscale disease spread depends on microscale topology. Applying the formalism of front propagation into unstable states, we derive the effective transport coefficients of the linear spreading: asymptotic speed, characteristic wavelength, and diffusion coefficient for the leading edge of the pulled fronts, and analyze their dependence on the underlying graph structure. We also derive the epidemic threshold for the system and study the front profile for various network configurations.