nils-herrmann/clean_scirepeval huggingface dataset:
Opinion formation in time-varying social networks: The case of Naming Game

We study the dynamics of the naming game as an opinion formation model on time-varying social networks. This agent-based model captures the essential features of the agreement dynamics by means of a memory-based negotiation process. Our study focuses on the impact of time-varying properties of the social network of the agents on the naming game dynamics. In particular, we perform a computational exploration of this model using simulations on top of real networks. We investigate the outcomes of the dynamics on two different types of time-varying data: (1) the networks vary on a day-to-day basis and (2) the networks vary within very short intervals of time (20 sec). In the first case, we find that networks with strong community structure hinder the system from reaching global agreement; the evolution of the naming game in these networks maintains clusters of coexisting opinions indefinitely leading to metastability. In the second case, we investigate the evolution of the naming game in perfect synchronization with the time evolution of the underlying social network shedding new light on the traditional emergent properties of the game that differ largely from what has been reported in the existing literature.
Characterizing Vainshtein solutions in massive gravity

We study static, spherically symmetric solutions in a recently proposed ghost-free model of nonlinear massive gravity. We focus on a branch of solutions where the helicity-0 mode can be strongly coupled within certain radial regions, giving rise to the Vainshtein effect. We truncate the analysis to scales below the gravitational Compton wavelength, and consider the weak field limit for the gravitational potentials, while keeping all nonlinearities of the helicity-0 mode. We determine analytically the number and properties of local solutions that exist asymptotically on large scales, and of local (inner) solutions that exist on small scales. We find two kinds of asymptotic solutions, one of which is asymptotically flat, while the other one is not, and also two types of inner solutions, one of which displays the Vainshtein mechanism, while the other exhibits a self-shielding behavior of the gravitational field. We analyze in detail in which cases the solutions match in an intermediate region. The asymptotically flat solutions connect only to inner configurations displaying the Vainshtein mechanism, while the nonasymptotically flat solutions can connect with both kinds of inner solutions. We show furthermore that there are some regions in the parameter space where global solutions do not exist, and characterize precisely in which regions of the phase space the Vainshtein mechanism takes place.
Patience vs. Impatience of Stock Traders

An ability to postpone one's execution without penalty provides an important strategic advantage in high-frequency trading. To elucidate competition between traders one has to formulate to a quantitative theory of formation of the execution price from market expectations and quotes. This theory was provided in 2005 by Foucault, Kadan and Kandel. I derive asymptotic distribution of the bids/offers as a function of the ratio of patient and impatient traders using the dynamic version of the Foucault, Kadan and Kandel dynamic Limit Order Book (LOB) model. Dynamic version of the LOB model allows stylized but sufficiently realistic representation of the trading markets. In particular, dynamic LOB allows simulation of the distribution of execution times and spreads from high-frequency quotes. Significant analytic progress is made towards understanding of trading as competition for immediacy of execution between traders. The results are qualitatively compared with empirical volume-at-price distribution of highly liquid stocks.
Implications of Higgs searches on the four-generation standard model.

Within the four-generation standard model, the Higgs couplings to gluons and to photons deviate in a significant way from the predictions of the three-generation standard model. As a consequence, large departures in several Higgs production and decay channels are expected. Recent Higgs search results, presented by ATLAS, CMS, and CDF, hint on the existence of a Higgs boson with a mass around 125 GeV. Using these results and assuming such a Higgs boson, we derive exclusion limits on the four-generation standard model. For m(H)=125 GeV, the model is excluded above 99.95% confidence level. For 124.5 GeV≤m(H)≤127.5 GeV, an exclusion limit above 99% confidence level is found.
Toehold Purchase Problem: A comparative analysis of two strategies

Toehold purchase, defined here as purchase of one share in a firm by an investor preparing a tender offer to acquire majority of shares in it, reduces by one the number of shares this investor needs for majority. In the paper we construct mathematical models for the toehold and no-toehold strategies and compare the expected profits of the investor and the probabilities of takeover the firm in both strategies. It turns out that the expected profits of the investor in both strategies coincide. On the other hand, the probability of takeover the firm using the toehold strategy is considerably higher comparing to the no-toehold strategy. In the analysis of the models we apply the apparatus of incomplete Beta functions and some refined bounds for central binomial coefficients.
Advanced Space Propulsion Based on Vacuum (Spacetime Metric) Engineering

A theme that has come to the fore in advanced planning for long-range space exploration is the concept that empty space itself (the quantum vacuum, or spacetime metric) might be engineered so as to provide energy/thrust for future space vehicles. Although far-reaching, such a proposal is solidly grounded in modern physical theory, and therefore the possibility that matter/vacuum interactions might be engineered for space-flight applications is not a priori ruled out. As examples, the current development of theoretical physics addresses such topics as warp drives, traversable wormholes and time machines that provide for such vacuum engineering possibilities. We provide here from a broad perspective the physics and correlates/consequences of the engineering of the spacetime metric.
ELECTRON-BEAM-INDUCED RADIO EMISSION FROM ULTRACOOL DWARFS

We present the numerical simulations for an electron-beam-driven and loss-cone-driven electron–cyclotron maser (ECM) with different plasma parameters and different magnetic field strengths for a relatively small region and short timescale in an attempt to interpret the recent discovered intense radio emission from ultracool dwarfs. We find that a large amount of electromagnetic (EM) field energy can be effectively released from the beam-driven ECM, which rapidly heats the surrounding plasma. A rapidly developed high-energy tail of electrons in velocity space (resulting from the heating process of the ECM) may produce the radio continuum depending on the initial strength of the external magnetic field and the electron beam current. Both significant linear polarization and circular polarization of EM waves can be obtained from the simulations. The spectral energy distributions of the simulated radio waves show that harmonics may appear from 10 to 70νpe (νpe is the electron plasma frequency) in the non-relativistic case and from 10 to 600νpe in the relativistic case, which makes it difficult to find the fundamental cyclotron frequency in the observed radio frequencies. A wide frequency band should therefore be covered by future radio observations.
Co-clustering for directed graphs: the Stochastic co-Blockmodel and spectral algorithm Di-Sim

Directed graphs have asymmetric connections, yet the current graph clustering methodologies cannot identify the potentially global structure of these asymmetries. We give a spectral algorithm called di-sim that builds on a dual measure of similarity that correspond to how a node (i) sends and (ii) receives edges. Using di-sim, we analyze the global asymmetries in the networks of Enron emails, political blogs, and the c elegans neural connectome. In each example, a small subset of nodes have persistent asymmetries; these nodes send edges with one cluster, but receive edges with another cluster. Previous approaches would have assigned these asymmetric nodes to only one cluster, failing to identify their sending/receiving asymmetries. Regularization and "projection" are two steps of di-sim that are essential for spectral clustering algorithms to work in practice. The theoretical results show that these steps make the algorithm weakly consistent under the degree corrected Stochastic co-Blockmodel, a model that generalizes the Stochastic Blockmodel to allow for both (i) degree heterogeneity and (ii) the global asymmetries that we intend to detect. The theoretical results make no assumptions on the smallest degree nodes. Instead, the theorem requires that the average degree grows sufficiently fast and that the weak consistency only applies to the subset of the nodes with sufficiently large leverage scores. The results results also apply to bipartite graphs.
Optimal portfolios in commodity futures markets

We develop a general approach to portfolio optimization in futures markets. Following the Heath–Jarrow–Morton (HJM) approach, we model the entire futures price curve at once as a solution of a stochastic partial differential equation. We also develop a general formalism to handle portfolios of futures contracts. In the portfolio optimization problem, the agent invests in futures contracts and a risk-free asset, and her objective is to maximize the utility from final wealth. In order to capture self-consistent futures price dynamics, we study a class of futures price curve models which admit a finite-dimensional realization. More precisely, we establish conditions under which the futures price dynamics can be realized in finite dimensions. Using the finite-dimensional realization, we derive a finite-dimensional form of the portfolio optimization problem and study its solution. We also give an economic interpretation of the coordinate process driving the finite-dimensional realization.
The geographic flow of music

The social media website last.fm provides a detailed snapshot of what its users in hundreds of cities listen to each week. After suitably normalizing this data, we use it to test one hypothesis related to the geographic flow of music: that some cities are consistently early adopters of new music (and early to snub stale music). To test this hypothesis, we adapt a method previously used to detect the leadership networks present in flocks of birds. We find empirical support for the claim that a similar leadership network exists among cities.
A Near-Term Quantum Computing Approach for Hard Computational Problems in Space Exploration

In this article, we show how to map a sampling of the hardest artificial intelligence problems in space exploration onto equivalent Ising models that then can be attacked using quantum annealing implemented in D-Wave machine. We overview the existing results as well as propose new Ising model implementations for quantum annealing. We review supervised and unsupervised learning algorithms for classification and clustering with applications to feature identification and anomaly detection. We introduce algorithms for data fusion and image matching for remote sensing applications. We overview planning problems for space exploration mission applications and algorithms for diagnostics and recovery with applications to deep space missions. We describe combinatorial optimization algorithms for task assignment in the context of autonomous unmanned exploration. Finally, we discuss the ways to circumvent the limitation of the Ising mapping using a "blackbox" approach based on ideas from probabilistic computing. In this article we describe the architecture of the D-Wave One machine and report its benchmarks. Results on random ensemble of problems in the range of up to 96 qubits show improved scaling for median core quantum annealing time compared with classical algorithms; whether this scaling persists for larger problem sizes is an open question. We also review previous results of D-Wave One benchmarking studies for solving binary classification problems with a quantum boosting algorithm which is shown to outperform AdaBoost. We review quantum algorithms for structured learning for multi-label classification and introduce a hybrid classical/quantum approach for learning the weights. Results of D-Wave One benchmarking studies for learning structured labels on four different data sets show a better performance compared with an independent Support Vector Machine approach with linear kernel.
Price and Quantity Trajectories: Second-order Dynamics

In two previous papers the author developed a second-order price adjustment (t\^atonnement) process. This paper extends the approach to include both quantity and price adjustments. We demonstrate three results: a analogue to physical energy, called "activity" arises naturally in the model, and is not conserved in general; price and quantity trajectories must either end at a local minimum of a scalar potential or circulate endlessly; and disturbances into a subspace of substitutable commodities decay over time. From this we argue, although we do not prove, that the model features global stability, combined with local instability, a characteristic of many real markets. Following these observations and a brief survey of empirical results for price-setting and consumption behavior in markets for "real" goods (as opposed to financial markets), we conjecture that Stigler and Becker's well-known theory of consumer preference opens the possibility of substantial degeneracy in commodity space, and therefore that price and quantity trajectories could lie on a relatively low-dimensional subspace within the full commodity space.
Social Aspects of Virtual Teams

There has been a transformation from individual work to team work in the last few decades (Ilgen, 1999), and many organizations use teams for many activities done by individuals in the past (Boyett & Conn, 1992 ; Katzenbach & Smith, 1993). In recent years, there has been a renewed interest in computermediated groups because of the increases in globalization of business operations leading to geographically dispersed executives and decision makers. However, what seems to be lacking is some focus in terms of problem settings and corresponding tools to support collaborative decision making. The research question of this study deals with the dynamics of virtual teams' members. A model, suggesting that team dynamics can increase the teams' output, is presented, and a methodology to examine the model is illustrated. An experiment was performed, in which subjects, who were grouped into teams, had to share information in order to complete a task. The findings indicate that the social aspect of the virtual team's discussion is negative than the social aspect of the face-to-face team's discussion, and that the virtual team's output is inferior to the face-to-face team's output. The virtual team is a common way of working nowadays, and with the growing use of Internet applications and firms' globalization it will expand in the future. Thus, the importance of the theoretical and practical implementation of the research will be discussed.
Effects of Social Influence on the Wisdom of Crowds

Wisdom of crowds refers to the phenomenon that the aggregate prediction or forecast of a group of individuals can be surprisingly more accurate than most individuals in the group, and sometimes { than any of the individuals comprising it. This article models the impact of social inuence on the wisdom of crowds. We build a minimalistic representation of individuals as Brownian particles coupled by means of social inuence. We demonstrate that the model can reproduce results of a previous empirical study. This allows us to draw more fundamental conclusions about the role of social inuence: In particular, we show that the question of whether social inuence has a positive or negative net eect on the wisdom of crowds is ill-dened. Instead, it is the starting conguration of the population, in terms of its diversity and accuracy, that directly determines how benecial social inuence actually is. The article further examines the scenarios under which social inuence promotes or impairs the wisdom of crowds.
Precise Relic WIMP Abundance and its Impact on Searches for Dark Matter Annihilation

If dark matter (DM) is a weakly interacting massive particle (WIMP) that is a thermal relic of the early Universe, then its total self-annihilation cross section is revealed by its present-day mass density. The canonical thermally averaged cross section for a generic WIMP is usually stated as 3*10^-26 cm^3s^-1, with unspecified uncertainty, and taken to be independent of WIMP mass. Recent searches for annihilation products of DM annihilation have just reached the sensitivity to exclude this canonical cross section for 100% branching ratio to certain final states and small WIMP masses. The ultimate goal is to probe all kinematically allowed final states as a function of mass and, if all states are adequately excluded, set a lower limit to the WIMP mass. Probing the low-mass region is further motivated due to recent hints for a light WIMP in direct and indirect searches. We revisit the thermal relic abundance calculation for a generic WIMP and show that the required cross section can be calculated precisely. It varies significantly with mass at masses below 10 GeV, reaching a maximum of 5.2*10^-26 cm^3s^-1 at masses around 0.3 GeV, and is 2.2*10^-26 cm^3s^-1 with feeble mass-dependence for masses above 10 GeV. These results, which differ significantly from the canonical value and have not been taken into account in searches for annihilation products from generic WIMPs, have a noticeable impact on the interpretation of present limits from Fermi-LAT and WMAP+ACT.
Thermodynamic Principles in Social Collaborations

A thermodynamic framework is presented to characterize the evolution of eciency, order, and quality in social content production systems, and this framework is applied to the analysis of Wikipedia. Contributing editors are characterized by their (creative) energy levels in terms of number of edits. We develop a denition of entropy that can be used to analyze the eciency of the system as a whole, and relate it to the evolution of powerlaw distributions and a metric of quality. The concept is applied to the analysis of eight years of Wikipedia editing data and results show that (1) Wikipedia has become more ecient during its evolution and (2) the entropy-based eciency metric has high correlation with observed readership of Wikipedia pages.
Group Foraging in Dynamic Environments

Previous human foraging experiments have shown that human groups routinely undermatch environmental resources much like other animal species. In this experiment, we test whether humans also selectively rely on others as information sources when the environmental state is uncertain, and we also test whether overt signals of other foragers' success influences group matching behavior and group adaptation to a changing environment. The results show evidence of reliance on social information in specific conditions, but participants were primarily influenced by their individual assessments of food location rather than the success of other foragers.
Crowd Memory: Learning in the Collective

Crowd algorithms often assume workers are inexperienced and thus fail to adapt as workers in the crowd learn a task. These assumptions fundamentally limit the types of tasks that systems based on such algorithms can handle. This paper explores how the crowd learns and remembers over time in the context of human computation, and how more realistic assumptions of worker experience may be used when designing new systems. We first demonstrate that the crowd can recall information over time and discuss possible implications of crowd memory in the design of crowd algorithms. We then explore crowd learning during a continuous control task. Recent systems are able to disguise dynamic groups of workers as crowd agents to support continuous tasks, but have not yet considered how such agents are able to learn over time. We show, using a real-time gaming setting, that crowd agents can learn over time, and `remember' by passing strategies from one generation of workers to the next, despite high turnover rates in the workers comprising them. We conclude with a discussion of future research directions for crowd memory and learning.
Leading the Collective: Social Capital and the Development of Leaders in Core-Periphery Organizations

Wikipedia and open source software projects have been cited as canonical examples of collectively intelligent organizations. Both organizations rely on large crowds of contributors to create knowledge goods. The crowds that emerge in both cases are not flat, but form a core-periphery network in which a few leaders contribute a large portion of the production and coordination work. This paper explores the social network processes by which leaders emerge from crowd-based organizations.
Biographical social networks on Wikipedia: a cross-cultural study of links that made history

It is arguable whether history is made by great men and women or vice versa, but undoubtably social connections shape history. Analysing Wikipedia, a global collective memory place, we aim to understand how social links are recorded across cultures. Starting with the set of biographies in the English Wikipedia we focus on the networks of links between these biographical articles on the 15 largest language Wikipedias. We detect the most central characters in these networks and point out culture-related peculiarities. Furthermore, we reveal remarkable similarities between distinct groups of language Wikipedias and highlight the shared knowledge about connections between persons across cultures.
Statistical properties of avalanches in networks.

We characterize the distributions of size and duration of avalanches propagating in complex networks. By an avalanche we mean the sequence of events initiated by the externally stimulated excitation of a network node, which may, with some probability, then stimulate subsequent excitations of the nodes to which it is connected, resulting in a cascade of excitations. This type of process is relevant to a wide variety of situations, including neuroscience, cascading failures on electrical power grids, and epidemiology. We find that the statistics of avalanches can be characterized in terms of the largest eigenvalue and corresponding eigenvector of an appropriate adjacency matrix that encodes the structure of the network. By using mean-field analyses, previous studies of avalanches in networks have not considered the effect of network structure on the distribution of size and duration of avalanches. Our results apply to individual networks (rather than network ensembles) and provide expressions for the distributions of size and duration of avalanches starting at particular nodes in the network. These findings might find application in the analysis of branching processes in networks, such as cascading power grid failures and critical brain dynamics. In particular, our results show that some experimental signatures of critical brain dynamics (i.e., power-law distributions of size and duration of neuronal avalanches) are robust to complex underlying network topologies.