nils-herrmann/clean_scirepeval huggingface dataset:
Chiral quantum walks

Given its importance to many other areas of physics, from condensed matter physics to thermodynamics, time-reversal symmetry has had relatively little influence on quantum information science. Here we develop a network-based picture of time-reversal theory, classifying Hamiltonians and quantum circuits as time-symmetric or not in terms of the elements and geometries of their underlying networks. Many of the typical circuits of quantum information science are found to exhibit time-asymmetry. Moreover, we show that time-asymmetry in circuits can be controlled using local gates only, and can simulate time-asymmetry in Hamiltonian evolution. We experimentally implement a fundamental example in which controlled time-reversal asymmetry in a palindromic quantum circuit leads to near-perfect transport. Our results pave the way for using time-symmetry breaking to control coherent transport, and imply that time-asymmetry represents an omnipresent yet poorly understood effect in quantum information science.
Second Life Physics: Virtual, real, or surreal?

Science teaching detached itself from Reality and became restricted to the classrooms and textbooks with their abundance of standardized and repetitive exercises, while students keep their own alternative conceptions. Papert, displeased with this inefficient learning process, as early as 1980 championed physics microworlds, where students could experience a variety of laws of motion, from Aristotle to Newton and Einstein or even “new” laws invented by the students themselves. While often mistakenly seen as a game, Second Life (SL), the online 3D virtual world hosted by Linden Lab, imposes essentially no rules on the residents beyond reasonable restrictions on improper behavior and the physical rules that guarantee its similitude to the real world. As a consequence SL qualifies itself as an environment for personal discovery and exploration as proposed by constructivist theories. The physical laws are implemented through the well-known physics engine Havok, whose design aims to provide game-players a consistent, “realistic” environment. Going beyond Reality limits, however, the Havok User Guide explicitly encourages developers to use several tricks to cheat the simulator in order to make games funnier or easier to play. As it is shown in this study, SL Physics is unexpectedly neither the Galilean/Newtonian “idealized” Physics nor a real world Physics virtualization, intentionally diverging from reality in such a way that it could be called hyper-real. As a matter of fact, if some of its features make objects behave “more realistically than real” ones, certain quantities, e.g. energy, have a totally different meaning in SL as compared to Physics. Far from considering it as a problem, however, the author argues that its hyper-reality may be a golden teaching opportunity, allowing surreal Physics simulations and epistemologically rich classroom discussions around the “what is a physical law?” issue, in good accordance with Papert’s never-implemented proposal.
Big Data as a Mediator in Science Teaching: A Proposal

We live in a digital world that, in 2010, crossed the mark of one zettabyte data. This huge amount of data processed on computers extremely fast with optimized techniques allows one to find insights in new and emerging types of data and content and to answer questions that were previously considered beyond reach. This is the idea of Big Data. Google now offers the Google Correlate analysis public tool that, from a search term or a series of temporal or regional data, provides a list of queries on Google whose frequencies follow patterns that best correlate with the data, according to the Pearson determination coefficient R2. Of course, "correlation does not imply causation." We believe, however, that there is potential for these big data tools to find unexpected correlations that may serve as clues to interesting phenomena, from the pedagogical and even scientific point of view. As far as we know, this is the first proposal for the use of Big Data in Science Teaching, of constructionist character, taking as mediators the computer and the public and free tools such as Google Correlate. It also has an epistemological bias, not being merely a training in computational infrastructure or predictive analytics, but aiming at providing students a better understanding of physical concepts, such as phenomena, observation, measurement, physical laws, theory, and causality. With it, they would be able to become good Big Data specialists, the so needed "data scientists" to solve the challenges of Big Data.
Networks of military alliances, wars, and international trade

The incidence of interstate wars has dropped dramatically over time: The number of wars per pair of countries per year from 1950 to 2000 was roughly a 10th as high as it was from 1820 to 1949. This significant decrease in the frequency of wars correlates with a substantial increase in the number of military alliances per country and the stability of those alliances. We show that one possible explanation of this is an accompanying expansion of international trade. Increased trade decreases countries’ incentives to attack each other and increases their incentives to defend each other, leading to a stable and peaceful network of military and trade alliances that is consistent with observed data. We investigate the role of networks of alliances in preventing (multilateral) interstate wars. We first show that, in the absence of international trade, no network of alliances is peaceful and stable. We then show that international trade induces peaceful and stable networks: Trade increases the density of alliances so that countries are less vulnerable to attack and also reduces countries’ incentives to attack an ally. We present historical data on wars and trade showing that the dramatic drop in interstate wars since 1950 is paralleled by a densification and stabilization of trading relationships and alliances. Based on the model we also examine some specific relationships, finding that countries with high levels of trade with their allies are less likely to be involved in wars with any other countries (including allies and nonallies), and that an increase in trade between two countries correlates with a lower chance that they will go to war with each other.
The Dawn of Open Access to Phylogenetic Data

The scientific enterprise depends critically on the preservation of and open access to published data. This basic tenet applies acutely to phylogenies (estimates of evolutionary relationships among species). Increasingly, phylogenies are estimated from increasingly large, genome-scale datasets using increasingly complex statistical methods that require increasing levels of expertise and computational investment. Moreover, the resulting phylogenetic data provide an explicit historical perspective that critically informs research in a vast and growing number of scientific disciplines. One such use is the study of changes in rates of lineage diversification (speciation – extinction) through time. As part of a meta-analysis in this area, we sought to collect phylogenetic data (comprising nucleotide sequence alignment and tree files) from 217 studies published in 46 journals over a 13-year period. We document our attempts to procure those data (from online archives and by direct request to corresponding authors), and report results of analyses (using Bayesian logistic regression) to assess the impact of various factors on the success of our efforts. Overall, complete phylogenetic data for of these studies are effectively lost to science. Our study indicates that phylogenetic data are more likely to be deposited in online archives and/or shared upon request when: (1) the publishing journal has a strong data-sharing policy; (2) the publishing journal has a higher impact factor, and; (3) the data are requested from faculty rather than students. Importantly, our survey spans recent policy initiatives and infrastructural changes; our analyses indicate that the positive impact of these community initiatives has been both dramatic and immediate. Although the results of our study indicate that the situation is dire, our findings also reveal tremendous recent progress in the sharing and preservation of phylogenetic data.
Second Life as a Platform for Physics Simulations and Microworlds: An Evaluation

Often mistakenly seen as a game, the online 3D immersive virtual world Second Life (SL) is itself a huge and sophisticated simulator of an entire Earthlike world. Differently from other metaverses where physical laws are not seriously taken into account, objects created in SL are automatically controlled by a powerful physics engine software. Despite that, it has been used mostly as a mere place for exploration and inquiry, with emphasis on group interaction. This article reports on a study conducted to evaluate the SL environment as a platform for physical simulations and microworlds. It begins by discussing a few relevant features of SL and a few differences found between it and traditional simulators e.g. Modellus. Finally, the SL environment as a platform for physical simulations and microworlds is evaluated. Some concrete examples of simulations in SL, including two of our own authorship, will be presented briefly in order to clarify and enrich both discussion and analysis. However, implementation of simulations in SL is not without drawbacks like the lack of experience many teachers have with programming and the differences found between SL Physics and Newtonian Physics. Despite of that, findings suggest it may possible for teachers to overcome these obstacles.
Optical detection and analysis of Pictor A's jet

New images from the Hubble Space Telescope of the FRII radio galaxy Pictor A reveal a number of jet knot candidates which coincide with previously detected radio and x-ray knots. Previous observations in x-ray and radio bands show the entire jet to be 1.9' long, with interesting variability, but an optical component was previously unknown. The discovered optical component is faint, and knot candidates must be teased out from a bright host galaxy. Using three broadband filters, we extract knot fluxes and upper-bounds on the flux for multiple knot candidates at wavelengths of 1600nm, 814nm and 475nm. We find that the data suggest that localized particle re-accleration events followed by synchrotron emission could explain the observed knot candidates, but those electrons could not supply enough x-ray flux to match prior observations. Our data provide key evidence suggesting a second, higher energy electron population which was previously hypothesized, but could not be confirmed.
When Politicians Talk: Assessing Online Conversational Practices of Political Parties on Twitter

Assessing political conversations in social media requires a deeper understanding of the underlying practices and styles that drive these conversations. In this paper, we present a computational approach for assessing online conversational practices of political parties. Following a deductive approach, we devise a number of quantitative measures from a discussion of theoretical constructs in sociological theory. The resulting measures make different - mostly qualitative - aspects of online conversational practices amenable to computation. We evaluate our computational approach by applying it in a case study. In particular, we study online conversational practices of German politicians on Twitter during the German federal election 2013. We find that political parties share some interesting patterns of behavior, but also exhibit some unique and interesting idiosyncrasies. Our work sheds light on (i) how complex cultural phenomena such as online conversational practices are amenable to quantification and (ii) the way social media such as Twitter are utilized by political parties.
Complex contagion process in spreading of online innovation

Diffusion of innovation can be interpreted as a social spreading phenomenon governed by the impact of media and social interactions. Although these mechanisms have been identified by quantitative theories, their role and relative importance are not entirely understood, as empirical verification has so far been hindered by the lack of appropriate data. Here we analyse a dataset recording the spreading dynamics of the world's largest Voice over Internet Protocol service to empirically support the assumptions behind models of social contagion. We show that the rate of spontaneous service adoption is constant, the probability of adoption via social influence is linearly proportional to the fraction of adopting neighbours, and the rate of service termination is time-invariant and independent of the behaviour of peers. By implementing the detected diffusion mechanisms into a dynamical agent-based model, we are able to emulate the adoption dynamics of the service in several countries worldwide. This approach enables us to make medium-term predictions of service adoption and disclose dependencies between the dynamics of innovation spreading and the socio-economic development of a country.
Absorption and injection models for open time-dependent quantum systems.

In the time-dependent simulation of pure states dealing with transport in open quantum systems, the initial state is located outside of the active region of interest. Using the superposition principle and the analytical knowledge of the free time evolution of such a state outside the active region, together with absorbing layers and remapping, a model for a very significant reduction of the computational burden associated with the numerical simulation of open time-dependent quantum systems is presented. The model is specially suited to study (many-particle and high-frequency effects) quantum transport, but it can also be applied to any other research field where the initial time-dependent pure state is located outside of the active region. From numerical simulations of open quantum systems described by the (effective mass) Schrödinger and (atomistic) tight-binding equations, a reduction of the computational burden of about two orders of magnitude for each spatial dimension of the domain with a negligible error is presented.
Influence Spread in Social Networks: A Study via a Fluid Limit of the Linear Threshold Model

Threshold based models have been widely used in characterizing collective behavior on social networks. An individual's threshold indicates the minimum level of influence that must be exerted, by other members of the population engaged in some activity, before the individual will join the activity. In this work, we begin with a homogeneous version of the Linear Threshold model proposed by Kempe et al. in the context of viral marketing, and generalize this model to arbitrary threshold distributions. We show that the evolution can be modeled as a discrete time Markov chain, and, by using a certain scaling, we obtain a fluid limit that provides an ordinary differential equation model (o.d.e.). We find that the threshold distribution appears in the o.d.e. via its hazard rate function. We demonstrate the accuracy of the o.d.e. approximation and derive explicit expressions for the trajectory of influence under the uniform threshold distribution. Also, for an exponentially distributed threshold, we show that the fluid dynamics are equivalent to the well-known SIR model in epidemiology. We also numerically study how other hazard functions (obtained from the Weibull and loglogistic distributions) provide qualitative different characteristics of the influence evolution, compared to traditional epidemic models, even in a homogeneous setting. We finally show how the model can be extended to a setting with multiple communities and conclude with possible future directions.
125 GeV Higgs from a chiral techniquark model

We consider the spin-zero spectrum of a strongly coupled gauge theory. In particular, we focus on the dynamical mass of the isosinglet scalar resonance in the presence of a four-fermion interaction external to the gauge dynamics. This is motivated by the extended technicolor framework for dynamical electroweak symmetry breaking. Applying the large-$N$ limit, we sum all the leading-order contributions, and find that the corrections to the mass of the isosinglet scalar resonance can be large, potentially reducing its value from ${\cal O}(1)$ TeV to the observed value of 125 GeV.
Interactions of Cultures and Top People of Wikipedia from Ranking of 24 Language Editions

Wikipedia is a huge global repository of human knowledge that can be leveraged to investigate interwinements between cultures. With this aim, we apply methods of Markov chains and Google matrix for the analysis of the hyperlink networks of 24 Wikipedia language editions, and rank all their articles by PageRank, 2DRank and CheiRank algorithms. Using automatic extraction of people names, we obtain the top 100 historical figures, for each edition and for each algorithm. We investigate their spatial, temporal, and gender distributions in dependence of their cultural origins. Our study demonstrates not only the existence of skewness with local figures, mainly recognized only in their own cultures, but also the existence of global historical figures appearing in a large number of editions. By determining the birth time and place of these persons, we perform an analysis of the evolution of such figures through 35 centuries of human history for each language, thus recovering interactions and entanglement of cultures over time. We also obtain the distributions of historical figures over world countries, highlighting geographical aspects of cross-cultural links. Considering historical figures who appear in multiple editions as interactions between cultures, we construct a network of cultures and identify the most influential cultures according to this network.
Option Pricing in a Dynamic Variance-Gamma Model

We present a discrete time stochastic volatility model in which the conditional distribution of the logreturns is a Variance-Gamma, that is a normal variance-mean mixture with Gamma mixing density. We assume that the Gamma mixing density is time varying and follows an affine Garch model, trying to capture persistence of volatility shocks and also higher order conditional dynamics in a parsimonious way. We select an equivalent martingale measure by means of the conditional Esscher transform as in Buhlmann et al. (1996) and show that this change of measure leads to a similar dynamics of the mixing distribution. The model admits a recursive procedure for the computation of the characteristic function of the terminal logprice, thus allowing semianalytical pricing as in Heston and Nandi (2000). From an empirical point of view, we check the ability of this model to calibrate SPX option data and we compare it with the Heston and Nandi (2000) model and with the Christoffersen, Heston and Jacobs (2006) model, that is based on Inverse Gaussian innovations. Moreover, we provide a detailed comparison with several variants of the Heston and Nandi model that shows the superiority of the Variance-Gamma innovations also from the point of view of historical MLE estimation.
Toward an understanding of foreground emission in the BICEP2 region

BICEP2 has reported the detection of a degree-scale B-mode polarization pattern in the Cosmic Microwave Background (CMB) and has interpreted the measurement as evidence for primordial gravitational waves. Motivated by the profound importance of the discovery of gravitational waves from the early Universe, we examine to what extent a combination of Galactic foregrounds and lensed E-modes could be responsible for the signal. We reanalyze the BICEP2 results and show that the 100 ×150 GHz and 150 ×150 GHz data are consistent with a cosmology with r=0.2 and negligible foregrounds, but also with a cosmology with r=0 and a significant dust polarization signal. We give independent estimates of the dust polarization signal in the BICEP2 region using a number of different approaches: (1) data-driven models based on Planck 353 GHz intensity, polarization fractions inferred from the same Planck data used by the BICEP2 team but corrected for CMB and CIB contributions, and polarization angles from starlight polarization data or the Planck sky model; (2) the same set of pre-Planck models used by the BICEP2 team but taking into account the higher polarization fractions observed in the CMB- and CIB-corrected map; (3) a measurement of neutral hydrogen gas column density NHI in the BICEP2 region combined with an extrapolation of a relation between HI column density and dust polarization derived by Planck; and (4) a dust polarization map based on digitized Planck data, which we only use as a final cross-check. While these approaches are consistent with each other, the expected amplitude of the dust polarization power spectrum remains uncertain by about a factor of three. The lower end of the prediction leaves room for a primordial contribution, but at the higher end the dust in combination with the standard CMB lensing signal could account for the BICEP2 observations, without requiring the existence of primordial gravitational waves. By measuring the cross-correlations between the pre-Planck templates used in the BICEP2 analysis and between different versions of a data-based template, we emphasize that cross-correlations between models are very sensitive to noise in the polarization angles and that measured cross-correlations are likely underestimates of the contribution of foregrounds to the map. These results suggest that BICEP1 and BICEP2 data alone cannot distinguish between foregrounds and a primordial gravitational wave signal, and that future Keck Array observations at 100 GHz and Planck observations at higher frequencies will be crucial to determine whether the signal is of primordial origin.
A fast current-driven instability in relativistic collisionless shocks

We report here on a fast current-driven instability at relativistic collisionless shocks, triggered by the perpendicular current carried by the supra-thermal particles as they gyrate around the background magnetic field in the shock precursor. We show that this instability grows faster than any other instability studied so far in this context, and we argue that it is likely to shape the physics of the shock and of particle acceleration in a broad parameter range.
Insights from the Wikipedia Contest (IEEE Contest for Data Mining 2011)

The Wikimedia Foundation has recently observed that newly joining editors on Wikipedia are increasingly failing to integrate into the Wikipedia editors' community, i.e. the community is becoming increasingly harder to penetrate. To sustain healthy growth of the community, the Wikimedia Foundation aims to quantitatively understand the factors that determine the editing behavior, and explain why most new editors become inactive soon after joining. As a step towards this broader goal, the Wikimedia foundation sponsored the ICDM (IEEE International Conference for Data Mining) contest for the year 2011. 
The objective for the participants was to develop models to predict the number of edits that an editor will make in future five months based on the editing history of the editor. Here we describe the approach we followed for developing predictive models towards this goal, the results that we obtained and the modeling insights that we gained from this exercise. In addition, towards the broader goal of Wikimedia Foundation, we also summarize the factors that emerged during our model building exercise as powerful predictors of future editing activity.