nils-herrmann/clean_scirepeval huggingface dataset:
Paradigms in Physics Education Research

In this paper, we describe two paradigms in physics education research (PER): recurrence-oriented and case-oriented PER. We connect theory on research methodologies in the social sciences to interviews with physics education researchers and examples of published PER to articulate the specific assumptions of recurrence- and case-oriented PER. We show that the different assumptions made in these two paradigms bear out in specific aims and research designs taken up by physics education researchers. In particular, recurrence-oriented research seeks reproducible, representative patterns and relationships; human behavior is modeled as governed by lawful (albeit probabilistic) relationships. Case-oriented research, in contrast, seeks to refine and develop theory by linking that theory to cases; human action is seen as shaped by the meanings that participants make of their local environments. We describe a third approach - question-oriented research - in which researchers take up both the recurrence- and case-oriented research paradigms.
The Origin of the Dynamical Quantum Non-locality

Non-locality is one of the hallmarks of quantum mechanics and is responsible for paradigmatic features such as entanglement[1] and the Aharonov-Bohm effect[2,3]. Non-locality comes in two "flavours": a \emph{kinematic non-locality}-- arising from the structure of the Hilbert space--[4-6] and a \emph{dynamical non-locality}-- arising from the quantum equations of motion--[2,3,5,6]. Despite of its main role in quantum information processing, kinematic non-locality is unable to induce any change in the probability distributions, so that the "action-at-a-distance" cannot manifest[5,6]. Conversely, dynamical non-locality does create explicit changes in probability, though in a "causality-preserving" manner[6,7]. Recently, the origin of the kinematic non-locality was related to the uncertainty principle[8], here we trace the origin of dynamical non-locality to the superposition principle. This relation adds to the more fundamental understanding of the nature of quantum dynamics and allows us to establish and identify how the uncertainty and the superposition principles determine the non-local character of the outcome of a quantum measurement. Thus, dynamical non-locality emerges as the responsible of the breakdown of the dynamical classical realism[9] and therefore, as key feature in the classical-quantum transition. Most importantly, being based on group theoretical and path integral formulations, our formulation admits immediate generalizations and extensions to, e.g., quantum field theory.
A data-driven study of influences in Twitter communities

This paper presents a quantitative study of Twitter, one of the most popular micro-blogging services, from the perspective of user influence. We crawl several datasets from the most active communities on Twitter and obtain 20.5 million user profiles, along with 420.2 million directed relations and 105 million tweets among the users. User influence scores are obtained from influence measurement services, Klout and PeerIndex. Our analysis reveals interesting findings of the structural properties of Twitter communities. Most importantly, we observe that whether a user retweets a message is strongly influenced by the first of his followees who posted that message. To capture such an effect, we propose the first influencer (FI) information diffusion model and show through extensive evaluation that compared to the widely adopted independent cascade model, the FI model is more stable and more accurate in predicting influence spreads in Twitter communities.
Macro photography with a tablet: applications on Science Teaching

In this work we present a simple way to get Macro photography (enlarged photographs) using a tablet or phone. We initially discuss the technique, which is essentially the accommodation of a drop of water on the camera lens. Next, we explore some applications to science teaching in primary and secondary levels. As discussed in the text, the simplicity and power of the technique may make it a good teaching tool for use in various disciplines such as Science, Biology and Physics.
Constraints on neutrino density and velocity isocurvature modes from WMAP-9 data

We nd that in the NDI and NVI cases larger isocurvature fractions are allowed than in the corresponding models with CDI. For uncorrelated perturbations, the upper limit to the primordial NDI (NVI) fraction is 24% (20%) at k = 0:002 Mpc 1 and 28% (16%) at k = 0:01 Mpc 1 . For maximally correlated (anticorrelated) perturbations, the upper limit to the NDI fraction is 3.0% (0.9%). The nonadiabatic contribution to the CMB temperature variance can be as large as 10% ({13%) for the NDI (NVI) modes. Bayesian model comparison favors pure adiabatic initial mode over the mixed primordial adiabatic and NDI, NVI, or CDI perturbations. At best, the betting odds for a mixed model (uncorrelated NDI) are 1:3.4 compared to the pure adiabatic model. For the phenomenological generally correlated mixed models the odds are about 1:100, whereas the slow-roll approach leads to 1:13 (NDI) and 1:51 (CDI).
First observation of the Λ(1405) line shape in electroproduction

We report the first observation of the line shape of the {Lambda}(1405) from electroproduction, and show that it is not a simple Breit-Wigner resonance. Electroproduction of K{sup +}{Lambda}(1405) off the proton was studied by using data from CLAS at Jefferson Lab in the range 1.0<Q{sup 2}<3.0 (GeV/c){sup 2}. The analysis utilized the decay channels {Sigma}{sup +} of the {Lambda}(1405) and p{pi}{sup 0} of the {Sigma}{sup +}. Neither the standard Particle Data Group resonance parameters, nor free parameters fitting to a single Breit-Wigner resonance represent the line shape. In our fits, the line shape corresponds approximately to predictions of a two-pole meson-baryon picture of the {Lambda}(1405), with a lower mass pole near 1368 MeV/c{sup 2} and a higher mass pole near 1423 MeV/c{sup 2}. Furthermore, with increasing photon virtuality the mass distribution shifts toward the higher mass pole.
A Divergence-Free Method to Extract Observables from Meson Correlation Functions

Correlation functions provide information on the properties of mesons in vacuum and of hot nuclear matter. In this Letter, we present a new method to derive a well-defined spectral representation for correlation functions. Combining this method with the quark gap equation and the inhomogeneous Bethe-Salpeter equation in the rainbow-ladder approximation, we calculate in-vacuum masses of light mesons and the electrical conductivity of the quark-gluon plasma. The analysis can be extended to other observables of strong-interaction systems.
ROTATIONAL SYNCHRONIZATION MAY ENHANCE HABITABILITY FOR CIRCUMBINARY PLANETS: KEPLER BINARY CASE STUDIES

We report a mechanism capable of reducing (or increasing) stellar activity in binary stars, thereby potentially enhancing (or destroying) circumbinary habitability. In single stars, stellar aggression toward planetary atmospheres causes mass-loss, which is especially detrimental for late-type stars, because habitable zones are very close and activity is long lasting. In binaries, tidal rotational breaking reduces magnetic activity, thus reducing harmful levels of X-ray and ultraviolet (XUV) radiation and stellar mass-loss that are able to erode planetary atmospheres. We study this mechanism for all confirmed circumbinary (p-type) planets. We find that main sequence twins provide minimal flux variation and in some cases improved environments if the stars rotationally synchronize within the first Gyr. Solar-like twins, like Kepler 34 and Kepler 35, provide low habitable zone XUV fluxes and stellar wind pressures. These wide, moist, habitable zones may potentially support multiple habitable planets. Solar-type stars with lower mass companions, like Kepler 47, allow for protected planets over a wide range of secondary masses and binary periods. Kepler 38 and related binaries are marginal cases. Kepler 64 and analogs have dramatically reduced stellar aggression due to synchronization of the primary, but are limited by the short lifetime. Kepler 16 appears to be inhospitable to planets due to extreme XUV flux. These results have important implications for estimates of the number of stellar systems containing habitable planets in the Galaxy and allow for the selection of binaries suitable for follow-up searches for habitable planets.
Attention and visibility in an information-rich world

As the rate of content production grows, we must make a staggering number of daily decisions about what information is worth acting on. For any flourishing online social media system, users can barely keep up with the new content shared by friends. How does the user-interface design help or hinder users' ability to find interesting content? We analyze the choices people make about which information to propagate on the social media sites Twitter and Digg. We observe regularities in behavior which can be attributed directly to cognitive limitations of humans, resulting from the different visibility policies of each site. We quantify how people divide their limited attention among competing sources of information, and we show how the user-interface design can mediate information spread.
Efficient Reinforcement Learning in Deterministic Systems with Value Function Generalization

We consider the problem of reinforcement learning over episodes of a finite-horizon deterministic system and as a solution propose optimistic constraint propagation (OCP), an algorithm designed to synthesize efficient exploration and value function generalization. We establish that when the true value function lies within a given hypothesis class, OCP selects optimal actions over all but at most K episodes, where K is the eluder dimension of the given hypothesis class. We establish further efficiency and asymptotic performance guarantees that apply even if the true value function does not lie in the given hypothesis class, for the special case where the hypothesis class is the span of pre-specified indicator functions over disjoint sets. We also discuss the computational complexity of OCP and present computational results involving two illustrative examples.
Dynamic AdS/QCD and the spectrum of walking gauge theories

We present a simple AdS/QCD model in which the formation of the chiral condensate is dynamically determined. The gauge dynamics is input through the running of the quark bilinear’s anomalous dimension, ?. The condensate provides a dynamically generated infrared wall in the computation of mesonic bound state masses and decay constants. As an example, we use the model, with perturbative computations of the running of ?, to study SU(3) gauge theory with a continuous number of quark flavors, Nf. We follow the behavior of the spectrum as we approach the conformal window through a walking gauge theory regime. We show such walking theories display a Berezinskii–Kosterlitz–Thoules phase transition, with Miransky scaling, as one approaches the edge of the conformal window at the critical value of Nf. We show that these walking theories possess an enhanced quark condensate, a light Higgs-like excitation, and argue that the nonperturbative contribution to S falls to zero. We also study the deformation of the Berezinskii–Kosterlitz–Thoules transition when the quarks have a current mass, which may be of use for understanding lattice simulations of walking theories.
The Pin-Bang Theory: Discovering The Pinterest World

Pinterest is an image-based online social network, which was launched in the year 2010 and has gained a lot of traction, ever since. Within 3 years, Pinterest has attained 48.7 million unique users. This stupendous growth makes it interesting to study Pinterest, and gives rise to multiple questions about it's users, and content. We characterized Pinterest on the basis of large scale crawls of 3.3 million user profiles, and 58.8 million pins. In particular, we explored various attributes of users, pins, boards, pin sources, and user locations, in detail and performed topical analysis of user generated textual content. The characterization revealed most prominent topics among users and pins, top image sources, and geographical distribution of users on Pinterest. We then investigated this social network from a privacy and security standpoint, and found traces of malware in the form of pin sources. Instances of Personally Identifiable Information (PII) leakage were also discovered in the form of phone numbers, BBM (Blackberry Messenger) pins, and email addresses. Further, our analysis demonstrated how Pinterest is a potential venue for copyright infringement, by showing that almost half of the images shared on Pinterest go uncredited. To the best of our knowledge, this is the first attempt to characterize Pinterest at such a large scale.
Formal multidimensional integrals, stuffed maps, and topological recursion

We show that the large N expansion in the multi-trace 1 formal hermitian matrix model is governed by the topological recursion of [Eynard and Orantin, 2007] with initial conditions. In terms of a 1d gas of eigenvalues, this model includes - on top of the squared Vandermonde - multilinear interactions of any order between the eigenvalues. In this problem, the initial data (W10,W20) of the topological recursion is characterized: for W10, by a non-linear, non-local Riemann-Hilbert problem on a discontinuity locus to determine ; for W20, by a related but linear, non-local Riemann-Hilbert problem on the discontinuity locus. In combinatorics, this model enumerates discrete surfaces (maps) whose elementary 2-cells can have any topology - W10 being the generating series of disks and W20 that of cylinders. In particular, by substitution one may consider maps whose elementary cells are themselves maps, for which we propose the name "stuffed maps". In a sense, our results complete the program of the "moment method" initiated in the 90s to compute the formal 1/N in the one hermitian matrix model.
Mathematical Formulation of Multilayer Networks

A network representation is useful for describing the structure of a large variety of complex systems. However, most real and engineered systems have multiple subsystems and layers of connectivity, and the data produced by such systems are very rich. Achieving a deep understanding of such systems necessitates generalizing ‘‘traditional’’ network theory, and the newfound deluge of data now makes it possible to test increasingly general frameworks for the study of networks. In particular, although adjacency matrices are useful to describe traditional single-layer networks, such a representation is insufficient for the analysis and description of multiplex and time-dependent networks. One must therefore develop a more general mathematical framework to cope with the challenges posed by multilayer complex systems. In this paper, we introduce a tensorial framework to study multilayer networks, and we discuss the generalization of several important network descriptors and dynamical processes—including degree centrality, clustering coefficients, eigenvector centrality, modularity, von Neumann entropy, and diffusion—for this framework. We examine the impact of different choices in constructing these generalizations, and we illustrate how to obtain known results for the special cases of single-layer and multiplex networks. Our tensorial approach will be helpful for tackling pressing problems in multilayer complex systems, such as inferring who is influencing whom (and by which media) in multichannel social networks and developing routing techniques for multimodal transportation systems.
Semi-analytic galaxy formation in f(R)-gravity cosmologies

Modifications of the equations of general relativity at large distances offer one possibility to explain the observed properties of our Universe without invoking a cosmological constant. Numerous proposals for such modified gravity cosmologies exist, but often their consequences for structure formation in the non-linear sector are not yet accurately known. In this work, we employ high-resolution numerical simulations of f(R)-gravity models coupled with a semi-analytic model (SAM) for galaxy formation to obtain detailed predictions for the evolution of galaxy properties. The f(R)-gravity models imply the existence of a `fifth-force', which is however locally suppressed, preserving the successes of general relativity on solar system scales. We show that dark matter haloes in f(R)-gravity models are characterized by a modified virial scaling with respect to the LCDM scenario, reflecting a higher dark matter velocity dispersion at a given mass. This effect is taken into account in the SAM by an appropriate modification of the mass--temperature relation. We find that the statistical properties predicted for galaxies (such as the stellar mass function and the cosmic star formation rate) in f(R)-gravity show generally only very small differences relative to LCDM, smaller than the dispersion between the results of different SAM models, which can be viewed as a measure of their systematic uncertainty. We also demonstrate that galaxy bias is not able to disentangle between f(R)-gravity and the standard cosmological scenario. However, f(R)-gravity imprints modifications in the linear growth rate of cosmic structures at large scale, which can be recovered from the statistical properties of large galaxy samples.
MOVING OBJECTS IN THE HUBBLE ULTRA DEEP FIELD

We identify proper motion objects in the Hubble Ultra Deep Field (UDF) using the optical data from the original UDF program in 2004 and the near-infrared data from the 128 orbit UDF 2012 campaign. There are 12 sources brighter than I = 27 mag that display >3σ significant proper motions. We do not find any proper motion objects fainter than this magnitude limit. Combining optical and near-infrared photometry, we model the spectral energy distribution of each point-source using stellar templates and state-of-the-art white dwarf models. For I ⩽ 27 mag, we identify 23 stars with K0–M6 spectral types and two faint blue objects that are clearly old, thick disk white dwarfs. We measure a thick disk white dwarf space density of 0.1–1.7 × 10−3 pc−3 from these two objects. There are no halo white dwarfs in the UDF down to I = 27 mag. Combining the Hubble Deep Field North, South, and the UDF data, we do not see any evidence for dark matter in the form of faint halo white dwarfs, and the observed population of white dwarfs can be explained with the standard Galactic models.
An electromagnetic analogue of gravitational wave memory

We present an electromagnetic analogue of gravitational wave memory. That is, we consider what change has occurred to a detector of electromagnetic radiation after the wave has passed. Rather than a distortion in the detector, as occurs in the gravitational wave case, we find a residual velocity (a ‘kick’) to the charges in the detector. In analogy with the two types of gravitational wave memory (‘ordinary’ and ‘nonlinear’) we find two types of electromagnetic kick.
The relativistic Black-Scholes model

The Black-Scholes equation, after a certain coordinate transformation, is equivalent to the heat equation. On the other hand the relativistic extension of the latter, the telegraphers equation, can be derived from the Euclidean version of the Dirac equation. Therefore, the relativistic extension of the Black-Scholes model follows from relativistic quantum mechanics quite naturally. We investigate this particular model for the case of European vanilla options. Due to the notion of locality incorporated in this way, one finds that the volatility frown-like effect appears when comparing to the original Black-Scholes model.
Physics at the CLIC e+e- Linear Collider -- Input to the Snowmass process 2013

This paper summarizes the physics potential of the CLIC high-energy e+e- linear collider. It provides input to the Snowmass 2013 process for the energy-frontier working groups on The Higgs Boson (HE1), Precision Study of Electroweak Interactions (HE2), Fully Understanding the Top Quark (HE3), as well as The Path Beyond the Standard Model -- New Particles, Forces, and Dimensions (HE4). It is accompanied by a paper describing the CLIC accelerator study, submitted to the Frontier Capabilities group of the Snowmass process.
Kernel Adaptive Metropolis-Hastings

A Kernel Adaptive Metropolis-Hastings algorithm is introduced, for the purpose of sampling from a target distribution with strongly nonlinear support. The algorithm embeds the trajectory of the Markov chain into a reproducing kernel Hilbert space (RKHS), such that the feature space covariance of the samples informs the choice of proposal. The procedure is computationally efficient and straightforward to implement, since the RKHS moves can be integrated out analytically: our proposal distribution in the original space is a normal distribution whose mean and covariance depend on where the current sample lies in the support of the target distribution, and adapts to its local covariance structure. Furthermore, the procedure requires neither gradients nor any other higher order information about the target, making it particularly attractive for contexts such as Pseudo-Marginal MCMC. Kernel Adaptive Metropolis-Hastings outperforms competing fixed and adaptive samplers on multivariate, highly nonlinear target distributions, arising in both real-world and synthetic examples.
Entangling Mobility and Interactions in Social Media

Daily interactions naturally define social circles. Individuals tend to be friends with the people they spend time with and they choose to spend time with their friends, inextricably entangling physical location and social relationships. As a result, it is possible to predict not only someone’s location from their friends’ locations but also friendship from spatial and temporal co-occurrence. While several models have been developed to separately describe mobility and the evolution of social networks, there is a lack of studies coupling social interactions and mobility. In this work, we introduce a model that bridges this gap by explicitly considering the feedback of mobility on the formation of social ties. Data coming from three online social networks (Twitter, Gowalla and Brightkite) is used for validation. Our model reproduces various topological and physical properties of the networks not captured by models uncoupling mobility and social interactions such as: i) the total size of the connected components, ii) the distance distribution between connected users, iii) the dependence of the reciprocity on the distance, iv) the variation of the social overlap and the clustering with the distance. Besides numerical simulations, a mean-field approach is also used to study analytically the main statistical features of the networks generated by a simplified version of our model. The robustness of the results to changes in the model parameters is explored, finding that a balance between friend visits and long-range random connections is essential to reproduce the geographical features of the empirical networks.