nils-herrmann/clean_scirepeval huggingface dataset:
Quantum phase transition in Heisenberg-Kitaev model

We explore the nature of the quantum phase transition between a magnetically ordered state with collinear spin pattern and a gapless $Z_2$ spin liquid in the Heisenberg-Kitaev model. We construct a slave particle mean field theory for the Heisenberg-Kitaev model in terms of complex fermionic spinons. It is shown that this theory, formulated in the appropriate basis, is capable of describing the Kitaev spin liquid as well as the transition between the gapless $Z_2$ spin liquid and the so-called stripy antiferromagnet. In particular, within a mean field theory, we have a discontinuous transition from the $Z_2$ spin liquid to the stripy antiferromagnet. We argue, however, that subtle spinon confinement effects, associated with the instability of gapped U(1) spin liquid in two spatial dimensions, are playing an important role at the transition. The possibility of an exotic continuous transition is briefly addressed.
A Meta-Analysis of State-of-the-Art Electoral Prediction From Twitter Data

Electoral prediction from Twitter data is an appealing research topic. It seems relatively straightforward and the prevailing view is overly optimistic. This is problematic because while simple approaches are assumed to be good enough, core problems are not addressed. Thus, this article aims to (1) provide a balanced and critical review of the state of the art; (2) cast light on the presume predictive power of Twitter data; and (3) propose some considerations to push forward the field. Hence, a scheme to characterize Twitter prediction methods is proposed. It covers every aspect from data collection to performance evaluation, through data processing and vote inference. Using that scheme, prior research is analyzed and organized to explain the main approaches taken up to date but also their weaknesses. This is the first meta-analysis of the whole body of research regarding electoral prediction from Twitter data. It reveals that its presumed predictive power regarding electoral prediction has been somewhat exaggerated: Social media may provide a glimpse on electoral outcomes but, up to now, research has not provided strong evidence to support it can currently replace traditional polls. Nevertheless, there are some reasons for optimism and, hence, further work on this topic is required, along with tighter integration with traditional electoral forecasting research.
On a symmetrization of diffusion processes

The latter author, together with collaborators, proposed a numerical scheme to calculate the price of barrier options. The scheme is based on a symmetrization of diffusion processes. The present paper aims to give a basis to the use of the numerical scheme for Heston and SABR-type stochastic volatility models. This will be done by showing a fairly general result on the symmetrization (in multi-dimension/multi-reflections). Further applications (to time-inhomogeneous diffusions/ to time-dependent boundaries/to curved boundaries) are also discussed.
Canonical Trends: Detecting Trend Setters in Web Data

Much information available on the web is copied, reused or rephrased. The phenomenon that multiple web sources pick up certain information is often called trend. A central problem in the context of web data mining is to detect those web sources that are first to publish information which will give rise to a trend. We present a simple and efficient method for finding trends dominating a pool of web sources and identifying those web sources that publish the information relevant to a trend before others. We validate our approach on real data collected from influential technology news feeds.
Nonparametric Link Prediction in Dynamic Networks

We propose a nonparametric link prediction algorithm for a sequence of graph snapshots over time. The model predicts links based on the features of its endpoints, as well as those of the local neighborhood around the endpoints. This allows for different types of neighborhoods in a graph, each with its own dynamics (e.g, growing or shrinking communities). We prove the consistency of our estimator, and give a fast implementation based on locality-sensitive hashing. Experiments with simulated as well as five real-world dynamic graphs show that we outperform the state of the art, especially when sharp fluctuations or nonlinearities are present.
New Dark Matter Detectors using DNA or RNA for Nanometer Tracking

Weakly Interacting Massive Particles (WIMPs) may constitute most of the matter in the Universe. The ability to detect the directionality of recoil nuclei will considerably facilitate detection of WIMPs. In this paper we propose a novel type of dark matter detector: detectors made of DNA or RNA could provide nanometer resolution for tracking, an energy threshold of 0.5 keV, and can operate at room temperature. When a WIMP from the Galactic Halo elastically scatters off of a nucleus in the detector, the recoiling nucleus then traverses hundreds of strings of single stranded nucleic acids (ssNA) with known base sequences and severs ssNA strands along its trajectory. The location of the break can be identified by amplifying and identifying the segments of cut ssNA using techniques well known to biologists. Thus the path of the recoiling nucleus can be tracked to nanometer accuracy. In one such detector concept, the transducers are nanometer-thick Au-foils of 1m x 1m, and the direction of recoiling nuclei is measured by "NA Tracking Chamber" consisting of ordered array of ssNA strands. Polymerase Chain Reaction (PCR) and ssNA sequencing are used to read-out the detector. The proposed detector is smaller and cheaper than other alternatives: 1 kg of gold and 0.1 to 4 kg of ssNA (depending on length and strand density), packed into 0.01m$^3$, can be used to study 10 GeV WIMPs. A variety of other detector target elements could be used in this detector to optimize for different WIMP masses and to identify WIMP properties. By leveraging advances in molecular biology, we aim to achieve about 1,000-fold better spatial resolution than in conventional WIMP detectors at reasonable cost.
A Concentration Theorem for Projections

X in R^D has mean zero and finite second moments. We show that there is a precise sense in which almost all linear projections of X into R^d (for d < D) look like a scale-mixture of spherical Gaussians -- specifically, a mixture of distributions N(0, sigma^2 I_d) where the weight of the particular sigma component is P (| X |^2 = sigma^2 D). The extent of this effect depends upon the ratio of d to D, and upon a particular coefficient of eccentricity of X's distribution. We explore this result in a variety of experiments.
A network theory analysis of football strategies

We showcase in this paper the use of some tools from network theory to describe the strategy of football teams. Using passing data made available by FIFA during the 2010 World Cup, we construct for each team a weighted and directed network in which nodes correspond to players and arrows to passes. The resulting network or graph provides a direct visual inspection of a team's strategy, from which we can identify play pattern, determine hot-spots on the play and localize potential weaknesses. Using different centrality measures, we can also determine the relative importance of each player in the game, the `popularity' of a player, and the effect of removing players from the game.
Dimensional transmutation by monopole condensation in QCD

We compare two competing conjectures of the color confinement in QCD, the monopole condensation and the Abelian dominance, and show that it is the monopole condensation which is responsible for the confinement. To demonstrate this we present a new gauge invariant integral expression of the one-loop QCD effective action which has no infra-red divergence. With this we show that, just as the GSO-projection restores the supersymmetry and modular invariance in NSR string theory, the color reflection invariance ("the C-projection") assures the gauge invariance and the stability of the monopole condensation. This establishes the monopole dominance in QCD. In doing so we point out critical defects in the calculation of the Savvidy-Nielsen-Olesen (SNO) effective action.
Stochastic variational inference

We develop stochastic variational inference, a scalable algorithm for approximating posterior distributions. We develop this technique for a large class of probabilistic models and we demonstrate it with two probabilistic topic models, latent Dirichlet allocation and the hierarchical Dirichlet process topic model. Using stochastic variational inference, we analyze several large collections of documents: 300K articles from Nature, 1.8M articles from The New York Times, and 3.8M articles from Wikipedia. Stochastic inference can easily handle data sets of this size and outperforms traditional variational inference, which can only handle a smaller subset. (We also show that the Bayesian nonparametric topic model outperforms its parametric counterpart.) Stochastic variational inference lets us apply complex Bayesian models to massive data sets.
Construction of a Lax Pair for the E6(1)q-Painlevé System

We construct a Lax pair for the $E^{(1)}_6 $ $q$-Painleve system from first principles by employing the general theory of semi-classical orthogonal polynomial systems characterised by divided-difference operators on discrete, quadratic lattices . Our study treats one special case of such lattices - the $q$-linear lattice - through a natural generalisation of the big $q$-Jacobi weight. As a by-product of our construction we derive the coupled first-order $q$-difference equations for the $E^{(1)}_6 $ $q$-Painleve system, thus verifying our identification. Finally we establish the correspondences of our result with the Lax pairs given earlier and separately by Sakai and Yamada, through explicit transformations.
Model-Based Clustering of Large Networks

We describe a network clustering framework, based on finite mixture models, that can be applied to discrete-valued networks with hundreds of thousands of nodes and billions of edge variables. Relative to other recent model-based clustering work for networks, we introduce a more flexible modeling framework, improve the variational-approximation estimation algorithm, discuss and implement standard error estimation via a parametric bootstrap approach, and apply these methods to much larger data sets than those seen elsewhere in the literature. The more flexible framework is achieved through introducing novel parameterizations of the model, giving varying degrees of parsimony, using exponential family models whose structure may be exploited in various theoretical and algorithmic ways. The algorithms are based on variational generalized EM algorithms, where the E-steps are augmented by a minorization-maximization (MM) idea. The bootstrapped standard error estimates are based on an efficient Monte Carlo network simulation idea. Last, we demonstrate the usefulness of the model-based clustering framework by applying it to a discrete-valued network with more than 131,000 nodes and 17 billion edge variables.
Financial instability from local market measures

We study the emergence of instabilities in a stylized model of a financial market, when different market actors calculate prices according to different (local) market measures. We derive typical properties for ensembles of large random markets using techniques borrowed from statistical mechanics of disordered systems. We show that, depending on the number of financial instruments available and on the heterogeneity of local measures, the market moves from an arbitrage-free phase to an unstable one, where the complexity of the market—as measured by the diversity of financial instruments—increases, and arbitrage opportunities arise. A sharp transition separates the two phases. Focusing on two different classes of local measures inspired by real market strategies, we are able to analytically compute the critical lines, corroborating our findings with numerical simulations.
VLA imaging of 12CO J = 1-0 and free-free emission in lensed submillimetre galaxies

We present a study using the Karl G. Jansky Very Large Array (VLA) of 12CO J = 1-0 emission in three strongly lensed submillimetre-selected galaxies (SMM?J16359, SMM?J14009 and SMM?J02399) at z = 2.52.9. These galaxies span LIR = 10111013?L?, offering an opportunity to compare the interstellar medium of luminous infrared galaxies and ultraluminous infrared galaxies at high redshift. We estimate molecular gas masses in the range of 240 X 109?M? using a method that assumes canonical underlying brightness temperature (Tb) ratios for star-forming and non-star-forming gas phases and a maximal star formation efficiency. A more simplistic method using XCO = 0.8 and the measured Tb ratios yields gas masses twice as high. In SMM?J14009 we find L CO 3-2'/L CO 1-0'=0.95+/-0.12, indicative of warm, star-forming gas, possibly influenced by the central active galactic nucleus (AGN). We set a gas mass limit of 3s \textless 6 X 108?M? for the Lyman break galaxy, A2218 #384, located in the same field as SMM?J16359 at z = 2.515. Finally, we use the rest-frame similar to 115?GHz freefree flux densities for SMM?J14009 and SMM?J02399 measurements tied directly to the photoionization rate of massive stars, and made possible by VLA's bandwidth to estimate star formation rates (SFRs) of 400600?M??yr-1 and to estimate the fraction of LIR due to AGN.
Suicide Ideation of Individuals in Online Social Networks

Suicide explains the largest number of death tolls among Japanese adolescents in their twenties and thirties. Suicide is also a major cause of death for adolescents in many other countries. Although social isolation has been implicated to influence the tendency to suicidal behavior, the impact of social isolation on suicide in the context of explicit social networks of individuals is scarcely explored. To address this question, we examined a large data set obtained from a social networking service dominant in Japan. The social network is composed of a set of friendship ties between pairs of users created by mutual endorsement. We carried out the logistic regression to identify users’ characteristics, both related and unrelated to social networks, which contribute to suicide ideation. We defined suicide ideation of a user as the membership to at least one active user-defined community related to suicide. We found that the number of communities to which a user belongs to, the intransitivity (i.e., paucity of triangles including the user), and the fraction of suicidal neighbors in the social network, contributed the most to suicide ideation in this order. Other characteristics including the age and gender contributed little to suicide ideation. We also found qualitatively the same results for depressive symptoms.
Improving neural networks by preventing co-adaptation of feature detectors

When a large feedforward neural network is trained on a small training set, it typically performs poorly on held-out test data. This "overfitting" is greatly reduced by randomly omitting half of the feature detectors on each training case. This prevents complex co-adaptations in which a feature detector is only helpful in the context of several other specific feature detectors. Instead, each neuron learns to detect a feature that is generally helpful for producing the correct answer given the combinatorially large variety of internal contexts in which it must operate. Random "dropout" gives big improvements on many benchmark tasks and sets new records for speech and object recognition.
On the origin of long-range correlations in texts

The complexity of human interactions with social and natural phenomena is mirrored in the way we describe our experiences through natural language. In order to retain and convey such a high dimensional information, the statistical properties of our linguistic output has to be highly correlated in time. An example are the robust observations, still largely not understood, of correlations on arbitrary long scales in literary texts. In this paper we explain how long-range correlations flow from highly structured linguistic levels down to the building blocks of a text (words, letters, etc..). By combining calculations and data analysis we show that correlations take form of a bursty sequence of events once we approach the semantically relevant topics of the text. The mechanisms we identify are fairly general and can be equally applied to other hierarchical settings.
Use of event-level neutrino telescope data in global fits for theories of new physics

We present a fast likelihood method for including event-level neutrino telescope data in parameter explorations of theories for new physics, and announce its public release as part of DarkSUSY 5.0.6. Our construction includes both angular and spectral information about neutrino events, as well as their total number. We also present a corresponding measure for simple model exclusion, which can be used for single models without reference to the rest of a parameter space. We perform a number of supersymmetric parameter scans with IceCube data to illustrate the utility of the method: example global fits and a signal recovery in the constrained minimal supersymmetric standard model (CMSSM), and a model exclusion exercise in a 7-parameter phenomenological version of the MSSM. The final IceCube detector con figuration will probe almost the entire focus-point region of the CMSSM, as well as a number of MSSM-7 models that will not otherwise be accessible to e. g. direct detection. Our method accurately recovers the mock signal, and provides tight constraints on model parameters and derived quantities. We show that the inclusion of spectral information significantly improves the accuracy of the recovery, providing motivation for its use in future IceCube analyses.
Inferring land use from mobile phone activity

Understanding the spatiotemporal distribution of people within a city is crucial to many planning applications. Obtaining data to create required knowledge, currently involves costly survey methods. At the same time ubiquitous mobile sensors from personal GPS devices to mobile phones are collecting massive amounts of data on urban systems. The locations, communications, and activities of millions of people are recorded and stored by new information technologies. This work utilizes novel dynamic data, generated by mobile phone users, to measure spatiotemporal changes in population. In the process, we identify the relationship between land use and dynamic population over the course of a typical week. A machine learning classification algorithm is used to identify clusters of locations with similar zoned uses and mobile phone activity patterns. It is shown that the mobile phone data is capable of delivering useful information on actual land use that supplements zoning regulations.
FERMI-LAT OBSERVATIONS AND A BROADBAND STUDY OF SUPERNOVA REMNANT CTB 109

CTB 109 (G109.1–1.0) is a Galactic supernova remnant (SNR) with a hemispherical shell morphology in X-rays and in the radio band. In this work, we report the detection of γ-ray emission coincident with CTB 109, using 37 months of data from the Large Area Telescope on board the Fermi Gamma-ray Space Telescope. We study the broadband characteristics of the remnant using a model that includes hydrodynamics, efficient cosmic-ray (CR) acceleration, nonthermal emission, and a self-consistent calculation of the X-ray thermal emission. We find that the observations can be successfully fit with two distinct parameter sets, one where the γ-ray emission is produced primarily by leptons accelerated at the SNR forward shock and the other where γ-rays produced by forward shock accelerated CR ions dominate the high-energy emission. Consideration of thermal X-ray emission introduces a novel element to the broadband fitting process, and while it does not rule out either the leptonic or the hadronic scenarios, it constrains the parameter sets required by the model to fit the observations. Moreover, the model that best fits the thermal and nonthermal emission observations is an intermediate case, where both radiation from accelerated electrons and hadrons contribute almost equally to the γ-ray flux observed.
BROADBAND SPECTRAL INVESTIGATIONS OF SGR J1550−5418 BURSTS

We present the results of our broadband spectral analysis of 42 SGR J1550−5418 bursts simultaneously detected with the Swift/X-ray Telescope (XRT) and the Fermi/Gamma-ray Burst Monitor (GBM), during the 2009 January active episode of the source. The unique spectral and temporal capabilities of the XRT windowed timing mode have allowed us to extend the GBM spectral coverage for these events down to the X-ray domain (0.5–10 keV). Our earlier analysis of the GBM data found that the SGR J1550−5418 burst spectra were described equally well with either a Comptonized model or with two blackbody functions; the two models were statistically indistinguishable. Our new broadband (0.5–200 keV) spectral fits show that, on average, the burst spectra are better described with two blackbody functions than with the Comptonized model. Thus, our joint XRT–GBM analysis clearly shows for the first time that the SGR J1550−5418 burst spectra might naturally be expected to exhibit a more truly thermalized character, such as a two-blackbody or even a multi-blackbody signal. Using the Swift and RXTE timing ephemeris for SGR J1550−5418 we construct the distribution of the XRT burst counts with spin phase and find that it is not correlated with the persistent X-ray emission pulse phase from SGR J1550−5418. These results indicate that the burst emitting sites on the neutron star need not to be co-located with hot spots emitting the bulk of the persistent X-ray emission. Finally, we show that there is a significant pulse phase dependence of the XRT burst counts, likely demonstrating that the surface magnetic field of SGR J1550−5418 is not uniform over the emission zones, since it is anticipated that regions with stronger surface magnetic field could trigger bursts more efficiently.
Chiral superfluid States in hybrid graphene heterostructures.

We study the "hybrid" heterostructure formed by one sheet of single-layer graphene (SLG) and one sheet of bilayer graphene (BLG) separated by a thin film of dielectric material. In general, it is expected that interlayer interactions can drive the system to a spontaneously broken-symmetry state characterized by interlayer phase coherence. The peculiarity of the SLG-BLG heterostructure is that the electrons in the two layers have different chiralities. We find that this difference causes the spontaneously broken-symmetry state to be N-fold degenerate. Moreover, we find that some of the degenerate states are chiral superfluid states, topologically distinct from the usual layer ferromagnetism. The chiral nature of the ground state opens the possibility to realize protected midgap states. The N-fold degeneracy of the ground state makes the physics of SLG-BLG hybrid systems analogous to the physics of 3He, in particular given the recent discovery of chiral superfluid states in this system.