nils-herrmann/clean_scirepeval huggingface dataset:
Scientific Utopia: I. Opening Scientific Communication

Existing norms for scientific communication are rooted in anachronistic practices of bygone eras making them needlessly inefficient. We outline a path that moves away from the existing model of scientific communication to improve the efficiency in meeting the purpose of public science—knowledge accumulation. We call for six changes: (a) full embrace of digital communication; (b) open access to all published research; (c) disentangling publication from evaluation; (d) breaking the “one article, one journal” model with a grading system for evaluation and diversified dissemination outlets; (e) publishing peer review; and (f) allowing open, continuous peer review. We address conceptual and practical barriers to change and provide examples showing how the suggested practices are being used already. The critical barriers to change are not technical or financial; they are social. Although scientists guard the status quo, they also have the power to change it.
A two measure model of dark energy and dark matter

In this work we construct a unified model of dark energy and dark matter. This is done with the following three elements: a gravitating scalar field, with a non-conventional kinetic term, as in the string theory tachyon; an arbitrary potential, V(); two measures — a metric measure ((−g)1/2) and a non-metric measure (Φ). The model has two interesting features: (i) For potentials which are unstable and would give rise to tachyonic scalar field, this model can stabilize the scalar field. (ii) The form of the dark energy and dark matter that results from this model is fairly insensitive to the exact form of the scalar field potential.
Neutrino- and antineutrino-induced reactions with nuclei between 1 and 50 GeV

Nuclear effects can have a significant impact on neutrino-nucleus interactions. In particular, data from neutrino experiments with broad energy distributions require complex theoretical models that are able to take all the relevant channels into account as well as incorporate nuclear effects in both initial and final-state interactions.  We investigate neutrino and antineutrino scattering on iron and carbon in the energy range from 1 to 30 GeV, which is relevant to current and coming experiments (MINOS, NOvA, and Minerva).  The Giessen Boltzmann--Uehling--Uhlenbeck (GiBUU) model, which implements all reaction channels relevant for neutrino energies under consideration, is used for an investigation of neutrino-nucleus reactions.  Our calculations are compared with the recent NOMAD and MINOS data for the integrated inclusive cross sections. Predictions are made for the differential cross sections for semiinclusive final states (pions, kaons, and nucleons) for the MINOS and NOvA beams.  Nuclear effects in the initial-state interactions may slightly change the inclusive nuclear cross section as compared to the free nucleon ones. Final-state interactions noticeably change the spectra of the outgoing hadrons. In the Minerva and NOvA experiments these effects should be visible in the kinetic energy distributions of the final pions, kaons, and nucleons. Secondary interactions play an important role for strangeness production.
Traveling baseball players’ problem in Korea

We study the so-called traveling tournament problem (TTP) to find an optimal tournament schedule. Differently from the original TTP, in which the total travel distance of all the participants is the objective function to minimize, we instead seek to maximize the fairness of the round robin tournament schedule of the Korean Baseball League. The standard deviation of the travel distances of teams is defined as the energy function, and the Metropolis Monte-Carlo method combined with the simulated annealing technique is applied to find the ground-state configuration. The resulting tournament schedule is found to satisfy all the constraint rules set by the Korean Baseball Organization, but with drastically increased fairness in traveling distances.
Crossover phenomenon in the performance of an Internet search engine

In this work we explore the ability of the Google search engine to find results for random N-letter strings. These random strings, dense over the set of possible N-letter words, address the existence of typos, acronyms, and other words without semantic meaning. Interestingly, we find that the probability of finding such strings sharply drops from one to zero at Nc = 6. The behavior of such order parameter suggests the presence of a transition-like phenomenon in the geometry of the search space. Furthermore, we define a susceptibility-like parameter which reaches a maximum in the neighborhood, suggesting the presence of criticality. We finally speculate on the possible connections to Ramsey theory.
Large-Scale Structure with Gravitational Waves I: Galaxy Clustering

Observed angular positions and redshifts of large-scale structure tracers such as galaxies are affected by gravitational waves through volume distortion and magnification effects. Thus, a gravitational wave background can in principle be probed through clustering statistics of large-scale structures. We calculate the observed angular clustering of galaxies in the presence of a gravitational wave background at linear order including all relativistic effects. For a scale-invariant spectrum of gravitational waves, the effects are most significant at the smallest multipoles (2≤l≤5), but typically suppressed by six or more orders of magnitude with respect to scalar contributions for currently allowed amplitudes of the inflationary gravitational wave background. We also discuss the most relevant second-order terms, corresponding to the distortion of tracer correlation functions by gravitational waves. These provide a natural application of the approach recently developed in Schmidt and Jeong {arXiv:1204.3625 .
Large-Scale Structure with Gravitational Waves II: Shear

The B (curl) mode of the correlation of galaxy ellipticities (shear) can be used to detect a stochastic gravitational wave background, such as that predicted by inflation. In this paper, we derive the tensor mode contributions to shear from both gravitational lensing and intrinsic alignments, using the gauge-invariant, full-sky results of Schmidt and Jeong {arXiv:1204.3625 . We find that the intrinsic alignment contribution, calculated using the linear alignment model, is larger than the lensing contribution by an order of magnitude or more, if the alignment strength for tensor modes is of the same order as for scalar modes. This contribution also extends to higher multipoles. These results make the prospects for probing tensor modes using galaxy surveys less pessimistic than previously thought, though still very challenging.
Motivic multiple zeta values and superstring amplitudes

The structure of tree-level open and closed superstring amplitudes is analyzed. For the open superstring amplitude we find a striking and elegant form, which allows one to disentangle its α′-expansion into several contributions accounting for different classes of multiple zeta values. This form is bolstered by the decomposition of motivic multiple zeta values, i.e. the latter encapsulate the α′-expansion of the superstring amplitude. Moreover, a morphism induced by the coproduct maps the α′-expansion onto a non-commutative Hopf algebra. This map represents a generalization of the symbol of a transcendental function. In terms of elements of this Hopf algebra the α′-expansion assumes a very simple and symmetric form, which carries all the relevant information. Equipped with these results we can also cast the closed superstring amplitude into a very elegant form.
Central Counterparty Risk

A clearing member of a Central Counterparty (CCP) is exposed to losses on their default fund and initial margin contributions. Such losses can be incurred whenever the CCP has insufficient funds to unwind the portfolio of a defaulting clearing member. This does not necessarily require the default of the CCP itself. In this note we aim to quantify the risk a financial institution has when facing a CCP. We show that a clearing member's CCP risk is given by a sum of exposures to each of the other clearing members. This arises because of the implicit default insurance that each member has provided in the form of mutualised, loss sharing collateral. We calculate the exposures by explicitly modeling the capital structure of a CCP as well as the loss distributions of the individual member portfolios. An important consideration in designing the model is the limited transparency with respect to the portfolio composition and collateral levels of individual clearing members. To overcome this we leverage the fact that, for a typical CCP, margin levels are risk-based. In particular, we parameterise the portfolio loss tail as a Pareto distribution and we calibrate this to the CCP defined probability of losses exceeding the posted initial margin levels. A key aspect of the model is that we explicitly take into account wrong-way risk, i.e. the fact that member defaults are more likely to occur in stressed market conditions, as well as potential contagion between a member's default and the losses on his portfolio.
Publishing and linking transport data on the web: extended version

Without Linked Data, transport data is limited to applications exclusively around transport. In this paper, we present a workflow for publishing and linking transport data on the Web. So we will be able to develop transport applications and to add other features which will be created from other datasets. This will be possible because transport data will be linked to these datasets.
 We apply this workflow to two datasets: NEPTUNE, a French standard describing a transport line, and Passim, a directory containing relevant information on transport services, in every French city.
Singularity strength based characterization of financial networks

Financial markets are well known examples of multi-fractal complex systems that have garnered much interest in their characterization through complex network theory. The recent studies have used correlation based distance metrics for defining and analyzing financial networks. In this work the singularity strength is employed to define a distance metric and the existence of hierarchical structure in the Johannesburg Stock Exchange is investigated. The multi-fractal nature of the financial market, which is otherwise hidden in the correlation coefficient based prescriptions, is analyzed through the use of the singularity strength based method. The presence of a super cluster is exhibited in the network which accounts for half of the network size and is homogeneous in the sectoral composition of the South African market.
Graph spectra and the detectability of community structure in networks

We study networks that display community structure--groups of nodes within which connections are unusually dense. Using methods from random matrix theory, we calculate the spectra of such networks in the limit of large size, and hence demonstrate the presence of a phase transition in matrix methods for community detection, such as the popular modularity maximization method. The transition separates a regime in which such methods successfully detect the community structure from one in which the structure is present but is not detected. By comparing these results with recent analyses of maximum-likelihood methods, we are able to show that spectral modularity maximization is an optimal detection method in the sense that no other method will succeed in the regime where the modularity method fails.
Carbon-dioxide emissions trading and hierarchical structure in worldwide finance and commodities markets.

In a highly interdependent economic world, the nature of relationships between financial entities is becoming an increasingly important area of study. Recently, many studies have shown the usefulness of minimal spanning trees (MST) in extracting interactions between financial entities. Here, we propose a modified MST network whose metric distance is defined in terms of cross-correlation coefficient absolute values, enabling the connections between anticorrelated entities to manifest properly. We investigate 69 daily time series, comprising three types of financial assets: 28 stock market indicators, 21 currency futures, and 20 commodity futures. We show that though the resulting MST network evolves over time, the financial assets of similar type tend to have connections which are stable over time. In addition, we find a characteristic time lag between the volatility time series of the stock market indicators and those of the EU CO(2) emission allowance (EUA) and crude oil futures (WTI). This time lag is given by the peak of the cross-correlation function of the volatility time series EUA (or WTI) with that of the stock market indicators, and is markedly different (>20 days) from 0, showing that the volatility of stock market indicators today can predict the volatility of EU emissions allowances and of crude oil in the near future.
Twelve fundamental and two sextet fermion flavors

We report extended simulation results and their new analysis in two important gauge theories with twelve fermion flavors in the fundamental SU(3) color representation and two fermions in the sextet representation. We probe the $N_f=12$ model with respect to the conformal window using mass deformed finite size scaling (FSS) theory driven by the fermion mass anomalous dimension. Our results at fixed gauge coupling show problems with the conformal scenario of the $N_f=12$ model. In the sextet model with two flavors, under the conformal hypothesis, we determine large values for the anomalous fermion mass dimension with $\gamma\geq 1$. Since our sextet analysis favors the chiral symmetry breaking hypothesis without conformality, the large exponent $\gamma$ could play an important role in understanding the composite Higgs mechanism. The new results discussed here include our extended data sets and exceed what was presented at the conference.
Optimal Multiple Stopping with Random Waiting Times

In the standard models for optimal multiple stopping problems it is assumed that between two exercises there is always a time period of deterministic length δ, the so-called refraction period. This prevents the optimal exercise times from bunching up together on top of the optimal stopping time for the one-exercise case. In this article we generalize the standard model by considering random refraction times. We develop the theory and reduce the problem to a sequence of ordinary stopping problems, thus extending the results for deterministic times. This requires an extension of the underlying filtrations in general. Furthermore, we consider the Markovian case and treat an example explicitly.
Model-based clustering in networks with Stochastic Community Finding

Aaron F. McDaid, University College Dublin, Ireland, aaronmcdaid@gmail.comThomas Brendan Murphy, University College Dublin, Ireland, brendan.murphy@ucd.ieNial Friel, University College Dublin, Ireland, nial.friel@ucd.ieNeil J. Hurley, University College Dublin, Ireland, neil.hurley@ucd.ieAbstract. In the model-based clustering of networks, blockmodelling may be used to identifyroles in the network. We identify a special case of the Stochastic Block Model (SBM) wherewe constrain the cluster-cluster interactions such that the density inside the clusters of nodesis expected to be greater than the density between clusters. This corresponds to the intuitionbehind community-ﬁnding methods, where nodes tend to clustered together if they link toeach other. We call this model Stochastic Community Finding (SCF) and present an eﬃcientMCMC algorithm which can cluster the nodes, given the network. The algorithm is evaluatedon synthetic data and is applied to a social network of interactions at a karate club and at amonastery, demonstrating how the SCF ﬁnds the ‘ground truth’ clustering where sometimesthe SBM does not. The SCF is only one possible form of constraint or specialization thatmay be applied to the SBM. In a more supervised context, it may be appropriate to use otherspecializations to guide the SBM.Keywords. Model-based clustering, MCMC, Social networks, Community ﬁnding, Blockmod-elling
Integral form of Yang-Mills equations and its gauge invariant conserved charges

Despite the fact that the integral form of the equations of classical electrodynamics is well known, the same is not true for non-abelian gauge theories. The aim of the present paper is threefold. First, we present the integral form of the classical Yang-Mills equations in the presence of sources, and then use it to solve the long standing problem of constructing conserved charges, for any field configuration, which are invariant under general gauge transformations and not only under transformations that go to a constant at spatial infinity. The construction is based on concepts in loop spaces and on a generalization of the non-abelian Stokes theorem for two-form connections. The third goal of the paper is to present the integral form of the self dual Yangs-Mills equations, and calculate the conserved charges associated to them. The charges are explicitly evaluated for the cases of monopoles, dyons, instantons and merons, and we show that in many cases those charges must be quantized. Our results are important in the understanding of global properties of non-abelian gauge theories.
Three lectures on free probability

These are notes from a three-lecture mini-course on free probability given at MSRI in the Fall of 2010 and repeated a year later at Harvard. The lectures were aimed at mathematicians and mathematical physicists working in combinatorics, probability, and random matrix theory. The first lecture was a staged rediscovery of free independence from first principles, the second dealt with the additive calculus of free random variables, and the third focused on random matrix models.
Exotic quantum spin models in spin-orbit-coupled Mott insulators.

We study cold atoms in an optical lattice with synthetic spin-orbit coupling in the Mott-insulator regime. We calculate the parameters of the corresponding tight-binding model using Peierls substitution and "localized Wannier states method" and derive the low-energy spin Hamiltonian for bosons and fermions. The spin Hamiltonian is a combination of Heisenberg model, quantum compass model and Dzyaloshinskii-Moriya interactions and it has a rich classical phase diagram with collinear, spiral and vortex phases. We discuss the state of the art of experiments to realize and detect magnetic orderings in strongly correlated optical lattices.
Seesaw mechanism with Occam’s razor

We discuss the seesaw mechanism which includes the minimum number of parameters for successful leptogenesis and three neutrino oscillations in the spirit of Occam’s razor. We show that models with two right-handed neutrinos with two texture zeros supported by Occam’s razor cannot t the observed neutrino parameters consistently for the normal light neutrino mass hierarchy. For the inverted light neutrino mass hierarchy, on the other hand, we nd that the models can t the observed neutrino parameters consistently. Besides, we show that the model predicts the maximal Dirac CP -phase of the neutrino mixing matrix in the measurable range in the foreseeable future for the inverted neutrino mass hierarchy. We also show that the predicted eective Majorana neutrino mass responsible for the neutrinoless double beta decay is around 50 meV which is also within reach of future experiments.
Mesh Learning for Classifying Cognitive Processes

A relatively recent advance in cognitive neuroscience has been multi-voxel pattern analysis (MVPA), which enables researchers to decode brain states and/or the type of information represented in the brain during a cognitive operation. MVPA methods utilize machine learning algorithms to distinguish among types of information or cognitive states represented in the brain, based on distributed patterns of neural activity. In the current investigation, we propose a new approach for representation of neural data for pattern analysis, namely a Mesh Learning Model. In this approach, at each time instant, a star mesh is formed around each voxel, such that the voxel corresponding to the center node is surrounded by its p-nearest neighbors. The arc weights of each mesh are estimated from the voxel intensity values by least squares method. The estimated arc weights of all the meshes, called Mesh Arc Descriptors (MADs), are then used to train a classifier, such as Neural Networks, k-Nearest Neighbor, Na\"ive Bayes and Support Vector Machines. The proposed Mesh Model was tested on neuroimaging data acquired via functional magnetic resonance imaging (fMRI) during a recognition memory experiment using categorized word lists, employing a previously established experimental paradigm (\"Oztekin & Badre, 2011). Results suggest that the proposed Mesh Learning approach can provide an effective algorithm for pattern analysis of brain activity during cognitive processing.
Hubble flows and gravitational potentials in observable Universe

In this paper, we consider the Universe deep inside of the cell of uniformity. At these scales, the Universe is filled with inhomogeneously distributed discrete structures (galaxies, groups and clusters of galaxies), which disturb the background Friedmann model. We propose mathematical models with conformally flat, hyperbolic and spherical spaces. For these models, we obtain the gravitational potential for an arbitrary number of randomly distributed inhomogeneities. In the cases of flat and hyperbolic spaces, the potential is finite at any point, including spatial infinity, and valid for an arbitrary number of gravitating sources. For both of these models, we investigate the motion of test masses (e.g., dwarf galaxies) in the vicinity of one of the inhomogeneities. We show that there is a distance from the inhomogeneity, at which the cosmological expansion prevails over the gravitational attraction and where test masses form the Hubble flow. For our group of galaxies, it happens at a few Mpc and the radius of the zero-acceleration sphere is of the order of 1 Mpc, which is very close to observations. Outside of this sphere, the dragging effect of the gravitational attraction goes very fast to zero.
Learning Continuous-Time Social Network Dynamics

We demonstrate that a number of sociology models for social network dynamics can be viewed as continuous time Bayesian networks (CTBNs). A sampling-based approximate inference method for CTBNs can be used as the basis of an expectation-maximization procedure that achieves better accuracy in estimating the parameters of the model than the standard method of moments algorithmfromthe sociology literature. We extend the existing social network models to allow for indirect and asynchronous observations of the links. A Markov chain Monte Carlo sampling algorithm for this new model permits estimation and inference. We provide results on both a synthetic network (for verification) and real social network data.
Improving Schema Matching with Linked Data

With today's public data sets containing billions of data items, more and more companies are looking to integrate external data with their traditional enterprise data to improve business intelligence analysis. These distributed data sources however exhibit heterogeneous data formats and terminologies and may contain noisy data. In this paper, we present a novel framework that enables business users to semi-automatically perform data integration on potentially noisy tabular data. This framework offers an extension to Google Refine with novel schema matching algorithms leveraging Freebase rich types. First experiments show that using Linked Data to map cell values with instances and column headers with types improves significantly the quality of the matching results and therefore should lead to more informed decisions.