nils-herrmann/clean_scirepeval huggingface dataset:
CLaSPS: A NEW METHODOLOGY FOR KNOWLEDGE EXTRACTION FROM COMPLEX ASTRONOMICAL DATA SETS

In this paper, we present the Clustering-Labels-Score Patterns Spotter (CLaSPS), a new methodology for the determination of correlations among astronomical observables in complex data sets, based on the application of distinct unsupervised clustering techniques. The novelty in CLaSPS is the criterion used for the selection of the optimal clusterings, based on a quantitative measure of the degree of correlation between the cluster memberships and the distribution of a set of observables, the labels, not employed for the clustering. CLaSPS has been primarily developed as a tool to tackle the challenging complexity of the multi-wavelength complex and massive astronomical data sets produced by the federation of the data from modern automated astronomical facilities. In this paper, we discuss the applications of CLaSPS to two simple astronomical data sets, both composed of extragalactic sources with photometric observations at different wavelengths from large area surveys. The first data set, CSC+, is composed of optical quasars spectroscopically selected in the Sloan Digital Sky Survey data, observed in the x-rays by Chandra and with multi-wavelength observations in the near-infrared, optical, and ultraviolet spectral intervals. One of the results of the application of CLaSPS to the CSC+ is the re-identification of a well-known correlation between the αOX parameter and the near-ultraviolet color, in a subset of CSC+ sources with relatively small values of the near-ultraviolet colors. The other data set consists of a sample of blazars for which photometric observations in the optical, mid-, and near-infrared are available, complemented for a subset of the sources, by Fermi γ-ray data. The main results of the application of CLaSPS to such data sets have been the discovery of a strong correlation between the multi-wavelength color distribution of blazars and their optical spectral classification in BL Lac objects and flat-spectrum radio quasars, and a peculiar pattern followed by blazars in the WISE mid-infrared colors space. This pattern and its physical interpretation have been discussed in detail in other papers by one of the authors.
Upper limit on the cosmological gamma-ray background

We show that the current extragalactic gamma-ray background (EGB) measurement below 100 GeV sets an upper limit on EGB itself at very high energy (VHE) above 100 GeV. The limit is conservative for the electromagnetic cascade emission from VHE EGB interacting with the cosmic microwave-to-optical background radiation not to exceed the current EGB measurement. The cascade component fits the measured VHE EGB spectrum rather well. However, once we add the contribution from known source classes, the Fermi VHE EGB observation exceeds or even violates the limit, which is approximated as ${E}^{2}dN/dEl4.5\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}5}(E/100\text{ }\text{ }\mathrm{GeV}{)}^{\ensuremath{-}0.7}\text{ }\text{ }\mathrm{MeV}/{\mathrm{cm}}^{2}/\mathrm{s}/\mathrm{sr}$. The upper limit above 100 GeV is useful in the future to probe the EGB origin and the new physics like axionlike particles and Lorentz-invariance violation.
Cosmic strings as emitters of extremely high energy neutrinos

We study massive particle radiation from cosmic string kinks, and its observability in extremely high energy neutrinos. In particular, we consider the emission of moduli --- weakly coupled scalar particles predicted in supersymmetric theories --- from the kinks of cosmic string loops. Since kinks move at the speed of light on strings, moduli are emitted with large Lorentz factors, and eventually decay into many pions and neutrinos via hadronic cascades. The produced neutrino flux has energy $E \gtrsim 10^{11} \rm{GeV}$, and is affected by oscillations and absorption (resonant and non-resonant). It is observable at upcoming neutrino telescopes such as JEM-EUSO, and the radio telescopes LOFAR and SKA, for a range of values of the string tension, and of the mass and coupling constant of the moduli.
Modeling two-language competition dynamics

During the last decade, much attention has been paid to language competition in the complex systems community, that is, how the fractions of speakers of several competing languages evolve in time. In this paper, we review recent advances in this direction and focus on three aspects. First, we consider the shift from two-state models to three-state models that include the possibility of bilingual individuals. The understanding of the role played by bilingualism is essential in sociolinguistics. In particular, the question addressed is whether bilingualism facilitates the coexistence of languages. Second, we will analyze the effect of social interaction networks and physical barriers. Finally, we will show how to analyze the issue of bilingualism from a game theoretical perspective.
On Noncommutative Corrections of Gravitational Energy in Teleparallel Gravity

We use the theory of teleparallelism equivalent to general relativity based on noncommutative spacetime coordinates. In this context, we write the corrections of the Schwarzschild solution. We propose the existence of a Weitzenbock spacetime that matches the corrected metric tensor. As an important result, we find the corrections of the gravitational energy in the realm of teleparallel gravity due to the noncommutativity of spacetime. Then we interpret such corrections as a manifestation of quantum theory in gravitational field.
Phenomenology of Philosophy of Science: OPERA data

I observe that, 
as the physics side of the OPERA-anomaly story is apparently unfolding, 
there can still be motivation for philosophy of science to analyze 
the six months of madness physicists spent chasing the dream of a new 
fundamental-physics revolution. 
 I here mainly report data on studies of the OPERA anomaly 
 that could be relevant for analyses from the 
 perspective of phenomenology of philosophy of science. 
 Most of what I report is an insider's perspective on 
 the debate that evolved from the original announcement 
 by the OPERA collaboration of evidence of superluminal neutrinos. 
 I also sketch out, from a broader perspective, 
 some of the objectives I view as achievable for 
 the phenomenology of philosophy of science.
Gender and Sexual Diversity Issues in Physics: The Audience Speaks

An account is presented of the special session on "Gender and Sexual Diversity Issues in Physics" which took place at the American Physical Society March Meeting 2012. The opinions of those who attended this session were solicited via an anonymous survey, the results of which are reported here. Drawing both upon ideas put forward by the speakers and audience from the meeting, a set of recommended actions is proposed for the Physics community to become more inclusive of LGBT+ people.
Giant topological insulator gap in graphene with 5d adatoms.

Two-dimensional topological insulators (2D TIs) have been proposed as platforms for many intriguing applications, ranging from spintronics to topological quantum information processing. Realizing this potential will likely be facilitated by the discovery of new, easily manufactured materials in this class. With this goal in mind, we introduce a new framework for engineering a 2D TI by hybridizing graphene with impurity bands arising from heavy adatoms possessing partially filled d shells, in particular, osmium and iridium. First-principles calculations predict that the gaps generated by this means exceed 0.2 eV over a broad range of adatom coverage; moreover, tuning of the Fermi level is not required to enter the TI state. The mechanism at work is expected to be rather general and may open the door to designing new TI phases in many materials.
Social influence in social advertising: evidence from field experiments

Social advertising uses information about consumers' peers, including peer affiliations with a brand, product, organization, etc., to target ads and contextualize their display. This approach can increase ad efficacy for two main reasons: peers' affiliations reflect unobserved consumer characteristics, which are correlated along the social network; and the inclusion of social cues (i.e., peers' association with a brand) alongside ads affect responses via social influence processes. For these reasons, responses may be increased when multiple social signals are presented with ads, and when ads are affiliated with peers who are strong, rather than weak, ties.
 We conduct two very large field experiments that identify the effect of social cues on consumer responses to ads, measured in terms of ad clicks and the formation of connections with the advertised entity. In the first experiment, we randomize the number of social cues present in word-of-mouth advertising, and measure how responses increase as a function of the number of cues. The second experiment examines the effect of augmenting traditional ad units with a minimal social cue (i.e., displaying a peer's affiliation below an ad in light grey text). On average, this cue causes significant increases in ad performance. Using a measurement of tie strength based on the total amount of communication between subjects and their peers, we show that these influence effects are greatest for strong ties. Our work has implications for ad optimization, user interface design, and central questions in social science research.
Robust Detection of Dynamic Community Structure in Networks

We describe techniques for the robust detection of community structure in some classes of time-dependent networks. Specifically, we consider the use of statistical null models for facilitating the principled identification of structural modules in semi-decomposable systems. Null models play an important role both in the optimization of quality functions such as modularity and in the subsequent assessment of the statistical validity of identified community structure. We examine the sensitivity of such methods to model parameters and show how comparisons to null models can help identify system scales. By considering a large number of optimizations, we quantify the variance of network diagnostics over optimizations ("optimization variance") and over randomizations of network structure ("randomization variance"). Because the modularity quality function typically has a large number of nearly degenerate local optima for networks constructed using real data, we develop a method to construct representative partitions that uses a null model to correct for statistical noise in sets of partitions. To illustrate our results, we employ ensembles of time-dependent networks extracted from both nonlinear oscillators and empirical neuroscience data.
Hadron yield correlation in quark combination models in high-energyAAcollisions

We study the hadron yield correlation in the combination models in high energy heavy ion collisions. We derive the relationship between the average yields of different hadrons produced in the combination of a system consisting of equal number of quarks and antiquarks. We present the results for the directly produced hadrons as well as those for the final hadrons including the strong and electromagnetic decay contributions. We also study the net quark influence by considering the case when the number of quarks is larger than that of the antiquarks. We make comparison with the data wherever possible.
Residual Component Analysis: Generalising PCA for more flexible inference in linear-Gaussian models

Probabilistic principal component analysis (PPCA) seeks a low dimensional representation of a data set in the presence of independent spherical Gaussian noise. The maximum likelihood solution for the model is an eigenvalue problem on the sample covariance matrix. In this paper we consider the situation where the data variance is already partially explained by other factors, for example sparse conditional dependencies between the covariates, or temporal correlations leaving some residual variance. We decompose the residual variance into its components through a generalised eigenvalue problem, which we call residual component analysis (RCA). We explore a range of new algorithms that arise from the framework, including one that factorises the covariance of a Gaussian density into a low-rank and a sparse-inverse component. We illustrate the ideas on the recovery of a protein-signaling network, a gene expression time-series data set and the recovery of the human skeleton from motion capture 3-D cloud data.
Quasi-Newton Methods: A New Direction

Four decades after their invention, quasi-Newton methods are still state of the art in unconstrained numerical optimization. Although not usually interpreted thus, these are learning algorithms that fit a local quadratic approximation to the objective function. We show that many, including the most popular, quasi-Newton methods can be interpreted as approximations of Bayesian linear regression under varying prior assumptions. This new notion elucidates some shortcomings of classical algorithms, and lights the way to a novel nonparametric quasi-Newton method, which is able to make more efficient use of available information at computational cost similar to its predecessors.
Information-Theoretic Semi-Supervised Metric Learning via Entropy Regularization

We propose a general information-theoretic approach to semi-supervised metric learning called SERAPH (SEmi-supervised metRic leArning Paradigm with Hypersparsity) that does not rely on the manifold assumption. Given the probability parameterized by a Mahalanobis distance, we maximize its entropy on labeled data and minimize its entropy on unlabeled data following entropy regularization. For metric learning, entropy regularization improves manifold regularization by considering the dissimilarity information of unlabeled data in the unsupervised part, and hence it allows the supervised and unsupervised parts to be integrated in a natural and meaningful way. Moreover, we regularize SERAPH by trace-norm regularization to encourage low-dimensional projections associated with the distance metric. The nonconvex optimization problem of SERAPH could be solved efficiently and stably by either a gradient projection algorithm or an EM-like iterative algorithm whose M-step is convex. Experiments demonstrate that SERAPH compares favorably with many well-known metric learning methods, and the learned Mahalanobis distance possesses high discriminability even under noisy environments.
Continuous Inverse Optimal Control with Locally Optimal Examples

Inverse optimal control, also known as inverse reinforcement learning, is the problem of recovering an unknown reward function in a Markov decision process from expert demonstrations of the optimal policy. We introduce a probabilistic inverse optimal control algorithm that scales gracefully with task dimensionality, and is suitable for large, continuous domains where even computing a full policy is impractical. By using a local approximation of the reward function, our method can also drop the assumption that the demonstrations are globally optimal, requiring only local optimality. This allows it to learn from examples that are unsuitable for prior methods.
On-Line Portfolio Selection with Moving Average Reversion

On-line portfolio selection has attracted increasing interests in machine learning and AI communities recently. Empirical evidence show that stock's high and low prices are temporary and stock price relatives are likely to follow the mean reversion phenomenon. While the existing mean reversion strategies are shown to achieve good empirical performance on many real datasets, they often make the single-period mean reversion assumption, which is not always satisfied, leading to poor performance in some real datasets. To overcome the limitation, this article proposes a multiple-period mean reversion, or so-called "Moving Average Reversion" (MAR), and a new on-line portfolio selection strategy named "On-Line Moving Average Reversion" (OLMAR), which exploits MAR by applying powerful online learning techniques. From our empirical results, we found that OLMAR can overcome the drawbacks of existing mean reversion algorithms and achieve significantly better results, especially on the datasets where existing mean reversion algorithms failed. In addition to superior performance, OLMAR also runs extremely fast, further supporting its practical applicability to a wide range of applications.
Machine Learning that Matters

Much of current machine learning (ML) research has lost its connection to problems of import to the larger world of science and society. From this perspective, there exist glaring limitations in the data sets we investigate, the metrics we employ for evaluation, and the degree to which results are communicated back to their originating domains. What changes are needed to how we conduct research to increase the impact that ML has? We present six Impact Challenges to explicitly focus the field's energy and attention, and we discuss existing obstacles that must be addressed. We aim to inspire ongoing discussion and focus on ML that matters.
Pion and kaon elastic form factors in a refined light-front model

Within the framework of light-front field theory, we reassess the electromagnetic form factors of the pion and kaon. Comparison with experiment is made for the full range of momentum transfer, q^2<0, including recent data. The light-front model's single regulator mass, m_R, of the \bar qq bound-state vertex function is initially adjusted to reproduce the weak decay constants, f_\pi\ and f_K, and both meson's charge radii, r_\pi\ and r_K. We study the behavior of these observables under variation of the quark masses and find an optimized parameter set, m_u=m_d, m_s and m_R, for which they are in sensibly better agreement with experiment than in a previous analysis; a feature also observed for the elastic form factors, in particular at small q^2. This model refinement is important in view of an extension to vector and heavy-light mesons.
Pairing and the Cooling of Neutron Stars

In this review, I present a brief summary of the impact of nucleon pairing at supra-nuclear densities on the cooling of neutron stars. I also describe how the recent observation of the cooling of the neutron star in the supernova remnant Cassiopeia A may provide us with the first direct evidence for the occurrence of such pairing. It also implies a size of the neutron 3P-F2 energy gap of the order of 0.1 MeV.
Quantum noncomutativity in quantum cosmology

In the present work, we study the noncommutative version of a quantum cosmology model. The model has a Friedmann-Robertson-Walker geometry, the matter content is a radiative perfect fluid and the spatial sections have positive constant curvatures. We work in the Schutz's variational formalism. We quantize the model and obtain the appropriate Wheeler-DeWitt equation. In this model the states are bounded. Therefore, we compute the discrete energy spectrum and the corresponding eigenfunctions. The energies depend on a noncommutative parameter ($\theta$). The solutions to the Wheeler-DeWitt equation are function of the scale factor ($a$) and a time variable ($\tau$), associated to the fluid. They also depend on an integer ($n$) and $\theta$. The most general solution ($\Psi(a,\tau)$) to the Wheeler-DeWitt equation is a sum, in the integer $n$, of the solutions mentioned above. We observe that, there is no $\Psi(a,\tau)$ satisfying the appropriate boundary conditions. Therefore, we conclude that it is not possible to obtain a wavefunction satisfying the appropriate boundary conditions for the present model with the considered noncommutativity.
Statistical Translation, Heat Kernels and Expected Distances

High dimensional structured data such as text and images is often poorly understood and misrepresented in statistical modeling. The standard histogram representation suffers from high variance and performs poorly in general. We explore novel connections between statistical translation, heat kernels on manifolds and graphs, and expected distances. These connections provide a new framework for unsupervised metric learning for text documents. Experiments indicate that the resulting distances are generally superior to their more standard counterparts.
A Utility Framework for Bounded-Loss Market Makers

We introduce a class of utility-based market makers that always accept orders at their risk-neutral prices. We derive necessary and sufficient conditions for such market makers to have bounded loss. We prove that hyperbolic absolute risk aversion utility market makers are equivalent to weighted pseudospherical scoring rule market makers. In particular, Hanson's logarithmic scoring rule market maker corresponds to a negative exponential utility market maker in our framework. We describe a third equivalent formulation based on maintaining a cost function that seems most natural for implementation purposes, and we illustrate how to translate among the three equivalent formulations. We examine the tradeoff between the market's liquidity and the market maker's worst-case loss. For a fixed bound on worst-case loss, some market makers exhibit greater liquidity near uniform prices and some exhibit greater liquidity near extreme prices, but no market maker can exhibit uniformly greater liquidity in all regimes. For a fixed minimum liquidity level, we give the lower bound of market maker's worst-case loss under some regularity conditions.
Limited Urban Growth: London's Street Network Dynamics since the 18th Century

We investigate the growth dynamics of Greater London defined by the administrative boundary of the Greater London Authority, based on the evolution of its street network during the last two centuries. This is done by employing a unique dataset, consisting of the planar graph representation of nine time slices of Greater London's road network spanning 224 years, from 1786 to 2010. Within this time-frame, we address the concept of the metropolitan area or city in physical terms, in that urban evolution reveals observable transitions in the distribution of relevant geometrical properties. Given that London has a hard boundary enforced by its long standing green belt, we show that its street network dynamics can be described as a fractal space-filling phenomena up to a capacitated limit, whence its growth can be predicted with a striking level of accuracy. This observation is confirmed by the analytical calculation of key topological properties of the planar graph, such as the topological growth of the network and its average connectivity. This study thus represents an example of a strong violation of Gibrat's law. In particular, we are able to show analytically how London evolves from a more loop-like structure, typical of planned cities, toward a more tree-like structure, typical of self-organized cities. These observations are relevant to the discourse on sustainable urban planning with respect to the control of urban sprawl in many large cities which have developed under the conditions of spatial constraints imposed by green belts and hard urban boundaries.
C to O-O Translation: Beyond the Easy Stuff

Can we reuse some of the huge code-base developed in C to take advantage of modern programming language features such as type safety, object-orientation, and contracts? This paper presents a source-to-source translation of C code into Eiffel, a modern object-oriented programming language, and the supporting tool C2Eif. The translation is completely automatic and supports the entire C language (ANSI, as well as many GNU C Compiler extensions, through CIL) as used in practice, including its usage of native system libraries and inlined assembly code. Our experiments show that C2Eif can handle C applications and libraries of significant size (such as vim and libgsl), as well as challenging benchmarks such as the GCC torture tests. The produced Eiffel code is functionally equivalent to the original C code, and takes advantage of some of Eiffel's features to produce safe and easy-to-debug translations.