nils-herrmann/clean_scirepeval huggingface dataset:
Using the Single Quark Transition Model to predict nucleon resonance amplitudes

We present predictions for the ∗ N ! N ∗ helicity amplitudes, where N ∗ is a member of the [70,1 − ] supermultiplet. We combine the results from the single quark transition model for the helicity amplitudes with the results of the covariant spectator quark model for the ∗ N ! N ∗ (1535) and ∗ N ! N ∗ (1520) transitions. The theoretical estimations from the covariant spectator quark model are used to calculate three independent functions A,B, and C of Q 2 , where Q 2 = q 2 and q is the momentum transfer. With the knowledge of the functions A,B, and C we estimate the helicity amplitudes for the transitions ∗ N ! N ∗ (1650), ∗ N ! N ∗ (1700), ∗ N ! �(1620), and ∗ N ! �(1700). The analysis is restricted to reactions with proton targets. The predictions for the transition amplitudes are valid for Q 2 > 2 GeV 2 .
Low-rank matrix recovery in poisson noise

This paper describes a new algorithm for recovering low-rank matrices from their linear measurements contaminated with Poisson noise: the Poisson noise Maximum Likelihood Singular Value thresholding (PMLSV) algorithm. We propose a convex optimization formulation with a cost function consisting of the sum of a likelihood function and a regularization function which is proportional to the nuclear norm of the matrix. Instead of solving the optimization problem directly by semi-definite program (SD-P), we derive an iterative singular value thresholding algorithm by expanding the likelihood function. We demonstrate the good performance of the proposed algorithm on recovery of solar flare images with Poisson noise: the algorithm is more efficient than solving SDP using the interior-point algorithm and it generates a good approximate solution compared to that solved from SDP.
Info-greedy sequential adaptive compressed sensing

We present an information-theoretic framework for sequential adaptive compressed sensing, Info-Greedy Sensing, where measurements are chosen to maximize the extracted information conditioned on the previous measurements. We lower bound the expected number of measurements for a given accuracy, and derive various forms of Info-Greedy Sensing algorithms under different signal and noise models, as well as under the sparse measurement vector constraint. We also show the Info-Greedy optimality of the bisection algorithm for k-sparse signals, as well as that of the iterative algorithm which measures using the maximum eigenvector of the posterior Gaussian signals. Numerical examples demonstrate the good performance of the proposed algorithms using simulated and real data.
Cortical Spatiotemporal Dimensionality Reduction for Visual Grouping

The visual systems of many mammals, including humans, are able to integrate the geometric information of visual stimuli and perform cognitive tasks at the first stages of the cortical processing. This is thought to be the result of a combination of mechanisms, which include feature extraction at the single cell level and geometric processing by means of cell connectivity. We present a geometric model of such connectivities in the space of detected features associated with spatiotemporal visual stimuli and show how they can be used to obtain low-level object segmentation. The main idea is to define a spectral clustering procedure with anisotropic affinities over data sets consisting of embeddings of the visual stimuli into higher-dimensional spaces. Neural plausibility of the proposed arguments will be discussed.
Quantization of a particle on a two-dimensional manifold of constant curvature

The formulation of quantum mechanics on spaces of constant curvature is studied. It is shown how a transition from a classical system to the quantum case can be accomplished by the quantization of the Noether momenta. These can be determined by means of Lie differentiation of the metric which defines the manifold. For the metric examined here, it is found that the resulting Schrodinger equation is separable and the spectrum and eigenfunctions can be investigated in detail.
Note on Legendre decomposition of the Pontryagin density in Kerr

In  ("Scalar field excited around a rapidly rotating black hole in Chern-Simons modified gravity"), Konno and Takahashi have recently developed some analytical results for the scalar field about a Kerr black hole in the decoupling limit of dynamical Chern-Simons gravity. This involved a decomposition of the source (the Pontryagin density) in terms of Legendre polynomials. Here we give a two-line expression for this decomposition which simplifies their quadruple sum. Our expressions are rational polynomials multiplying Legendre functions of the second kind, or equivalently rational polynomials multiplying hypergeometric functions.
Projecting Ising Model Parameters for Fast Mixing

Inference in general Ising models is difficult, due to high treewidth making tree-based algorithms intractable. Moreover, when interactions are strong, Gibbs sampling may take exponential time to converge to the stationary distribution. We present an algorithm to project Ising model parameters onto a parameter set that is guaranteed to be fast mixing, under several divergences. We find that Gibbs sampling using the projected parameters is more accurate than with the original parameters when interaction strengths are strong and when limited time is available for sampling.
Splitting methods for nonconvex composite optimization

We consider the problem of minimizing the sum of a smooth function h with a bounded Hessian, and a nonsmooth function. We assume that the latter function is a composition of a proper closed function P and a surjective linear map M, with the proximal mappings of τP , τ > 0, simple to compute. This problem is nonconvex in general and encompasses many important applications in engineering and machine learning. In this paper, we examined two types of splitting methods for solving this nonconvex optimization problem: alternating direction method of multipliers and proximal gradient algorithm. For the direct adaptation of the alternating direction method of multipliers, we show that, if the penalty parameter is chosen sufficiently large and the sequence generated has a cluster point, then it gives a stationary point of the nonconvex problem. We also establish convergence of the whole sequence under an additional assumption that the functions h and P are semi-algebraic. Furthermore, when M is the identity so that the proximal gradient algorithm can be efficiently applied, we show that any cluster point is stationary under a slightly more flexible constant step-size rule than what is known in the literature for a nonconvex h.
Structured Learning via Logistic Regression

A successful approach to structured learning is to write the learning objective as a joint function of linear parameters and inference messages, and iterate between updates to each. This paper observes that if the inference problem is "smoothed" through the addition of entropy terms, for fixed messages, the learning objective reduces to a traditional (non-structured) logistic regression problem with respect to parameters. In these logistic regression problems, each training example has a bias term determined by the current set of messages. Based on this insight, the structured energy function can be extended from linear factors to any function class where an "oracle" exists to minimize a logistic loss.
Decision-theoretic approaches to non-knowledge in economics

We review two strands of conceptual approaches to the formal representation of a decision maker's non-knowledge at the initial stage of a static one-person, one-shot decision problem in economic theory. One focuses on representations of non-knowledge in terms of probability measures over sets of mutually exclusive and exhaustive consequence-relevant states of Nature, the other deals with unawareness of potentially important events by means of sets of states that are less complete than the full set of consequence-relevant states of Nature. We supplement our review with a brief discussion of unresolved matters in both approaches.
Co-following on twitter

We present an in-depth study of co-following on Twitter based on the observation that two Twitter users whose followers have similar friends are also similar, even though they might not share any direct links or a single mutual follower. We show how this observation contributes to (i) a better understanding of language-agnostic user classification on Twitter, (ii) eliciting opportunities for Computational Social Science, and (iii) improving online marketing by identifying cross-selling opportunities. We start with a machine learning problem of predicting a user's preference among two alternative choices of Twitter friends. We show that co-following information provides strong signals for diverse classification tasks and that these signals persist even when the most discriminative features are removed. Going beyond mere classification performance optimization, we present applications of our methodology to Computational Social Science. Here we confirm stereotypes such as that the country singer Kenny Chesney (@kennychesney) is more popular among @GOP followers, whereas Lady Gaga (@ladygaga) enjoys more support from @TheDemocrats followers. In the domain of marketing we give evidence that celebrity endorsement is reflected in co-following and we demonstrate how our methodology can be used to reveal the audience similarities between not so obvious entites such as Apple and Puma.
The Arcsine law and an asymptotic behavior of orthogonal polynomials (Mathematical Aspects of Quantum Fields and Related Topics)

Interacting Fock space connects the study of quantum probability theory, classical random variables, and orthogonal polynomials. It is a pre-Hilbert space associated with creation, preservation, and annihilation processes. We prove that if three processes are asymptotically commutative, the arcsine law arises as the "large quantum number limits." As a corollary, it is shown that for many probability measures, asymptotic behavior of orthogonal polynomials is described by the arcsine function. A weaker form of asymptotic commutativity provides us a discretized arcsine law.
Reducing Offline Evaluation Bias in Recommendation Systems

Recommendation systems have been integrated into the majority of large online systems. They tailor those systems to individual users by filtering and ranking information according to user profiles. This adaptation process influences the way users interact with the system and, as a consequence, increases the difficulty of evaluating a recommendation algorithm with historical data (via offline evaluation). This paper analyses this evaluation bias and proposes a simple item weighting solution that reduces its impact. The efficiency of the proposed solution is evaluated on real world data extracted from Viadeo professional social network.
Equilibration of a finite-temperature binary Bose gas formed by population transfer

We consider an equilibrium single-species homogeneous Bose gas from which a proportion of the atoms are instantaneously and coherently transferred to a second species, thereby forming a binary Bose gas in a non-equilibrium initial state. We study the ensuing evolution towards a new equilibrium, mapping the dynamics and final equilibrium state out as a function of the population transfer and the interspecies interactions by means of classical field methods. While in certain regimes, the condensate fractions are largely unaffected by the population transfer process, in others, particularly for immiscible interactions, one or both condensate fractions are vastly reduced to a new equilibrium value.
Information and statistics: a new paradigm in theoretical nuclear physics

Theoretical predictions of physical observables often involve extrapolations to regions that are poorly constrained by laboratory experiments and astrophysical observations. Without properly quantified theoretical errors, such model predictions are of very limited utility. In this contribution we use maximum-likelihood estimation to compute theoretical errors and assess correlations between physical observables. We illustrate the power and elegance of these methods using examples of both pedagogical and realistic interest. In particular, we implement a gaussian approximation to the likelihood function to develop a new relativistic effective interaction constrained by ground-state properties of finite nuclei, their monopole response, and masses of neutron stars.
Gauging Quantum States: From Global to Local Symmetries in Many-Body Systems

We present an operational procedure to transform global symmetries into local symmetries at the level of individual quantum states, as opposed to typical gauging prescriptions for Hamiltonians or Lagrangians. We then construct a compatible gauging map for operators, which preserves locality and reproduces the minimal coupling scheme for simple operators. By combining this construction with the formalism of projected entangled-pair states (PEPS), we can show that an injective PEPS for the matter fields is gauged into a G-injective PEPS for the combined gauge-matter system, which potentially has topological order. We derive the corresponding parent Hamiltonian, which is a frustration-free gauge-theory Hamiltonian closely related to the Kogut-Susskind Hamiltonian at zero coupling constant. We can then introduce gauge dynamics at finite values of the coupling constant by applying a local filtering operation. This scheme results in a low-parameter family of gauge-invariant states of which we can accurately probe the phase diagram, as we illustrate by studying a Z(2) gauge theory with Higgs matter.
Closing the stop gap

Light stops are a hallmark of the most natural realizations of weak-scale supersymmetry. While stops have been extensively searched for, there remain open gaps around and below the top mass, due to similarities of stop and top signals with current statistics. We propose a new fast-track avenue to improve light stop searches for R-parity conserving supersymmetry, by comparing top cross section measurements to the theoretical prediction. Stop masses below ∼ 180 GeV can now be ruled out for a light neutralino. The possibility of a stop signal contaminating the top mass measurement is also brieﬂy addressed.
Planets and Stellar Activity: Hide and Seek in the CoRoT-7 system

Since the discovery of the transiting Super-Earth CoRoT-7b, several investigations have been made of the number and precise masses of planets present in the system, but they all yield different results, owing to the star's high level of activity. Radial velocity (RV) variations induced by stellar activity therefore need to be modelled and removed to allow a reliable detection of all planets in the system. We re-observed CoRoT-7 in January 2012 with both HARPS and the CoRoT satellite, so that we now have the benefit of simultaneous RV and photometric data. We fitted the off-transit variations in the CoRoT lightcurve using a harmonic decomposition similar to that implemented in Queloz et al. (2009). This fit was then used to model the stellar RV contribution, according to the methods described by Aigrain et al. (2011). This model was incorporated into a Monte Carlo Markov Chain in order to make a precise determination of the orbits of CoRoT-7b and CoRoT-7c. We also assess the evidence for the presence of one or two additional planetary companions.
Fitting the annual modulation in DAMA with neutrons from muons and neutrinos.

The DAMA/LIBRA experiment searches for evidence of dark matter scattering off nuclei. Data from DAMA show 9.2 σ evidence for an annual modulation, consistent with dark matter having a cross section around 2 × 10(-40) cm(2). However, this is excluded by other direct detection experiments. We propose an alternative source of annual modulation in the form of neutrons, which have been liberated from material surrounding the detector by a combination of (8)B solar neutrinos and atmospheric muons. The phase of the muon modulation lags 30 days behind the data; however, we show that adding the modulated neutrino component shifts the phase of the combined signal forward. In addition, we estimate that neutrinos and muons need ∼ 1000 m(3) of scattering material in order to generate enough neutrons to constitute the signal. With current data, our model gives as good a fit as dark matter, and we discuss prospects for future experiments to discriminate between the two.
REVISION OF EARTH-SIZED KEPLER PLANET CANDIDATE PROPERTIES WITH HIGH-RESOLUTION IMAGING BY THE HUBBLE SPACE TELESCOPE

We present the results of our Hubble Space Telescope program and describe how our analysis methods were used to re-evaluate the habitability of some of the most interesting Kepler planet candidates. Our program observed 22 Kepler Object of Interest (KOI) host stars, several of which were found to be multiple star systems unresolved by Kepler. We use our high-resolution imaging to spatially resolve the stellar multiplicity of Kepler-296, KOI-2626, and KOI-3049, and develop a conversion to the Kepler photometry (Kp) from the F555W and F775W filters on WFC3/UVIS. The binary system Kepler-296 (five planets) has a projected separation of 0 &farcs; 217 ?> (80 AU); KOI-2626 (one planet candidate) is a triple star system with a projected separation of 0 &farcs; 201 ?> (70 AU) between the primary and secondary components and 0 &farcs; 161 ?> (55 AU) between the primary and tertiary; and the binary system KOI-3049 (one planet candidate) has a projected separation of 0 &farcs; 464 ?> (225 AU). We use our measured photometry to fit the separated stellar components to the latest Victoria–Regina Stellar Models with synthetic photometry to conclude that the systems are coeval. The components of the three systems range from mid-K dwarf to mid-M dwarf spectral types.We solved for the planetary properties of each system analytically and via an MCMC algorithm using our independent stellar parameters. The planets range from ∼ 1.6 to ∼ 4.2 R ⨁ ?> , mostly Super Earths and mini-Neptunes. As a result of the stellar multiplicity, some planets previously in the Habitable Zone are, in fact, not, and other planets may be habitable depending on their assumed stellar host.
Unveiling the Origin of Fast Radio Bursts by Optical Follow Up Observations

We discuss how we can detect and identify counterparts of fast radio bursts (FRBs) in future optical follow up observations of FRBs if real-time alert of FRBs becomes available. We consider kilonovae as candidates of FRB optical counterparts, as expected in the case that FRBs originate from mergers of double neutron star binaries. Although theoretical predictions on luminosities of kilonovae are still highly uncertain, recent models suggest that kilonovae can be detected at redshifts up to z $\sim$ 0.3 within the range of the uncertainties. We expect $\sim$ 1--5 unrelated supernovae (SNe) down to a similar variability magnitude in 5 days interval within the typical error radius of a FRB. We show that, however, a kilonova can be distinguished from these SNe by its rapid decay and/or color evolution, making it possible to verify the existence of a kilonova associated with a FRB. We also discuss the case that SNe Ia are FRB optical counterparts, as it might be if FRBs originate from double white dwarf binaries. Verification of this scenario is also possible, since the chance probability of finding a SNe Ia having consistent explosion time with that of a FRB within the FRB error region is small (typically $\lesssim$ 0.01).
Robust Optimization using Machine Learning for Uncertainty Sets

Our goal is to build robust optimization problems for making decisions based on complex data from the past. In robust optimization (RO) generally, the goal is to create a policy for decision-making that is robust to our uncertainty about the future. In particular, we want our policy to best handle the the worst possible situation that could arise, out of an uncertainty set of possible situations. Classically, the uncertainty set is simply chosen by the user, or it might be estimated in overly simplistic ways with strong assumptions; whereas in this work, we learn the uncertainty set from data collected in the past. The past data are drawn randomly from an (unknown) possibly complicated high-dimensional distribution. We propose a new uncertainty set design and show how tools from statistical learning theory can be employed to provide probabilistic guarantees on the robustness of the policy.
Cosmological Constant in a Quantum Gravity Theory for a Piecewise-linear Spacetime

We study the quantum contributions to the classical cosmological constant in a quantum gravity theory for GR with matter on a piecewise linear spacetime corresponding to a triangulation of a smooth manifold. We use the effective action approach and a special path-integral (PI) measure which depends on a free parameter, while matter is modeled by a massive self-interacting scalar field. The effective cosmological constant (CC) is given as a sum of 3 terms: the classical CC, the quantum gravity CC and the matter CC. We show that the free parameters of the theory can be chosen such that the classical CC cancels the matter CC so that the effective CC is given by the QG CC. Since the value of the quantum gravity CC is determined by the PI measure only, the PI measure parameter can be chosen such that the effective CC gives the observed value. This is equivalent to the statement that the experimentally observed CC value belongs to the spectrum of the CC operator in this QG theory.
A Synonym Based Approach of Data Mining in Search Engine Optimization

In today’s era with the rapid growth of information on the web, makes users turn to search engines as a replacement of traditional media. This makes sorting of particular information through billions of webpages and displaying the relevant data makes the task tough for the search engine. Remedy for this is SEO (Search Engine Optimization), i.e. having a website optimized in such a way that it will display the relevant webpages based on ranking. This is the main reason that makes search engine optimization a prominent position in online world. This paper present a synonym based data mining approach for SEO that makes the task of improving the ranking of the website much easier way and user will get answer to their query easily through any of search engine available in market.
Identifying Higher-order Combinations of Binary Features

Finding statistically significant interactions between binary variables is computationally and statistically challenging in high-dimensional settings, due to the combinatorial explosion in the number of hypotheses. Terada et al. recently showed how to elegantly address this multiple testing problem by excluding non-testable hypotheses. Still, it remains unclear how their approach scales to large datasets. 
We here proposed strategies to speed up the approach by Terada et al. and evaluate them thoroughly in 11 real-world benchmark datasets. We observe that one approach, incremental search with early stopping, is orders of magnitude faster than the current state-of-the-art approach.