nils-herrmann/clean_scirepeval huggingface dataset:
Internal Symmetry of Space-Time Connections with Torsion

In this brief article an internal symmetry of a generic metric compatible space-time connection, metric and generalized volume element is introduced. The symmetry arises naturally by considering a space-time connection containing a generic torsion tensor, and would otherwise be missed in the absence of torsion. When the transformation is applied to the Hilbert-Einstein action it is shown that by a choice of gauge all possible field theories arising from the Hilbert-Einstein action are equivalent to the standard theory of gravity described by general relativity.
Linking GloVe with word2vec

The Global Vectors for word representation (GloVe), introduced by Jeffrey Pennington et al. is reported to be an efficient and effective method for learning vector representations of words. State-of-the-art performance is also provided by skip-gram with negative-sampling (SGNS) implemented in the word2vec tool. In this note, we explain the similarities between the training objectives of the two models, and show that the objective of SGNS is similar to the objective of a specialized form of GloVe, though their cost functions are defined differently.
Pion-pion cross section from proton-proton collisions at the LHC

The zero-degree calorimeters (ZDC) installed in the ALICE, ATLAS and CMS experiments at the LHC, make possible simultaneous detection of forward-backward leading neutrons, pp=>nXn. Such data with sufficiently high statistics could be a source of information about the pion-pion total cross section at high energies, provided that the absorption corrections, which are expected to be strong, are well understood. Otherwise, making a plausible assumption about the magnitude of the pion-pion cross section, one can consider such measurements as a way to study the absorption effects, which is the main focus of the present paper. These effects introduced at the amplitude level, are found to be different for the pion fluxes, which either conserve or flip the nucleon helicity. The pion fluxes from both colliding protons are essentially reduced by absorption, moreover, there is a common absorption suppression factor, which breaks down the factorized form of the cross section. We also evaluate the feed-down corrections related to the initial/final state inelastic processes possessing a rapidity gap, and found them to be small in the kinematic range under consideration. The contribution of other iso-vector Reggeons, spin-flip natural parity rho and a2, and spin non-flip unnatural parity a1 are also evaluated and found to be rather small.
Justifying answer sets using argumentation

An answer set is a plain set of literals which has no further structure that would explain why certain literals are part of it and why others are not. We show how argumentation theory can help to explain why a literal is or is not contained in a given answer set by defining two justification methods, both of which make use of the correspondence between answer sets of a logic program and stable extensions of the assumption-based argumentation (ABA) framework constructed from the same logic program. Attack Trees justify a literal in argumentation-theoretic terms, i.e. using arguments and attacks between them, whereas ABA-Based Answer Set Justifications express the same justification structure in logic programming terms, that is using literals and their relationships. Interestingly, an ABA-Based Answer Set Justification corresponds to an admissible fragment of the answer set in question, and an Attack Tree corresponds to an admissible fragment of the stable extension corresponding to this answer set.
Discreteness of time in the evolution of the universe

In this paper, we will first derive the Wheeler-DeWitt equation for the generalized geometry which occurs in M-theory. Then we will observe that M2-branes act as probes for this generalized geometry, and as M2-branes have an extended structure, their extended structure will limits the resolution to which this generalized geometry can be defined. We will demonstrate that this will deform the Wheeler-DeWitt equation for the generalized geometry. We analyze such a deformed Wheeler-DeWitt equation in the minisuperspace approximation and observe that this deformation can be used as a solution to the problem of time. This is because this deformation gives rise to time crystals in our universe due to the spontaneous breaking of time re-parametrization invariance.
Cosmological tests using the angular size of galaxy clusters

We use measurements of the galaxy-cluster angular size versus redshift to test and compare the standard model (Lambda CDM) and the R-h = ct Universe. We show that the latter fits the data with a reduced chi(2)(dof) = 0.786 for a Hubble constant H-0 = 72.6(-3.4)(+3.8) km s(-1) Mpc(-1), and H-0 is the sole parameter in this model. By comparison, the optimal flat Lambda cold dark matter (Lambda CDM) model, with two free parameters (including Omega(m) = 0.50 and H-0 = 73.9(-9.5)(+10.6) km s(-1) Mpc(-1)), fits the angular-size data with a reduced chi(2)(dof) = 0.806. On the basis of their chi(2)(dof) values alone, both models appear to account for the data very well in spite of the fact that the R-h = ct Universe expands at a constant rate, while Lambda CDM does not. However, because of the different number of free parameters in these models, selection tools, such as the Bayes Information Criterion, favour R-h = ct over Lambda CDM with a likelihood of similar to 86 per cent versus similar to 14 per cent. These results impact the question of galaxy growth at large redshifts. Previous work suggested an inconsistency with the underlying cosmological model unless elliptical and disc galaxies grew in size by a surprisingly large factor similar to 6 from z similar to 3 to 0. The fact that both Lambda CDM and R-h = ct fit the cluster-size measurements quite well casts some doubt on the suggestion that the unexpected result with individual galaxies may be due to the use of an incorrect expansion scenario, rather than astrophysical causes, such as mergers and/or selection effects.
Wess-Zumino-Witten Terms in Graphene Landau Levels.

We consider the interplay between the antiferromagnetic and Kekulé valence bond solid orderings in the zero energy Landau levels of neutral monolayer and bilayer graphene. We establish the presence of Wess-Zumino-Witten terms between these orders: this implies that their quantum fluctuations are described by the deconfined critical theories of quantum spin systems. We present implications for experiments, including the possible presence of excitonic superfluidity in bilayer graphene.
Quantum versus classical annealing of Ising spin glasses

Benchmarking quantum simulation Finding a solution to a problem often amounts to an optimization problem and thus can be recast in terms of the lowest-energy state of a system. To find such ground states, mathematical methods based on annealing were developed. To reach the ground state more quickly than with the earlier classical methods, a quantum-mechanical approach was proposed; however, the evidence for quantum speed-up is contradictory. Heim et al. show that the results depend on how the problem is described and how the optimization routine is implemented. This development should be valuable for benchmarking quantum machines. Science, this issue p. 215 For quantum machines to have a competitive advantage, they have to be posed the right question. Quantum annealers use quantum fluctuations to escape local minima and find low-energy configurations of a physical system. Strong evidence for superiority of quantum annealing (QA) has come from comparing QA implemented through quantum Monte Carlo (QMC) simulations to classical annealing. Motivated by recent experiments, we revisit the question of when quantum speedup may be expected. Although a better scaling is seen for QA in two-dimensional Ising spin glasses, this advantage is due to time discretization artifacts and measurements that are not possible on a physical quantum annealer. Simulations in the physically relevant continuous time limit, on the other hand, do not show superiority. Our results imply that care must be taken when using QMC simulations to assess the potential for quantum speedup.
Constructing black hole entropy from gravitational collapse

Based on a recent proposal for the gravitational entropy of free gravitational fields, we investigate the thermodynamic properties of black hole formation through gravitational collapse in the framework of the semitetrad 1+1+2 covariant formalism. In the simplest case of an Oppenheimer-Snyder-Datt collapse we prove that the change in gravitational entropy outside a collapsing body is related to the variation of the surface area of the body itself, even before the formation of horizons. As a result, we are able to relate the Bekenstein-Hawking entropy of the black hole endstate to the variation of the vacuum gravitational entropy outside the collapsing body.
Metric recovery from directed unweighted graphs

We analyze directed, unweighted graphs obtained from $x_i\in \mathbb{R}^d$ by connecting vertex $i$ to $j$ iff $|x_i - x_j| < \epsilon(x_i)$. Examples of such graphs include $k$-nearest neighbor graphs, where $\epsilon(x_i)$ varies from point to point, and, arguably, many real world graphs such as co-purchasing graphs. We ask whether we can recover the underlying Euclidean metric $\epsilon(x_i)$ and the associated density $p(x_i)$ given only the directed graph and $d$. 
We show that consistent recovery is possible up to isometric scaling when the vertex degree is at least $\omega(n^{2/(2+d)}\log(n)^{d/(d+2)})$. Our estimator is based on a careful characterization of a random walk over the directed graph and the associated continuum limit. As an algorithm, it resembles the PageRank centrality metric. We demonstrate empirically that the estimator performs well on simulated examples as well as on real-world co-purchasing graphs even with a small number of points and degree scaling as low as $\log(n)$.
Visual Sentiment Prediction with Deep Convolutional Neural Networks

Images have become one of the most popular types of media through which users convey their emotions within online social networks. Although vast amount of research is devoted to sentiment analysis of textual data, there has been very limited work that focuses on analyzing sentiment of image data. In this work, we propose a novel visual sentiment prediction framework that performs image understanding with Deep Convolutional Neural Networks (CNN). Specifically, the proposed sentiment prediction framework performs transfer learning from a CNN with millions of parameters, which is pre-trained on large-scale data for object recognition. Experiments conducted on two real-world datasets from Twitter and Tumblr demonstrate the effectiveness of the proposed visual sentiment analysis framework.
Study of isospin nonconservation in the framework of spectral distribution theory

The observed isospin-symmetry breaking in light nuclei are caused not only by the Coulomb interaction but by the isovector one and two body plus isotensor two body nuclear interactions as well. Spectral distribution theory which treats nuclear spectroscopy and other structural properties in a statistical framework was earlier applied to isospin conserving Hamiltonians only. In this paper we extend that to include the nuclear interactions non-scalar in isospin and work out examples in sd shell to calculate the linear term in the isobaric mass-multiplet equation originating from these non-scalar parts.
Discrete Solitons in Waveguide Arrays with Long-Range Linearly Coupled Effect

We study the influences to the discrete soliton (DS) by introducing linearly long-range nonlocal interactions, which give rise to the off-diagonal elements of the linearly coupled matrix in the discrete nonlinear schrodinger equation to be filled bynon-zero terms. Theoretical analysis and numerical simulations find that the DS under this circumstance can exhibit strong digital effects: the fundamental DS is a narrow one, which occupies nearly only one waveguide, the dipole and double-monopole solitons, which occupy two waveguides, can be found in self-focusing and -defocusing nonlinearities, respectively. Stable flat-top solitons and their stagger counterparts, which occupy a controllable number of waveguides, can also be obtained through this system. Such digital properties may give rise to additional data processing applications and have potential in fabricating digital optical devices in all-optical networks.
Misassembly detection using paired-end sequence reads and optical mapping data

A crucial problem in genome assembly is the discovery and correction of misassembly errors in draft genomes. We develop a method called misSEQuel that enhances the quality of draft genomes by identifying misassembly errors and their breakpoints using paired-end sequence reads and optical mapping data. Our method also fulfills the critical need for open source computational methods for analyzing optical mapping data. We apply our method to various assemblies of the loblolly pine, Francisella tularensis, rice and budgerigar genomes. We generated and used stimulated optical mapping data for loblolly pine and F.tularensis and used real optical mapping data for rice and budgerigar.  Our results demonstrate that we detect more than 54% of extensively misassembled contigs and more than 60% of locally misassembled contigs in assemblies of F.tularensis and between 31% and 100% of extensively misassembled contigs and between 57% and 73% of locally misassembled contigs in assemblies of loblolly pine. Using the real optical mapping data, we correctly identified 75% of extensively misassembled contigs and 100% of locally misassembled contigs in rice, and 77% of extensively misassembled contigs and 80% of locally misassembled contigs in budgerigar. Availability and implementation: misSEQuel can be used as a post-processing step in combination with any genome assembler and is freely available at http://www.cs.colostate.edu/seq/. Contact: muggli@cs.colostate.edu Supplementary information: Supplementary data are available at Bioinformatics online.
Canonical structure of the E10 model and supersymmetry

A coset model based on the hyperbolic Kac–Moody algebra E10 has been conjectured to underly eleven-dimensional supergravity and M theory. In this note we study the canonical structure of the bosonic model for finite- and infinite-dimensional groups. In the case of finite-dimensional groups like GL(n) we exhibit a convenient set of variables with Borel-type canonical brackets. The generalisation to the Kac–Moody case requires a proper treatment of the imaginary roots that remains elusive. As a second result, we show that the supersymmetry constraint of D = 11 supergravity can be rewritten in a suggestive way using E10 algebra data. Combined with the canonical structure, this rewriting explains the previously observed association of the canonical constraints with null roots of E10. We also exhibit a basic incompatibility between local supersymmetry and the K(E10) ‘R symmetry’, that can be traced back to the presence of imaginary roots and to the unfaithfulness of the spinor representations occurring in the present formulation of the E10 worldline model, and that may require a novel type of bosonisation/fermionisation for its resolution. This appears to be a key challenge for future progress with E10.
Detectable close-in planets around white dwarfs through late unpacking

Although 25%-50% of white dwarfs (WDs) display evidence for remnant planetary systems, their orbital architectures and overall sizes remain unknown. Vibrant close-in (~1 Solar radius) circumstellar activity is detected at WDs spanning many Gyrs in age, suggestive of planets further away. Here we demonstrate how systems with 4 and 10 closely-packed planets that remain stable and ordered on the main sequence can become unpacked when the star evolves into a WD and experience pervasive inward planetary incursions throughout WD cooling. Our full-lifetime simulations run for the age of the Universe and adopt main sequence stellar masses of 1.5, 2.0 and 2.5 Solar masses, which correspond to the mass range occupied by the progenitors of typical present-day WDs. These results provide (i) a natural way to generate an ever-changing dynamical architecture in post-main-sequence planetary systems, (ii) an avenue for planets to achieve temporary close-in orbits that are potentially detectable by transit photometry, and (iii) a dynamical explanation for how residual asteroids might pollute particularly old WDs.
Solar magnetic fields and terrestrial climate

Solar irradiance is considered one of the main natural factors affecting terrestrial climate, and its variations are included in most numerical models estimating the effects of natural versus anthropogenic factors for climate change. Solar wind causing geomagnetic disturbances is another solar activity agent whose role in climate change is not yet fully estimated but is a subject of intense research. For the purposes of climate modeling, it is essential to evaluate both the past and the future variations of solar irradiance and geomagnetic activity which are ultimately due to the variations of solar magnetic fields. Direct measurements of solar magnetic fields are available for a limited period, but can be reconstructed from geomagnetic activity records. Here we present a reconstruction of total solar irradiance based on geomagnetic data, and a forecast of the future irradiance and geomagnetic activity relevant for the expected climate change.
Bell tests with optimal local hidden variable models

An alternative method of detection-loophole-free Bell test is proposed using local hidden variable (LHV) models with optimal detection efficiencies. A framework for constructing such optimal LHV models is presented. Optimal LHV models for maximally and non-maximally entangled twopartite states are constructed to reproduce the quantum correlations within the critical detection efficiencies. The LHV models are shown to be completely equivalent with the existing twopartite Bell inequalities in their optimized setups, and to have even lower critical efficiencies in the LHV modes' own optimized setups. Applications in Bell tests and in device-independent quantum information processing are discussed.
Varying constants quantum cosmology

We discuss minisuperspace models within the framework of varying physical constants theories including Λ-term. In particular, we consider the varying speed of light (VSL) theory and varying gravitational constant theory (VG) using the specific ansätze for the variability of constants: c(a) = c0 an and G(a)=G0 aq. We find that most of the varying c and G minisuperspace potentials are of the tunneling type which allows to use WKB approximation of quantum mechanics. Using this method we show that the probability of tunneling of the universe ``from nothing'' (a=0) to a Friedmann geometry with the scale factor at is large for growing c models and is strongly suppressed for diminishing c models. As for G varying, the probability of tunneling is large for G diminishing, while it is small for G increasing. In general, both varying c and G change the probability of tunneling in comparison to the standard matter content (cosmological term, dust, radiation) universe models.
Stable Bound Orbits in Six-dimensional Myers-Perry Black Holes

The existence of stable bound orbits of test particles is one of the most characteristic properties in black hole spacetimes. In higher-dimensional black holes, due to the dependence of gravity on the spacetime dimensions, there is no stable bound orbit balanced by Newtonian gravitational monopole force and centrifugal force, although this balance occurs in the four-dimensional Kerr black hole. In this paper, however, the existence of stable bound orbits of massive and massless particles is shown in six-dimensional singly spinning Myers-Perry black holes with a value of the spin parameter larger than a critical value. The innermost stable bound orbits and the outermost stable bound orbits are found on the rotational axis.
BayesCAT: Bayesian co‐estimation of alignment and tree

Traditionally, phylogeny and sequence alignment are estimated separately: first estimate a multiple sequence alignment and then infer a phylogeny based on the sequence alignment estimated in the previous step. However, uncertainty in the alignment is ignored, resulting, possibly, in overstated certainty in phylogeny estimates. We develop a joint model for co‐estimating phylogeny and sequence alignment which improves estimates from the traditional approach by accounting for uncertainty in the alignment in phylogenetic inferences. Our insertion and deletion (indel) model allows arbitrary‐length overlapping indel events and a general distribution for indel fragment size. We employ a Bayesian approach using MCMC to estimate the joint posterior distribution of a phylogenetic tree and a multiple sequence alignment. Our approach has a tree and a complete history of indel events mapped onto the tree as the state space of the Markov Chain while alternative previous approaches have a tree and an alignment. A large state space containing a complete history of indel events makes our MCMC approach more challenging, but it enables us to infer more information about the indel process. The performances of this joint method and traditional sequential methods are compared using simulated data as well as real data. Software named BayesCAT (Bayesian Co‐estimation of Alignment and Tree) is available at https://github.com/heejungshim/BayesCAT.
Community-based immunization strategies for epidemic control

Understanding the epidemic dynamics, and finding out efficient techniques to control it, is a challenging issue. A lot of research has been done on targeted immunization strategies, exploiting various global network topological properties. However, in practice, information about the global structure of the contact network may not be available. Therefore, immunization strategies that can deal with a limited knowledge of the network structure are required. In this paper, we propose targeted immunization strategies that require information only at the community level. Results of our investigations on the SIR epidemiological model, using a realistic synthetic benchmark with controlled community structure, show that the community structure plays an important role in the epidemic dynamics. An extensive comparative evaluation demonstrates that the proposed strategies are as efficient as the most influential global centrality based immunization strategies, despite the fact that they use a limited amount of information. Furthermore, they outperform alternative local strategies, which are agnostic about the network structure, and make decisions based on random walks.
Diversifying Sparsity Using Variational Determinantal Point Processes

We propose a novel diverse feature selection method based on determinantal point processes (DPPs). Our model enables one to flexibly define diversity based on the covariance of features (similar to orthogonal matching pursuit) or alternatively based on side information. We introduce our approach in the context of Bayesian sparse regression, employing a DPP as a variational approximation to the true spike and slab posterior distribution. We subsequently show how this variational DPP approximation generalizes and extends mean-field approximation, and can be learned efficiently by exploiting the fast sampling properties of DPPs. Our motivating application comes from bioinformatics, where we aim to identify a diverse set of genes whose expression profiles predict a tumor type where the diversity is defined with respect to a gene-gene interaction network. We also explore an application in spatial statistics. In both cases, we demonstrate that the proposed method yields significantly more diverse feature sets than classic sparse methods, without compromising accuracy.