nils-herrmann/clean_scirepeval huggingface dataset:
Internal structure of exotic hadrons by high-energy exclusive reactions

We propose to use high-energy exclusive reactions for probing internal structure of exotic hadron candidates. First, the constituent counting rule of perturbative QCD can be used for finding internal configurations of an exotic hadron candidate. It is because the number of constituents ($n$), which participate in the exclusive reaction, is found by the scaling behavior of the cross section $d\sigma/dt \propto 1/s^{n-2}$ at large momentum transfer, where $s$ is the center-of-mass energy squared. As an example, we show that the internal structure of $\Lambda \, (1405)$ should be found, for example, by the reaction $\pi^- + p \to K^0 + \Lambda (1405)$. Second, the internal structure of exotic hadron candidates should be investigated by hadron tomography with generalized parton distributions (GPDs) and generalized distribution amplitudes (GDAs) in exclusive reactions. Exotic nature should be reflected in the GPDs which contain two factors, longitudinal parton distributions as indicated by the constituent counting rule and transverse form factors as suggested by the hadron size. The GDAs should be investigated by the two-photon process $\gamma^* \gamma \to h\bar h$, for example $h=f_0$ or $a_0$, in electron-positron annihilation. Since the GDAs contain information on a time-like form factor, exotic nature should be found by studying the $h\bar h$ invariant mass dependence of the cross section. The internal structure of exotic hadron candidates should be clarified by the exclusive reactions at facilities such as J-PARC and KEKB.
Black hole horizons and quantum charged particles

We point out a structural similarity between the characterization of black hole apparent horizons as stable, marginally outer trapped surfaces (MOTS) and the quantum description of a non-relativistic charged particle moving in given magnetic and electric fields on a closed surface. Specifically, the spectral problem of the MOTS-stability operator corresponds to a stationary quantum particle with a formal fine-structure constant α of negative sign. We discuss how such analogy enriches both problems, illustrating this with the insights into the MOTS-spectral problem gained from the analysis of the spectrum of the quantum charged particle Hamiltonian.
A massively parallel algorithm for constructing the BWT of large string sets

We present a new scalable, lightweight algorithm to incrementally construct the BWT and FM-index of large string sets such as those produced by Next Generation Sequencing. The algorithm is designed for massive parallelism and can effectively exploit the combination of low capacity high bandwidth memory and slower external system memory typical of GPU accelerated systems. Particularly, for a string set of n characters from an alphabet with \sigma symbols, it uses a constant amount of high-bandwidth memory and at most 3n log(\sigma) bits of system memory. Given that deep memory hierarchies are becoming a pervasive trait of high performance computing architectures, we believe this to be a relevant feature. The implementation can handle reads of arbitrary length and is up to 2 and respectively 6.5 times faster than state-of-the-art for short and long genomic reads
Likelihood smoothing using gravitational wave surrogate models

Likelihood surfaces in the parameter space of gravitational wave signals can contain many secondary maxima, which can prevent search algorithms from finding the global peak and correctly mapping the distribution. Traditional schemes to mitigate this problem maintain the number of secondary maxima and thus retain the possibility that the global maximum will remain undiscovered. By contrast, the recently proposed technique of likelihood transform can modify the structure of the likelihood surface to reduce its complexity. We present a practical method to carry out a likelihood transform using a Gaussian smoothing kernel, utilising gravitational wave surrogate models to perform the smoothing operation analytically. We demonstrate the approach with Newtonian and post-Newtonian waveform models for an inspiralling circular compact binary.
Deep Directed Generative Autoencoders

For discrete data, the likelihood $P(x)$ can be rewritten exactly and parametrized into $P(X = x) = P(X = x | H = f(x)) P(H = f(x))$ if $P(X | H)$ has enough capacity to put no probability mass on any $x'$ for which $f(x')\neq f(x)$, where $f(\cdot)$ is a deterministic discrete function. The log of the first factor gives rise to the log-likelihood reconstruction error of an autoencoder with $f(\cdot)$ as the encoder and $P(X|H)$ as the (probabilistic) decoder. The log of the second term can be seen as a regularizer on the encoded activations $h=f(x)$, e.g., as in sparse autoencoders. Both encoder and decoder can be represented by a deep neural network and trained to maximize the average of the optimal log-likelihood $\log p(x)$. The objective is to learn an encoder $f(\cdot)$ that maps $X$ to $f(X)$ that has a much simpler distribution than $X$ itself, estimated by $P(H)$. This "flattens the manifold" or concentrates probability mass in a smaller number of (relevant) dimensions over which the distribution factorizes. Generating samples from the model is straightforward using ancestral sampling. One challenge is that regular back-propagation cannot be used to obtain the gradient on the parameters of the encoder, but we find that using the straight-through estimator works well here. We also find that although optimizing a single level of such architecture may be difficult, much better results can be obtained by pre-training and stacking them, gradually transforming the data distribution into one that is more easily captured by a simple parametric model.
Inflation in scale-invariant theories of gravity

Thanks to the Planck Collaboration, we know the value of the scalar spectral index of primordial fluctuations with unprecedented precision. In addition, the joint analysis of the data from Planck, BICEP2, and KEK has further constrained the value of the tensor-to-scalar ratio $r$ so that chaotic inflationary scenarios seem to be disfavoured. Inspired by these results, we look for a model that yields a value of $r$ that is larger than the one predicted by the Starobinsky model but is still within the new constraints. We show that purely quadratic, renormalizable, and scale-invariant gravity, implemented by loop-corrections, satisfies these requirements.
‘The physics of life,’ an undergraduate general education biophysics course

Improving the scientific literacy of non-scientists is an important aim, both because of the ever-increasing impact of science on our lives and because understanding science enriches our experience of the natural world. One route to improving scientific literacy is via general education undergraduate courses—i.e. courses for students not majoring in the sciences or engineering. Because it encompasses a variety of important scientific concepts, demonstrates connections between basic science and real-world applications and illustrates the creative ways in which scientific insights develop, biophysics is a useful subject with which to promote scientific literacy. I describe here a course on biophysics for non-science-major undergraduates recently developed at the University of Oregon (Eugene, OR, USA), noting its design, which spans both macroscopic and microscopic topics, and the specific content of a few of its modules. I also describe evidence-based pedagogical approaches adopted in teaching the course and aspects of course enrollment and evaluation.
Fair bilateral prices in Bergman's model

Bielecki and Rutkowski (2014) introduced and studied a generic nonlinear market model, which includes several risky assets, multiple funding accounts and margin accounts. In this paper, we examine the pricing and hedging of contract both from the perspective of the hedger and the counterparty with arbitrary initial endowments. We derive inequalities for unilateral prices and we give the range for either fair bilateral prices or bilaterally profitable prices. We also study the monotonicity of a unilateral price with respect to the initial endowment. Our study hinges on results for BSDE driven by continuous martingales obtained in Nie and Rutkowski (2014), but we also derive the pricing PDEs for path-independent contingent claims of European style in a Markovian framework.
Observation of Majorana fermions in ferromagnetic atomic chains on a superconductor

A possible sighting of Majorana states Nearly 80 years ago, the Italian physicist Ettore Majorana proposed the existence of an unusual type of particle that is its own antiparticle, the so-called Majorana fermion. The search for a free Majorana fermion has so far been unsuccessful, but bound Majorana-like collective excitations may exist in certain exotic superconductors. Nadj-Perge et al. created such a topological superconductor by depositing iron atoms onto the surface of superconducting lead, forming atomic chains (see the Perspective by Lee). They then used a scanning tunneling microscope to observe enhanced conductance at the ends of these chains at zero energy, where theory predicts Majorana states should appear. Science, this issue p. 602; see also p. 547 Scanning tunneling microscopy is used to observe signatures of Majorana states at the ends of iron atom chains.  Using high-resolution spectroscopic imaging techniques, we show that the onset of superconductivity, which gaps the electronic density of states in the bulk of the Fe chains, is accompanied by the appearance of zero-energy end-states. This spatially resolved signature provides strong evidence, corroborated by other observations, for the formation of a topological phase and edge-bound Majorana fermions in our atomic chains.
Testing the nonlinear stability of Kerr-Newman black holes

The nonlinear stability of Kerr-Newman black holes (KNBHs) is investigated by performing numerical simulations within the full Einstein-Maxwell theory. We take as initial data a KNBH with mass M, angular momentum to mass ratio a and charge Q. Evolutions are performed to scan this parameter space within the intervals 0≤a/M≤0.994 and 0≤Q/M≤0.996, corresponding to an extremality parameter a/a_max (amax≡ √(M^2−Q^2)) ranging from 0 to 0.995. These KNBHs are evolved, together with a small bar-mode perturbation, up to a time of order 120M. Our results suggest that for small Q/a, the quadrupolar oscillation modes depend solely on a/a_max, a universality also apparent in previous perturbative studies in the regime of small rotation. Using as a stability criterion the absence of significant relative variations in the horizon areal radius and BH spin, we find no evidence for any developing instability.
Universe as a cosmic string

The cosmology of brane-induced gravity in six infinite dimensions is investigated. It is shown that a brane with Friedmann-Robertson-Walker symmetries necessarily acts as a source of cylindrically symmetric gravitational waves, so-called Einstein-Rosen waves. Their existence essentially distinguishes this model from its codimension-one counterpart and necessitates solving the nonlinear system of bulk and brane-matching equations. A numerical analysis is performed and two qualitatively different and dynamically separated classes of cosmologies are derived: Degravitating solutions for which the Hubble parameter settles to zero despite the presence of a novanishing energy density on the brane, and superaccelerating solutions for which Hubble grows unbounded. The parameter space of both the stable and unstable regime is derived and observational consequences are discussed: It is argued that the degravitating regime does not allow for a phenomenologically viable cosmology. On the other hand, the superaccelerating solutions are potentially viable; however, their unstable behavior questions their physical relevance.
Exploring Critical Collapse in the Semilinear Wave Equation using Space-Time Finite Elements

1 Department of Mathematics and Statistics, South Dakota State University, Brookings, SD 57007, USA and2 Center for Research in Extreme Scale Technologies,Indiana University, Bloomington, IN 47405, USA(Dated: October 8, 2014)A fully implicit numerical approach based on the space-time nite element method is implementedfor the semilinear wave equation in 1(space) + 1(time) dimensions to explore critical collapse andsearch for self-similar solutions. Previous work studied this behavior by exploring the threshold ofsingularity formation using time marching nite di erence techniques while this work introducesan adaptive time parallel numerical method to the problem. The semilinear wave equation witha p = 7 term is examined in spherical symmetry. The impact of mesh re nement and the timeadditive Schwarz preconditioner in conjunction with Krylov Subspace Methods are examined.
A General Duality Relation with Applications in Quantitative Risk Management

A fundamental problem in risk management is the robust aggregation of different sources of risk in a situation where little or no data are available to infer information about their dependencies. A popular approach to solving this problem is to formulate an optimization problem under which one maximizes a risk measure over all multivariate distributions that are consistent with the available data. In several special cases of such models, there exist dual problems that are easier to solve or approximate, yielding robust bounds on the aggregated risk. In this chapter we formulate a general optimization problem, which can be seen as a doubly infinite linear programming problem, and we show that the associated dual generalizes several well known special cases and extends to new risk management models we propose.
BayesPy: Variational Bayesian Inference in Python

BayesPy is an open-source Python software package for performing variational Bayesian inference. It is based on the variational message passing framework and supports conjugate exponential family models. By removing the tedious task of implementing the variational Bayesian update equations, the user can construct models faster and in a less error-prone way. Simple syntax, flexible model construction and efficient inference make BayesPy suitable for both average and expert Bayesian users. It also supports some advanced methods such as stochastic and collapsed variational inference.
Probit Normal Correlated Topic Models

The logistic normal distribution has recently been adapted via the transformation of multivariate Gaus- sian variables to model the topical distribution of documents in the presence of correlations among topics. In this paper, we propose a probit normal alternative approach to modelling correlated topical structures. Our use of the probit model in the context of topic discovery is novel, as many authors have so far con- centrated solely of the logistic model partly due to the formidable inefficiency of the multinomial probit model even in the case of very small topical spaces. We herein circumvent the inefficiency of multinomial probit estimation by using an adaptation of the diagonal orthant multinomial probit in the topic models context, resulting in the ability of our topic modelling scheme to handle corpuses with a large number of latent topics. An additional and very important benefit of our method lies in the fact that unlike with the logistic normal model whose non-conjugacy leads to the need for sophisticated sampling schemes, our ap- proach exploits the natural conjugacy inherent in the auxiliary formulation of the probit model to achieve greater simplicity. The application of our proposed scheme to a well known Associated Press corpus not only helps discover a large number of meaningful topics but also reveals the capturing of compellingly intuitive correlations among certain topics. Besides, our proposed approach lends itself to even further scalability thanks to various existing high performance algorithms and architectures capable of handling millions of documents.
Tight Regret Bounds for Stochastic Combinatorial Semi-Bandits

A stochastic combinatorial semi-bandit is an online learning problem where at each step a learning agent chooses a subset of ground items subject to constraints, and then observes stochastic weights of these items and receives their sum as a payoff. In this paper, we close the problem of computationally and sample efficient learning in stochastic combinatorial semi-bandits. In particular, we analyze a UCB-like algorithm for solving the problem, which is known to be computationally efficient; and prove $O(K L (1 / \Delta) \log n)$ and $O(\sqrt{K L n \log n})$ upper bounds on its $n$-step regret, where $L$ is the number of ground items, $K$ is the maximum number of chosen items, and $\Delta$ is the gap between the expected returns of the optimal and best suboptimal solutions. The gap-dependent bound is tight up to a constant factor and the gap-free bound is tight up to a polylogarithmic factor.
Research Update on Extreme-Mass-Ratio Inspirals

The inspirals of stellar-mass mass compact objects into massive black holes in the centres of galaxies are one of the most important sources of gravitational radiation for space- based detectors like LISA or eLISA. These extreme-mass-ratio inspirals (EMRIs) will enable an ambitious research program with implications for astrophysics, cosmology, and fundamental physics. This article is a summary of the talks delivered at the plenary session on EMRIs at the 10th International LISA Symposium. It contains research updates on the following topics: astrophysics of EMRIs; EMRI science potential; and EMRI modeling.
Stable FLRW solutions in Generalized Massive Gravity

We present exact FLRW solutions in generalized massive gravity where the mass parameters are naturally promoted to Lorentz-invariant functions of the Stuckelberg fields. This new dependence relaxes the constraint that would otherwise prevent massive gravity from possessing exact FLRW solutions. It does so without the need to introduce additional degrees of freedom. We find self-accelerating cosmological solutions and show that, with a mild restriction on the region of phase space, these cosmological solutions exhibit full stability, i.e. absence of ghosts and gradient instabilities for all the tensor, vector and scalar modes, for all cosmic time. We perform the full decoupling limit analysis, including vector degrees of freedom, which can be used to confirm the existence of an active Vainshtein mechanism about these solutions.
Minimax Analysis of Active Learning

This work establishes distribution-free upper and lower bounds on the minimax label complexity of active learning with general hypothesis classes, under various noise models. The results reveal a number of surprising facts. In particular, under the noise model of Tsybakov (2004), the minimax label complexity of active learning with a VC class is always asymptotically smaller than that of passive learning, and is typically significantly smaller than the best previously-published upper bounds in the active learning literature. In high-noise regimes, it turns out that all active learning problems of a given VC dimension have roughly the same minimax label complexity, which contrasts with well-known results for bounded noise. In low-noise regimes, we find that the label complexity is well-characterized by a simple combinatorial complexity measure we call the star number. Interestingly, we find that almost all of the complexity measures previously explored in the active learning literature have worst-case values exactly equal to the star number. We also propose new active learning strategies that nearly achieve these minimax label complexities.
A simple test for spacetime symmetry

This paper presents a simple method for investigating spacetime symmetry for a given metric. The method makes use of the curvature conditions that are obtained from the Killing equations. We use the solutions of the curvature conditions to compute an upper bound on the number of Killing vector fields, as well as Killing–Yano (KY) tensors and closed conformal KY tensors. We also use them in the integration of the Killing equations. By means of the method, we thoroughly investigate KY symmetry of type D vacuum solutions such as the Kerr metric in four dimensions. The method is also applied to a large variety of physical metrics in four and five dimensions.
On the Computational Efficiency of Training Neural Networks

It is well-known that neural networks are computationally hard to train. On the other hand, in practice, modern day neural networks are trained efficiently using SGD and a variety of tricks that include different activation functions (e.g. ReLU), over-specification (i.e., train networks which are larger than needed), and regularization. In this paper we revisit the computational complexity of training neural networks from a modern perspective. We provide both positive and negative results, some of them yield new provably efficient and practical algorithms for training certain types of neural networks.
Parity-violating nucleon-nucleon force in the 1/N(c) expansion.

Several experimental investigations have observed parity violation (PV) in nuclear systems-a consequence of the weak force between quarks. We apply the 1/N(c) expansion of QCD to the P-violating T-conserving component of the nucleon-nucleon (NN) potential. We show there are two leading-order operators, both of which affect p[over →]p scattering at order N(c). We find an additional four operators at order N(c)(0)sin(2)θ(W) and six at O(1/N(c)). Pion exchange in the PV NN force is suppressed by 1/N(c) and sin(2)θ(W), providing a quantitative explanation for its nonobservation up to this time. The large-N(c) hierarchy of other PV NN force mechanisms is consistent with estimates of the couplings in phenomenological models. The PV observed in p[over →]p scattering data is compatible with natural values for the strong and weak coupling constants: there is no evidence of fine-tuning.
Synonymous and nonsynonymous distances help untangle convergent evolution and recombination

When estimating a phylogeny from a multiple sequence alignment, researchers often assume the absence of recombination. However, if recombination is present, then tree estimation and all downstream analyses will be impacted, because different segments of the sequence alignment support different phylogenies. Similarly, convergent selective pressures at the molecular level can also lead to phylogenetic tree incongruence across the sequence alignment. Current methods for detection of phylogenetic incongruence are not equipped to distinguish between these two different mechanisms and assume that the incongruence is a result of recombination or other horizontal transfer of genetic information. We propose a new recombination detection method that can make this distinction, based on synonymous codon substitution distances. Although some power is lost by discarding the information contained in the nonsynonymous substitutions, our new method has lower false positive probabilities than the comparable recombination detection method when the phylogenetic incongruence signal is due to convergent evolution. We apply our method to three empirical examples, where we analyze: (1) sequences from a transmission network of the human immunodeficiency virus, (2) tlpB gene sequences from a geographically diverse set of 38 Helicobacter pylori strains, and (3) hepatitis C virus sequences sampled longitudinally from one patient.
Constructing a taxonomy of fine-grained human movement and activity motifs through social media

Profiting from the emergence of web-scale social data sets, numerous recent studies have systematically explored human mobility patterns over large populations and large time scales. Relatively little attention, however, has been paid to mobility and activity over smaller time-scales, such as a day. Here, we use Twitter to identify people's frequently visited locations along with their likely activities as a function of time of day and day of week, capitalizing on both the content and geolocation of messages. We subsequently characterize people's transition pattern motifs and demonstrate that spatial information is encoded in word choice.