nils-herrmann/clean_scirepeval huggingface dataset:
Detectability of small-scale magnetic fields in early-type stars

Strong, globally-organized magnetic fields are found for a small fraction of O, B, and A stars. At the same time, many theoretical and indirect observational studies have suggested the ubiquitous presence of weak localized magnetic fields at the surfaces of massive stars. However, no direct detections of such fields have been reported yet.  We have carried out the first comprehensive theoretical investigation of the spectropolarimetric observational signatures of the structured magnetic fields. These calculations are applied to interpret null results of the recent magnetic surveys of massive stars.  The intensity and circular polarization spectra of early-type stars were simulated using detailed polarized radiative transfer calculations with LTE model atmospheres. Similar to observational analyses, the mean Stokes I and V line profiles were obtained by applying a multiline averaging technique. Different spectropolarimetric observables were examined for multiple realizations of randomly distributed radial magnetic field spots on different spatial scales.  We characterize the amplitude of the circular polarization profiles and the mean longitudinal magnetic field as a function of magnetic spot sizes. The dependence of these observables on the effective temperature, projected rotational velocity, and inclination angle is also investigated. Using results of the recently completed Magnetism in Massive Stars (MiMeS) survey, we derive upper limits on the small-scale magnetic fields compatible with the MiMeS nondetections.  According to our simulations, existing spectropolarimetric observations of sharp-lined massive stars rule out the presence of any small-scale fields stronger than 50-250 G, depending on the typical spot sizes. For broad-lined stars, the observations constrain such fields to being below approximately 1 kG.
AMS-02 results support the secondary origin of cosmic ray positrons.

We show that the recent AMS-02 positron fraction measurement is consistent with a secondary origin for positrons and does not require additional primary sources such as pulsars or dark matter. The measured positron fraction at high energy saturates the previously predicted upper bound for secondary production, obtained by neglecting radiative losses. This coincidence, which will be further tested by upcoming AMS-02 data at higher energy, is a compelling indication for a secondary source. Within the secondary model, the AMS-02 data imply a cosmic ray propagation time in the Galaxy of <10(6) yr and an average traversed interstellar matter density of ~1 cm(-3), comparable to the density of the Milky Way gaseous disk, at a rigidity of 300 GV.
Classical and Quantum Algorithms for Testing Equivalence of Group Extensions

While efficient algorithms are known for solving many important problems related to groups, no efficient algorithm is known for determining whether two arbitrary groups are isomorphic. The particular case of 2-nilpotent groups, a special type of central extension, is widely believed to contain the essential hard cases. However, looking specifically at central extensions, the natural formulation of being "the same" is not isomorphism but rather "equivalence," which requires an isomorphism to preserves the structure of the extension. In this paper, we show that equivalence of central extensions can be computed efficiently on a classical computer when the groups are small enough to be given by their multiplication tables. However, in the model of black box groups, which allows the groups to be much larger, we show that equivalence can be computed efficiently on a quantum computer but not a classical one (under common complexity assumptions). Our quantum algorithm demonstrates a new application of the hidden subgroup problem for general abelian groups.
The most powerful particles in the Universe: a cosmic smash

This year we are celebrating 101 years since the discovery of cosmic rays. They are whizzing all around the Universe, and they occur at very different energies, including the highest particle energies that exist. However, theory predicts an abrupt suppression (a "cutoff") above a specific huge energy. This is difficult to verify, the measurements are controversial, but it provides a unique opportunity to probe established concepts of physics - like Lorentz Invariance - under extreme conditions. If the observations will ultimately contradict this "cutoff", this could require a fundamental pillar of physics to be revised.
Granular association rules for multi-valued data

Granular association rule is a new approach to reveal patterns hide in many-to-many relationships of relational databases. Different types of data such as nominal, numeric and multi-valued ones should be dealt with in the process of rule mining. In this paper, we study multi-valued data and develop techniques to filter out strong however uninteresting rules. An example of such rule might be “male students rate movies released in 1990s that are not thriller.” This kind of rules, called negative granular association rules, often overwhelms positive ones which are more useful. To address this issue, we filter out negative granules such as “not thriller” in the process of granule generation. In this way, only positive granular association rules are generated and strong ones are mined. Experimental results on the movielens data set indicate that most rules are negative, and our technique is effective to filter them out.
Cold-start recommendation through granular association rules

Recommender systems are popular in e-commerce as they suggest items of interest to users. Researchers have addressed the cold-start problem where either the user or the item is new. However, the situation with both new user and new item has seldom been considered. In this paper, we propose a cold-start recommendation approach to this situation based on granular association rules. Specifically, we provide a means for describing users and items through information granules, a means for generating association rules between users and items, and a means for recommending items to users using these rules. Experiments are undertaken on a publicly available dataset MovieLens. Results indicate that rule sets perform similarly on the training and the testing sets, and the appropriate setting of granule is essential to the application of granular association rules.
Regge-Wheeler equation, linear stability, and greybody factors for dirty black holes

So-called "dirty" black holes are those surrounded by non-zero stress-energy, rather than vacuum. The presence of the non-zero stress-energy modifies key features of the black hole, such as the surface gravity, Regge-Wheeler equation, linear stability, and greybody factors in a rather nontrivial way. Working within the inverse-Cowling approximation, (effectively the test-field limit), we shall present general forms for the Regge-Wheeler equation for linearized spin 0, spin 1, and axial spin 2 perturbations on an arbitrary static spherically symmetric background spacetime. Using very general features of the background spacetime, (in particular the classical energy conditions for the stress-energy surrounding the black hole), we extract several interesting and robust bounds on the behaviour of such systems, including rigorous bounds on the greybody factors for dirty black holes.
Somoclu: An Efficient Distributed Library for Self-Organizing Maps

Somoclu is a C++ tool for training self-organizing maps on large data sets using a high-performance cluster. It builds on MPI for distributing the workload across the nodes of the cluster. It is also able to boost training by using CUDA if graphics processing units are available. A sparse kernel is included, which is useful for high-dimensional but sparse data, such as the vector spaces common in text mining workflows. The code is released under GNU GPLv3 licence.
Parton physics on a Euclidean lattice.

I show that the parton physics related to correlations of quarks and gluons on the light cone can be studied through the matrix elements of frame-dependent, equal-time correlators in the large momentum limit. This observation allows practical calculations of parton properties on a Euclidean lattice. As an example, I demonstrate how to recover the leading-twist quark distribution by boosting an equal-time correlator to a large momentum.
Are Financial Markets an aspect of Quantum World

Writing the article-Time independent pricing of options in range bound markets; the question in the title came naturally to my mind. It is stated, in the above article, that in certain market conditions the stock price is subjected to an equation that exactly matches a time independent Schrodinger equation. The time independent equation for options valuation is used further to explain a stock market phenomenon that resembles an alpha particle decay tunneling effect. The transmission coefficient for the stock price tunneling effect it is also deduced. Although, it may not have important impact in quantum physics, the philosophical aspects residing in the use of quantum mechanics for stock market specific are very important.
Core collapse and horizontal-branch morphology in Galactic globular clusters

Stellar collision rates in globular clusters (GCs) do not appear to correlate with horizontal branch (HB) morphology, sug- gesting that dynamics does not play a role in the second-parameter problem. However, core densities and collision rates derived from surface-brightness may be significantly underestimated as the surface-brightness profile of GCs is not necessarily a good indicator of the dynamical state of GC cores. Core-collapse may go unnoticed if high central densities of dark remnants are present.  We test whether GC HB morphology data supports a dynamical contribution to the so-called second-parameter effect.  To remove first-parameter dependence we fitted the maximum effective temperature along the HB as a function of metal- licity in a sample of 54 Milky Way GCs. We plotted the residuals to the fit as a function of second-parameter candidates, namely dynamical age and total luminosity. We considered dynamical age (i.e. the ratio between age and half-light relaxation time) among possible second-parameters. We used a set of direct N-body simulations, including ones with dark remnants to illustrate how core density peaks, due to core collapse, in a dynamical-age range similar to that in which blue HBs are overabundant with respect to the metallicity expectation, especially for low-concentration initial conditions.  GC total luminosity shows nonlinear behavior compatible with the self-enrichment picture. However, the data are amenable to a different interpretation based on a dynamical origin of the second-parameter effect. Enhanced mass-stripping in the late red-giant- branch phase due to stellar interactions in collapsing cores is a viable candidate mechanism. In this picture, GCs with HBs bluer than expected based on metallicity are those undergoing core-collapse.
ON THE ORIGIN OF THE B-STARS IN THE GALACTIC CENTER

We present a new directly observable statistic that uses sky position (x, y) and proper motion (vx, vy) of stars near the massive black hole in the Galactic center to identify populations with high orbital eccentricities. It is most useful for stars with large orbital periods for which dynamical accelerations are difficult to determine. We apply this statistic to a data set of B-stars with a projected radii 0.″1 < p < 25″ (∼0.004–1 pc) from the massive black hole in the Galactic center. We compare the results with those from N-body simulations to distinguish between scenarios for their formation. We find that the scenarios favored by the data correlate strongly with particular K-magnitude intervals, corresponding to different zero-age, main-sequence (MS) masses and lifetimes. Stars with 14 ≲ mK ≲ 15 (15–20 M☉, tMS = 8–13 Myr) match a disk formation origin well, whereas those with mK ⩾ 15 (<15 M☉, tMS > 13 Myr), if isotropically distributed, form a population that is more eccentric than thermal, which suggests a Hills binary-disruption origin.
Non-perturbative Massive Solutions in Gravitational Higgs Mechanism

We construct exact non-perturbative massive solutions in the gravitational Higgs mechanism. They confirm the conclusions of , which are based on non-perturbative Hamiltonian analysis for the rel- evant metric degrees of freedom, that while perturbatively unitarity may not be evident, no negative norm state is present in the full nonlinear theory. The non-perturbative massive solutions do not appear to exhibit instabil- ities and describe vacuum configurations which are periodic in time, including purely longitudinal solutions with isotropic periodically expanding and contracting spatial dimensions, "cosmological strings" with only one peri- odically expanding and contracting spatial dimension, and also purely non-longitudinal ("traceless") periodically expanding and contracting solutions with constant spatial volume. As an aside we also discuss massive solutions in New Massive Gravity. While such solutions are present in the linearized theory, we argue that already at the next-to-linear (quadratic) order in the equations of motion (and, more generally, for weak-field configurations) there are no massive solutions.
High Level Pattern Classification via Tourist Walks in Networks

Complex networks refer to large-scale graphs with nontrivial connection patterns. The salient and interesting features that the complex network study offer in comparison to graph theory are the emphasis on the dynamical properties of the networks and the ability of inherently uncovering pattern formation of the vertices. In this paper, we present a hybrid data classification technique combining a low level and a high level classifier. The low level term can be equipped with any traditional classification techniques, which realize the classification task considering only physical features (e.g., geometrical or statistical features) of the input data. On the other hand, the high level term has the ability of detecting data patterns with semantic meanings. In this way, the classification is realized by means of the extraction of the underlying network's features constructed from the input data. As a result, the high level classification process measures the compliance of the test instances with the pattern formation of the training data. Out of various high level perspectives that can be utilized to capture semantic meaning, we utilize the dynamical features that are generated from a tourist walker in a networked environment. Specifically, a weighted combination of transient and cycle lengths generated by the tourist walk is employed for that end. Interestingly, our study shows that the proposed technique is able to further improve the already optimized performance of traditional classification techniques.
Universal extra dimensions after Higgs discovery

We show bounds on five- and six-dimensional universal extra dimension (UED) models from the latest results of the Higgs searches at the LHC and from the electroweak precision data for the $S$ and $T$ parameters. We consider the minimal UED model in five dimensions and the ones in six dimensions, compactified on ${T}^{2}/{Z}_{2}$, ${T}^{2}/({Z}_{2}\ifmmode\times\else\texttimes\fi{}{Z}_{2}^{\ensuremath{'}})$, ${T}^{2}/{Z}_{4}$, ${S}^{2}$, ${S}^{2}/{Z}_{2}$, the real projective plane, and the projective sphere. The highest possible ultraviolet cutoff scale for each UED model is evaluated from the electroweak vacuum stability by solving the renormalization group equation of the Higgs self-coupling. This scale turns out to be lower than the conventional one obtained from the perturbativity of the gauge coupling. The resultant 95% C.L. lower bounds on the first Kaluza\char21{}Klein scale from the LHC results and from the $S$, $T$ analysis are 600 and 700 GeV in the minimal UED model, while those in the six-dimensional UED models are 800\char21{}1300 GeV and 900\char21{}1500 GeV, respectively.
Unsatisfiable Cores for Constraint Programming

Constraint Programming (CP) solvers typically tackle optimization problems by repeatedly finding solutions to a problem while placing tighter and tighter bounds on the solution cost. This approach is somewhat naive, especially for soft-constraint optimization problems in which the soft constraints are mostly satisfied. Unsatisfiable-core approaches to solving soft constraint problems in Boolean Satisfiability (e.g. MAXSAT) force all soft constraints to hold initially. When solving fails they return an unsatisfiable core, as a set of soft constraints that cannot hold simultaneously. Using this information the problem is relaxed to allow certain soft constraint(s) to be violated and solving continues. Since Lazy Clause Generation (LCG) solvers can also return unsatisfiable cores we can adapt the MAXSAT unsatisfiable core approach to CP. We implement the original MAXSAT unsatisfiable core solving algorithms WPM1, MSU3 in a state-of-the-art LCG solver and show that there exist problems which benefit from this hybrid approach.
The Extended Parameter Filter

The parameters of temporal models, such as dynamic Bayesian networks, may be modelled in a Bayesian context as static or atemporal variables that in fluence transition probabilities at every time step. Particle filters fail for models that include such variables, while methods that use Gibbs sampling of parameter variables may incur a per-sample cost that grows linearly with the length of the observation sequence. Storvik (2002) devised a method for incremental computation of exact sufficient statistics that, for some cases, reduces the per-sample cost to a constant. In this paper, we demonstrate a connection between Storvik's filter and a Kalman filter in parameter space and establish more general conditions under which Storvik's filter works. Drawing on an analogy to the extended Kalman filter, we develop and analyze, both theoretically and experimentally, a Taylor approximation to the parameter posterior that allows Storvik's method to be applied to a broader class of models. Our experiments on both synthetic examples and real applications show improvement over existing methods.
Optimization of stochastic database cracking

Variant Stochastic cracking is a significantly more resilient approach to adaptive indexing. It showed [1]that Stochastic cracking uses each query as a hint on how to reorganize data, but not blindly so; it gains resilience and avoids performance bottlenecks by deliberately applying certain arbitrary choices in its decision making. Therefore bring, adaptive indexing forward to a mature formulation that confers the workload-robustness that previous approaches lacked. Original cracking relies on the randomness of the workloads to converge well.
Optimal dividend problem for a generalized compound Poisson risk model

In this note we study the optimal dividend problem for a company whose surplus process, in the absence of dividend payments, evolves as a generalized compound Poisson model in which the counting process is a generalized Poisson process. This model including the classical risk model and the Polya-Aeppli risk model as special cases. The objective is to find a dividend policy so as to maximize the expected discounted value of dividends which are paid to the shareholders until the company is ruined. We show that under some conditions the optimal dividend strategy is formed by a barrier strategy.
Cover tree Bayesian reinforcement learning

This paper proposes an online tree-based Bayesian approach for reinforcement learning. For inference, we employ a generalised context tree model. This defines a distribution on multivariate Gaussian piecewise-linear models, which can be updated in closed form. The tree structure itself is constructed using the cover tree method, which remains efficient in high dimensional spaces. We combine the model with Thompson sampling and approximate dynamic programming to obtain effective exploration policies in unknown environments. The flexibility and computational simplicity of the model render it suitable for many reinforcement learning problems in continuous state spaces. We demonstrate this in an experimental comparison with a Gaussian process model, a linear model and simple least squares policy iteration.
Entropic measures of individual mobility patterns

Understanding human mobility from a microscopic point of view may represent a fundamental breakthrough for the development of a statistical physics for cognitive systems and it can shed light on the applicability of macroscopic statistical laws for social systems. Even if the complexity of individual behaviors prevents a true microscopic approach, the introduction of mesoscopic models allows the study of the dynamical properties for the non-stationary states of the considered system. We propose to compute various entropy measures of the individual mobility patterns obtained from GPS data that record the movements of private vehicles in the Florence district, in order to point out new features of human mobility related to the use of time and space and to define the dynamical properties of a stochastic model that could generate similar patterns. Moreover, we can relate the predictability properties of human mobility to the distribution of time passed between two successive trips. Our analysis suggests the existence of a hierarchical structure in the mobility patterns which divides the performed activities into three different categories, according to the time cost, with different information contents. We show that a Markov process defined by using the individual mobility network is not able to reproduce this hierarchy, which seems the consequence of different strategies in the activity choice. Our results could contribute to the development of governance policies for a sustainable mobility in modern cities.
Turtle: Identifying frequent k-mers with cache-efficient algorithms

Counting the frequencies of k-mers in read libraries is often a first step in the analysis of high-throughput sequencing data. Infrequent k-mers are assumed to be a result of sequencing errors. The frequent k-mers constitute a reduced but error-free representation of the experiment, which can inform read error correction or serve as the input to de novo assembly methods. Ideally, the memory requirement for counting should be linear in the number of frequent k-mers and not in the, typically much larger, total number of k-mers in the read library.



We present a novel method that balances time, space and accuracy requirements to efficiently extract frequent k-mers even for high-coverage libraries and large genomes such as human. Our method is designed to minimize cache misses in a cache-efficient manner by using a pattern-blocked Bloom filter to remove infrequent k-mers from consideration in combination with a novel sort-and-compact scheme, instead of a hash, for the actual counting. Although this increases theoretical complexity, the savings in cache misses reduce the empirical running times. A variant of method can resort to a counting Bloom filter for even larger savings in memory at the expense of false-negative rates in addition to the false-positive rates common to all Bloom filter-based approaches. A comparison with the state-of-the-art shows reduced memory requirements and running times.



The tools are freely available for download at http://bioinformatics.rutgers.edu/Software/Turtle and http://figshare.com/articles/Turtle/791582.