nils-herrmann/clean_scirepeval huggingface dataset:
A new electromagnetic code for ICRF antenna in EAST

The demand for an effective tool to help in the design of ion cyclotron radio frequency (ICRF) antenna system for fusion experiment has driven the development of predictive codes. A new electromagnetic code based on the method of moments (MOM) is described in the paper. The code computes the electromagnetic field by the solution of the electric field integral equation. The structure of ICRF antennas are discretized with triangular mesh. By using the new code, the scattering parameter and the surface current are given and compared with the result by commercial code CST. Moreover, the power spectra are studied with different toroidal phases for heating and current drive. Good agreement of simulation results between the new code and CST are obtained. The code has been validated against CST for EAST ICRF antenna.
Problem-free time-dependent variational principle for open quantum systems.

Methods of quantum nuclear wave-function dynamics have become very efficient in simulating large isolated systems using the time-dependent variational principle (TDVP). However, a straightforward extension of the TDVP to the density matrix framework gives rise to methods that do not conserve the energy in the isolated system limit and the total system population for open systems where only energy exchange with environment is allowed. These problems arise when the system density is in a mixed state and is simulated using an incomplete basis. Thus, the basis set incompleteness, which is inevitable in practical calculations, creates artificial channels for energy and population dissipation. To overcome this unphysical behavior, we have introduced a constrained Lagrangian formulation of TDVP applied to a non-stochastic open system Schrödinger equation [L. Joubert-Doriol, I. G. Ryabinkin, and A. F. Izmaylov,  (2014)]. While our formulation can be applied to any variational ansatz for the system density matrix, derivation of working equations and numerical assessment is done within the variational multiconfiguration Gaussian approach for a two-dimensional linear vibronic coupling model system interacting with a harmonic bath.
Web Template Extraction Based on Hyperlink Analysis

Web templates are one of the main development resources for website engineers. Templates allow them to increase productivity by plugin content into already formatted and prepared pagelets. For the final user templates are also useful, because they provide uniformity and a common look and feel for all webpages. However, from the point of view of crawlers and indexers, templates are an important problem, because templates usually contain irrelevant information such as advertisements, menus, and banners. Processing and storing this information is likely to lead to a waste of resources (storage space, bandwidth, etc.). It has been measured that templates represent between 40% and 50% of data on the Web. Therefore, identifying templates is essential for indexing tasks. In this work we propose a novel method for automatic template extraction that is based on similarity analysis between the DOM trees of a collection of webpages that are detected using menus information. Our implementation and experiments demonstrate the usefulness of the technique.
XQOWL: An Extension of XQuery for OWL Querying and Reasoning

One of the main aims of the so-called Web of Data is to be able to handle heterogeneous resources where data can be expressed in either XML or RDF. The design of programming languages able to handle both XML and RDF data is a key target in this context. In this paper we present a framework called XQOWL that makes possible to handle XML and RDF/OWL data with XQuery. XQOWL can be considered as an extension of the XQuery language that connects XQuery with SPARQL and OWL reasoners. XQOWL embeds SPARQL queries (via Jena SPARQL engine) in XQuery and enables to make calls to OWL reasoners (HermiT, Pellet and FaCT++) from XQuery. It permits to combine queries against XML and RDF/OWL resources as well as to reason with RDF/OWL data. Therefore input data can be either XML or RDF/OWL and output data can be formatted in XML (also using RDF/OWL XML serialization).
A Fuzzy Logic Programming Environment for Managing Similarity and Truth Degrees

FASILL (acronym of "Fuzzy Aggregators and Similarity Into a Logic Language") is a fuzzy logic programming language with implicit/explicit truth degree annotations, a great variety of connectives and unification by similarity. FASILL integrates and extends features coming from MALP (Multi-Adjoint Logic Programming, a fuzzy logic language with explicitly annotated rules) and Bousi~Prolog (which uses a weak unification algorithm and is well suited for flexible query answering). Hence, it properly manages similarity and truth degrees in a single framework combining the expressive benefits of both languages. This paper presents the main features and implementations details of FASILL. Along the paper we describe its syntax and operational semantics and we give clues of the implementation of the lattice module and the similarity module, two of the main building blocks of the new programming environment which enriches the FLOPER system developed in our research group.
Improving the Deductive System DES with Persistence by Using SQL DBMS's

This work presents how persistent predicates have been included in the in-memory deductive system DES by relying on external SQL database management systems. We introduce how persistence is supported from a user-point of view and the possible applications the system opens up, as the deductive expressive power is projected to relational databases. Also, we describe how it is possible to intermix computations of the deductive engine and the external database, explaining its implementation and some optimizations. Finally, a performance analysis is undertaken, comparing the system with current relational database systems.
Sequential Kernel Herding: Frank-Wolfe Optimization for Particle Filtering

Recently, the Frank-Wolfe optimization algorithm was suggested as a procedure to obtain adaptive quadrature rules for integrals of functions in a reproducing kernel Hilbert space (RKHS) with a potentially faster rate of convergence than Monte Carlo integration (and "kernel herding" was shown to be a special case of this procedure). In this paper, we propose to replace the random sampling step in a particle filter by Frank-Wolfe optimization. By optimizing the position of the particles, we can obtain better accuracy than random or quasi-Monte Carlo sampling. In applications where the evaluation of the emission probabilities is expensive (such as in robot localization), the additional computational cost to generate the particles through optimization can be justified. Experiments on standard synthetic examples as well as on a robot localization task indicate indeed an improvement of accuracy over random and quasi-Monte Carlo sampling.
HOG based fast human detection

Objects recognition in image is one of the most difficult problems in computer vision. It is also an important step for the implementation of several existing applications that require highlevel image interpretation. Therefore, there is a growing interest in this research area during the last years. In this paper, we present an algorithm for human detection and recognition in realtime, from images taken by a CCD camera mounted on a car-like mobile robot. The proposed technique is based on Histograms of Oriented Gradient (HOG) and SVM classifier. The implementation of our detector has provided good results, and can be used in robotics tasks.
Interference phenomena in the $J^P=1/2^-$-wave in $\eta$ photoproduction

The recent precise experimental results for the photoproduction of $\eta$-mesons off the neutron measured with the Crystal Ball/TAPS calorimeter at the MAMI accelerator have been investigated in detail in the framework of the Bonn-Gatchina coupled channel model. The main result is that the narrow structure observed in the excitation function of $\gamma n \rightarrow n\eta$ can be reproduced fully with a particular interference pattern in the $J^P=1/2^-$ partial wave. Introduction of the narrow resonance $N(1685)$ with the properties reported in earlier publications deteriorates the quality of the fit.
Equitability of Dependence Measure

A measure of dependence is said to be equitable if it gives similar scores to equally noisy relationship of different types. In practice, we do not know what kind of functional relationship is underlying two given observations, Hence the equitability of dependence measure is critical in analysis and by scoring relationships according to an equitable measure one hopes to find important patterns of any type of further examination. In this paper, we introduce our definition of equitability of a dependence measure, which is naturally from this initial description, and Further more power-equitable(weak-equitable) is introduced which is of the most practical meaning in evaluating the equitablity of a dependence measure.
Margins of discrete Bayesian networks

Bayesian network models with latent variables are widely used in statistics and machine learning. In this paper we provide a complete algebraic characterization of Bayesian network models with latent variables when the observed variables are discrete and no assumption is made about the state-space of the latent variables. We show that it is algebraically equivalent to the so-called nested Markov model, meaning that the two are the same up to inequality constraints on the joint probabilities. In particular these two models have the same dimension. The nested Markov model is therefore the best possible description of the latent variable model that avoids consideration of inequalities, which are extremely complicated in general. A consequence of this is that the constraint finding algorithm of Tian and Pearl (UAI 2002, pp519-527) is complete for finding equality constraints. 
Latent variable models suffer from difficulties of unidentifiable parameters and non-regular asymptotics; in contrast the nested Markov model is fully identifiable, represents a curved exponential family of known dimension, and can easily be fitted using an explicit parameterization.
Virial tests for post-Newtonian stationary black-hole—disk systems

We investigated hydrodynamical post-Newtonian models of selfgravitating stationary black-hole-disk systems. The post-Newtonian scheme presented here and also in our recent paper is a continuation of previous, purely Newtonian studies of selfgravitating hydrodynamical disks rotating according to the Keplerian rotation law. The post-Newtonian relativistic corrections are significant even at the 1PN level. The 1PN correction to the angular velocity can be of the order of 10% of its Newtonian value. It can be expressed as a combination of geometric and hydrodynamical terms. Moreover, in contrast to the Newtonian Poincaré-Wavre theorem, it depends both on the distance from the rotation axis and the distance from the equatorial plane. In the technical part of this note we derive virial relations valid up to 1PN order. We show that they are indeed satisfied by our numerical solutions.
Association Rule Mining using Maximum Entropy

Recommendations based on behavioral data may be faced with ambiguous statistical evidence. We consider the case of association rules, relevant e.g.~for query and product recommendations. For example: Suppose that a customer belongs to categories A and B, each of which is known to have positive correlation with buying product C, how do we estimate the probability that she will buy product C? 
For rare terms or products there may not be enough data to directly produce such an estimate --- perhaps we never directly observed a connection between A, B, and C. What can we do when there is no support for estimating the probability by simply computing the observed frequency? In particular, what is the right thing to do when A and B give rise to very different estimates of the probability of C? 
We consider the use of maximum entropy probability estimates, which give a principled way of extrapolating probabilities of events that do not even occur in the data set! Focusing on the basic case of three variables, our main technical contributions are that (under mild assumptions): 1) There exists a simple, explicit formula that gives a good approximation of maximum entropy estimates, and 2) Maximum entropy estimates based on a small number of samples are provably tightly concentrated around the true maximum entropy frequency that arises if we let the number of samples go to infinity. 
Our empirical work demonstrates the surprising precision of maximum entropy estimates, across a range of real-life transaction data sets. In particular we observe the average absolute error on maximum entropy estimates is a factor $3$--$14$ less compared to using independence or extrapolation estimates, when the data used to make the estimates has low support. We believe that the same principle can be used to synthesize probability estimates in many settings.
Cosmological perturbations in a mimetic matter model

We investigate the cosmological evolution of a mimetic matter model with arbitrary scalar potential. The cosmological reconstruction\char22{}which is the method for constructing a model for an arbitrary evolution of the scale factor\char22{}is explicitly performed for different choices of potential. The cases where the mimetic matter model shows the evolution as cold dark matter (CDM), the $w\mathrm{CDM}$ model, dark matter and dark energy with a dynamical $Om(z)$ [where $Om(z)\ensuremath{\equiv}[(H(z)/{H}_{0}{)}^{2}\ensuremath{-}1]/[(1+z{)}^{3}\ensuremath{-}1]$], and phantom dark energy with a phantom-nonphantom crossing are presented in detail. The cosmological perturbations for such evolutions are studied in the mimetic matter model. For instance, the evolution behavior of the matter density contrast (which is different than the usual one, i.e., $\stackrel{\ifmmode\ddot\else\textasciidieresis\fi{}}{\ensuremath{\delta}}+2H\stackrel{\ifmmode \dot{}\else \textperiodcentered \fi{}}{\ensuremath{\delta}}\ensuremath{-}{\ensuremath{\kappa}}^{2}\ensuremath{\rho}\ensuremath{\delta}/2=0$) is investigated. The possibility of a peculiar evolution of $\ensuremath{\delta}$ in the model under consideration is shown. Special attention is paid to the behavior of the matter density contrast near the future singularity, where the decay of perturbations may occur much earlier than the singularity.
Smooth quantum dynamics of the mixmaster universe

We present a quantum version of the vacuum Bianchi IX model by implementing ane coherent state quantization combined with a Born-Oppenheimer-like adiabatic approximation. The analytical treatment is carried out on both quantum and semiclassical levels. The resolution of the classical singularity occurs by means of a repulsive potential generated by our quantization procedure. The quantization of the oscillatory degrees of freedom produces a radiation energy density term in the semiclassical constraint equation. The Friedmann-like lowest energy eigenstates of the system are found to be dynamically stable.
Investigation of a chaotic spiking neuron model

Chaos provides many interesting properties that can be used to achieve computational tasks. Such properties are sensitivity to initial conditions, space filling, control and synchronization. Chaotic neural models have been devised to exploit such properties. In this paper, a chaotic spiking neuron model is investigated experimentally. This investigation is performed to understand the dynamic behaviours of the model. The aim of this research is to investigate the dynamics of the nonlinear dynamic state neuron (NDS) experimentally. The experimental approach has revealed some quantitative and qualitative properties of the NDS model such as the control mechanism, the reset mechanism, and the way the model may exhibit dynamic behaviours in phase space. It is shown experimentally in this paper that both the reset mechanism and the self-feed back control mechanism are important for the NDS model to work and to stabilise to one of the large number of available unstable periodic orbits (UPOs) that are embedded in its attractor. The experimental investigation suggests that the internal dynamics of the NDS neuron provide a rich set of dynamic behaviours that can be controlled and stabilised. These wide range of dynamic behaviours may be exploited to carry out information processing tasks.
Analyses of Statistical Structures in Economic Indices

The complex, time-dependent statistical structures observed in the Dow Jones Industrial Average on a typical trading day are modeled with Lorentzian functions. The resonant-like structures are characterized by the values of the basic ratio: the average lifetime of the individual states associated with a given structural form divided by the average interval between adjacent states. Values of the ratio are determined for three structural forms characterized by the average intervals: 50 to 100 seconds (the fine structure), approximately10 minutes, and approximately1 hour (the intermediate structures I and II). During the trading day the values of the basic ratio associated with the fine structure of the index are found to lie in the narrow range from 0.49 to 0.52. This finding is characteristic of the highly statistical nature of many-body systems typified by daily trading. It is therefore proposed that the value of this ratio, determined in the first hour-or-so on a given day, be used to provide information concerning the likely performance of the fine, statistical component of the index for the remainder of the trading day. For the intermediate structures the basic ratios are approximately 0.6 and therefore they too can be analyzed as individual states.  Analytical economics; Lorentzian analyses of statistical structures in the Dow Jones Industrial Average; basic parameters of economic indices.
Optimal survey schemes for stochastic gradient descent with applications to M-estimation

Iterative stochastic approximation methods are widely used to solve M-estimation problems, in the context of predictive learning in particular. In certain situations that shall be undoubtedly more and more common in the Big Data era, the datasets available are so massive that computing statistics over the full sample is hardly feasible, if not unfeasible. A natural and popular approach to gradient descent in this context consists in substituting the “full data” statistics with their counterparts based on subsamples picked at random of manageable size. It is the main purpose of this paper to investigate the impact of survey sampling with unequal inclusion probabilities on stochastic gradient descent-based M-estimation methods. Precisely, we prove that, in presence of some a priori information, one may significantly increase statistical accuracy in terms of limit variance, when choosing appropriate first order inclusion probabilities. These results are described by asymptotic theorems and are also supported by illustrative numerical experiments.
On solutions of the Pauli equation in non-static de Sitter metrics

A particle with spin 1/2 is investigated both in expanding and oscillating cosmological de Sitter models. It is shown that these space-time geometries admit existence of the non-relativistic limit in the covariant Dirac equation. Procedure for transition to the Pauli approximation is conducted in the equations in the variables $(t, r)$, obtained after separating the angular dependence of $(\theta, \phi)$ from the wave function. The non-relativistic systems of equations in the variables $(t, r)$ is solved exactly in both models. The constructed wave functions do not represent stationary states with fixed energy, however the corresponding probability density does not depend on the time.
The impact of galactic feedback on the circumgalactic medium

Galactic feedback strongly affects the way galactic environments are enriched. We examine this connection by performing a suite of cosmological hydrodynamic simulations, exploring a range of parameters based on the galaxy formation model developed in Vogelsberger et al. We examine the effects of AGN feedback, wind mass loading, wind specific energy, and wind metal-loading on the properties of the circumgalactic medium (CGM) of galaxies with M_(halo) > 10^(11) M⊙. Note that while the V13 model was tuned to match observations including the stellar mass function, no explicit tuning was done for the CGM. The wind energy per unit outflow mass has the most significant effect on the CGM enrichment. High-energy winds launch metals far beyond the virial radius. AGN feedback also has a significant effect, but only at z < 3. We compare to high-redshift HI and CIV observations. All our simulations produce the observed number of Damped Lyman α Absorbers. At lower column density, several of our simulations produce enough Lyman Limit Systems (LLS) 100 kpc from the galaxy, but in all cases the LLS abundance falls off with distance faster than observations, with too few LLS at 200 kpc. Further, in all models the CIV abundance drops off too sharply with distance, with too little CIV 100–200 kpc from the galaxy. Higher energy wind models produce more extended CIV but also produce less stars, in tension with star formation rate density observations. This highlights the fact that circumgalactic observations are a strong constraint on galactic feedback models.
Mass constraints to Sco X-1 from Bowen fluorescence and deep near-infrared spectroscopy

More than 50 years after the dawn of X-ray astronomy, the dynamical parameters of the prototypical X-ray binary Sco X-1 are still unknown. We combine a Monte Carlo analysis, which includes all the previously known orbital parameters of the system, along with the K-correction to set dynamical constraints to the masses of the compact object (M1<1.73 Msun) and the companion star (0.28 Msun<M2<0.70 Msun). For the case of a canonical neutron star mass of M1= 1.4 Msun, the orbital inclination is found to be lower than 40 degree. We also present the best near-infrared spectrum of the source to date. There is no evidence of donor star features on it, but we are able to constrain the veiling factor as a function of the spectral type of the secondary star. The combination of both techniques restricts the spectral type of the donor to be later than K4 and luminosity class IV. It also constrains the contribution of the companion light to the infrared emission of Sco X-1 to be lower than 33 percent. This implies that the accretion related luminosity of the system in the K band is larger than 4 x 10E35 erg/s.