The magnetic exchange parameters and anisotropy of the quasi-two dimensional antiferromagnet NiPS3_3

Neutron inelastic scattering has been used to measure the magnetic excitations in powdered NiPS$_3$, a quasi-two dimensional antiferromagnet with spin $S = 1$ on a honeycomb lattice. The spectra show clear, dispersive magnons with a $\sim 7$ meV gap at the Brillouin zone center. The data were fitted using a Heisenberg Hamiltonian with a single-ion anisotropy assuming no magnetic exchange between the honeycomb planes. Magnetic exchange interactions up to the third intraplanar nearest-neighbour were required. The fits show robustly that NiPS$_3$ has an easy axis anisotropy with $\Delta = 0.3$ meV and that the third nearest-neighbour has a strong antiferromagnetic exchange of $J_3 = -6.90$ meV. The data can be fitted reasonably well with either $J_1 < 0$ or $J_1 > 0$, however the best quantitative agreement with high-resolution data indicate that the nearest-neighbour interaction is ferromagnetic with $J_1 = 1.9$ meV and that the second nearest-neighbour exchange is small and antiferromagnetic with $J_2 = -0.1$ meV. The dispersion has a minimum in the Brillouin zone corner that is slightly larger than that at the Brillouin zone center, indicating that the magnetic structure of NiPS$_3$ is close to being unstable.Comment: 21 pages, 7 figures, 33 reference

Gravitational and electromagnetic emission by magnetized coalescing binary systems

We discuss the possibility to obtain an electromagnetic emission accompanying the gravitational waves emitted in the coalescence of a compact binary system. Motivated by the existence of black hole configurations with open magnetic field lines along the rotation axis, we consider a magnetic dipole in the system, the evolution of which leads to (i) electromagnetic radiation, and (ii) a contribution to the gravitational radiation, the luminosity of both being evaluated. Starting from the observations on magnetars, we impose upper limits for both the electromagnetic emission and the contribution of the magnetic dipole to the gravitational wave emission. Adopting this model for the evolution of neutron star binaries leading to short gamma ray bursts, we compare the correction originated by the electromagnetic field to the gravitational waves emission, finding that they are comparable for particular values of the magnetic field and of the orbital radius of the binary system. Finally we calculate the electromagnetic and gravitational wave energy outputs which result comparable for some values of magnetic field and radius.Comment: 9 pages, 3 figures, to appear in Astroph. Sp.Scienc

Web ontology representation and reasoning via fragments of set theory

In this paper we use results from Computable Set Theory as a means to represent and reason about description logics and rule languages for the semantic web. Specifically, we introduce the description logic \mathcal{DL}\langle 4LQS^R\rangle(\D)--admitting features such as min/max cardinality constructs on the left-hand/right-hand side of inclusion axioms, role chain axioms, and datatypes--which turns out to be quite expressive if compared with \mathcal{SROIQ}(\D), the description logic underpinning the Web Ontology Language OWL. Then we show that the consistency problem for \mathcal{DL}\langle 4LQS^R\rangle(\D)-knowledge bases is decidable by reducing it, through a suitable translation process, to the satisfiability problem of the stratified fragment $4LQS^R$ of set theory, involving variables of four sorts and a restricted form of quantification. We prove also that, under suitable not very restrictive constraints, the consistency problem for \mathcal{DL}\langle 4LQS^R\rangle(\D)-knowledge bases is \textbf{NP}-complete. Finally, we provide a $4LQS^R$-translation of rules belonging to the Semantic Web Rule Language (SWRL)

HEOS 1 helium observations in the solar wind

Results of alpha-particle observations performed by the European satellite HEOS 1, in the period from December 9, 1968, to April 13, 1969, and from September 6, 1969, to April 15, 1970, are presented. The average bulk velocities of protons V sub p and alpha-particles V sub alpha appear to be equal; however, due to an instrumental bias, the possibility of V sub alpha being lower than V sub p cannot be ruled out. Comparison with observations of Vela 3 and Explorer 34 satellites gives evidence of a dependence of helium abundance on the solar cycle. The problem of the stability of differences between the bulk velocities of protons and alpha-particles is investigated. The behavior of alpha-particles through interplanetary shock waves is illustrated in connection with magnetic field measurements

Towards explainable data-to-text generation

In recent years there has been a renewed burst of interest in systems able to textually summarize data, producing natural language text as a description of input data series. Many of the recently proposed approaches to solve the data-to-text task are based on Machine Learning (ML) and ultimately rely on Deep Learning (DL) techniques. This technological choice often prevents the system from enjoying explainability properties. In this paper we outline our ongoing research and present a framework that is ML/DL free and is conceived to be compliant with xAI requirements. In particular we design ASP/Python programs that enable explicit control of the abstraction process, descriptions' accuracy and relevance handling, and amount of synthesis. We provide a critical analysis of the xAI features that should be implemented and a working proof of concept that addresses crucial aspects in the abstraction of data. In particular we discuss: how to model and output the abstraction accuracy of a concept w.r.t. data; how to identify what to say with controlled synthesis level: i.e., the key descriptive elements to be addressed in the data; how to represent abstracted information by means of visual annotation to charts. The main advantages of such approach are a trustworthy and reliable description, a transparent methodology, logically provable output, and measured accuracy that can control natural language modulation of descriptions

Nonstationary Kalman filter for estimation of accurate and consistent car-following data

Difficulty in obtaining accurate car-following data has traditionally been regarded as a considerable drawback in understanding real phenomena and has affected development and validation of traffic micro-simulation models. Recent advancements in digital technology have opened up new horizons in researching this field. In spite of the high precision of these techniques, estimating time series data of speeds and accelerations from positions with the required accuracy is still a demanding task. The core of the problem is to filter noisy trajectory data of each vehicle without altering platoon data consistency, i.e. speeds and accelerations of following vehicles have to be estimated so that resulting inter-vehicle spacings are equal to the “real” one. Otherwise negative spacings can also easily occur. The task was achieved in this study by considering vehicles of a platoon as a sole dynamic system and reducing several estimation problems to a single consistent one. This process was accomplished by means of a non-stationary Kalman filter that uses measurements and time-varying error information from differential GPS devices. The Kalman filter, not applicable to estimate the speeds of one vehicle from its position measurements alone, was fruitfully applied here to the speed estimation of the whole platoon by including inter-vehicle spacings as additional measurements (assumed as reference measurements). The closed solution of an optimisation problem ensuring strict observation of the “true” inter-vehicle spacings concludes the estimation process. The stationary counterpart of the devised filter is suitable to be applied to position data whichever technique is used, e.g. video cameras

A regularized procedure to generate a deep learning model for topology optimization of electromagnetic devices

The use of behavioral models based on deep learning (DL) to accelerate electromagnetic field computations has recently been proposed to solve complex electromagnetic problems. Such problems usually require time-consuming numerical analysis, while DL allows achieving the topo-logically optimized design of electromagnetic devices using desktop class computers and reasonable computation times. An unparametrized bitmap representation of the geometries to be optimized, which is a highly desirable feature needed to discover completely new solutions, is perfectly managed by DL models. On the other hand, optimization algorithms do not easily cope with high dimensional input data, particularly because it is difficult to enforce the searched solutions as feasible and make them belong to expected manifolds. In this work, we propose the use of a variational autoencoder as a data regularization/augmentation tool in the context of topology optimization. The optimization was carried out using a gradient descent algorithm, and the DL neural network was used as a surrogate model to accelerate the resolution of single trial cases in the due course of optimization. The varia-tional autoencoder and the surrogate model were simultaneously trained in a multi-model custom training loop that minimizes total loss—which is the combination of the two models’ losses. In this paper, using the TEAM 25 problem (a benchmark problem for the assessment of electromagnetic numerical field analysis) as a test bench, we will provide a comparison between the computational times and design quality for a “classical” approach and the DL-based approach. Preliminary results show that the variational autoencoder manages regularizing the resolution process and transforms a constrained optimization into an unconstrained one, improving both the quality of the final solution and the performance of the resolution process

Large parallel and perpendicular electric fields on electron spatial scales in the terrestrial bow shock

Large parallel ($\leq$ 100 mV/m) and perpendicular ($\leq$ 600 mV/m) electric fields were measured in the Earth's bow shock by the vector electric field experiment on the Polar satellite. These are the first reported direct measurements of parallel electric fields in a collisionless shock. These fields exist on spatial scales comparable to or less than the electron skin depth (a few kilometers) and correspond to magnetic field-aligned potentials of tens of volts and perpendicular potentials up to a kilovolt. The perpendicular fields are amongst the largest ever measured in space, with energy densities of $\epsilon_0 E^2/ n k_b T_e$ of order 10%. The measured parallel electric field implies that the electrons can be demagnetized, which may result in stochastic (rather than coherent) electron heating