Net Fisher information measure versus ionization potential and dipole polarizability in atoms

The net Fisher information measure, defined as the product of position and momentum Fisher information measures and derived from the non-relativistic Hartree-Fock wave functions for atoms with Z=1-102, is found to correlate well with the inverse of the experimental ionization potential. Strong direct correlations of the net Fisher information are also reported for the static dipole polarizability of atoms with Z=1-88. The complexity measure, defined as the ratio of the net Onicescu information measure and net Fisher information, exhibits clearly marked regions corresponding to the periodicity of the atomic shell structure. The reported correlations highlight the need for using the net information measures in addition to either the position or momentum space analogues. With reference to the correlation of the experimental properties considered here, the net Fisher information measure is found to be superior than the net Shannon information entropy.Comment: 16 pages, 6 figure

A simple method for the evaluation of the information content and complexity in atoms. A proposal for scalability

We present a very simple method for the calculation of Shannon, Fisher, Onicescu and Tsallis entropies in atoms, as well as SDL and LMC complexity measures, as functions of the atomic number Z. Fractional occupation probabilities of electrons in atomic orbitals are employed, instead of the more complicated continuous electron probability densities in position and momentum spaces, used so far in the literature. Our main conclusions are compatible with the results of more sophisticated approaches and correlate fairly with experimental data. We obtain for the Tsallis entropic index the value q=1.031, which shows that atoms are very close to extensivity. A practical way towards scalability of the quantification of complexity for systems with more components than the atom is indicated. We also discuss the issue if the complexity of the electronic structure of atoms increases with Z. A Pair of Order-Disorder Indices (PODI), which can be introduced for any quantum many-body system, is evaluated in atoms. We conclude that "atoms are ordered systems, which do not grow in complexity as Z increases".Comment: Preprint, 25 pages, 15 figures, 1 Tabl

Complexity and neutron stars structure

We apply the statistical measure of complexity introduced by Lopez-Ruiz, Mancini and Calbet to neutron stars structure. Neutron stars is a classical example where the gravitational field and quantum behavior are combined and produce a macroscopic dense object. Actually, we continue the recent application of Sanudo and Pacheco to white dwarfs structure. We concentrate our study on the connection between complexity and neutron star properties, like maximum mass and the corresponding radius, applying a specific set of realistic equation of states. Moreover, the effect of the strength of the gravitational field on the neutron star structure and consequently on the complexity measure is also investigated. It is seen that neutron stars, consistent with astronomical observations so far, are ordered systems (low complexity), which cannot grow in complexity as their mass increases. This is a result of the interplay of gravity, the short-range nuclear force and the very short-range weak interaction.Comment: Preprint, 23 pages, 28 figure

Efficient intent classification and entity recognition for university administrative services employing deep learning models

The design and implementation of a domain specific conversational agent requires efficient Natural Language Understanding (NLU). The task is harder when multiple languages have to be supported, and training datasets can be beneficial. This work focuses on the development of an intelligent system, an automated multilingual customer service conversational agent (chatbot) for university students, which supports both Greek and English and combines Intent Classification or Intent Extraction (IE) and Named Entity Recognition (NER) to understand the content (i.e. type of actions conveyed and respective entities) of users' messages. We focus on the development of the fundamental tasks required by a conversational agent to provide customer services in the education industry and manage requests with instant responses and increased customer satisfaction. Instead of handling IE and NER separately, as it is common in the related work, we develop a joint model that combines Bidirectional Long Short-Term Memory (BiLSTM) and Conditional Random Fields (CRF) layers and generates outputs both for IE and NER. We introduce a novel, open access dataset for customer services in education industry, the UniWay dataset, that has been used for training and evaluating our model, comprises students' questions in English and Greek about essential information related to their studies. A comparative evaluation of the proposed model versus state-of-the-art standalone and joint model solutions in UniWay and xSID datasets, results in improvement of the performance for the IE task up to 1.4% and it is on par with the state-of-the-art for the NER task. These results justify the intuition that closed domains can benefit from less sophisticated architectures, but less costly in terms of computational and memory resources, that jointly resolve multiple NLU tasks