Light-induced switching in pDTE - FICO 1D photonic structures

We propose the design of 1D photonic crystals and microcavities in which fluorine-indium codoped cadmium oxide (FICO) nanocrystal based layers and layers of diarylethene-based polyester (pDTE) are alternated or embedded in a microcavity. The irradiation with UV light results in two different behaviours: i) it dopes the FICO nanocrystals inducing a blue shift of their plasmonic resonances; ii) it changes the real part of the refractive index of the photochromic pDTE polymer. These two behaviours are combined in the proposed photonic structures and can be useful for switchable filters and cavities for light emission.Comment: 9 pages, 5 figure

Magneto-optical switching in microcavities based on a TGG defect sandwiched between periodic and disordered one-dimensional photonic structures

The employment of magneto-optical materials to fabricate photonic crystals gives the unique opportunity to achieve optical tuning by applying a magnetic field. In this study we have simulated the transmission spectrum of a microcavity in which the Bragg reflectors are made with silica (SiO2) and yttria (Y2O3) and the defect layer is made with TGG (Tb3Ga5O12). We show that the application of an external magnetic field results in a tuning of the defect mode of the microcavity. In the simulations we have considered the wavelength dependence of the refractive indexes and the Verdet constants of the materials. A tuning of the defect mode of about 22 nm with a magnetic field of 5 T, at low temperature (8 K), is demonstrated. Furthermore, we discuss the possibility to tune a microcavity with disordered photonic structures as reflectors. In the presence of the magnetic field such microcavity shows a shift of resonances in a broad range of wavelengths. This study presents a method of contactless optical tuning.Comment: 8 pages, 4 figure

A probabilistic model of the electron transport in films of PbSe nanocrystals arranged in a cubic lattice

The fabrication of nanocrystal (NC) films, starting from colloidal dispersion, is a very attractive topic in condensed matter physics community. NC films can be employed for transistors, light emitting diodes, laser, and solar cells. For this reason the understanding of the film conductivity is of major importance. In this paper we describe a probabilistic model that allow to predict the conductivity of the NC films, in this case of a cubic lattice of Lead Selenide NCs. The model is based on the hopping probability between NCs show a comparison with experimental data reported in literature.Comment: 5 pages, 4 figure

Efficient Axiomatization of OWL 2 EL Ontologies from Data by means of Formal Concept Analysis: (Extended Version)

We present an FCA-based axiomatization method that produces a complete EL TBox (the terminological part of an OWL 2 EL ontology) from a graph dataset in at most exponential time. We describe technical details that allow for efficient implementation as well as variations that dispense with the computation of extremely large axioms, thereby rendering the approach applicable albeit some completeness is lost. Moreover, we evaluate the prototype on real-world datasets.This is an extended version of an article accepted at AAAI 2024

Learning General Concept Inclusions in Probabilistic Description Logics

Probabilistic interpretations consist of a set of interpretations with a shared domain and a measure assigning a probability to each interpretation. Such structures can be obtained as results of repeated experiments, e.g., in biology, psychology, medicine, etc. A translation between probabilistic and crisp description logics is introduced and then utilised to reduce the construction of a base of general concept inclusions of a probabilistic interpretation to the crisp case for which a method for the axiomatisation of a base of GCIs is well-known

Most specific consequences in the description logic EL

The notion of a most specific consequence with respect to some terminological box is introduced, conditions for its existence in the description logic EL and its variants are provided, and means for its computation are developed. Algebraic properties of most specific consequences are explored. Furthermore, several applications that make use of this new notion are proposed and, in particular, it is shown how given terminological knowledge can be incorporated in existing approaches for the axiomatization of observations. For instance, a procedure for an incremental learning of concept inclusions from sequences of interpretations is developed

Learning description logic axioms from discrete probability distributions over description graphs: Extended Version

Description logics in their standard setting only allow for representing and reasoning with crisp knowledge without any degree of uncertainty. Of course, this is a serious shortcoming for use cases where it is impossible to perfectly determine the truth of a statement. For resolving this expressivity restriction, probabilistic variants of description logics have been introduced. Their model-theoretic semantics is built upon so-called probabilistic interpretations, that is, families of directed graphs the vertices and edges of which are labeled and for which there exists a probability measure on this graph family. Results of scientific experiments, e.g., in medicine, psychology, or biology, that are repeated several times can induce probabilistic interpretations in a natural way. In this document, we shall develop a suitable axiomatization technique for deducing terminological knowledge from the assertional data given in such probabilistic interpretations. More specifically, we consider a probabilistic variant of the description logic EL⊥, and provide a method for constructing a set of rules, so-called concept inclusions, from probabilistic interpretations in a sound and complete manner