ZnPSe3_3 as ultrabright indirect bandgap system with microsecond excitonic lifetimes

We report an optical characterization of ZnPSe$_3$ crystals that demonstrates indirect band gap characteristics in combination with unusually strong photoluminescence. We found evidence of interband recombination from excitonic states with microsecond lifetimes. Through optical characterization, we reconstructed the electronic band scheme relevant for fundamental processes of light absorption, carrier relaxation and radiative recombination. The investigation of the radiative processes in the presence of magnetic field revealed spin polarization of fundamental electronic states. This observation opens a pathway towards controlling the spin of excitonic states in technologically relevant microsecond timescales

Fine structure of K\mathrm{K}-excitons in multilayers of transition metal dichalcogenides

Reflectance and magneto-reflectance experiments together with theoretical modelling based on the $\mathbf{k\cdot p}$ approach have been employed to study the evolution of direct bandgap excitons in MoS$_2$ layers with a thickness ranging from mono- to trilayer. The extra excitonic resonances observed in MoS$_2$ multilayers emerge as a result of the hybridization of Bloch states of each sub-layer due to the interlayer coupling. The properties of such excitons in bi- and trilayers are classified by the symmetry of corresponding crystals. The inter- and intralayer character of the reported excitonic resonances is fingerprinted with the magneto-optical measurements: the excitonic $g$-factors of opposite sign and of different amplitude are revealed for these two types of resonances. The parameters describing the strength of the spin-orbit interaction are estimated for bi- and trilayer MoS$_2$.Comment: 14 pages, 10 figure

Bound -> free and bound -> bound multichannel emission spectra from selectively excited Rydberg states in the ZnAr and CdAr van der Waals complexes

Multichannel dispersed emission spectra recorded upon a selective excitation of Rydberg electronic energy states in the ZnAr and CdAr van der Waals (vdW) complexes are analysed as a proof-of-concept of the future experimental approach. Simulations of the emission spectra are based on ab-initio calculated interatomic potentials and transition dipole moments (TDMs). Experimental set-up that is under construction along with the experimental procedure are discussed

Land cover classification with multi-sensor fusion of partly missing data

We describe a system that uses decision tree-based tools for seamless acquisition of knowledge for classification of remotely sensed imagery. We concentrate on three important problems in this process: information fusion, model understandability, and handling of missing data. Importance of multi-sensor information fusion and the use of decision tree classifiers for such problems have been well-studied in the literature. However, these studies have been limited to the cases where all data sources have a full coverage for the scene under consideration. Our contribution in this paper is to show how decision tree classifiers can be learned with alternative (surrogate) decision nodes and result in models that are capable of dealing with missing data during both training and classification to handle cases where one or more measurements do not exist for some locations. We present detailed performance evaluation regarding the effectiveness of these classifiers for information fusion and feature selection, and study three different methods for handling missing data in comparative experiments. The results show that surrogate decisions incorporated into decision tree classifiers provide powerful models for fusing information from different data layers while being robust to missing data. © 2009 American Society for Photogrammetry and Remote Sensing

Interactive training of advanced classifiers for mining remote sensing image archives

Advances in satellite technology and availability of down-loaded images constantly increase the sizes of remote sensing image archives. Automatic content extraction, classification and content-based retrieval have become highly desired goals for the development of intelligent remote sensing databases. The common approach for mining these databases uses rules created by analysts. However, incorporating GIS information and human expert knowledge with digital image processing improves remote sensing image analysis. We developed a system that uses decision tree classifiers for interactive learning of land cover models and mining of image archives. Decision trees provide a promising solution for this problem because they can operate on both numerical (continuous) and categorical (discrete) data sources, and they do not require any assumptions about neither the distributions nor the independence of attribute values. This is especially important for the fusion of measurements from different sources like spectral data, DEM data and other ancillary GIS data. Furthermore, using surrogate splits provides the capability of dealing with missing data during both training and classification, and enables handling instrument malfunctions or the cases where one or more measurements do not exist for some locations. Quantitative and qualitative performance evaluation showed that decision trees provide powerful tools for modeling both pixel and region contents of images and mining of remote sensing image archives

Mining and Filtering Multi-level Spatial Association Rules with ARES

In spatial data mining, a common task is the discovery of spatial association rules from spatial databases. We propose a distributed system, named ARES that takes advantage of the use of a multi-relational approach to mine spatial association rules. It supports spatial database coupling and discovery of multi-level spatial association rules as a means for spatial data exploration. We also present some criteria to bias the search and to filter the discovered rules according to user's expectations. Finally, we show the applicability of our proposal to two different real world domains, namely, document image processing and geo-referenced analysis of census data