Cross-Country Efficiency Comparison between Italian and Spanish Public Universities in the period 2000-2005

The growing internationalization of European Higher Education requires more emphasis on crosscountry comparisons. In this paper, an efficiency analysis of Italian and Spanish universities is conducted; as well as from a comparative perspective. The efficiency scores are obtained using Data Envelopment Analysis. The results demonstrate a good average efficiency in both countries relative to each “countryspecific” frontier; but when compared together, Italian universities seem relatively more efficient. Malmquist indexes show, in both cases, efficiency improvements in the period considered. In the Italian case, this improvement is due to major “technological changes”; that is, the introduction of some structural reforms in the sector (e.g. Bachelor/Master curricula). In the Spanish case, there is an improvement in “pure” efficiency, which is due to new funding models. Further stages of the study underline the role of “regional effects”, probably due to different socio-economic conditions in Italy, and to the decentralization process in Spain

Non-Extensive Entropy Econometrics for Low Frequency Series

The second edition of Non-extensive Entropy Econometrics for Low Frequency Series provides a new and robust power-law-based, non-extensive entropy econometrics approach to the economic modelling of ill-behaved inverse problems. Particular attention is paid to national account-based general equilibrium models known for their relative complexity. This new proposed approach could extend the frontier of theoretical and applied econometrics

Applications of information theory in filtering and sensor management

“A classical sensor tasking methodology is analyzed in the context of generating sensor schedules for monitoring resident space objects (RSOs). This approach, namely maximizing the expected Kullback-Leibler divergence in a measurement update, is evaluated from a probabilistic perspective to determine the accuracy of the conventional approach. In this investigation, a newdivergence-based approach is proposed to circumvent themyopic nature of the measure, forecasting the potential information contribution to a time of interest and leveraging the system dynamics and measurement model to do so. The forecasted objective exploits properties of a batch measurement update to frequently exhibit faster optimization times when compared to an accumulation of the conventional myopic employment. The forecasting approach additionally affords the ability to emphasize tracking performance at the point in time to which the information is mapped. The forecasted divergence is lifted into the multitarget domain and combined with a collision entropy objective. The addition of the collision consideration assists the tasking policy in avoiding scenarios in which determining the origin of a measurement is difficult, ameliorating issues when executing the sensor schedule. The properties of the divergencebased and collision entropy-based objectives are explored to determine appropriate optimization schemes that can enable their use in real-time application. It is demonstrated through a single-target tasking simulation that the forecasted measure successfully outperforms traditional approaches with regard to tracking performance at the forecasted time. This simulation is followed by a multitarget tasking scenario in which different optimization strategies are analyzed, illustrating the feasibility of the proposed tasking policy and evaluating the solution from both schedule quality and runtime perspectives”--Abstract, page iii

Non-Extensive Entropy Econometrics for Low Frequency Series

The second edition of Non-extensive Entropy Econometrics for Low Frequency Series provides a new and robust power-law-based, non-extensive entropy econometrics approach to the economic modelling of ill-behaved inverse problems. Particular attention is paid to national account-based general equilibrium models known for their relative complexity. This new proposed approach could extend the frontier of theoretical and applied econometrics

Dataset shift in land-use classification for optical remote sensing

Multimodal dataset shifts consisting of both concept and covariate shifts are addressed in this study to improve texture-based land-use classification accuracy for optical panchromatic and multispectral remote sensing. Multitemporal and multisensor variances between train and test data are caused by atmospheric, phenological, sensor, illumination and viewing geometry differences, which cause supervised classification inaccuracies. The first dataset shift reduction strategy involves input modification through shadow removal before feature extraction with gray-level co-occurrence matrix and local binary pattern features. Components of a Rayleigh quotient-based manifold alignment framework is investigated to reduce multimodal dataset shift at the input level of the classifier through unsupervised classification, followed by manifold matching to transfer classification labels by finding across-domain cluster correspondences. The ability of weighted hierarchical agglomerative clustering to partition poorly separated feature spaces is explored and weight-generalized internal validation is used for unsupervised cardinality determination. Manifold matching solves the Hungarian algorithm with a cost matrix featuring geometric similarity measurements that assume the preservation of intrinsic structure across the dataset shift. Local neighborhood geometric co-occurrence frequency information is recovered and a novel integration thereof is shown to improve matching accuracy. A final strategy for addressing multimodal dataset shift is multiscale feature learning, which is used within a convolutional neural network to obtain optimal hierarchical feature representations instead of engineered texture features that may be sub-optimal. Feature learning is shown to produce features that are robust against multimodal acquisition differences in a benchmark land-use classification dataset. A novel multiscale input strategy is proposed for an optimized convolutional neural network that improves classification accuracy to a competitive level for the UC Merced benchmark dataset and outperforms single-scale input methods. All the proposed strategies for addressing multimodal dataset shift in land-use image classification have resulted in significant accuracy improvements for various multitemporal and multimodal datasets.Thesis (PhD)--University of Pretoria, 2016.National Research Foundation (NRF)University of Pretoria (UP)Electrical, Electronic and Computer EngineeringPhDUnrestricte

Information Geometry

This Special Issue of the journal Entropy, titled “Information Geometry I”, contains a collection of 17 papers concerning the foundations and applications of information geometry. Based on a geometrical interpretation of probability, information geometry has become a rich mathematical field employing the methods of differential geometry. It has numerous applications to data science, physics, and neuroscience. Presenting original research, yet written in an accessible, tutorial style, this collection of papers will be useful for scientists who are new to the field, while providing an excellent reference for the more experienced researcher. Several papers are written by authorities in the field, and topics cover the foundations of information geometry, as well as applications to statistics, Bayesian inference, machine learning, complex systems, physics, and neuroscience