On Computing Entity Relatedness in Wikipedia, with Applications

Many text mining tasks, such as clustering, classification, retrieval, and named entity linking, benefit from a measure of relatedness between entities in a knowledge graph. We present a thorough study of all entity relatedness measures in recent literature based on Wikipedia as the knowledge graph. To facilitate this study, we introduce a new dataset with human judgments of entity relatedness. No clear dominance is seen between measures based on textual similarity and graph proximity. Some of the better measures involve expensive global graph computations. We propose a new, space-efficient, computationally lightweight, two-stage framework for relatedness computation. In the first stage, a small weighted subgraph is dynamically grown around the two query entities; in the second stage, relatedness is derived based on computations on this subgraph. Our system shows better agreement with human judgment than existing proposals both on the new dataset and on an established one. Our framework also shows improvements with respect to the state-of-the-art on three different extrinsic evaluations in the domains of ranking entity pairs, entity linking, and synonym extraction

Landslide-related sediment yield of a large apenninic catchment

Diverse sources of information, which describes landslide movement, hillslope-channel connectivity and sedimentation rates, are analyzed to detect trends that took place during the last 12 thousands years. We estimate the landslide-related sediment production rates by combining measured landslide velocities and geometries and historical landslide frequency. Coarse sediment deposition rates are measured throughout the Holocene by means of dating and stratigraphy of the alluvial fan and terraced deposits. The comparison between present-day hillslope sediment production and Holocene averaged sediment deposition rates confirms that landsliding is the main agent conveying sediments to higher order trunk streams. The connectivity between hillslopes and the stream network is well developed and no significant sediment sinks influence the sediment transport process. However fluctuations of sediment delivery rates at the outlet of the catchment took place during Holocene and are likely associated to periods of increased hillslope sediment production and channel discharge caused by climatic forcin

Performance Optimization of a Heavy Class Helicopter Engine Installation Using Genetic Algorithms Coupled With CFD Simulations

Aerodynamic design and optimization of engine installation is a pivotal part of the helicopter design process. To this purpose, an adaptive problem-independent and reliable optimization methodology would be particularly valuable for accomplishment of such goal. The application of advanced evolutionary algorithms coupled with CFD solvers for the accurate flow solution of validated numerical models represents a very powerful tool for the parametric design and optimization of engine installation components. Within the JTI Clean Sky FP7 project \u201cHeavyCopter\u201d the consortium constituted by the University of Padova (UNIPD) and the spin-off company HIT09 developed an automatic optimization loop based on an in-house genetic algorithm called GeDEA, and applied it to engine installation design of a heavy-class helicopter. This paper illustrates the application of the above mentioned optimization loop both at cruise and hover reference flight conditions for such a helicopter. The algorithm pursues the minimization of the total pressure losses at the air intakes while keeping the flow distortion at the engine inlet at the lowest level; regarding the exhausts, the back-pressure is minimized in order to increase the power output of the engine while preserving the entrainment ratio. The results highlight significantly improved performance margins with respect to the baseline both for intakes and exhaust

Multi-criteria Multi-constrained Aerodynamic Optimization of Civil Tiltrotor Empennage Surfaces

The present work describes the overall optimization strategy that has been adopted for the enhancement of the aerodynamic performance of a civil tiltrotor empennage surfaces. The optimization process has been designed around GeDEA-II, a Multi-Objective Evolutionary Algorithm developed at University of Padua. The optimization algorithm has been used in two different cases: A two-dimensional optimization of the empennage airfoil and a three-dimensional optimization of the empennage winglets, patented by Leonardo Helicopters under the name of finlets. Results demonstrate the effectiveness of the optimization strategies for both the cases. A parametric study of the empennage planform has also been conducted with the aid of an artificial neural network, in order to assess the variations in aerodynamic performance for different geometries