A unifying representation for a class of dependent random measures

We present a general construction for dependent random measures based on thinning Poisson processes on an augmented space. The framework is not restricted to dependent versions of a specific nonparametric model, but can be applied to all models that can be represented using completely random measures. Several existing dependent random measures can be seen as specific cases of this framework. Interesting properties of the resulting measures are derived and the efficacy of the framework is demonstrated by constructing a covariate-dependent latent feature model and topic model that obtain superior predictive performance

Are the deficits in navigational abilities present in the Williams syndrome related to deficits in the backward inhibition?

Williams syndrome (WS) is associated with a distinct profile of relatively proficient skills within the verbal domain compared to the severe impairment of visuo-spatial processing. Abnormalities in executive functions and deficits in planning ability and spatial working memory have been described. However, to date little is known about the influence of executive function deficits on navigational abilities in WS. This study aimed at analyzing in WS individuals a specific executive function, the backward inhibition (BI) that allows individuals to flexibly adapt to continuously changing environments. A group of WS individuals and a mental age- and gender-matched group of typically developing children were subjected to three task-switching experiments requiring visuospatial or verbal material to be processed. Results showed that WS individuals exhibited clear BI deficits during visuospatial task-switching paradigms and normal BI effect during verbal task-switching paradigm. Overall, the present results suggest that the BI involvement in updating environment representations during navigation may influence WS navigational abilitie

Preliminary results of P-wave and S-wave measurements by seismic dilatometer test (SPDMT) in Mirandola (Italy)

A trial seismic dilatometer-VP (SPDMT) has been recently developed to measure the compressional wave velocity VP, in addition to the shear wave velocity VS and to the DMT geotechnical parameters. The new SPDMT is the combination of the traditional mechanical flat dilatometer (DMT) with an appropriate seismic module placed above the DMT blade. The SPDMT module consist in a probe outfitted with two receivers for measuring the P-wave velocity, along with two receivers for measuring the S-wave velocity. The paper describes the SPDMT equipment, the test procedure and the interpretation of VP and VS measurements, together with some considerations on the potential geotechnical applications which can benefit from the contemporary measurement of the two propagation velocities. Finally, the paper illustrates preliminary results of P-wave and S-wave measurements by SPDMT compared to several cross-hole, down-hole and suspension logging data at the Mirandola test site (Italy), a soft alluvial site which was investigated within the InterPACIFIC (Intercomparison of methods for site parameter and velocity profile characterization) project

3D Generative Model Latent Disentanglement via Local Eigenprojection

Designing realistic digital humans is extremely complex. Most data-driven generative models used to simplify the creation of their underlying geometric shape do not offer control over the generation of local shape attributes. In this paper, we overcome this limitation by introducing a novel loss function grounded in spectral geometry and applicable to different neural-network-based generative models of 3D head and body meshes. Encouraging the latent variables of mesh variational autoencoders (VAEs) or generative adversarial networks (GANs) to follow the local eigenprojections of identity attributes, we improve latent disentanglement and properly decouple the attribute creation. Experimental results show that our local eigenprojection disentangled (LED) models not only offer improved disentanglement with respect to the state-of-the-art, but also maintain good generation capabilities with training times comparable to the vanilla implementations of the models. Our code and pre-trained models are available at github.com/simofoti/LocalEigenprojDisentangled

Investigation of suitable sites for wave energy converters around Sicily (Italy)

Abstract. An analysis of wave energy along the coasts of Sicily (Italy) is presented with the aim of selecting possible sites for the implementation of wave energy converters (WECs). The analysis focuses on the selection of hotspot areas of energy concentration. A third-generation model was adopted to reconstruct the wave data along the coast over a period of 14 years. The reconstruction was performed using the wave and wind data from the European Centre for Medium-Range Weather Forecasts. The analysis of wave energy allowed us to characterise the most energetic zones, which are located on the western side of Sicily and on the Strait of Sicily. Moreover, the estimate of the annual wave power on the entire computational domain identified eight interesting sites. The main features of the sites include relatively high wave energy and proximity to the coast, which makes them possible sites for the implementation of WEC farms