Geometric Deep Learning: a Temperature Based Analysis of Graph Neural Networks

We examine a Geometric Deep Learning model as a thermodynamic system treating the weights as non-quantum and non-relativistic particles. We employ the notion of temperature previously defined in [7] and study it in the various layers for GCN and GAT models. Potential future applications of our findings are discussed.Comment: Published on Proceedings of GSI 202

Folded fabric tunes rock deformation and failure mode in the upper crust

The micro-mechanisms of brittle failure affect the bulk mechanical behaviour and permeability of crustal rocks. In low-porosity crystalline rocks, these mechanisms are related to mineralogy and fabric anisotropy, while confining pressure, temperature and strain rates regulate the transition from brittle to ductile behaviour. However, the effects of folded anisotropic fabrics, widespread in orogenic settings, on the mechanical behaviour of crustal rocks are largely unknown. Here we explore the deformation and failure behaviour of a representative folded gneiss, by combining the results of triaxial deformation experiments carried out while monitoring microseismicity with microstructural and damage proxies analyses. We show that folded crystalline rocks in upper crustal conditions exhibit dramatic strength heterogeneity and contrasting failure modes at identical confining pressure and room temperature, depending on the geometrical relationships between stress and two different anisotropies associated to the folded rock fabric. These anisotropies modulate the competition among quartz- and mica-dominated microscopic damage processes, resulting in transitional brittle to semi-brittle modes under P and T much lower than expected. This has significant implications on scales relevant to seismicity, energy resources, engineering applications and geohazards

DiagText: manual do usuário.

DiagText é uma ferramenta que teve como objetivo inicial auxiliar o processo de extração de informações de documentos textuais que descrevem doenças de culturas agrícolas para formação de uma árvore de decisão baseada nos sintomas das doenças avaliadas.bitstream/item/11847/1/doc84.pd

Modulated model predictive control with optimized overmodulation

Finite Set Model Predictive Control (FS-MPC) has many advantages, such as a fast dynamic response and an intuitive implementation. For these reasons, it has been thoroughly researched during the last decade. However, the wave form produced by FS-MPC has a switching component whose spread spectrum remains a major disadvantage of the strategy. This paper discusses a modulated model predictive control that guarantees a spectrum switching frequency in the linear modulation range and extends its optimized response to the overmodulation region. Due to the equivalent high gain of the predictive control, and to the limit on the voltage actuation of the power converter, it is expected that the actuation voltage will enter the overmodulation region during large reference changes or in response to load impacts. An optimized overmodulation strategy that converges towards FS-MPC’s response for large tracking errors is proposed for this situation. This technique seamlessly combines PWM’s good steadystate switching performance with FS-MPC’s high dynamic response during large transients. The constant switching frequency is achieved by incorporating modulation of the predicted current vectors in the model predictive control of the currents in a similar fashion as conventional Space-Vector Pulse Width Modulation (SV-PWM) is used to synthesize an arbitrary voltage reference. Experimental results showing the proposed strategy’s good steady-state switching performance, its FS-MPC-like transient response and the seamless transition between modes of operation are presented for a permanent magnet synchronous machine drive

Sedimentological, mineralogical and geochemical features of late quaternary sediment profiles from the Southern Tuscany Hg Mercury District (Italy): Evidence for the presence of pre-industrial mercury and arsenic concentrations

Southern Tuscany (Italy) is an important metallogenic district that hosts relevant S-polymetallic deposits that have intensely been exploited for centuries. Consequently, potential toxic elements, such as Hg and As, are widely distributed in the surrounding environment. In this paper, an extensive sedimentological, mineralogical and geochemical study of two Late Quaternary sediment profiles, partially outcropping along the coast of southern Tuscany (Ansedonia area), was carried out to evaluate the contents and mobility of Hg and As with the aims to contribute to the definition of the geochemical baseline of southern Tuscany before the human intervention and evaluate the potential dispersion of these harmful elements. The sedimentological, mineralogical and geochemical (major elements) features revealed that the studied profiles are mostly related to the local geological characteristics and the Quaternary geological history of the area. The concentrations and the normalized patterns of trace and rare earth elements highlighted the absence of any anthropogenic activity. This implies that the studied samples are to be regarded as good proxies for evaluating the geochemical baseline of southern Tuscany before the intense mining activity. The enrichment factors (EF) of most trace elements were indeed lower or close to 2, indicating a variability close to the average concentration of the Upper Continental Crust (UCC), while other elements slightly enriched, such as Pb, were in agreement with the natural baseline reported for southern Tuscany. Mercury and As displayed EF values >40 when compared to the average contents of UCC, although they decrease down to 4 when compared to the suggested baseline for southern Tuscany. The higher Hg and As contents detected in this study, inferred to natural sources, evidenced (i) the great natural variability occurring in largely mineralized areas and (ii) the importance of estimating reference environmental parameters in order to avoid misleading interpretations of the detected anomalies. Moreover, the results of leaching test on sediment samples denoted a relatively low mobility of Hg and As, suggesting that these elements are preferentially mobilized by transport of clastic sediments and such anomalies may be preserved for relatively long times in Quaternary sediments. However, leachable Hg (0.6-9.7 μg/L) and As (2.1-42.2 μg/L) concentrations are significantly high when compared to those of the Italian limit for groundwater (1 μg/L for Hg and 10 μg/L for As). Quaternary sediments from southern Tuscany could then be a potential, though natural, source of Hg and As to groundwater systems

Viscoelasticity measurements by an optofluidic micro-rheometer

During the last decades, microrheology attracted a significant attention thanks to the possibility of investigating the viscoelastic properties of complex systems (e.g. cells and soft materials) at micrometer scale. The inherent low-consumption of sample offered by microrheology makes it the ideal candidate to study the rheological properties of precious/limited materials. In active microrheology, optical or magnetic forces enable trapping and manipulation of micro-probes in the fluid under test. The probe's response to external stimuli is used to derive the rheological properties of the surrounding medium. While this approach has been already reported in the scientific literature mainly using optical tweezers [1], in this document we propose a different system configuration based on a dual beam laser trap, previously exploited to realize a simple viscometer [2,3]. The here proposed device has all the features of a rheometer, also allowing to measure the elastic properties, and has the advantage of requiring a lower beam intensity while being able to apply larger forces with respect to standard optical tweezers. Additionally the system can be easily integrated in a glass substrate, requiring just an external connection to a CW-laser source and a low-magnification objective for sample observation

DETECTION OF BUILDING ROOFS AND FACADES FROM AERIAL LASER SCANNING DATA USING DEEP LEARNING

In this work we test the power of prediction of deep learning for detection of buildings from aerial laser scanner point cloud information. Automatic extraction of built features from remote sensing data is of extreme interest for many applications. In particular latest paradigms of 3D mapping of buildings, such as CityGML and BIM, can benefit from an initial determination of building geometries. In this work we used a LiDAR dataset of urban environment from the ISPRS benchmark on urban object detection. The dataset is labelled with eight classes, two were used for this investigation: roof and facades. The objective is to test how TensorFlow neural network for deep learning can predict these two classes. Results show that for “roof” and “facades” semantic classes respectively, recall is 84% and 76% and precision is 72% and 63%. The number and distribution of correct points well represent the geometry, thus allowing to use them as support for CityGML and BIM modelling. Further tuning of the hidden layers of the DL model will likely improve results and will be tested in future investigations