Young children do not integrate visual and haptic information

Several studies have shown that adults integrate visual and haptic information (and information from other modalities) in a statistically optimal fashion, weighting each sense according to its reliability. To date no studies have investigated when this capacity for cross-modal integration develops. Here we show that prior to eight years of age, integration of visual and haptic spatial information is far from optimal, with either vision or touch dominating totally, even in conditions where the dominant sense is far less precise than the other (assessed by discrimination thresholds). For size discrimination, haptic information dominates in determining both perceived size and discrimination thresholds, while for orientation discrimination vision dominates. By eight-ten years, the integration becomes statistically optimal, like adults. We suggest that during development, perceptual systems require constant recalibration, for which cross-sensory comparison is important. Using one sense to calibrate the other precludes useful combination of the two sources

Estimation of thermophysical properties from in-situ measurements in all seasons: quantifying and reducing errors using dynamic grey-box methods

Robust characterisation of the thermal performance of buildings from in-situ measurements requires error analysis to evaluate the certainty of estimates. A method for the quantification of systematic errors on the thermophysical properties of buildings obtained using dynamic grey-box methods is presented, and compared to error estimates from the average method. Different error propagation methods (accounting for equipment uncertainties) were introduced to reflect the different mathematical description of heat transfer in the static and dynamic approaches. Thermophysical properties and their associated errors were investigated using two case studies monitored long term. The analysis showed that the dynamic method (and in particular a three thermal resistance and two thermal mass model) reduced the systematic error compared to the static method, even for periods of low internal-to-external average temperature difference. It was also shown that the use of a uniform error as suggested in the ISO 9869-1:2014 Standard would generally be misrepresentative. The study highlighted that dynamic methods for the analysis of in-situ measurements may provide robust characterisation of the thermophysical behaviour of buildings and extend their application beyond the winter season in temperate climates (e.g., for quality assurance and informed decision making purposes) in support of closing the performance gap

Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective

Neural-symbolic computing has now become the subject of interest of both academic and industry research laboratories. Graph Neural Networks (GNN) have been widely used in relational and symbolic domains, with widespread application of GNNs in combinatorial optimization, constraint satisfaction, relational reasoning and other scientific domains. The need for improved explainability, interpretability and trust of AI systems in general demands principled methodologies, as suggested by neural-symbolic computing. In this paper, we review the state-of-the-art on the use of GNNs as a model of neural-symbolic computing. This includes the application of GNNs in several domains as well as its relationship to current developments in neural-symbolic computing.Comment: Updated version, draft of accepted IJCAI2020 Survey Pape

Distributed control in virtualized networks

The increasing number of the Internet connected devices requires novel solutions to control the next generation network resources. The cooperation between the Software Defined Network (SDN) and the Network Function Virtualization (NFV) seems to be a promising technology paradigm. The bottleneck of current SDN/NFV implementations is the use of a centralized controller. In this paper, different scenarios to identify the pro and cons of a distributed control-plane were investigated. We implemented a prototypal framework to benchmark different centralized and distributed approaches. The test results have been critically analyzed and related considerations and recommendations have been reported. The outcome of our research influenced the control plane design of the following European R&D projects: PLATINO, FI-WARE and T-NOVA

New insights into crustal structure, Cenozoic magmatism, CO2 degassing and seismogenesis in the southern Apennines and Irpinia region from local earthquake tomography

We present high-resolution Vp and Vp/Vs models of the southern Apennines (Italy) computed using local earthquakes recorded from 2006 to 2011 with a graded inversion scheme that progressively resolves the crustal structure, from the large scale of the Apennines belt to the local scale of the normal-fault system. High-Vp bodies defined in the upper and mid crust under the external Apennines are interpreted as extensive mafic intrusions revealing anorogenic magmatism episodes that broadened on the Adriatic domain during Paleogene. Under the mountain belt, a low-Vp region, annular to the Neapolitan volcanic district, indicates the existence of a thermal/fluid anomaly in the mid crust, coinciding with a shallow Moho and diffuse degassing of deeply derived CO2. In the belt axial zone, low Vp/Vs gas-pressurized rock volumes under the Apulian carbonates correlate to high heat flow, strong CO2-dominated gas emissions of mantle origin and shallow carbonate reservoirs with pressurized CO2 gas caps. We hypothesize that the pressurized fluid volumes located at the base of the active fault system influence the rupture process of large normal-faulting earthquakes, like the 1980 Mw6.9 Irpinia event, and that major asperities are confined within the high-Vp Apulian carbonates. This study confirms once more that pre-existing structures of the Pliocene Apulian belt controlled the rupture propagation during the Irpinia earthquake. The main shock broke a 30 km long, NE-dipping seismogenic structure, whereas delayed ruptures (both the 20 s and the 40 s sub-events) developed on antithetic faults, reactivating thrust faults located at the eastern edge of the Apulian belt

Energy Density Functionals From the Strong-Coupling Limit Applied to the Anions of the He Isoelectronic Series

Anions and radicals are important for many applications including environmental chemistry, semiconductors, and charge transfer, but are poorly described by the available approximate energy density functionals. Here we test an approximate exchange-correlation functional based on the exact strong-coupling limit of the Hohenberg-Kohn functional on the prototypical case of the He isoelectronic series with varying nuclear charge $Z<2$, which includes weakly bound negative ions and a quantum phase transition at a critical value of $Z$, representing a big challenge for density functional theory. We use accurate wavefunction calculations to validate our results, comparing energies and Kohn-Sham potentials, thus also providing useful reference data close to and at the quantum phase transition. We show that our functional is able to bind H$^-$ and to capture in general the physics of loosely bound anions, with a tendency to strongly overbind that can be proven mathematically. We also include corrections based on the uniform electron gas which improve the results.Comment: Accepted for the JCP Special Topic Issue "Advances in DFT Methodology

Density functional theory for strongly-correlated bosonic and fermionic ultracold dipolar and ionic gases

We introduce a density functional formalism to study the ground-state properties of strongly-correlated dipolar and ionic ultracold bosonic and fermionic gases, based on the self-consistent combination of the weak and the strong coupling limits. Contrary to conventional density functional approaches, our formalism does not require a previous calculation of the interacting homogeneous gas, and it is thus very suitable to treat systems with tunable long-range interactions. Due to its asymptotic exactness in the regime of strong correlation, the formalism works for systems in which standard mean-field theories fail.Comment: 5 pages, 2 figure

Spin Resolution of the Electron-Gas Correlation Energy: Positive same-spin contribution

The negative correlation energy per particle of a uniform electron gas of density parameter $r_s$ and spin polarization $\zeta$ is well known, but its spin resolution into up-down, up-up, and down-down contributions is not. Widely-used estimates are incorrect, and hamper the development of reliable density functionals and pair distribution functions. For the spin resolution, we present interpolations between high- and low-density limits that agree with available Quantum Monte Carlo data. In the low-density limit for $\zeta = 0$, we find that the same-spin correlation energy is unexpectedly positive, and we explain why. We also estimate the up and down contributions to the kinetic energy of correlation.Comment: new version, to appear in PRB Rapid Communicatio

An automatic deep learning approach for coronary artery calcium segmentation

Coronary artery calcium (CAC) is a significant marker of atherosclerosis and cardiovascular events. In this work we present a system for the automatic quantification of calcium score in ECG-triggered non-contrast enhanced cardiac computed tomography (CT) images. The proposed system uses a supervised deep learning algorithm, i.e. convolutional neural network (CNN) for the segmentation and classification of candidate lesions as coronary or not, previously extracted in the region of the heart using a cardiac atlas. We trained our network with 45 CT volumes; 18 volumes were used to validate the model and 56 to test it. Individual lesions were detected with a sensitivity of 91.24%, a specificity of 95.37% and a positive predicted value (PPV) of 90.5%; comparing calcium score obtained by the system and calcium score manually evaluated by an expert operator, a Pearson coefficient of 0.983 was obtained. A high agreement (Cohen's k = 0.879) between manual and automatic risk prediction was also observed. These results demonstrated that convolutional neural networks can be effectively applied for the automatic segmentation and classification of coronary calcifications