When Does Output Feedback Enlarge the Capacity of the Interference Channel?

In this paper, the benefits of channel-output feedback in the Gaussian interference channel (G-IC) are studied under the effect of additive Gaussian noise. Using a linear deterministic (LD) model, the signal to noise ratios (SNRs) in the feedback links beyond which feedback plays a significant role in terms of increasing the individual rates or the sum-rate are approximated. The relevance of this work lies on the fact that it identifies the feedback SNRs for which in any G-IC one of the following statements is true: (a) feedback does not enlarge the capacity region; (b) feedback enlarges the capacity region and the sum-rate is greater than the largest sum-rate without feedback; and (c) feedback enlarges the capacity region but no significant improvement is observed in the sum-rate

Evolution of residual stresses induced by machining in a Nickel based alloy under static loading at room temperature

Tensile residual stresses are very often generated on the surface when machining nickel alloys. In order to determine their influence on the final mechanical behaviour of the component residual stress stability should be considered. In the present work the evolution of residual stresses induced by machining in Inconel 718 under static loading at room temperature has been studied. An Inconel 718 disc has been face turned and specimens for tensile tests have been extracted from the disc. Then surface residual stresses have been measured by X-ray diffraction for initial state and different loading levels. Finally, a finite element model has been fitted to experimental results and the study has been extended for more loading conditions. For the studied case, it has been observed that tensile residual stresses remain stable when applying elastic loads but they increase at higher loads close to the yield stress of the material

The Sailor diagram – A new diagram for the verification of two-dimensional vector data from multiple models

A new diagram is proposed for the verification of vector quantities generated by multiple models against a set of observations. It has been designed with the objective, as in the Taylor diagram, of providing a visual diagnostic tool which allows an easy comparison of simulations by multiple models against a reference dataset. However, the Sailor diagram extends this ability to two-dimensional quantities such as currents, wind, horizontal fluxes of water vapour and other geophysical variables by adding features which allow us to evaluate directional properties of the data as well. The diagram is based on the analysis of the two-dimensional structure of the mean squared error matrix between model and observations. This matrix is separated in a part corresponding to the bias and the relative rotation of the two orthogonal directions (empirical orthogonal functions; EOFs) which best describe the vector data. Since there is no truncation of the retained EOFs, these orthogonal directions explain the total variability of the original dataset. We test the performance of this new diagram to identify the differences amongst the reference dataset and a series of model outputs by using some synthetic datasets and real-world examples with time series of variables such as wind, current and vertically integrated moisture transport. An alternative setup for spatially varying time-fixed fields is shown in the last examples, in which the spatial average of surface wind in the Northern and Southern Hemisphere according to different reanalyses and realizations from ensembles of CMIP5 models are compared. The Sailor diagrams presented here show that it is a tool which helps in identifying errors due to the bias or the orientation of the simulated vector time series or fields. The R implementation of the diagram presented together with this paper allows us also to easily retrieve the individual diagnostics of the different components of the mean squared error and additional diagnostics which can be presented in tabular form.This research has been supported by the Spanish Government’s MINECO grant and ERDF (grant no. CGL2016- 76561-R) and the UPV/EHU (grant no. GIU17/02)

Membrane-containing virus particles exhibit the mechanics of a composite material for genome protection

The protection of the viral genome during extracellular transport is an absolute requirement for virus survival and replication. In addition to the almost universal proteinaceous capsids, certain viruses add a membrane layer that encloses their double-stranded (ds) DNA genome within the protein shell. Using the membrane-containing enterobacterial virus PRD1 as a prototype, and a combination of nanoindentation assays by atomic force microscopy and finite element modelling, we show that PRD1 provides a greater stability against mechanical stress than that achieved by the majority of dsDNA icosahedral viruses that lack a membrane. We propose that the combination of a stiff and brittle proteinaceous shell coupled with a soft and compliant membrane vesicle yields a tough composite nanomaterial well-suited to protect the viral DNA during extracellular transport

A multiscale material model for metallic powder compaction during hot isostatic pressing

The prediction of the distortions during Near-Net-Shape Hot Isostatic Pressing (NNS-HIP) is an intrinsic multiscale problem where the local interactions among particles determine the macroscopic distortions taking place during the sintering and densification of a component. In this work, a multiscale approach is proposed to solve this problem. In particular, a viscoplastic constitutive model capable of predicting macroscopic contractions during a HIP process with high accuracy has been developed, implemented and validated. The macroscopic model incorporates the mechanical behaviour predicted at the meso-scale by means of multiple-particle finite element models (MP-FEM) of an agglomerate of powder particles. The model is validated through the prediction of distortions during HIP of a full scale industrial case. It is concluded that adding the microscopic information of the HIP process to simulate the contractions at the macroscopic level results in a considerable improvement of the accuracy of the predictions

Approximate Capacity Region of the Two-User Gaussian Interference Channel with Noisy Channel-Output Feedback

In this paper, the capacity region of the linear deterministic interference channel with noisy channel-output feedback (LD-IC-NF) is fully characterized. The proof of achievability is based on random coding arguments and rate splitting; blockMarkov superposition coding; and backward decoding. The proof of converse reuses some of the existing outer bounds and includes new ones obtained using genie-aided models. Following the insight gained from the analysis of the LD-IC-NF, an achievability region and a converse region for the two-user Gaussian interference channel with noisy channel-output feedback (GIC-NF) are presented. Finally, the achievability region and the converse region are proven to approximate the capacity region of the G-IC-NF to within 4.4 bits

The worst-case data-generating probability measure in statistical learning

The worst-case data-generating (WCDG) probability measure is introduced as a tool for characterizing the generalization capabilities of machine learning algorithms. Such a WCDG probability measure is shown to be the unique solution to two different optimization problems: (a) The maximization of the expected loss over the set of probability measures on the datasets whose relative entropy with respect to a reference measure is not larger than a given threshold; and (b) The maximization of the expected loss with regularization by relative entropy with respect to the reference measure. Such a reference measure can be interpreted as a prior on the datasets. The WCDG cumulants are finite and bounded in terms of the cumulants of the reference measure. To analyze the concentration of the expected empirical induced by the WCDG probability measure, the notion of (,δ)-robustness of models is introduced. Closed-form expressions are presented for the sensitivity of the expected loss for a fixed model. These tools result in the characterization of a novel expression for the generalization error of arbitrary machine learning algorithms. This exact expression is provided in terms of the WCDG probability measure and leads to an upper bound that is equal to the sum of the mutual information and the lautum information between the models and the datasets, up to a constant factor. This upper bound is achieved by a Gibbs algorithm. This finding reveals that an exploration into the generalization error of the Gibbs algorithm facilitates the derivation of overarching insights applicable to any machine learning algorithm

Data-driven modeling and monitoring of fuel cell performance

A mathematical framework that provides practical guidelines for user adoption is proposed for fuel cell performance evaluation. By leveraging the mathematical framework, two measures that describe the average and worst-case performance are presented. To facilitate the computation of the performance measures in a practical setting, we model the distribution of the voltages at different current points as a Gaussian process. Then the minimum number of samples needed to estimate the performance measures is obtained using information-theoretic notions. Furthermore, we introduce a sensing algorithm that finds the current points that are maximally informative about the voltage. Observing the voltages at the points identified by the proposed algorithm enables the user to estimate the voltages at the unobserved points. The proposed performance measures and the corresponding results are validated on a fuel cell dataset provided by an industrial user whose conclusion coincides with the judgement from the fuel cell manufacturer

Green spaces and cognitive development in primary schoolchildren

© 2015, National Academy of Sciences. All rights reserved. Exposure to green space has been associated with better physical and mental health. Although this exposure could also influence cognitive development in children, available epidemiological evidence on such an impact is scarce. This study aimed to assess the association between exposure to green space and measures of cognitive development in primary schoolchildren. This study was based on 2,593 schoolchildren in the second to fourth grades (7-10 y) of 36 primary schools in Barcelona, Spain (2012-2013). Cognitive development was assessed as 12-mo change in developmental trajectory of working memory, superior working memory, and inattentiveness by using four repeated (every 3 mo) computerized cognitive tests for each outcome. We assessed exposure to green space by characterizing outdoor surrounding greenness at home and school and during commuting by using high-resolution (5 m x5 m) satellite data on greenness (normalized difference vegetation index). Multilevel modeling was used to estimate the associations between green spaces and cognitive development. We observed an enhanced 12-mo progress in working memory and superior working memory and a greater 12-mo reduction in inattentiveness associated with greenness within and surrounding school boundaries and with total surrounding greenness index (including greenness surrounding home, commuting route, and school). Adding a traffic-related air pollutant (elemental carbon) to models explained 20-65% of our estimated associations between school greenness and 12-mo cognitive development. Our study showed a beneficial association between exposure to green space and cognitive development among schoolchildren that was partly mediated by reduction in exposure to air pollution