196 research outputs found

    Adversarial training to improve robustness of adversarial deep neural classifiers in the NOvA experiment

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    The NOvA experiment is a long-baseline neutrino oscillation experiment. Consisting of two functionally identical detectors situated off-axis in Fermilab’s NuMI neutrino beam. The Near Detector observes the unoscillated beam at Fermilab, while the Far Detector observes the oscillated beam 810 km away. This allows for measurements of the oscillation probabilities for multiple oscillation channels, ν_µ → ν_µ, anti ν_µ → anti ν_µ, ν_µ → ν_e and anti ν_µ → anti ν_e, leading to measurements of the neutrino oscillation parameters, sinθ_23, ∆m^2_32 and δ_CP. These measurements are produced from an extensive analysis of the recorded data. Deep neural networks are deployed at multiple stages of this analysis. The Event CVN network is deployed for the purposes of identifying and classifying the interaction types of selected neutrino events. The effects of systematic uncertainties present in the measurements on the network performance are investigated and are found to cause negligible variations. The robustness of these network trainings is therefore demonstrated which further justifies their current usage in the analysis beyond the standard validation. The effects on the network performance for larger systematic alterations to the training datasets beyond the systematic uncertainties, such as an exchange of the neutrino event generators, are investigated. The differences in network performance corresponding to the introduced variations are found to be minimal. Domain adaptation techniques are implemented in the AdCVN framework. These methods are deployed for the purpose of improving the Event CVN robustness for scenarios with systematic variations in the underlying data

    Efficient Experimental and Data-Centered Workflow for Microstructure-Based Fatigue Data – Towards a Data Basis for Predictive AI Models

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    Background Early fatigue mechanisms for various materials are yet to be unveiled for the (very) high-cycle fatigue (VHCF) regime. This can be ascribed to a lack of available data capturing initial fatigue damage evolution, which continues to adversely affect data scientists and computational modeling experts attempting to derive microstructural dependencies from small sample size data and incomplete feature representations. Objective The aim of this work is to address this lack and to drive the digital transformation of materials such that future virtual component design can be rendered more reliable and more efficient. Achieving this relies on fatigue models that comprehensively capture all relevant dependencies. Methods To this end, this work proposes a combined experimental and data post-processing workflow to establish multimodal fatigue crack initiation and propagation data sets efficiently. It evolves around fatigue testing of mesoscale specimens to increase damage detection sensitivity, data fusion through multimodal registration to address data heterogeneity, and image-based data-driven damage localization. Results A workflow with a high degree of automation is established, that links large distortion-corrected microstructure data with damage localization and evolution kinetics. The workflow enables cycling up to the VHCF regime in comparatively short time spans, while maintaining unprecedented time resolution of damage evolution. Resulting data sets capture the interaction of damage with microstructural features and hold the potential to unravel a mechanistic understanding. Conclusions The proposed workflow lays the foundation for future data mining and data-driven modeling of microstructural fatigue by providing statistically meaningful data sets extendable to a wide range of materials

    Blind assessment for stereo images considering binocular characteristics and deep perception map based on deep belief network

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    © 2018 Elsevier Inc. In recent years, blind image quality assessment in the field of 2D image/video has gained the popularity, but its applications in 3D image/video are to be generalized. In this paper, we propose an effective blind metric evaluating stereo images via deep belief network (DBN). This method is based on wavelet transform with both 2D features from monocular images respectively as image content description and 3D features from a novel depth perception map (DPM) as depth perception description. In particular, the DPM is introduced to quantify longitudinal depth information to align with human stereo visual perception. More specifically, the 2D features are local histogram of oriented gradient (HoG) features from high frequency wavelet coefficients and global statistical features including magnitude, variance and entropy. Meanwhile, the global statistical features from the DPM are characterized as 3D features. Subsequently, considering binocular characteristics, an effective binocular weight model based on multiscale energy estimation of the left and right images is adopted to obtain the content quality. In the training and testing stages, three DBN models for the three types features separately are used to get the final score. Experimental results demonstrate that the proposed stereo image quality evaluation model has high superiority over existing methods and achieve higher consistency with subjective quality assessments

    Sonographic assessment of normal and abnormal fetal development; early and late aspects.

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    New methods for studying complex diseases via genetic association studies

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    Genome-wide association studies (GWAS) have delivered many novel insights about the etiology of many common heritable diseases. However, in most disorders studied by GWAS, the known single nucleotide polymorphisms (SNPs) associated with the disease do not account for a large portion of the genetic factors underlying the condition. This suggests that many of the undiscovered variants contributing to the risk of common diseases have weak effects or are relatively rare. This thesis introduces novel adaptations of techniques for improving detection power for both of these types of risk variants, and reports the results of analyses applying these methods to real datasets for common diseases. Chapter 2 describes a novel approach to improve the detection of weak-effect risk variants that is based on an adaptive sampling technique known as Distilled Sensing (DS). This procedure entails utilization of a portion of the total sample to exclude from consideration regions of the genome where there is no evidence of genetic association, and then testing for association with a greatly reduced number of variants in the remaining sample. Application of the method to simulated data sets and GWAS data from studies of age-related macular degeneration (AMD) demonstrated that, in many situations, DS can have superior power over traditional meta-analysis techniques to detect weak-effect loci. Chapter 3 describes an innovative pipeline to screen for rare variants in next generation sequencing (NGS) data. Since rare variants, by definition, are likely to be present in only a few individuals even in large samples, efficient methods to screen for rare causal variants are critical for advancing the utility of NGS technology. Application of our approach, which uses family-based data to identify candidate rare variants that could explain aggregation of disease in some pedigrees, resulted in the discovery of novel protein-coding variants linked to increased risk for Alzheimer's disease (AD) in African Americans. The techniques presented in this thesis address different aspects of the "missing heritability" problem and offer efficient approaches to discover novel risk variants, and thereby facilitate development of a more complete picture of genetic risk for common diseases

    Modeling and characterization of the mechanical and damping response of carbon nanotube nanocomposites

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    "Modeling and Characterization of the Mechanical and Damping Response of Carbon Nanotube Nanocomposites" ABSTRACT: Multifunctionality is a current trend in material design. The fast growing needs of industries are challenging the design of structures made of advanced lightweight composites that possess the capability of performing multiple functions. The superior mechanical properties of carbon nanotubes (CNTs) - besides the excellent electrical and thermal properties - make them ideal candidates to be used as reinforcement llers in composite materials. CNT nanocomposites, made of suitable polymeric matrices lled with carbon nanotubes, have shown enhanced mechanical and electrical features with an additional extraordinary feature, namely, a high structural damping capacity. The main objective of this work is to explore the mechanical and damping response of CNT nanocomposites, aiming at reaching a better understanding of the macroscopic behavior of the nanocomposite materials by taking into account their complex micro/nanostructural features. The practical goal is to effectively explore the potential exploitation of these nanostructured materials in demanding structural applications. In order to investigate and optimize not only the mechanical properties but also the damping capacity of CNT/polymer composites, a specic analytical model, based on the Eshelby and Mory-Tanaka approaches, is here presented. The proposed model is an effective tool for predicting nonlinear stress-strain curves, energy dissipation mechanisms and hysteresis of nanocomposite materials. A great deal of studies were conducted on the ability of CNT-reinforced materials to absorb vibrations and noise (damping capacity), analyzing the orientation, dispersion and aspect ratio of CNTs as main parameters that affect the mechanical and damping properties. As a step forward from the current state of the art, the present work suggests an innovative theoretical method to describe the macroscopic response of nanocomposites and explore energy dissipation mechanisms arising from the shear slippage of nanotubes within the hosting matrix. The major mechanism through which energy is dissipated, the stick-slip mechanism, can be properly treated by introducing a plastic eigenstrain in the CNT inclusions whose evolutive law is accordingly shaped after the physical phenomenology. A set of numerical tests are performed to estimate the elastic properties and the nonlinear response of nanocomposites, characterizing the hysteresis loops in the stress-strain curves. Parametric studies are conducted to investigate the in uence of the main constitutive parameters of the model on the mechanical response including the damping capacity. The numerical simulations revealed that the interfacial shear strength, the CNT volume fraction, the exponent of the evolution law for the plastic eigenstrain, as well as the strain amplitude, have a signicant effect on the hysteresis of CNT nanocomposites. Moreover, it is shown that an optimal combination of these micro-structural parameters can be achieved via differential evolutionary algorithms that allow to maximize the damping capacity, while preserving the high elastic properties of the nanostructured materials. Such approach further enables the calibration of the model and design the nanomaterial in order to provide an effective response according to the structural vibration control requirements and high mechanical performance goals. The validation of the effectiveness of the predictive computational tool, together with its theoretical framework, is also sought via an ad hoc experimental approach. The experimental campaign featuring mechanical tests on a variety of CNT nanocomposite materials was indeed a fundamental step towards the renement of the model and a reasonable tuning of the model parameters. In addition, a morphology investigation of the prepared CNT/polymer composites was a decisive step to dene the microstructural properties. The experimental activities highlighted and conrmed the relevance of several morphological aspects, such as the actual CNT aspect ratio variability within the nanocomposite, the CNT dispersion and agglomeration degree and the polymer matrix chemical structure, to mention but a few. Those results shed light to which nanocomposites constituents features can influence the macroscopic response of the material. The conducted experimental work aimed also at identifying and introducing parameters that can better enhance the nanocomposite mechanical and damping behavior, by investigating also aspects of the fabrication processes that can help improve the CNT dispersion or the CNT adhesion like, for instance, the CNT functionalization. These experimental findings allowed a final model update by overcoming the main limitations, generally present in the most common micromechanical theories for multi-phase materials, i.e., (i) the perfect nanoller dispersion and distribution in the surrounding matrix, and (ii) the perfect interfacial adhesion between the carbon nanotubes and polymer chains
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