26 research outputs found
Delay-Based Macromodeling of Long Interconnects from Frequency-Domain Terminal Responses
No full textHigh-Performance Passive Macromodeling Algorithms for Parallel Computing Platforms
No full textThis paper presents a comprehensive strategy for fast generation of passive macromodels of linear devices and interconnects on parallel computing hardware. Starting from a raw characterization of the structure in terms of frequency-domain tabulated scattering responses, we perform a rational curve fitting and a postprocessing passivity enforcement. Both algorithms are parallelized and cast in a form that is suitable for deployment on shared-memory multicore platforms. Particular emphasis is placed on the passivity characterization step, which is performed using two complementary strategies. The first uses an iterative restarted and deflated rational Arnoldi process to extract the imaginary Hamiltonian eigenvalues associated with the model. The second is based on an accuracy-controlled adaptive sampling. Various parallelization strategies are discussed for both schemes, with particular care on load balancing between different computing threads and memory occupation. The resulting parallel macromodeling flow is demonstrated on a number of medium- and large-scale structures, showing good scalability up to 16 computational core
Subgradient Techniques for Passivity Enforcement of Linear Device and Interconnect Macromodels
No full textThis paper presents a class of nonsmooth convex optimization methods for the passivity enforcement of reduced-order macromodels of electrical interconnects, packages, and linear passive devices. Model passivity can be lost during model extraction or identification from numerical field solutions or direct measurements. Nonpassive models may cause instabilities in transient system-level simulation, therefore a suitable postprocessing is necessary in order to eliminate any passivity violations. Different from leading numerical schemes on the subject, passivity enforcement is formulated here as a direct frequency-domain norm minimization through perturbation of the model state-space parameters. Since the dependence of this norm on the parameters is nonsmooth, but continuous and convex, we resort to the use of subdifferentials and subgradients, which are used to devise two different algorithms. We provide a theoretical proof of the global optimality for the solution computed via both schemes. Numerical results confirm that these algorithms achieve the global optimum in a finite number of iterations within a prescribed accuracy leve
FAbry STabilization indEX (FASTEX) : an innovative tool for the assessment of clinical stabilization in Fabry disease
No full textTwo disease severity scoring systems, the Mainz Severity Score Index (MSSI) and Fabry Disease Severity Scoring System (DS3), have been validated for quantifying the disease burden of Fabry disease. We aimed to develop a dynamic mathematical model [the FASTEX (FAbry STabilization indEX)] to assess the clinical stability. A multidisciplinary panel of experts in Fabry disease first defined a novel score of severity [raw score (RS)] based on three domains with a small number items in each domain (nervous system domain: pain, cerebrovascular events; renal domain: proteinuria, glomerular filtration rate; cardiac domain: echocardiography parameters, electrocardiograph parameters and New York Heart Association class) and evaluated the clinical stability over time. The RS was tested in 28 patients (15 males, 13 females) with the classic form of Fabry disease. There was good statistical correlation between the newly established RS and a weighted score (WS), with DS3 and MSSI (R (2) = 0.914, 0.949, 0.910 and 0.938, respectively). In order to refine the RS further, a WS, which was expressed as a percentage value, was calculated. This was based on the relative clinical significance of each item within the domain with the panel agreeing on the attribution of a different weight of clinical damage to a specific organ system. To test the variation of the clinical burden over time, the RS was repeated after 1 year. The panel agreed on a cut-off of a 20% change from baseline as the clinical WS to define clinical stability. The FASTEX model showed good correlation with the clinical assessment and with clinical variation over time in all patients
UPV-Symanto at eRisk 2021: Mental Health Author Profiling for Early Risk Prediction on the Internet
No full text[EN] This paper presents the contributions of the UPV-Symanto team, a collaboration between Symanto Research and the PRHLT Center, in the eRisk 2021 shared tasks on gambling addiction, self-harm detection and prediction of depression levels. We have used a variety of models and techniques, including
Transformers, hierarchical attention networks with multiple linguistic features, a dedicated early alert decision mechanism, and temporal modelling of emotions. We trained the models using additional training data that we collected and annotated thanks to expert psychologists. Our emotions-over-time model obtained the best results for the depression severity task in terms of ACR (and second best according to ADODL). For the self-harm detection task, our Transformer-based model obtained the best absolute result in terms of ERDE5 and we ranked equal first in terms of speed and latency.The authors from Universitat Politècnica de València thank the EU-FEDER Comunitat Valenciana
2014-2020 grant IDIFEDER/2018/025. The work of Paolo Rosso was in the framework of the
research project PROMETEO/2019/121 (DeepPattern) by the Generalitat Valenciana. We would
like to thank the two anonymous reviewers who helped us improve this paper.Basile, A.; Chinea-Ríos, M.; Uban, A.; Müller, T.; Rössler, L.; Yenikent, S.; Chulvi-Ferriols, MA.... (2021). UPV-Symanto at eRisk 2021: Mental Health Author Profiling for Early Risk Prediction on the Internet. CEUR. 908-927. https://riunet.upv.es/handle/10251/190670S90892
Groupwise registration for longitudinal MRI analysis of glioma based on deep learning
No full textGlioma progression is monitored by routine MR scanning, enabling tumor growth evaluation with respect to earlier time-points. This growth may present both as a mass effect and as an extension of abnormalities into previously healthy tissue. To accurately quantify tumor growth and tumor-induced deformations, longitudinal intrasubject image registration is often used. However, such registration in cases with large deformations and tissue change is highly challenging. Longitudinal image registration may benefit from groupwise strategies in which multiple images are concurrently aligned. This avoids introducing bias towards an a priori-selected reference image. However, existing learning-based methods for image registration mostly concern pair-wise approaches. Moreover, the few proposed learning-based methods for groupwise registration are designed for the analysis of images without pathologies and are prone to fail to register glioma images. To bridge this gap, we present a learning-based method for the non-linear registration of longitudinal glioma images. We adapt an existing learning-based groupwise method to handle tumor infiltration by means of cost-function masking. The proposed method is able to register glioma images despite the presence of non-correspondences across the time-points by focusing on the normal-appearing tissue similarity. We train the framework both in one resolution and with a multi-stage strategy exploring multiple resolutions. We evaluate on a dataset from the Glioma Longitudinal AnalySiS consortium and compare it to conventional groupwise registration methods. We achieve comparable Dice coefficients, with higher SSIM and more detailed registrations. These evaluation metrics are further improved when trained as a multi-stage method. The proposed framework preserves the diffeomorphic conditions and the geometric centrality of the deformation fields, while significantly reducing the runtime to under a minute. The proposed methods may serve as an alternative to conventional toolboxes to provide further insight into glioma growth.Biomedical Engineerin
Giuseppe Capecelatro (1744-1836). Un arcivescovo tra politica e diritto
No full textAmong the many essays of Giuseppe Capecelatro – in which it’s evident the influence of Pietro Giannone - is of particular interest the Discorso istorico-politico dell’origine, del progresso e della decadenza del potere de’ chierici su le signorie temporali (published anonymously in Naples in 1788) in which the author took part in the controversy conducted by the Bourbon government against the Church on the so-called tribute of Chinea. The agreement reached in 1791 between the King of Naples and the Pope sent in disgrace the authors - who wrote the essays against the Chinea - in front of the Bourbon government. For this reason Capecelatro didn’t hesitate to cooperate with the revolutionaries of 1799 to reform the municipality of Taranto
Capecelatro, Giuseppe
No full textArchbishop of Taranto lived between late 1700 and early 1800. He was the author of Discorso istorico-politico dell’origine, del progresso e della decadenza del potere de’ chierici su le signorie temporali(Philadelphia sd but Naples) and Riflessioni sul discorso istorico-politico, dialogo del sig. Censorini italiano col sig. Ramour francese (Philadelphia, 1789). The two essays, placed on the Index by S. Office, are part of the controversy conducted by the Bourbon government against the Church on the so called tribute of 'Chinea'
