Neural-Based Nonlinear Device Models for Intermodulation Analysis

A new procedure to learn a nonlinear model together with its derivative parameters using a composite neural network is presented.So far neural networks have never been used to extract large-signal device model accounting for distortion parameters.Applying this method to FET devices leads to nonlinear models for current- voltage functions which allow improved prediction of weak and mildly device nonlinearities in the whole bias region. The resulting models have demonstrated to be suitable for both small-signal and large-signal analyses,including intermodulation distortion prediction

Generic flow profiles induced by a beating cilium

We describe a multipole expansion for the low Reynolds number fluid flows generated by a localized source embedded in a plane with a no-slip boundary condition. It contains 3 independent terms that fall quadratically with the distance and 6 terms that fall with the third power. Within this framework we discuss the flows induced by a beating cilium described in different ways: a small particle circling on an elliptical trajectory, a thin rod and a general ciliary beating pattern. We identify the flow modes present based on the symmetry properties of the ciliary beat.Comment: 12 pages, 6 figures, to appear in EPJ

Boosting Few-Shot Learning with Disentangled Self-Supervised Learning and Meta-Learning for Medical Image Classification

Background and objective: Employing deep learning models in critical domains such as medical imaging poses challenges associated with the limited availability of training data. We present a strategy for improving the performance and generalization capabilities of models trained in low-data regimes. Methods: The proposed method starts with a pre-training phase, where features learned in a self-supervised learning setting are disentangled to improve the robustness of the representations for downstream tasks. We then introduce a meta-fine-tuning step, leveraging related classes between meta-training and meta-testing phases but varying the granularity level. This approach aims to enhance the model's generalization capabilities by exposing it to more challenging classification tasks during meta-training and evaluating it on easier tasks but holding greater clinical relevance during meta-testing. We demonstrate the effectiveness of the proposed approach through a series of experiments exploring several backbones, as well as diverse pre-training and fine-tuning schemes, on two distinct medical tasks, i.e., classification of prostate cancer aggressiveness from MRI data and classification of breast cancer malignity from microscopic images. Results: Our results indicate that the proposed approach consistently yields superior performance w.r.t. ablation experiments, maintaining competitiveness even when a distribution shift between training and evaluation data occurs. Conclusion: Extensive experiments demonstrate the effectiveness and wide applicability of the proposed approach. We hope that this work will add another solution to the arsenal of addressing learning issues in data-scarce imaging domains.Comment: 20 pages, 4 figures, 4 tables. Submitted to Elsevier on 25 March 202

The challenge and response to global tourism in the post-modern era: the commodification, reconfiguration and mutual transformation of Habana Vieja, Cuba

There is a growing literature on the symbolic and cultural meanings of tourism and the ways in which cities are increasingly competing for tourists through the promotion of cultural assets and different forms of spectacle in the `tourist bubble'. To date, research on the role and impact of tourism in cities has largely been confined to those in Western, post-industrial economies. This paper examines the growth of cultural tourism in the central area of Havana, Cuba, and explores the range of unique, devolved, state-owned enterprises that are attempting to use tourism as a funding mechanism to achieve improvements in the social and cultural fabric of the city for the benefit of residents. The paper concludes with an assessment of the implications of this example for our understanding of how the pressures for restructuring and commodification can be moderated at the city level. Copyright 2008 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution

Persistent homology to analyse 3D faces and assess body weight gain

In this paper, we analyse patterns in face shape variation due to weight gain. We propose the use of persistent homology descriptors to get geometric and topological information about the configuration of anthropometric 3D face landmarks. In this way, evaluating face changes boils down to comparing the descriptors computed on 3D face scans taken at different times. By applying dimensionality reduction techniques to the dissimilarity matrix of descriptors, we get a space in which each face is a point and face shape variations are encoded as trajectories in that space. Our results show that persistent homology is able to identify features which are well related to overweight and may help assessing individual weight trends. The research was carried out in the context of the European project SEMEOTICONS, which developed a multisensory platform which detects and monitors over time facial signs of cardio-metabolic risk

Rasgos dentales no-métricos en una muestra pre-conquista calchaquí de Argentina, América del Sur

The present study was carried out with a Calchaquí human Pre-Conquest sample from Northwest of Argentina, with the aim of exploring the dental morphology patterns in this population. This study was carried out by means of a macroscopic analysis in permanent dentitions of 7 skulls. 40 dental non-mtetric traits were recorded using the ASU Dental Anthropology System. Percentages >70% was found only in 4 traits. Calchaquí sample studied here is near to these values in shovel shape expression, but the results of this study suggest that a Sinodont pattern is no clear for this sample. To conclude, the present investigation provides additional, insightful elements for a description of biological factors in the process of dental morphologic diversification associated to regional and temporal ranges in this region of Argentina.Facultad de Ciencias Naturales y Muse

Low-Bias-Complexity Ku-band GaN MMIC Doherty Power Amplifier

This paper present a two-stage Doherty power amplifier designed to maximize the efficiency at 6 dB back-off while minimizing the complexity in terms of bias voltages. The amplifier has been manufactured on a GaN-SiC 150 nm monolithic microwave integrated circuit technology. The fabricated chip, measured in continuous wave conditions, maintains a linear gain higher than 13 dB, a saturated output power in excess of 34 dBm, with a power-added efficiency higher than 20% both at saturation and at 6 dB output back-off, over the 14.5 GHz-17.25 GHz band, favorably comparing with the present state of the art for similar applications