Learning Speech Emotion Representations in the Quaternion Domain

The modeling of human emotion expression in speech signals is an important, yet challenging task. The high resource demand of speech emotion recognition models, combined with the general scarcity of emotion-labelled data are obstacles to the development and application of effective solutions in this field. In this paper, we present an approach to jointly circumvent these difficulties. Our method, named RH-emo, is a novel semi-supervised architecture aimed at extracting quaternion embeddings from real-valued monoaural spectrograms, enabling the use of quaternion-valued networks for speech emotion recognition tasks. RH-emo is a hybrid real/quaternion autoencoder network that consists of a real-valued encoder in parallel to a real-valued emotion classifier and a quaternion-valued decoder. On the one hand, the classifier permits to optimization of each latent axis of the embeddings for the classification of a specific emotion-related characteristic: valence, arousal, dominance, and overall emotion. On the other hand, quaternion reconstruction enables the latent dimension to develop intra-channel correlations that are required for an effective representation as a quaternion entity. We test our approach on speech emotion recognition tasks using four popular datasets: IEMOCAP, RAVDESS, EmoDB, and TESS, comparing the performance of three well-established real-valued CNN architectures (AlexNet, ResNet-50, VGG) and their quaternion-valued equivalent fed with the embeddings created with RH-emo. We obtain a consistent improvement in the test accuracy for all datasets, while drastically reducing the resources' demand of models. Moreover, we performed additional experiments and ablation studies that confirm the effectiveness of our approach

Effects of photoperiod on epididymal and sperm morphology in a wild rodent, the viscacha (Lagostomus maximus maximus)

The viscacha (Lagostomus maximus maximus) is a seasonal South American wild rodent. The adult males exhibit an annual reproductive cycle with periods of maximum and minimum gonadal activity. Four segments have been identified in the epididymis of this species: initial, caput, corpus, and cauda. The main objective of this work was to relate the seasonal morphological changes observed in the epididymal duct with the data from epididymal sperm during periods of activity and gonadal regression using light and scanning electron microscopy (SEM). Under light and electron microscopy, epididymal corpus and cauda showed marked seasonal variations in structural parameters and in the distribution of different cellular populations of epithelium. Initial and caput segments showed mild morphological variations between the two periods. Changes in epididymal sperm morphology were observed in the periods analyzed and an increased number of abnormal gametes were found during the regression period. During this period, anomalies were found mainly in the head, midpiece, and neck, while in the activity period, defects were found only in the head. Our results confirm that the morphological characteristics of the epididymal segments, as well as sperm morphology, undergo significant changes during the reproductive cycle of Lagostomus.Fil: Cruceño, A. M.. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia. Cátedra de Histología; ArgentinaFil: De Rosas, J. C.. Consejo Nacional de Investigaciones Científicas y Tecnicas. Centro Cientifico Tecnologico Mendoza. Instituto Histologia y Embriologia de Mendoza "Dr. M. Burgos"; Argentina. Universidad Nacional de Cuyo. Facultad de Ciencias Médicas; ArgentinaFil: Foscolo, Mabel Rosa. Consejo Nacional de Investigaciones Científicas y Tecnicas. Centro Cientifico Tecnologico Mendoza. Instituto Histologia y Embriologia de Mendoza "Dr. M. Burgos"; ArgentinaFil: Chaves, E. M.. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia. Cátedra de Histología; ArgentinaFil: Scardapane, L.. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia. Cátedra de Histología; ArgentinaFil: Dominguez, S.. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia. Cátedra de Histología; ArgentinaFil: Aguilera Merlo, C.. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia. Cátedra de Histología; Argentin

Multifunctional Core@Satellite Magnetic Particles for Magnetoresistive Biosensors

Magnetoresistive (MR) biosensors combine distinctive features such as small size, low cost, good sensitivity, and propensity to be arrayed to perform multiplexed analysis. Magnetic nanoparticles (MNPs) are the ideal target for this platform, especially if modified not only to overcome their intrinsic tendency to aggregate and lack of stability but also to realize an interacting surface suitable for biofunctionalization without strongly losing their magnetic response. Here, we describe an MR biosensor in which commercial MNP clusters were coated with gold nanoparticles (AuNPs) and used to detect human IgG in water using an MR biochip that comprises six sensing regions, each one containing five U-shaped spin valve sensors. The isolated AuNPs (satellites) were stuck onto an aggregate of individual iron oxide crystals (core) so that the resulting core@satellite magnetic particles (CSMPs) could be functionalized by the photochemical immobilization technique an easy procedure that leads to oriented antibodies immobilized upright onto gold. The morphological, optical, hydrodynamic, magnetic, and surface charge properties of CSMPs were compared with those exhibited by the commercial MNP clusters showing that the proposed coating procedure endows the MNP clusters with stability and ductility without being detrimental to magnetic properties. Eventually, the high-performance MR biosensor allowed us to detect human IgG in water with a detection limit of 13 pM (2 ng mL-1). Given its portability, the biosensor described in this paper lends itself to a point-of-care device; moreover, the features of the MR biochip also make it suitable for multiplexed analysis

Lung ultrasonography for long-term follow-up of COVID-19 survivors compared to chest CT scan

Background: While lung ultrasonography (LUS) has utility for the evaluation of the acute phase of COVID-19 related lung disease, its role in long-term follow-up of this condition has not been well described. The objective of this study is to compare LUS and chest computed tomography (CT) results in COVID-19 survivors with the intent of defining the utility of LUS for long-term follow-up of COVID-19 respiratory disease. Methods: Prospective observational study that enrolled consecutive survivors of COVID-19 with acute hypoxemic respiratory failure (HARF) admitted to the Respiratory Intensive Care Unit. Three months following hospital discharge, patients underwent LUS, chest CT, body plethysmography and laboratory testing, the comparison of which forms the basis of this report. Results: 38 patients were enrolled, with a total of 190 lobes analysed: men 27/38 (71.1%), mean age 60.6 y (SD 10.4). LUS findings and pulmonary function tests outcomes were compared between patients with and without ILD, showing a statistically significant difference in terms of LUS score (p: 0.0002), FEV1 (p: 0.0039) and FVC (p: 0.012). ROC curve both in lobe by lobe and in patient's overall analysis revealed an outstanding ILD discrimination ability of LUS (AUC: 0.94 and 0.95 respectively) with a substantial Cohen's coefficient (K: 0.74 and 0.69). Conclusions: LUS has an outstanding discrimination ability compared to CT in identifying an ILD of at least mild grade in the post COVID-19 follow-up. LUS should be considered as the first-line tool in follow-up programs, while chest CT could be performed based on LUS findings

Fibrosing Progressive Interstitial Lung Disease in Rheumatoid Arthritis: A Multicentre Italian Study

Background: The INBUILD study demonstrated the efficacy of nintedanib in the treatment of progressive fibrosing interstitial lung disease different to idiopathic pulmonary fibrosis, including rheumatoid arthritis (RA)-related ILD. Nevertheless, the prevalence of RA-ILD patients that may potentially benefit from nintedanib remains unknown. Objectives and methods: The aim of the present multicentre study was to investigate the prevalence and possible associated factors of fibrosing progressive patterns in a cross-sectional cohort of RA-ILD patients. Results: One hundred and thirty-four RA-ILD patients with a diagnosis of RA-ILD, who were confirmed at high-resolution computed tomography and with a follow-up of at least 24 months, were enrolled. The patients were defined as having a progressive fibrosing ILD in case of a relative decline in forced vital capacity > 10% predicted and/or an increased extent of fibrotic changes on chest imaging in a 24-month period. Respiratory symptoms were excluded to reduce possible bias due to the retrospective interpretation of cough and dyspnea. According to radiologic features, ILD was classified as usual interstitial pneumonia (UIP) in 50.7% of patients, nonspecific interstitial pneumonia in 19.4%, and other patterns in 29.8%. Globally, a fibrosing progressive pattern was recorded in 36.6% of patients (48.5% of patients with a fibrosing pattern) with a significant association to the UIP pattern. Conclusion: We observed that more than a third of RA-ILD patients showed a fibrosing progressive pattern and might benefit from antifibrotic treatment. This study shows some limitations, such as the retrospective design. The exclusion of respiratory symptoms' evaluation might underestimate the prevalence of progressive lung disease but increases the value of results.Background: The INBUILD study demonstrated the efficacy of nintedanib in the treatment of progressive fibrosing interstitial lung disease different to idiopathic pulmonary fibrosis, including rheumatoid arthritis (RA)-related ILD. Nevertheless, the prevalence of RA-ILD patients that may potentially benefit from nintedanib remains unknown. Objectives and methods: The aim of the present multicentre study was to investigate the prevalence and possible associated factors of fibrosing progressive patterns in a cross-sectional cohort of RA-ILD patients. Results: One hundred and thirty-four RA-ILD patients with a diagnosis of RA-ILD, who were confirmed at high-resolution computed tomography and with a follow-up of at least 24 months, were enrolled. The patients were defined as having a progressive fibrosing ILD in case of a relative decline in forced vital capacity > 10% predicted and/or an increased extent of fibrotic changes on chest imaging in a 24-month period. Respiratory symptoms were excluded to reduce possible bias due to the retrospective interpretation of cough and dyspnea. According to radiologic features, ILD was classified as usual interstitial pneumonia (UIP) in 50.7% of patients, nonspecific interstitial pneumonia in 19.4%, and other patterns in 29.8%. Globally, a fibrosing progressive pattern was recorded in 36.6% of patients (48.5% of patients with a fibrosing pattern) with a significant association to the UIP pattern. Conclusion: We observed that more than a third of RA-ILD patients showed a fibrosing progressive pattern and might benefit from antifibrotic treatment. This study shows some limitations, such as the retrospective design. The exclusion of respiratory symptoms’ evaluation might underestimate the prevalence of progressive lung disease but increases the value of results

A calibrated multiexit neural network for detecting urothelial cancer cells

Deep convolutional networks have become a powerful tool for medical imaging diagnostic. In pathology, most efforts have been focused in the subfield of histology, while cytopathology (which studies diagnostic tools at the cellular level) remains underexplored. In this paper, we propose a novel deep learning model for cancer detection from urinary cytopathology screening images. We leverage recent ideas from the field of multioutput neural networks to provide a model that can efficiently train even on small-scale datasets, such as those typically found in real-world scenarios. Additionally, we argue that calibration (i.e., providing confidence levels that are aligned with the ground truth probability of an event) has been a major shortcoming of prior works, and we experiment a number of techniques to provide a well-calibrated model. We evaluate the proposed algorithm on a novel dataset, and we show that the combination of focal loss, multiple outputs, and temperature scaling provides a model that is significantly more accurate and calibrated than a baseline deep convolutional network

Flexible generative adversarial networks with non-parametric activation functions

Generative adversarial networks (GANs) have become widespread models for complex density estimation tasks such as image generation or image-to-image synthesis. At the same time, training of GANs can suffer from several problems, either of stability or convergence, sometimes hindering their effective deployment. In this paper we investigate whether we can improve GAN training by endowing the neural network models with more flexible activation functions compared to the commonly used rectified linear unit (or its variants). In particular, we evaluate training a deep convolutional GAN wherein all hidden activation functions are replaced with a version of the kernel activation function (KAF), a recently proposed technique for learning non-parametric nonlinearities during the optimization process. On a thorough empirical evaluation on multiple image generation benchmarks, we show that the resulting architectures learn to generate visually pleasing images in a fraction of the number of the epochs, eventually converging to a better solution, even when we equalize (or even lower) the number of free parameters. Overall, this points to the importance of investigating better and more flexible architectures in the context of GANs

In codice ratio: OCR of handwritten Latin documents using deep convolutional networks

Automatic transcription of historical handwritten documents is a challenging research problem, requiring in general expensive transcriptions from expert paleographers. In Codice Ratio is designed to be an end-to-end architecture requiring instead limited labeling effort, whose aim is the automatic transcription of a portion of the Vatican Secret Archives (one of the largest historical libraries in the world). In this paper, we describe in particular the design of our OCR component for Latin characters. To this end, we first annotated a large corpus of Latin characters with a custom crowdsourcing platform. Leveraging over recent progresses in deep learning, we designed and trained a deep convolutional network achieving an overall accuracy of 96% over the entire dataset, which is one of the highest results reported in the literature so far. Our training data are publicly available