Dermatological remedies in the traditional pharmacopoeia of Vulture-Alto Bradano, inland southern Italy

Dermatological remedies make up at least one-third of the traditional pharmacopoeia in southern Italy. The identification of folk remedies for the skin is important both for the preservation of traditional medical knowledge and in the search for novel antimicrobial agents in the treatment of skin and soft tissue infection (SSTI). Our goal is to document traditional remedies from botanical, animal, mineral and industrial sources for the topical treatment of skin ailments. In addition to SSTI remedies for humans, we also discuss certain ethnoveterinary applications. Field research was conducted in ten communities in the Vulture-Alto Bradano area of the Basilicata province, southern Italy. We randomly sampled 112 interviewees, stratified by age and gender. After obtaining prior informed consent, we collected data through semi-structured interviews, participant-observation, and small focus groups techniques. Voucher specimens of all cited botanic species were deposited at FTG and HLUC herbaria located in the US and Italy. We report the preparation and topical application of 116 remedies derived from 38 plant species. Remedies are used to treat laceration, burn wound, wart, inflammation, rash, dental abscess, furuncle, dermatitis, and other conditions. The pharmacopoeia also includes 49 animal remedies derived from sources such as pigs, slugs, and humans. Ethnoveterinary medicine, which incorporates both animal and plant derived remedies, is addressed. We also examine the recent decline in knowledge regarding the dermatological pharmacopoeia. The traditional dermatological pharmacopoeia of Vulture-Alto Bradano is based on a dynamic folk medical construct of natural and spiritual illness and healing. Remedies are used to treat more than 45 skin and soft tissue conditions of both humans and animals. Of the total 165 remedies reported, 110 have never before been published in the mainland southern Italian ethnomedical literature

Outcome and its predictors in Guillain–Barré syndrome

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A meta-learning approach for training explainable graph neural networks

In this article, we investigate the degree of explainability of graph neural networks (GNNs). The existing explainers work by finding global/local subgraphs to explain a prediction, but they are applied after a GNN has already been trained. Here, we propose a meta-explainer for improving the level of explainability of a GNN directly at training time, by steering the optimization procedure toward minima that allow post hoc explainers to achieve better results, without sacrificing the overall accuracy of GNN. Our framework (called MATE, MetA-Train to Explain) jointly trains a model to solve the original task, e.g., node classification, and to provide easily processable outputs for downstream algorithms that explain the model's decisions in a human-friendly way. In particular, we meta-train the model's parameters to quickly minimize the error of an instance-level GNNExplainer trained on-the-fly on randomly sampled nodes. The final internal representation relies on a set of features that can be ``better'' understood by an explanation algorithm, e.g., another instance of GNNExplainer. Our model-agnostic approach can improve the explanations produced for different GNN architectures and use any instance-based explainer to drive this process. Experiments on synthetic and real-world datasets for node and graph classification show that we can produce models that are consistently easier to explain by different algorithms. Furthermore, this increase in explainability comes at no cost to the accuracy of the model

Guillain-Barré syndrome: a century of progress

In 1916, Guillain, Barré and Strohl reported on two cases of acute flaccid paralysis with high cerebrospinal fluid protein levels and normal cell counts — novel findings that identified the disease we now know as Guillain–Barré syndrome (GBS). 100 years on, we have made great progress with the clinical and pathological characterization of GBS. Early clinicopathological and animal studies indicated that GBS was an immune-mediated demyelinating disorder, and that severe GBS could result in secondary axonal injury; the current treatments of plasma exchange and intravenous immunoglobulin, which were developed in the 1980s, are based on this premise. Subsequent work has, however, shown that primary axonal injury can be the underlying disease. The association of Campylobacter jejuni strains has led to confirmation that anti-ganglioside antibodies are pathogenic and that axonal GBS involves an antibody and complement-mediated disruption of nodes of Ranvier, neuromuscular junctions and other neuronal and glial membranes. Now, ongoing clinical trials of the complement inhibitor eculizumab are the first targeted immunotherapy in GBS

SmartFog: Training the Fog for the energy-saving analytics of Smart-Meter data

In this paper, we characterize the main building blocks and numerically verify the classification accuracy and energy performance of SmartFog, a distributed and virtualized networked Fog technological platform for the support for Stacked Denoising Auto-Encoder (SDAE)-based anomaly detection in data flows generated by Smart-Meters (SMs). In SmartFog, the various layers of an SDAE are pretrained at different Fog nodes, in order to distribute the overall computational efforts and, then, save energy. For this purpose, a new Adaptive Elitist Genetic Algorithm (AEGA) is “ad hoc” designed to find the optimized allocation of the SDAE layers to the Fog nodes. Interestingly, the proposed AEGA implements a (novel) mechanism that adaptively tunes the exploration and exploitation capabilities of the AEGA, in order to quickly escape the attraction basins of local minima of the underlying energy objective function and, then, speed up the convergence towards global minima. As a matter of fact, the main distinguishing feature of the resulting SmartFog paradigm is that it accomplishes the joint integration on a distributed Fog computing platform of the anomaly detection functionality and the minimization of the resulting energy consumption. The reported numerical tests support the effectiveness of the designed technological platform and point out that the attained performance improvements over some state-of-the-art competing solutions are around 5%, 68% and 30% in terms of detection accuracy, execution time and energy consumption, respectively

Compressing deep-quaternion neural networks with targeted regularisation

In recent years, hyper-complex deep networks (such as complex-valued and quaternion-valued neural networks - QVNNs) have received a renewed interest in the literature. They find applications in multiple fields, ranging from image reconstruction to 3D audio processing. Similar to their real-valued counterparts, quaternion neural networks require custom regularisation strategies to avoid overfitting. In addition, for many real-world applications and embedded implementations, there is the need of designing sufficiently compact networks, with few weights and neurons. However, the problem of regularising and/or sparsifying QVNNs has not been properly addressed in the literature as of now. In this study, the authors show how to address both problems by designing targeted regularisation strategies, which can minimise the number of connections and neurons of the network during training. To this end, they investigate two extensions of l1and structured regularisations to the quaternion domain. In the authors' experimental evaluation, they show that these tailored strategies significantly outperform classical (realvalued) regularisation approaches, resulting in small networks especially suitable for low-power and real-time applications

Complex-valued neural networks with nonparametric activation functions

Complex-valued neural networks (CVNNs) are a powerful modeling tool for domains where data can be naturally interpreted in terms of complex numbers. However, several analytical properties of the complex domain (such as holomorphicity) make the design of CVNNs a more challenging task than their real counterpart. In this paper, we consider the problem of flexible activation functions (AFs) in the complex domain, i.e., AFs endowed with sufficient degrees of freedom to adapt their shape given the training data. While this problem has received considerable attention in the real case, very limited literature exists for CVNNs, where most activation functions are generally developed in a split fashion (i.e., by considering the real and imaginary parts of the activation separately) or with simple phase-amplitude techniques. Leveraging over the recently proposed kernel activation functions, and related advances in the design of complex-valued kernels, we propose the first fully complex, nonparametric activation function for CVNNs, which is based on a kernel expansion with a fixed dictionary that can be implemented efficiently on vectorized hardware. Several experiments on common use cases, including prediction and channel equalization, validate our proposal when compared to real-valued neural networks and CVNNs with fixed activation functions

PHYDI. Initializing parameterized hypercomplex neural networks as identity functions

Neural models based on hypercomplex algebra systems are growing and prolificating for a plethora of applications, ranging from computer vision to natural language processing. Hand in hand with their adoption, parameterized hypercomplex neural networks (PHNNs) are growing in size and no techniques have been adopted so far to control their convergence at a large scale. In this paper, we study PHNNs convergence and propose parameterized hypercomplex identity initialization (PHYDI), a method to improve their convergence at different scales, leading to more robust performance when the number of layers scales up, while also reaching the same performance with fewer iterations. We show the effectiveness of this approach in different benchmarks and with common PHNNs with ResNets- and Transformer-based architecture. The code is available at https://github.com/ispamm/PHYDI

L3DAS23: Learning 3D Audio Sources for Audio-Visual Extended Reality

The primary goal of the L3DAS (Learning 3D Audio Sources) project is to stimulate and support collaborative research studies concerning machine learning techniques applied to 3D audio signal processing. To this end, the L3DAS23 Challenge, presented at IEEE ICASSP 2023, focuses on two spatial audio tasks of paramount interest for practical uses: 3D speech enhancement (3DSE) and 3D sound event localization and detection (3DSELD). Both tasks are evaluated within augmented reality applications. The aim of this paper is to describe the main results obtained from this challenge. We provide the L3DAS23 dataset, which comprises a collection of first-order Ambisonics recordings in reverberant simulated environments. Indeed, we maintain some general characteristics of the previous L3DAS challenges, featuring a pair of first-order Ambisonics microphones to capture the audio signals and involving multiple-source and multiple-perspective Ambisonics recordings. However, in this new edition, we introduce audio-visual scenarios by including images that depict the frontal view of the environments as captured from the perspective of the microphones. This addition aims to enrich the challenge experience, giving participants tools for exploring a combination of audio and images for solving the 3DSE and 3DSELD tasks. In addition to a brand-new dataset, we provide updated baseline models designed to take advantage of audio-image pairs. To ensure accessibility and reproducibility, we also supply supporting API for an effortless replication of our results. We support the dataset download and the use of the baseline models via extensive instructions provided on the official GitHub repository at https://github.com/l3das/L3DAS23. Lastly, we present the results achieved by the participants of the L3DAS23 Challenge. For more comprehensive information and in-depth details about the challenge, we invite the reader to visit the L3DAS Project website at http://www.l3das.com/icassp2023

Combined Sparse Regularization for Nonlinear Adaptive Filters

Nonlinear adaptive filters often show some sparse behavior due to the fact that not all the coefficients are equally useful for the modeling of any nonlinearity. Recently, a class of proportionate algorithms has been proposed for nonlinear filters to leverage sparsity of their coefficients. However, the choice of the norm penalty of the cost function may be not always appropriate depending on the problem. In this paper, we introduce an adaptive combined scheme based on a block-based approach involving two nonlinear filters with different regularization that allows to achieve always superior performance than individual rules. The proposed method is assessed in nonlinear system identification problems, showing its effectiveness in taking advantage of the online combined regularization.Comment: This is a corrected version of the paper presented at EUSIPCO 2018 and published on IEEE https://ieeexplore.ieee.org/document/855295