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

Re-identification of objects from aerial photos with hybrid siamese neural networks

In this paper, we consider the task of re-identifying the same object in different photos taken from separate positions and angles during aerial reconnaissance, which is a crucial task for the maintenance and surveillance of critical large-scale infrastructure. To effectively hybridize deep neural networks with available domain expertise for a given scenario, we propose a customized pipeline, wherein a domain-dependent object detector is trained to extract the assets (i.e., sub-components) present on the objects, and a siamese neural network learns to re-identify the objects, exploiting both visual features (i.e., the image crops corresponding to the assets) and the graphs describing the relations among their constituting assets. We describe a real-world application concerning the re-identification of electric poles in the Italian energy grid, showing our pipeline to significantly outperform siamese networks trained from visual information alone. We also provide a series of ablation studies of our framework to underline the effect of including topological asset information in the pipeline, learnable positional embeddings in the graphs, and the effect of different types of graph neural networks on the final accuracy

CASTNet: Community-Attentive Spatio-Temporal Networks for Opioid Overdose Forecasting

Opioid overdose is a growing public health crisis in the United States. This crisis, recognized as "opioid epidemic," has widespread societal consequences including the degradation of health, and the increase in crime rates and family problems. To improve the overdose surveillance and to identify the areas in need of prevention effort, in this work, we focus on forecasting opioid overdose using real-time crime dynamics. Previous work identified various types of links between opioid use and criminal activities, such as financial motives and common causes. Motivated by these observations, we propose a novel spatio-temporal predictive model for opioid overdose forecasting by leveraging the spatio-temporal patterns of crime incidents. Our proposed model incorporates multi-head attentional networks to learn different representation subspaces of features. Such deep learning architecture, called "community-attentive" networks, allows the prediction of a given location to be optimized by a mixture of groups (i.e., communities) of regions. In addition, our proposed model allows for interpreting what features, from what communities, have more contributions to predicting local incidents as well as how these communities are captured through forecasting. Our results on two real-world overdose datasets indicate that our model achieves superior forecasting performance and provides meaningful interpretations in terms of spatio-temporal relationships between the dynamics of crime and that of opioid overdose.Comment: Accepted as conference paper at ECML-PKDD 201

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

A primer to common major gastrointestinal post-surgical anatomy on CT—a pictorial review

The post-operative abdomen can be challenging and knowledge of normal post-operative anatomy is important for diagnosing complications. The aim of this pictorial essay is to describe a few selected common, major gastrointestinal surgeries, their clinical indications and depict their normal post-operative computed tomography (CT) appearance. This essay provides some clues to identify the surgeries, which can be helpful especially when surgical history is lacking: recognition of the organ(s) involved, determination of what was resected and familiarity with the type of anastomoses used

Clinical and functional characterization of a novel mutation in lamin a/c gene in a multigenerational family with arrhythmogenic cardiac laminopathy.

none17noneAkinori Kimura;Cinzia Forleo;Monica Carmosino;Nicoletta Resta;Alessandra Rampazzo;Rosanna Valecce;Sandro Sorrentino;Massimo Iacoviello;Francesco Pisani;Giuseppe Procino;Andrea Gerbino;Arnaldo Scardapane;Cristiano Simone;Martina Calore;Silvia Torretta;Maria Svelto;Stefano FavaleAkinori, Kimura; Cinzia, Forleo; Monica, Carmosino; Nicoletta, Resta; Rampazzo, Alessandra; Rosanna, Valecce; Sandro, Sorrentino; Massimo, Iacoviello; Francesco, Pisani; Giuseppe, Procino; Andrea, Gerbino; Arnaldo, Scardapane; Cristiano, Simone; Calore, Martina; Silvia, Torretta; Maria, Svelto; Stefano, Faval

Radiological evaluation of colorectal anastomoses

Background and aims: The purpose of this study was to determine the accuracy, interobserver variability, timing and discordance with relaparotomy of postoperative radiological examination of colorectal anastomoses. Patient/methods: From 2000 to 2005, 429 patients underwent an

Quaternion neural networks for 3D sound source localization in reverberant environments

Localization of sound sources in 3D sound fields is an extremely challenging task, especially when the environments are reverberant and involve multiple sources. In this work, we propose a deep neural network to analyze audio signals recorded by 3D microphones and localize sound sources in a spatial sound field. In particular, we consider first-order Ambisonics microphones to capture 3D acoustic signals and represent them by spherical harmonic decomposition in the quaternion domain. Moreover, to improve the localization performance, we use quaternion input features derived from the acoustic intensity, which is strictly related to the direction of arrival (DOA) of a sound source. The proposed network architecture involves both quaternion-valued convolutional and recurrent layers. Results show that the proposed method is able to exploit both the quaternion-valued representation of ambisonic signals and to improve the localization performance with respect to existing methods