Modeling Taxi Drivers' Behaviour for the Next Destination Prediction

In this paper, we study how to model taxi drivers' behaviour and geographical information for an interesting and challenging task: the next destination prediction in a taxi journey. Predicting the next location is a well studied problem in human mobility, which finds several applications in real-world scenarios, from optimizing the efficiency of electronic dispatching systems to predicting and reducing the traffic jam. This task is normally modeled as a multiclass classification problem, where the goal is to select, among a set of already known locations, the next taxi destination. We present a Recurrent Neural Network (RNN) approach that models the taxi drivers' behaviour and encodes the semantics of visited locations by using geographical information from Location-Based Social Networks (LBSNs). In particular, RNNs are trained to predict the exact coordinates of the next destination, overcoming the problem of producing, in output, a limited set of locations, seen during the training phase. The proposed approach was tested on the ECML/PKDD Discovery Challenge 2015 dataset - based on the city of Porto -, obtaining better results with respect to the competition winner, whilst using less information, and on Manhattan and San Francisco datasets.Comment: preprint version of a paper submitted to IEEE Transactions on Intelligent Transportation System

Deep learning techniques for biomedical data processing

The interest in Deep Learning (DL) has seen an exponential growth in the last ten years, producing a significant increase in both theoretical and applicative studies. On the one hand, the versatility and the ability to tackle complex tasks have led to the rapid and widespread diffusion of DL technologies. On the other hand, the dizzying increase in the availability of biomedical data has made classical analyses, carried out by human experts, progressively more unlikely. Contextually, the need for efficient and reliable automatic tools to support clinicians, at least in the most demanding tasks, has become increasingly pressing. In this survey, we will introduce a broad overview of DL models and their applications to biomedical data processing, specifically to medical image analysis, sequence processing (RNA and proteins) and graph modeling of molecular data interactions. First, the fundamental key concepts of DL architectures will be introduced, with particular reference to neural networks for structured data, convolutional neural networks, generative adversarial models, and siamese architectures. Subsequently, their applicability for the analysis of different types of biomedical data will be shown, in areas ranging from diagnostics to the understanding of the characteristics underlying the process of transcription and translation of our genetic code, up to the discovery of new drugs. Finally, the prospects and future expectations of DL applications to biomedical data will be discussed

A multi-stage GAN for multi-organ chest X-ray image generation and segmentation

Multi-organ segmentation of X-ray images is of fundamental importance for computer aided diagnosis systems. However, the most advanced semantic segmentation methods rely on deep learning and require a huge amount of labeled images, which are rarely available due to both the high cost of human resources and the time required for labeling. In this paper, we present a novel multi-stage generation algorithm based on Generative Adversarial Networks (GANs) that can produce synthetic images along with their semantic labels and can be used for data augmentation. The main feature of the method is that, unlike other approaches, generation occurs in several stages, which simplifies the procedure and allows it to be used on very small datasets. The method has been evaluated on the segmentation of chest radiographic images, showing promising results. The multistage approach achieves state-of-the-art and, when very few images are used to train the GANs, outperforms the corresponding single-stage approach

On the approximation capability of GNNs in node classification/regression tasks

Graph neural networks (GNNs) are a broad class of connectionist models for graph processing. Recent studies have shown that GNNs can approximate any function on graphs, modulo the equivalence relation on graphs defined by theWeisfeiler-Lehman (WL) test. However, these results suffer from some limitations, both because they were derived using the Stone-Weierstrass theorem — which is existential in nature — and because they assume that the target function to be approximated must be continuous. Furthermore, all current results are dedicated to graph classification/regression tasks, where the GNN must produce a single output for the whole graph, while also node classification/regression problems, in which an output is returned for each node, are very common. In this paper, we propose an alternative way to demonstrate the approximation capability of GNNs that overcomes these limitations. Indeed, we show that GNNs are universal approximators in probability for node classification/regression tasks, as they can approximate any measurable function that satisfies the 1-WL-equivalence on nodes. The proposed theoretical framework allows the approximation of generic discontinuous target functions and also suggests the GNN architecture that can reach a desired approximation. In addition, we provide a bound on the number of the GNN layers required to achieve the desired degree of approximation, namely 2r − 1, where r is the maximum number of nodes for the graphs in the domain

Generated or Not Generated (GNG): The Importance of Background in the Detection of Fake Images

Facial biometrics are widely used to reliably and conveniently recognize people in photos, in videos, or from real-time webcam streams. It is therefore of fundamental importance to detect synthetic faces in images in order to reduce the vulnerability of biometrics-based security systems. Furthermore, manipulated images of faces can be intentionally shared on social media to spread fake news related to the targeted individual. This paper shows how fake face recognition models may mainly rely on the information contained in the background when dealing with generated faces, thus reducing their effectiveness. Specifically, a classifier is trained to separate fake images from real ones, using their representation in a latent space. Subsequently, the faces are segmented and the background removed, and the detection procedure is performed again, observing a significant drop in classification accuracy. Finally, an explainability tool (SHAP) is used to highlight the salient areas of the image, showing that the background and face contours crucially influence the classifier decision

analysis of brain nmr images for age estimation with deep learning

Abstract During the last decade, deep learning and Convolutional Neural Networks (CNNs) have produced a devastating impact on computer vision, yielding exceptional results on a variety of problems, including analysis of medical images. Recently, these techniques have been extended to 3D images with the downside of a large increase in the computational load. In particular, state-of-the-art CNNs have been used for brain Nuclear Magnetic Resonance (NMR) imaging, with the aim of estimating the patients' age. In fact, a large discrepancy between the real and the estimated age is a clear alarm for the onset of neurodegenerative diseases, such as some types of early dementia and Alzheimer's disease. In this paper, we propose an effective alternative to 3D convolutions that guarantees a significant reduction of the computational requirements for this kind of analysis. The proposed architectures achieve comparable results with the competitor 3D methods, requiring only a fraction of the training time and GPU memory

Impact of the Covid 19 outbreaks on the italian twitter vaccination debat: a network based analysis

Vaccine hesitancy, or the reluctance to be vaccinated, is a phenomenon that has recently become particularly significant, in conjunction with the vaccination campaign against COVID-19. During the lockdown period, necessary to control the spread of the virus, social networks have played an important role in the Italian debate on vaccination, generally representing the easiest and safest way to exchange opinions and maintain some form of sociability. Among social network platforms, Twitter has assumed a strategic role in driving the public opinion, creating compact groups of users sharing similar views towards the utility, uselessness or even dangerousness of vaccines. In this paper, we present a new, publicly available, dataset of Italian tweets, TwitterVax, collected in the period January 2019--May 2022. Considering monthly data, gathered into forty one retweet networks -- where nodes identify users and edges are present between users who have retweeted each other -- we performed community detection within the networks, analyzing their evolution and polarization with respect to NoVax and ProVax users through time. This allowed us to clearly discover debate trends as well as identify potential key moments and actors in opinion flows, characterizing the main features and tweeting behavior of the two communities

Hearing threshold estimation by Auditory Steady State Responses (ASSR) in children

Hearing threshold identification in very young children is always problematic and challenging. Electrophysiological testing such as auditory brainstem responses (ABR) is still considered the most reliable technique for defining the hearing threshold. However, over recent years there has been increasing evidence to support the role of auditory steady-state response (ASSR). Retrospective study. Forty-two children, age range 3-189 months, were evaluated for a total of 83 ears. All patients were affected by sensorineural hearing loss (thresholds ≥ 40 dB HL according to a click-ABR assessment). All patients underwent ABRs, ASSR and pure tone audiometry (PTA), with the latter performed according to the child’s mental and physical development. Subjects were divided into two groups: A and B. The latter performed all hearing investigations at the same time as they were older than subjects in group A, and it was then possible to achieve electrophysiological and PTA tests in close temporal sequence. There was no significant difference between the threshold levels identified at the frequencies tested (0.25, 0.5, 1, 2 and 4 kHz), by PTA, ABR and ASSR between the two groups (Mann Whitney U test, p < 0.05). Moreover, for group A, there was no significant difference between the ASSR and ABR thresholds when the children were very young and the PTA thresholds subsequently identified at a later stage. Our results show that ASSR can be considered an effective procedure and a reliable test, particularly when predicting hearing threshold in very young children at lower frequencies (including 0.5 kHz)

Rehabilitation of Severe to Profound Sensorineural Hearing Loss in Adults: Audiological Outcomes

The aim of this article is to describe the audiological patterns of 71 adult patients presenting severe to profound sensorineural hearing loss, who were rehabilitated by cochlear implants (CIs) and hearing aids. This is a retrospective study in a university setting, where the clinical records of 71 adult patients were reviewed and processed. Speech intelligibility was evaluated at one aided ear (CI) or at both aided ears (double CI or a combination of CI and hearing aid [HA]). Patients with a bilateral CI or with a bimodal hearing setup (CI and HA) performed better than those with a single CI; data from the phonetic matrices test showed that there was a statistically significant difference among patients aided by a single CI versus binaural setup (double CI or CI + HA). In particular, patients aided by a bilateral CI, or by a CI and HA, showed an improvement in the functional results of the speech tests, compared to patients using a single CI. Binaural hearing (either with a bilateral CI or bimodal) allows an improvement in the functional results at the speech tests, compared to the use of a CI only