Machine Learning Approaches to Human Body Shape Analysis

Soft biometrics, biomedical sciences, and many other fields of study pay particular attention to the study of the geometric description of the human body, and its variations. Although multiple contributions, the interest is particularly high given the non-rigid nature of the human body, capable of assuming different poses, and numerous shapes due to variable body composition. Unfortunately, a well-known costly requirement in data-driven machine learning, and particularly in the human-based analysis, is the availability of data, in the form of geometric information (body measurements) with related vision information (natural images, 3D mesh, etc.). We introduce a computer graphics framework able to generate thousands of synthetic human body meshes, representing a population of individuals with stratified information: gender, Body Fat Percentage (BFP), anthropometric measurements, and pose. This contribution permits an extensive analysis of different bodies in different poses, avoiding the demanding, and expensive acquisition process. We design a virtual environment able to take advantage of the generated bodies, to infer the body surface area (BSA) from a single view. The framework permits to simulate the acquisition process of newly introduced RGB-D devices disentangling different noise components (sensor noise, optical distortion, body part occlusions). Common geometric descriptors in soft biometric, as well as in biomedical sciences, are based on body measurements. Unfortunately, as we prove, these descriptors are not pose invariant, constraining the usability in controlled scenarios. We introduce a differential geometry approach assuming body pose variations as isometric transformations of the body surface, and body composition changes covariant to the body surface area. This setting permits the use of the Laplace-Beltrami operator on the 2D body manifold, describing the body with a compact, efficient, and pose invariant representation. We design a neural network architecture able to infer important body semantics from spectral descriptors, closing the gap between abstract spectral features, and traditional measurement-based indices. Studying the manifold of body shapes, we propose an innovative generative adversarial model able to learn the body shapes. The method permits to generate new bodies with unseen geometries as a walk on the latent space, constituting a significant advantage over traditional generative methods

Additive models for energy markets

This Dissertation explores the capability of additive models to describe prices in energy markets, by focusing in particular on the specific case of electricity and natural gas. In Chapter 1 we study a dynamic portfolio optimization problem designed for intraday electricity trading. In Chapter 2 we introduce a no-arbitrage tractable framework based on the Heath- Jarrow-Morton approach for a multicommodity energy forward market. Chapter 3 deals with a thorough empirical study of a two-factor model derived by the framework of Chapter 2, with an application to the German power futures market. Finally, in Chapter 4 we discuss option pricing for additive factor models by Fourier transform methods. We introduce a two-factor futures price model with jumps in order to capture the implied volatility smile of European electricity options. An application to the European Energy Exchange Power Derivatives market is presented

Extradural hemorrhagic spinal cavernous angioma in a paucisymptomatic child: A rare case with review of the current literature

Background: Cavernous angiomas, also referred to as cavernous hemangiomas or cavernomas (CMs), are vascular malformative benign neoplasms that may develop in any part of the central nervous system. Spinal CMs are uncommon (overall incidence rate of 0.04-0.05%). Pure epidural CMs account for 1-2% of all spinal CMs and 4% of all spinal epidural tumors. Diagnosis is extremely rare in the pediatric age. To the best of our knowledge, only 10 cases have been described so far. The treatment of choice is microsurgical resection. Case description: We describe here the rare case of a cervicothoracic hemorrhagic spinal epidural cavernoma in a paucisymptomatic, 8-year-old female Bangladeshi child. C7-T2 laminectomy with excision of a scarcely defined, capsulated dark red lesion was performed with good recovery. Conclusion: Spinal epidural cavernomas are rare. Childhood presentation is even rarer. The reason could be found in a greater "compliance" and to a rarer occurrence of acute bleeding in children, thus resulting in a delayed diagnosis. Surgical excision is the gold standard of treatment

Capturing the power options smile by an additive two-factor model for overlapping futures prices

Piccirilli M, Schmeck MD, Vargiolu T. Capturing the power options smile by an additive two-factor model for overlapping futures prices. Center for Mathematical Economics Working Papers. Vol 625. Bielefeld: Center for Mathematical Economics; 2019.In this paper we introduce an additive two-factor model for electricity futures prices based on Normal Inverse Gaussian Lévy processes, that fulfills a no-overlapping-arbitrage (NOA) condition. We compute European option prices by Fourier transform methods, introduce a specific calibration procedure that takes into account no-arbitrage constraints and fit the model to power option settlement prices of the European Energy Exchange (EEX). We show that our model is able to reproduce the different levels and shapes of the implied volatility (IV) profiles displayed by options with a variety of delivery periods

THE EFFECT OF MOZART’S MUSIC IN SEVERE EPILEPSY: FUNCTIONAL AND MORPHOLOGICAL FEATURES

Music is a very important factor in everyday life, involving mood, emotions and memories. The effect of music on the brain is very debated. Certainly, music activates a complex network of neurones in auditory areas, mesolimbic areas, cerebellum and multisensory areas. In particular, music exerts its effects on the brain of patients with epilepsy, having a dichotomous influence: it can either be seizure-promoting in musicogenic epilepsy or antiepileptic. Several studies have shown that seizure-prone neural networks may be stimulated by certain periodicities while other frequencies may prevent seizure activity. There are a lot of data in the literature about the so-called "Mozart effect" (Rauscher et al. 1993). In previous studies we observed that in institutionalized subjects with severe/profound intellectual disability and drug-resistant epilepsy, a systematic music listening protocol reduced the frequency of seizures in about 50% of the cases. In this study we are conducting a survey on the observation of what happens to the brain of patients suffering from drug-resistant epilepsy through electroencephalographic investigations, brain MRI and behavioural analysis before and after six months of listening to Mozart music (Sonata K.448). The first step is to present the data of the first patient under investigation