385 research outputs found
Measuring the risk of a nonlinear portfolio with fat tailed risk factors through probability conserving transformation
This paper presents a new heuristic for fast approximation of VaR (Value-at-Risk) and CVaR (conditional Value-at-Risk) for financial portfolios, where the net worth of a portfolio is a non-linear function of possibly non-Gaussian risk factors. The proposed method is based on mapping non-normal marginal distributions into normal distributions via a probability conserving transformation and then using a quadratic, i.e. Delta–Gamma, approximation for the portfolio value. The method is very general and can deal with a wide range of marginal distributions of risk factors, including non-parametric distributions. Its computational load is comparable with the Delta–Gamma–Normal method based on Fourier inversion. However, unlike the Delta–Gamma–Normal method, the proposed heuristic preserves the tail behaviour of the individual risk factors, which may be seen as a significant advantage. We demonstrate the utility of the new method with comprehensive numerical experiments on simulated as well as real financial data
Coping with Data Scarcity in Deep Learning and Applications for Social Good
The recent years are experiencing an extremely fast evolution of the Computer Vision and
Machine Learning fields: several application domains benefit from the newly developed
technologies and industries are investing a growing amount of money in Artificial Intelligence.
Convolutional Neural Networks and Deep Learning substantially contributed to the rise and
the diffusion of AI-based solutions, creating the potential for many disruptive new businesses.
The effectiveness of Deep Learning models is grounded by the availability of a huge
amount of training data. Unfortunately, data collection and labeling is an extremely expensive
task in terms of both time and costs; moreover, it frequently requires the collaboration of
domain experts.
In the first part of the thesis, I will investigate some methods for reducing the cost
of data acquisition for Deep Learning applications in the relatively constrained industrial
scenarios related to visual inspection. I will primarily assess the effectiveness of Deep Neural
Networks in comparison with several classical Machine Learning algorithms requiring a
smaller amount of data to be trained. Hereafter, I will introduce a hardware-based data
augmentation approach, which leads to a considerable performance boost taking advantage of
a novel illumination setup designed for this purpose. Finally, I will investigate the situation in
which acquiring a sufficient number of training samples is not possible, in particular the most
extreme situation: zero-shot learning (ZSL), which is the problem of multi-class classification
when no training data is available for some of the classes. Visual features designed for image
classification and trained offline have been shown to be useful for ZSL to generalize towards
classes not seen during training. Nevertheless, I will show that recognition performances
on unseen classes can be sharply improved by learning ad hoc semantic embedding (the
pre-defined list of present and absent attributes that represent a class) and visual features, to
increase the correlation between the two geometrical spaces and ease the metric learning
process for ZSL.
In the second part of the thesis, I will present some successful applications of state-of-the-
art Computer Vision, Data Analysis and Artificial Intelligence methods. I will illustrate
some solutions developed during the 2020 Coronavirus Pandemic for controlling the disease
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evolution and for reducing virus spreading. I will describe the first publicly available
dataset for the analysis of face-touching behavior that we annotated and distributed, and
I will illustrate an extensive evaluation of several computer vision methods applied to the
produced dataset. Moreover, I will describe the privacy-preserving solution we developed
for estimating the \u201cSocial Distance\u201d and its violations, given a single uncalibrated image
in unconstrained scenarios. I will conclude the thesis with a Computer Vision solution
developed in collaboration with the Egyptian Museum of Turin for digitally unwrapping
mummies analyzing their CT scan, to support the archaeologists during mummy analysis
and avoiding the devastating and irreversible process of physically unwrapping the bandages
for removing amulets and jewels from the body
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Measuring the risk of financial portfolios with nonlinear instruments and non-Gaussian risk factors
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The focus of my research has been computationally efficient means of computing measures of
risk for portfolios of nonlinear financial instruments when the risk factors might be possibly
non-Gaussian. In particular, the measures of risk chosen have been Value-at-Risk (VaR)
and conditional Value-at-Risk (CVaR). I have studied the problem of computation of risk
in two types of financial portfolios with nonlinear instruments which depend on possibly
non-Gaussian risk factors:
1. Portfolios of European stock options when the stock return distribution may not be
Gaussian;
2. Portfolios of sovereign bonds (which are nonlinear in the underlying risk factor, i.e.
the short rate) when the risk factor may or may not be Gaussian.
Addressing both these problems need a wide array of mathematical tools both from the
field of applied statistics (Delta-Gamma-Normal models, characteristic function inversion,
probability conserving transformation) and systems theory (Vasicek stochastic differential
equation model, Kalman filter). A new heuristic is proposed for addressing the first problem,
while an empirical study is presented to support the use of filter-based models for addressing
the second problem. In addition to presenting a discussion of these underlying mathematical
tools, the dissertation also presents comprehensive numerical experiments in both cases,
with simulated as well as rea
Enhancing visual embeddings through weakly supervised captioning for zero-shot learning
Visual features designed for image classification have shown to be useful in zero-shot learning (ZSL) when generalizing towards classes not seen during training. In this paper, we argue that a more effective way of building visual features for ZSL is to extract them through captioning, in order not just to classify an image but, instead, to describe it. However, modern captioning models rely on a massive level of supervision, e.g up to 15 extended descriptions per instance provided by humans, which is simply not available for ZSL benchmarks. In the latter in fact, the available annotations inform about the presence/absence of attributes within a fixed list only. Worse, attributes are seldom annotated at the image level, but rather, at the class level only: because of this, the annotation cannot be visually grounded. In this paper, we deal with such a weakly supervised regime to train an end-to-end LSTM captioner, whose backbone CNN image encoder can provide better features for ZSL. Our enhancement of visual features, called 'VisEn', is compatible with any generic ZSL method, without requiring changes in its pipeline (a part from adapting hyper-parameters). Experimentally, VisEn is capable of sharply improving recognition performance on unseen classes, as we demonstrate thorough an ablation study which encompasses different ZSL approaches. Further, on the challenging fine-grained CUB dataset, VisEn improves by margin state-of-the-art methods, by using visual descriptors of one order of magnitude smaller
Value-at-Risk for fixed-income portfolios: a Kalman filtering approach
We propose a way of measuring the risk of a sovereign debt portfolio by using a simple two-factor short rate model. The model is calibrated from data and then the changes in the bond prices are simulated by using a Kalman filter. The bond prices being simulated remain arbitrage-free, in contrast with principal component analysis-based strategies for simulation and risk measurement of debt portfolios. In liquid sovereign debt markets, a risk measurement methodology which allows the future bond price scenarios to be arbitrage-free may be seen as a potentially more realistic way of measuring the debt portfolio risk due to interest rate fluctuations. We demonstrate the performance of this methodology with calibration and backtesting, both on simulated data as well as on a real portfolio of US government bonds
The impact of successful cross-competencies on a career in tourism in Italy: the meeting point between the student's perceptions and the requirements for professionals
The paper aims to understand how the changes occurring in the tourism sector are affecting the labor market in Italy, with a special focus on the relevance of successful cross competences (SCC). It focuses on comparing the relevance of these competencies in the perception of both students preparing to enter the tourism field and tour operators. The two-step study combined qualitative analysis that put forth specific characteristics of the tourism labor market in Italy through interviews with experts, and quantitative analysis that correlated the requirements of the tour operators to the ideas students have of what competencies tour operators entering the field should have. The results evinced differing perceptions of SCC and their relative importance in professional fields. Students manifest to miss awareness of the importance of SCC for their future careers. Furthermore, organizational ability, self-control and self-esteem were perceived by tour operators as the most important competencies to be acquired
The Global strategy for women\u27s, children\u27s and adolescents\u27 health (2016-2030): a roadmap based on evidence and country experience.
The Global strategy for women’s, children’s and adolescents’ health (2016–2030) provides a roadmap for ending preventable deaths of women, children and adolescents by 2030 and helping them achieve their potential for and rights to health and well-being in all settings.1 The global strategy has three objectives: survive (end preventable deaths); thrive (ensure health and well-being); and transform (expand enabling environments). These objectives are aligned with 17 targets within nine of the sustainable development goals (SDGs),2 including SDG 3 on health and other SDGs related to the political, social, economic and environmental determinants of health and sustainable development.
Like the SDGs, the global strategy is universal in scope and multisectoral in action, aiming for transformative change across numerous challenging areas for health and sustainable development (Box 1).1The strategy was developed through evidence reviews and syntheses and a global stakeholder consultation,3,4 and draws on new thinking about priorities and approaches for health and sustainable development.4 Particular attention was given to experience gained and lessons learnt by countries during implementation of the previous Global strategy for women’s and children’s health (2010–2015)5 and achieving the millennium development goals (MDGs).6,7 A five-year operational framework with up-to-date technical resources has also been developed to support country-led implementation of the global strategy. This framework will be regularly updated until 2030.1,
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