Improved synthesis of the hypoxia probe 5-deutero-5-fluoro-5-deoxy-azomycin arabinoside (FAZA) as a model process for tritium radiolabeling

Peer reviewedPublisher PD

Autoregressive process parameters estimation from Compressed Sensing measurements and Bayesian dictionary learning

The main contribution of this thesis is the introduction of new techniques which allow to perform signal processing operations on signals represented by means of compressed sensing. Exploiting autoregressive modeling of the original signal, we obtain a compact yet representative description of the signal which can be estimated directly in the compressed domain. This is the key concept on which the applications we introduce rely on. In fact, thanks to proposed the framework it is possible to gain information about the original signal given compressed sensing measurements. This is done by means of autoregressive modeling which can be used to describe a signal through a small number of parameters. We develop a method to estimate these parameters given the compressed measurements by using an ad-hoc sensing matrix design and two different coupled estimators that can be used in different scenarios. This enables centralized and distributed estimation of the covariance matrix of a process given the compressed sensing measurements in a efficient way at low communication cost. Next, we use the characterization of the original signal done by means of few autoregressive parameters to improve compressive imaging. In particular, we use these parameters as a proxy to estimate the complexity of a block of a given image. This allows us to introduce a novel compressive imaging system in which the number of allocated measurements is adapted for each block depending on its complexity, i.e., spatial smoothness. The result is that a careful allocation of the measurements, improves the recovery process by reaching higher recovery quality at the same compression ratio in comparison to state-of-the-art compressive image recovery techniques. Interestingly, the parameters we are able to estimate directly in the compressed domain not only can improve the recovery but can also be used as feature vectors for classification. In fact, we also propose to use these parameters as more general feature vectors which allow to perform classification in the compressed domain. Remarkably, this method reaches high classification performance which is comparable with that obtained in the original domain, but with a lower cost in terms of dataset storage. In the second part of this work, we focus on sparse representations. In fact, a better sparsifying dictionary can improve the Compressed Sensing recovery performance. At first, we focus on the original domain and hence no dimensionality reduction by means of Compressed Sensing is considered. In particular, we develop a Bayesian technique which, in a fully automated fashion, performs dictionary learning. More in detail, using the uncertainties coming from atoms selection in the sparse representation step, this technique outperforms state-of-the-art dictionary learning techniques. Then, we also address image denoising and inpainting tasks using the aforementioned technique with excellent results. Next, we move to the compressed domain where a better dictionary is expected to provide improved recovery. We show how the Bayesian dictionary learning model can be adapted to the compressive case and the necessary assumptions that must be made when considering random projections. Lastly, numerical experiments confirm the superiority of this technique when compared to other compressive dictionary learning techniques

A note on the fate of the Landau-Yang theorem in non-Abelian gauge theories

Using elementary considerations of Lorentz invariance, Bose symmetry and BRST invariance, we argue why the decay of a massive color-octet vector state into a pair of on-shell massless gluons is possible in a non-Abelian SU(N) Yang-Mills theory, we constrain the form of the amplitude of the process and offer a simple understanding of these results in terms of effective-action operators.Comment: 7 pages. v2: typos corrected, one reference adde

On the secrecy of compressive cryptosystems under finite-precision representation of sensing matrices

In recent years, the Compressed Sensing (CS) framework has been shown to be an effective private key cryptosystem. If infinite precision is available, then it has been shown that spherical secrecy can be achieved. However, despite its theoretically proven secrecy properties, the only practically feasible implementations involve the use of Bernoulli sensing matrices. In this work, we show that different distributions employing a much larger finite alphabet can be considered. More in detail, we consider the use of quantized Gaussian sensing matrices and experimentally show that, besides being suitable for practical implementation, they can achieve higher secrecy with respect to Bernoulli sensing matrices. Furthermore, we show that this approach can be used to tune the secrecy of the CS cryptosystems based on the available machine precision

Comparing e-Fuels and Electrification for Decarbonization of Heavy-Duty Transports

The freight sector is expected to keep, or even increase, its fundamental role for the major modern economies, and therefore actions to limit the growing pressure on the environment are urgent. The use of electricity is a major option for the decarbonization of transports; in the heavy-duty segment, it can be implemented in different ways: besides full electric-battery powertrains, electricity can be used to supply catenary roads, or can be chemically stored in liquid or gaseous fuels (e-fuels). While the current EU legislation adopts a tailpipe Tank-To-Wheels approach, which results in zero emissions for all direct uses of electricity, a Well-To-Wheels (WTW) method would allow accounting for the potential benefits of using sustainable fuels such as e-fuels. In this article, we have performed a WTW-based comparison and modelling of the options for using electricity to supply heavy-duty vehicles: e-fuels, eLNG, eDiesel, and liquid Hydrogen. Results showed that the direct use of electricity can provide high Greenhouse Gas (GHG) savings, and also in the case of the e-fuels when low-carbonintensity electricity is used for their production. While most studies exclusively focus on absolute GHG savings potential, considerations of the need for new infrastructures, and the technological maturity of some options, are fundamental to compare the different technologies. In this paper, an assessment of such technological and non-technological barriers has been conducted, in order to compare alternative pathways for the heavy-duty sector. Among the available options, the flexibility of using drop-in, energy-dense liquid fuels represents a clear and substantial immediate advantage for decarbonization. Additionally, the novel approach adopted in this paper allows us to quantify the potential benefits of using e-fuels as chemical storage able to accumulate electricity from the production peaks of variable renewable energies, which would otherwise be wasted due to grid limitations

Challenges and opportunities of process modelling renewable advanced fuels

The Paris COP21 held on December 2015 represented a step forward global GHG emission reduction: this led to intensify research efforts in renewables, including biofuels and bioliquids. However, addressing sustainable biofuels and bioliquid routes and value chains which can limit or reverse the ILUC (indirect land-use change effect) is of paramount importance. Given this background condition, the present study targets the analysis and modelling a new integrated biomass conversion pathway to produce renewable advanced fuels, enabling the issue of indirect land-use change (ILUC) of biofuels to be tackled. The bioenergy chain under investigation integrates the decentralized production of biogas through anaerobic digestion and its upgrading to biomethane, followed by a centralized conversion to liquid transport fuels, involving methane reforming into syngas, Fischer–Tropsch (FT) synthesis, and methanol synthesis. The methodology adopted in this work stem from extensive literature review of suitable bio/thermo-chemical conversion technologies and their process modelling using a commercial flow-diagram simulation software is carried out. The major significance of the study is to understand the different modelling approaches, to allow the estimation of process yields and mass/energy balances: in such a way, this work aims at providing guidance to process modellers targeting qualitative and quantitative assessments of biomass to biofuels process routes. Beyond FT products, additional process pathways have been also explored, such as MeOH synthesis from captured CO2 and direct methane to methanol synthesis (DMTM). The analysis demonstrated that it is possible to model such innovative integrated processes through the selected simulation tool. However, research is still needed as regards the DMTM process, where studies about modelling this route through the same tool have not been yet identified in the literature

Adversarial Learning of Mappings Onto Regularized Spaces for Biometric Authentication

We present AuthNet: a novel framework for generic biometric authentication which, by learning a regularized mapping instead of a classification boundary, leads to higher performance and improved robustness. The biometric traits are mapped onto a latent space in which authorized and unauthorized users follow simple and well-behaved distributions. In turn, this enables simple and tunable decision boundaries to be employed in order to make a decision. We show that, differently from the deep learning and traditional template-based authentication systems, regularizing the latent space to simple target distributions leads to improved performance as measured in terms of Equal Error Rate (EER), accuracy, False Acceptance Rate (FAR) and Genuine Acceptance Rate (GAR). Extensive experiments on publicly available datasets of faces and fingerprints confirm the superiority of AuthNet over existing methods