Development of a decision analytic model to support decision making and risk communication about thrombolytic treatment

Background Individualised prediction of outcomes can support clinical and shared decision making. This paper describes the building of such a model to predict outcomes with and without intravenous thrombolysis treatment following ischaemic stroke. Methods A decision analytic model (DAM) was constructed to establish the likely balance of benefits and risks of treating acute ischaemic stroke with thrombolysis. Probability of independence, (modified Rankin score mRS ≤ 2), dependence (mRS 3 to 5) and death at three months post-stroke was based on a calibrated version of the Stroke-Thrombolytic Predictive Instrument using data from routinely treated stroke patients in the Safe Implementation of Treatments in Stroke (SITS-UK) registry. Predictions in untreated patients were validated using data from the Virtual International Stroke Trials Archive (VISTA). The probability of symptomatic intracerebral haemorrhage in treated patients was incorporated using a scoring model from Safe Implementation of Thrombolysis in Stroke-Monitoring Study (SITS-MOST) data. Results The model predicts probabilities of haemorrhage, death, independence and dependence at 3-months, with and without thrombolysis, as a function of 13 patient characteristics. Calibration (and inclusion of additional predictors) of the Stroke-Thrombolytic Predictive Instrument (S-TPI) addressed issues of under and over prediction. Validation with VISTA data confirmed that assumptions about treatment effect were just. The C-statistics for independence and death in treated patients in the DAM were 0.793 and 0.771 respectively, and 0.776 for independence in untreated patients from VISTA. Conclusions We have produced a DAM that provides an estimation of the likely benefits and risks of thrombolysis for individual patients, which has subsequently been embedded in a computerised decision aid to support better decision-making and informed consent

Graph Laplacian–Based Strategies and Convex Optimization via Primal-Dual Methods

This thesis focuses on the analysis of different variational approaches for solving inverse problems. In the first part, we examine the graph Laplacian operator within an l2-l1 framework, where q is less than 1. A key challenge in using this linear operator is its dependence on an initial reconstruction, which can be obtained through a general reconstruction method. However, we demonstrate that, under very weak assumptions on the chosen reconstruction method, the resulting strategy is both convergent and stable, achieving high quality final reconstructions. Additionally, we analyze the fractional graph Laplacian operator, showing that the use of fractional powers can surpass the standard approach by providing more detailed final images. The second part of this thesis considers a more general framework, where the optimization problem consists of the sum of a differentiable term and a non-smooth but convex term. The variable metric approach we propose results in a convergent method that fixes a priori the number of nested iterations required to compute inexact approximations of the proximal gradient step. We also introduce an iterated Tikhonov-based strategy, which accelerates convergence while maintaining high-quality reconstructions. In the context of image deblurring, the variable metric approach can be reinterpreted as a right preconditioning strategy. Therefore, the final section is devoted to the analysis of a left preconditioning approach.This thesis focuses on the analysis of different variational approaches for solving inverse problems. In the first part, we examine the graph Laplacian operator within an l2-l1 framework, where q is less than 1. A key challenge in using this linear operator is its dependence on an initial reconstruction, which can be obtained through a general reconstruction method. However, we demonstrate that, under very weak assumptions on the chosen reconstruction method, the resulting strategy is both convergent and stable, achieving high quality final reconstructions. Additionally, we analyze the fractional graph Laplacian operator, showing that the use of fractional powers can surpass the standard approach by providing more detailed final images. The second part of this thesis considers a more general framework, where the optimization problem consists of the sum of a differentiable term and a non-smooth but convex term. The variable metric approach we propose results in a convergent method that fixes a priori the number of nested iterations required to compute inexact approximations of the proximal gradient step. We also introduce an iterated Tikhonov-based strategy, which accelerates convergence while maintaining high-quality reconstructions. In the context of image deblurring, the variable metric approach can be reinterpreted as a right preconditioning strategy. Therefore, the final section is devoted to the analysis of a left preconditioning approach

A 2-pyridyl-2,1-borazaronaphthalene derivative as forefather of a new class​ of bidentate ligands: synthesis and application in luminescent Ir(III) complexes

Borazaro compounds (or azaborines) are aromatic compounds in which a C=C unit is replaced by an isoelectronic B-N unit. The possibility to generate chemical diversity has led to an increasing interest in azaborines, especially in the fields of biomedical research and optoelectonics. In particular, Dewar’s synthesis of borazaronaphthalene is a common starting step to obtain different 1,2-azaborines via nucleophilic substitution on the boron atom. Here we present the synthesis of a novel 1,2-azaborine (i.e. 4-methyl-2-(pyridin-2-yl)-2,1-borazaronaphthalene, named FAAH) via functionalization of 2-chloro-4-methyl-2,1-borazaronaphthalene with a 2-pyridyl unit. FAAH can be used as an anionic bidentate ligand for transition metal complexes, since it can chelate the metal center with both the pyridine and the azaborine nitrogen atoms. FAAH was used for the synthesis of a series of neutral luminescent Ir(III) complexes (named FAV, FAB and FAR) of general formula [Ir(C^N )2(FAA)], where C^N indicates three different cyclometalating ligands: i.e. 2-phenylpyridine in the case of FAV; 2-(2,4-difluorophenyl)pyridine in the case of FAB; 2-methyl-3-phenylquinoxaline in the case of FAR. The reaction yields are quite low, however it was always possible to characterize all the compounds by means of NMR spectroscopy. A complete photophysical and theoretical characterization is also presented. FAAH displays a good chemical stability and a high photoluminescence quantum yield (up to 28 % in solution). On the contrary, the Iridium complexes undergo degradation over time in solution. Despite this stability problem, it was possible to get a good understanding of the photophysics of the three complexes: the emission of both FAV and FAB is observed around 500 nm and arises from a 3LC state centered on the azaborine ligand. In the case of FAR, the emitting state is basically 3MLCT/3LLCT in nature and the resulting broad and unstructured emission band is centered around 700 nm

Improving JackDaw : algorithm linearization, streaming and visualization

LAUREA MAGISTRALEIn un panorama in cui il numero di malware continua a crescere, i sistemi di analisi automatica stanno diventando essenziali. Jackdaw vuole essere uno strumento di analisi globale basato su una combinazione di tecniche di analisi statica e dinamica, capace di estrarre comportamenti di alto livello dai binari di malware e in grado di operare sotto forma di servizio on-line a cui gli analisti possano sottoporre i binari perché siano analizzati. Inoltre, Jackdaw potrà essere usato per creare una mappa dei malware che renda possibile visualizzare e comprendere l’evoluzione del malware nel tempo, in maniera automatica basandosi sui risultati delle analisi. Il mio obiettivo consiste nel migliorare il sistema esistente abbassando la complessità temporale dell’analisi da NP ad almeno polinomiale, ristrutturare il sistema perché funzioni in maniera continuativa e creare una mappa del malware dinamica in tempo reale.In a growing malware panorama, automatic analysis systems are be- coming essential. Jackdaw aims to be a global analysis tool based on both static a dynamic analysis techniques which extracts high-level behaviours from malware binaries, operating as an on-line service to which analysts can submit binaries to be analysed. Furthermore, Jackdaw is meant to allow the creation of a malware map that makes possible to visualise and understand the malware evolution in time, automatically based on the malware analysis results. My goal consists of improving the existing framework by decreas- ing the temporal complexity of the analysis from NP to at least poly- nomial, restructuring the system to work as a stream service and cre- ating a dynamical, real-time malware map

Learning optical flow from still images

This paper deals with the scarcity of data for training optical flow networks, highlighting the limitations of existing sources such as labeled synthetic datasets or unlabeled real videos. Specifically, we introduce a framework to generate accurate ground-truth optical flow annotations quickly and in large amounts from any readily available single real picture. Given an image, we use an off-the-shelf monocular depth estimation network to build a plausible point cloud for the observed scene. Then, we virtually move the camera in the reconstructed environment with known motion vectors and rotation angles, allowing us to synthesize both a novel view and the corresponding optical flow field connecting each pixel in the input image to the one in the new frame. When trained with our data, state-of-the-art optical flow networks achieve superior generalization to unseen real data compared to the same models trained either on annotated synthetic datasets or unlabeled videos, and better specialization if combined with synthetic images.Comment: CVPR 2021. Project page with supplementary and code: https://mattpoggi.github.io/projects/cvpr2021aleotti

Rainfall Thresholding and Susceptibility assessment of rainfall induced landslides: application to landslide management in St Thomas, Jamaica

The final publication is available at Springer via http://dx.doi.org/10.1007/s10064-009-0232-zThe parish of St Thomas has one of the highest densities of landslides in Jamaica, which impacts the residents, local economy and the built and natural environment. These landslides result from a combination of steep slopes, faulting, heavy rainfall and the presence of highly weathered volcanics, sandstones, limestones and sandstone/shale series and are particularly prevalent during the hurricane season (June–November). The paper reports a study of the rainfall thresholds and landslide susceptibility assessment to assist the prediction, mitigation and management of slope instability in landslide-prone areas of the parish