La famille nucléaire au 21e siècle : quelle place pour le père?

Depuis les débuts de la deuxième vague féministe, la participation quasi-universelle des femmes au marché du travail a causé la perte, pour les pères de famille, du rôle de pourvoyeur exclusif que ces derniers avaient tenu depuis si longtemps. Les femmes hétérosexuelles de la société contemporaine sont dorénavant plus scolarisées et atteignent des carrières plus avancées que leurs contreparties masculines. Alors que les femmes soutiennent davantage la famille par leurs emplois, les hommes doivent proportionnellement assumer des rôles plus actifs et participatifs à la maison. En cette période de transition, de transformation et de redéfinition rapides des rôles genrés à l’intérieur de l’économie familiale hétérosexuelle, les pères participent de manière plus équitable à l’éducation des enfants et à l’entretien du domicile, ces rôles ayant longtemps été attribués presque exclusivement aux femmes. Ces nouvelles responsabilités détonnent avec les représentations stéréotypées des hommes dans la culture populaire, ainsi qu’avec les modèles d’identification de la masculinité normative que les hommes ont requis et requièrent toujours sous l’égide de l’hétérosexualité imposée. De ce fait, les hommes occidentaux de moins de cinquante ans se trouvent souvent à cheval entre deux mondes d’exigences concurrentes. La place que peuvent et doivent prendre les pères au sein de leurs familles requiert un questionnement critique. L’ironie de la place du père peut être comprise comme la tension entre les normes du genre rétrogrades ou conservatrices et les exigences – à la fois politiques et pragmatiques – de la masculinité de la classe moyenne contemporaine. Cette tension fondamentale et structurante de la masculinité contemporaine est vécue par l’anxiété d’association au genre, ainsi qu’à travers des difficultés filiales, paternelles et intergénérationnelles. Ce mémoire de maîtrise adopte une approche déconstructiviste envers l’analyse de constructions contemporaines de la masculinité reproductive. À travers les gender studies, la théorie queer ainsi que la psychanalyse, le mémoire offre des lectures analytiques du discours de croissance personnelle de John Stoltenberg, de la fiction autobiographique de Karl Ove Knausgaard, ainsi que de la série télévisée américaine Dexter.

A mobile image enhancement technology for visually impaired

In this thesis, an image enhancement application is developed for low-vision patients when they use iPhones to see images/watch videos. The thesis has two contributions. The first contribution is the new image enhancement algorithm which combines human vision features. The new image enhancement algorithm is modified from a wavelet transform based image enhancement algorithm developed by Dr. Jinshan Tang. Different from the original algorithm, the new image enhancement algorithm combines human visual feature into the algorithm and thus can make the new algorithm more effective. Experimental simulation results show that the proposed algorithm has better visual results than the algorithm without combining visual features. The second contribution of this thesis is the development of a mobile image enhancement application. In this application, users with low-vision can see clearer images on an iPhone which is installed with the application I have developed.

La famille nucléaire au 21e siècle : quelle place pour le père?

Depuis les débuts de la deuxième vague féministe, la participation quasi-universelle des femmes au marché du travail a causé la perte, pour les pères de famille, du rôle de pourvoyeur exclusif que ces derniers avaient tenu depuis si longtemps. Les femmes hétérosexuelles de la société contemporaine sont dorénavant plus scolarisées et atteignent des carrières plus avancées que leurs contreparties masculines. Alors que les femmes soutiennent davantage la famille par leurs emplois, les hommes doivent proportionnellement assumer des rôles plus actifs et participatifs à la maison. En cette période de transition, de transformation et de redéfinition rapides des rôles genrés à l’intérieur de l’économie familiale hétérosexuelle, les pères participent de manière plus équitable à l’éducation des enfants et à l’entretien du domicile, ces rôles ayant longtemps été attribués presque exclusivement aux femmes. Ces nouvelles responsabilités détonnent avec les représentations stéréotypées des hommes dans la culture populaire, ainsi qu’avec les modèles d’identification de la masculinité normative que les hommes ont requis et requièrent toujours sous l’égide de l’hétérosexualité imposée. De ce fait, les hommes occidentaux de moins de cinquante ans se trouvent souvent à cheval entre deux mondes d’exigences concurrentes. La place que peuvent et doivent prendre les pères au sein de leurs familles requiert un questionnement critique. L’ironie de la place du père peut être comprise comme la tension entre les normes du genre rétrogrades ou conservatrices et les exigences – à la fois politiques et pragmatiques – de la masculinité de la classe moyenne contemporaine. Cette tension fondamentale et structurante de la masculinité contemporaine est vécue par l’anxiété d’association au genre, ainsi qu’à travers des difficultés filiales, paternelles et intergénérationnelles. Ce mémoire de maîtrise adopte une approche déconstructiviste envers l’analyse de constructions contemporaines de la masculinité reproductive. À travers les gender studies, la théorie queer ainsi que la psychanalyse, le mémoire offre des lectures analytiques du discours de croissance personnelle de John Stoltenberg, de la fiction autobiographique de Karl Ove Knausgaard, ainsi que de la série télévisée américaine Dexter.

Supporting Scientific Research Through Machine and Deep Learning: Fluorescence Microscopy and Operational Intelligence Use Cases

Although the debate of what data science is has a long history and has not reached a complete consensus yet, Data Science can be summarized as the process of learning from data. Guided by the above vision, this thesis presents two independent data science projects developed in the scope of multidisciplinary applied research. The first part analyzes fluorescence microscopy images typically produced in life science experiments, where the objective is to count how many marked neuronal cells are present in each image. Aiming to automate the task for supporting research in the area, we propose a neural network architecture tuned specifically for this use case, cell ResUnet (c-ResUnet), and discuss the impact of alternative training strategies in overcoming particular challenges of our data. The approach provides good results in terms of both detection and counting, showing performance comparable to the interpretation of human operators. As a meaningful addition, we release the pre-trained model and the Fluorescent Neuronal Cells dataset collecting pixel-level annotations of where neuronal cells are located. In this way, we hope to help future research in the area and foster innovative methodologies for tackling similar problems. The second part deals with the problem of distributed data management in the context of LHC experiments, with a focus on supporting ATLAS operations concerning data transfer failures. In particular, we analyze error messages produced by failed transfers and propose a Machine Learning pipeline that leverages the word2vec language model and K-means clustering. This provides groups of similar errors that are presented to human operators as suggestions of potential issues to investigate. The approach is demonstrated on one full day of data, showing promising ability in understanding the message content and providing meaningful groupings, in line with previously reported incidents by human operators.

Lie symmetries in cortical inspired CNNs

Our scope in this thesis is to propose architectures of CNNs in such a way to model the early visual pathway, including the Lateral Geniculate Nucleus and the Horizontal Connectivity of the primary visual cortex. Moreover, we will show how cortically inspired architectures allow to perform contrast perceptual invariance as well as grouping and the emergence of visual percepts. Particularly, the LGN is modeled with a first layer l0 containing a single filter Ψ0 that pre-filters the image I. Since the RPs of the LGN cells can be modeled as a LoG, we expect to obtain a radially symmetric filter with a similar shape; to this end, we prove the rotational invariance of Ψ0 and we study the influence of this filter to the subsequent layer. Indeed, we compare the statistic distribution of the filters in the second layer l1 of our architecture with the statistic distribution of the RPs of V1 cells of a macaque. Then, we model the horizontal connectivity of V1 implementing a transition kernel K1 to the layer l1. In this setting, we study the vector fields and the association fields induced by the connectivity kernel K1. To this end, we first approximate the filters bank in l1 with a Gabor function and use the parameters just found to re-parameterize the kernel. Thanks to this step, the kernel is now re-parameterized into a sub-Riemmanian space R2 × S1. Now we are able to compare the vector and association fields induced by K1 with the models of the horizontal connectivity.

Tensor-Train decomposition for image classification problems

In these last years a great effort has been put in the development of new techniques for automatic object classification, also due to the consequences in many applications such as medical imaging or driverless cars. To this end, several mathematical models have been developed from logistic regression to neural networks. A crucial aspect of these so called classification algorithms is the use of algebraic tools to represent and approximate the input data. In this thesis, we examine two different models for image classification based on a particular tensor decomposition named Tensor-Train (TT) decomposition. The use of tensor approaches preserves the multidimensional structure of the data and the neighboring relations among pixels. Furthermore the Tensor-Train, differently from other tensor decompositions, does not suffer from the curse of dimensionality making it an extremely powerful strategy when dealing with high-dimensional data. It also allows data compression when combined with truncation strategies that reduce memory requirements without spoiling classification performance. The first model we propose is based on a direct decomposition of the database by means of the TT decomposition to find basis vectors used to classify a new object. The second model is a tensor dictionary learning model, based on the TT decomposition where the terms of the decomposition are estimated using a proximal alternating linearized minimization algorithm with a spectral stepsize.

Variational and learning models for image and time series inverse problems

Inverse problems are at the core of many challenging applications. Variational and learning models provide estimated solutions of inverse problems as the outcome of specific reconstruction maps. In the variational approach, the result of the reconstruction map is the solution of a regularized minimization problem encoding information on the acquisition process and prior knowledge on the solution. In the learning approach, the reconstruction map is a parametric function whose parameters are identified by solving a minimization problem depending on a large set of data. In this thesis, we go beyond this apparent dichotomy between variational and learning models and we show they can be harmoniously merged in unified hybrid frameworks preserving their main advantages. We develop several highly efficient methods based on both these model-driven and data-driven strategies, for which we provide a detailed convergence analysis. The arising algorithms are applied to solve inverse problems involving images and time series. For each task, we show the proposed schemes improve the performances of many other existing methods in terms of both computational burden and quality of the solution. In the first part, we focus on gradient-based regularized variational models which are shown to be effective for segmentation purposes and thermal and medical image enhancement. We consider gradient sparsity-promoting regularized models for which we develop different strategies to estimate the regularization strength. Furthermore, we introduce a novel gradient-based Plug-and-Play convergent scheme considering a deep learning based denoiser trained on the gradient domain. In the second part, we address the tasks of natural image deblurring, image and video super resolution microscopy and positioning time series prediction, through deep learning based methods. We boost the performances of supervised, such as trained convolutional and recurrent networks, and unsupervised deep learning strategies, such as Deep Image Prior, by penalizing the losses with handcrafted regularization terms.

Numerical and experimental analysis of 3D printed smart structures and systems

Three dimensional (3D) printers of continuous fiber reinforced composites, such as MarkTwo (MT) by Markforged, can be used to manufacture such structures. To date, research works devoted to the study and application of flexible elements and CMs realized with MT printer are only a few and very recent. A good numerical and/or analytical tool for the mechanical behavior analysis of the new composites is still missing. In addition, there is still a gap in obtaining the material properties used (e.g. elastic modulus) as it is usually unknown and sensitive to printing parameters used (e.g. infill density), making the numerical simulation inaccurate. Consequently, the aim of this thesis is to present several work developed. The first is a preliminary investigation on the tensile and flexural response of Straight Beam Flexures (SBF) realized with MT printer and featuring different interlayer fiber volume-fraction and orientation, as well as different laminate position within the sample. The second is to develop a numerical analysis within the Carrera' s Unified Formulation (CUF) framework, based on component-wise (CW) approach, including a novel preprocessing tool that has been developed to account all regions printed in an easy and time efficient way. Among its benefits, the CUF-CW approach enables building an accurate database for collecting first natural frequencies modes results, then predicting Young' s modulus based on an inverse problem formulation. To validate the tool, the numerical results are compared to the experimental natural frequencies evaluated using a digital image correlation method. Further, we take the CUF-CW model and use static condensation to analyze smart structures which can be decomposed into a large number of similar components. Third, the potentiality of MT in combination with topology optimization and compliant joints design (CJD) is investigated for the realization of automated machinery mechanisms subjected to inertial loads.

Association between pre-treatment indicators and compliance to neoadjuvant/perioperative chemotherapy in operable gastric cancer

Background and aims: perioperative treatment is currently the gold standard approach for locally advanced gastric cancer (GC). Unfortunately, the phenomenon of patients dropping out of treatment has been frequently observed. The primary aims of this study were to verify if routine blood parameters, the inflammatory response markers, sarcopenia, and the depletion of adipose tissues were associated with compliance with neoadjuvant/perioperative chemotherapy. Methods and study design: sarcopenia and adipose indices were calculated with a CT scan before starting chemotherapy and before surgery. Blood samples were considered before the first and second cycles of chemotherapy. Results: A total of 84 patients with localized operable GC, were identified between September 2010 and January 2021. Forty-four patients (52.4%) did not complete the treatment according to the number of cycles planned/performed. Eight patients (9.5%) decided to suspend chemotherapy, seven patients (8.3%) discontinued because of clinical decision-making, 14 patients (16.7%) because of toxicity, and 15 patients (17.9%) for miscellaneous causes. Sarcopenia before starting chemotherapy was found to be present in 38 patients (50.7%) while it was in 47 patients (60%) at the CT scan before the gastrectomy. In multivariable analysis, both for changes tending to have a value of PLR at basal and in the second control a higher one than the cut-off (OR = 5.03, 95% CI: 1.34 - 18.89, p-value = 0.017), and for PLR which increased from a lower to a higher value in second control with respect to the cut off (OR = 4.64, 95% CI: 1.02 -21.02, p-value = 0.047) resulted associated with incomplete compliance. Conclusions: among the biological indicators, changes in the value of PLR with a tendency towards increasing compared to the cut-off appear to be an immediate indicator of incomplete compliance with neoadjuvant/perioperative treatment. More information is needed to reduce the causes of interruption.

Resistance and opposition to the Macedonian kingship in the ancient Iranian world

This thesis deals with the criticism of Macedonian kingship in the ancient Iranian world. The question of indigenous opposition and resistance to the Greeks and Macedonians has been little addressed by ancient historians. The study therefore adopts a different, interdisciplinary perspective and seeks to understand where the utterly negative portrayal of Alexander and the Macedonians found in most Iranian sources stems from. The first part deals with the subject by first examining the acts of violence committed by Alexander and his men against the Iranians during the expedition to Asia that might have led to such a portrayal in the Iranian sources. I have focused on looting, massacres and insults to deities, such as the looting of temples or the destruction of many settlements in ancient Iran handed down in classical sources. To this end, an important part was devoted to the analysis of archaeological sources, especially the signs of destruction in areas such as Persia and Sogdiana. In the second part, the image of Alexander and his successors, although mentioned much less frequently, as it appears in pre-Islamic Iranian literature, is analysed in detail, focusing on the faults and cruelties attributed to them against the Iranians, but especially against their religion. These are mostly Zoroastrian religious sources, whose clergy preserved a demonic image of the Macedonian kings. In the third and final part, further examples of offences committed by the Diadochi and Seleucids against the Iranians in the classical tradition are collected. At the same time, it is examined how the Hellenistic rulers of Iranian origin, e.g. the Arsacids and the Orontids, opposed not only militarily but also ideologically the Macedonian tradition represented by the kingdoms of Macedonian descent and chose a pro-Iranian tradition that was clearly different from the Greco-Roman one.

Le radici del monoteismo israelitico come riflesso dell'idea di regalita divina in epoca pre-esilica

Questa tesi è un contributo al dibattito meta-terminologico sull'uso scientifico del termine "monoteismo" in relazione alla religione dell'Israele antico. L'attenzione è rivolta principalmente a un tema specifico: l'esplorazione della nozione teistica di "esistenza" divina (implicita nell'uso di "monoteismo" come lente di osservazione) e il problema della sua applicazione alle concettualizzazioni della divinità che emergono nella Bibbia ebraica. In primo luogo, il "monoteismo" come termine e concetto viene ricondotto alle sue origini storiche nell'ambiente intellettuale del platonismo di Cambridge nell'Inghilterra del XVII secolo. Poi, si affronta il dibattito contemporaneo sull'uso del termine "monoteismo" in relazione alla religione dell'Israele antico e si evidenzia il ruolo dell'"esistenza" teistica come lente distorcente nella lettura dei testi biblici. La maggior parte della tesi sostiene questo assunto con una lettura esegetica dettagliata di tre passi biblici scelti come casi di studio: Sal 82; 1Re 18,20-40* e Zc 14,9. Queste analisi mostrano come la nozione teistica di un'esistenza divina astratta non sia in grado di spiegare la rappresentazione del divino che emerge da questi testi. Allo stesso tempo, il potere divino come categoria euristica viene proposto come un'alternativa più adatta a spiegare queste concettualizzazioni della divinità. L'ultima sezione elabora ulteriormente questi risultati. Qui la regalità di YHWH, come immagine metaforica del suo potere, viene utilizzata per descrivere i cambiamenti nella concettualizzazione di questa divinità. L'argomentazione finale è che in nessuna parte del materiale biblico affrontato in questa tesi si trova una nozione simile a quella di esistenza divina astratta. Poiché tale nozione è implicita nell'uso del termine "monoteismo", questi risultati richiedono una considerazione ancora più attenta del suo uso nel dibattito scientifico.

Regularization meets GreenAI: a new framework for image reconstruction in life sciences applications

Ill-conditioned inverse problems frequently arise in life sciences, particularly in the context of image deblurring and medical image reconstruction. These problems have been addressed through iterative variational algorithms, which regularize the reconstruction by adding prior knowledge about the problem's solution. Despite the theoretical reliability of these methods, their practical utility is constrained by the time required to converge. Recently, the advent of neural networks allowed the development of reconstruction algorithms that can compute highly accurate solutions with minimal time demands. Regrettably, it is well-known that neural networks are sensitive to unexpected noise, and the quality of their reconstructions quickly deteriorates when the input is slightly perturbed. Modern efforts to address this challenge have led to the creation of massive neural network architectures, but this approach is unsustainable from both ecological and economic standpoints. The recently introduced GreenAI paradigm argues that developing sustainable neural network models is essential for practical applications. In this thesis, we aim to bridge the gap between theory and practice by introducing a novel framework that combines the reliability of model-based iterative algorithms with the speed and accuracy of end-to-end neural networks. Additionally, we demonstrate that our framework yields results comparable to state-of-the-art methods while using relatively small, sustainable models. In the first part of this thesis, we discuss the proposed framework from a theoretical perspective. We provide an extension of classical regularization theory, applicable in scenarios where neural networks are employed to solve inverse problems, and we show there exists a trade-off between accuracy and stability. Furthermore, we demonstrate the effectiveness of our methods in common life science-related scenarios. In the second part of the thesis, we initiate an exploration extending the proposed method into the probabilistic domain. We analyze some properties of deep generative models, revealing their potential applicability in addressing ill-posed inverse problems.

Quantitative analysis of pre- and post-operative valvular annulus deformations following valvular insufficiency

Valvular insufficiency is a growingly common valvular heart disease that frequently is associated with regurgitation. Atrioventricular incompetency can lead to overall ventricular and atrial enlargement, volume overload, heart impairment and, if not treated, can culminate in heart failure. With the advances in technology and the increasing interest in devices that have lower post-operative burden on patients, transcatheter mitral and tricuspid valve repair systems are going through a phase of rapid development and growing use. In this work, we aimed to quantitatively assess the morphology of mitral and tricuspid annuli in patients who underwent transcatheter valve repair with MitraClip/TriClip, before and after the intervention, using three-dimensional transoesophageal echocardiography images, in order to evaluate the geometrical changes of the annulus following the intervention. For our purposes, firstly, we implemented a tool for the visualization and navigation of the volumetric data across the cardiac cycle. Then, in order to track the annulus over the cardiac cycle, we extracted five rotational slices from the volume data, selected two initial points on each slice, and tracked these points across the cardiac cycle using KLT algorithm. In a first stage we led a parameters optimization for the tracking method, and we studied the sensitivity of the KLT algorithm to the initialization points, that are manually chosen by the user. In a second stage, we analysed 10 subjects (5 for mitral regurgitation and 5 for tricuspid regurgitation), tracking their annulus before and after valve repairment. In conclusion, we found in all our 10 subjects that immediately after the intervention there is a shortening of the major diameters of the valves, mainly the shortest diameter, due to the clip application, that leads to a reduction of the perimeter and the area of the annulus.

Characterization of a prototype for cone beam breast computed tomography

The project aims to experiment the Cone Beam Breast Computed Tomography technique using a standard digital mammography system. The work is focused on the definition of a protocol of quality measurements for the pre-clinical evaluation of the machine. The paper is developed in two parts. The first is specifically concerned with the methods used to define the image quality and dosimetry aspects specific for digital mammography devices. A complete characterization of the system has been performed according to the applicable IEC standards to assure the performances of the equipment and define the quality levels. Due to the lack of a quality control protocol dedicated to CBBCT mammography scanner, a new equivalent test procedure has been proposed. The second part of the paper is focused on the evaluation, through quantitative and visual analyzes, of the CBCT exam feasibility in the hardware and software conditions currently proposed by IMS Giotto. The prototype was in fact developed differing from the technical choices of competing companies and developed for a different intended use. The main difference with respect to the existing breast CT scanners is the possibility of performing on the same system the CBBCT scanning but also all the mammographic techniques. In this thesis, we aim to assess whether, in the current setup, considering a dosimetric range very close to that used in the clinic, the tests produce results that can be considered acceptable or at least indicative of the feasibility of the entire project from a commercial point of view. For this purpose, the final reconstruction images, obtained by two previously developed software, are analyzed.

Advancing Bag-of-visual-words Representations For Lesion Classification In Retinal Images.

Diabetic Retinopathy (DR) is a complication of diabetes that can lead to blindness if not readily discovered. Automated screening algorithms have the potential to improve identification of patients who need further medical attention. However, the identification of lesions must be accurate to be useful for clinical application. The bag-of-visual-words (BoVW) algorithm employs a maximum-margin classifier in a flexible framework that is able to detect the most common DR-related lesions such as microaneurysms, cotton-wool spots and hard exudates. BoVW allows to bypass the need for pre- and post-processing of the retinographic images, as well as the need of specific ad hoc techniques for identification of each type of lesion. An extensive evaluation of the BoVW model, using three large retinograph datasets (DR1, DR2 and Messidor) with different resolution and collected by different healthcare personnel, was performed. The results demonstrate that the BoVW classification approach can identify different lesions within an image without having to utilize different algorithms for each lesion reducing processing time and providing a more flexible diagnostic system. Our BoVW scheme is based on sparse low-level feature detection with a Speeded-Up Robust Features (SURF) local descriptor, and mid-level features based on semi-soft coding with max pooling. The best BoVW representation for retinal image classification was an area under the receiver operating characteristic curve (AUC-ROC) of 97.8% (exudates) and 93.5% (red lesions), applying a cross-dataset validation protocol. To assess the accuracy for detecting cases that require referral within one year, the sparse extraction technique associated with semi-soft coding and max pooling obtained an AUC of 94.2 ± 2.0%, outperforming current methods. Those results indicate that, for retinal image classification tasks in clinical practice, BoVW is equal and, in some instances, surpasses results obtained using dense detection (widely believed to be the best choice in many vision problems) for the low-level descriptors.