A GCV based Arnoldi-Tikhonov regularization method

For the solution of linear discrete ill-posed problems, in this paper we consider the Arnoldi-Tikhonov method coupled with the Generalized Cross Validation for the computation of the regularization parameter at each iteration. We study the convergence behavior of the Arnoldi method and its properties for the approximation of the (generalized) singular values, under the hypothesis that Picard condition is satisfied. Numerical experiments on classical test problems and on image restoration are presented

Embedded techniques for choosing the parameter in Tikhonov regularization

This paper introduces a new strategy for setting the regularization parameter when solving large-scale discrete ill-posed linear problems by means of the Arnoldi-Tikhonov method. This new rule is essentially based on the discrepancy principle, although no initial knowledge of the norm of the error that affects the right-hand side is assumed; an increasingly more accurate approximation of this quantity is recovered during the Arnoldi algorithm. Some theoretical estimates are derived in order to motivate our approach. Many numerical experiments, performed on classical test problems as well as image deblurring are presented

A deep representation for depth images from synthetic data

Convolutional Neural Networks (CNNs) trained on large scale RGB databases have become the secret sauce in the majority of recent approaches for object categorization from RGB-D data. Thanks to colorization techniques, these methods exploit the filters learned from 2D images to extract meaningful representations in 2.5D. Still, the perceptual signature of these two kind of images is very different, with the first usually strongly characterized by textures, and the second mostly by silhouettes of objects. Ideally, one would like to have two CNNs, one for RGB and one for depth, each trained on a suitable data collection, able to capture the perceptual properties of each channel for the task at hand. This has not been possible so far, due to the lack of a suitable depth database. This paper addresses this issue, proposing to opt for synthetically generated images rather than collecting by hand a 2.5D large scale database. While being clearly a proxy for real data, synthetic images allow to trade quality for quantity, making it possible to generate a virtually infinite amount of data. We show that the filters learned from such data collection, using the very same architecture typically used on visual data, learns very different filters, resulting in depth features (a) able to better characterize the different facets of depth images, and (b) complementary with respect to those derived from CNNs pre-trained on 2D datasets. Experiments on two publicly available databases show the power of our approach

From source to target and back: symmetric bi-directional adaptive GAN

The effectiveness of generative adversarial approaches in producing images according to a specific style or visual domain has recently opened new directions to solve the unsupervised domain adaptation problem. It has been shown that source labeled images can be modified to mimic target samples making it possible to train directly a classifier in the target domain, despite the original lack of annotated data. Inverse mappings from the target to the source domain have also been evaluated but only passing through adapted feature spaces, thus without new image generation. In this paper we propose to better exploit the potential of generative adversarial networks for adaptation by introducing a novel symmetric mapping among domains. We jointly optimize bi-directional image transformations combining them with target self-labeling. Moreover we define a new class consistency loss that aligns the generators in the two directions imposing to conserve the class identity of an image passing through both domain mappings. A detailed qualitative and quantitative analysis of the reconstructed images confirm the power of our approach. By integrating the two domain specific classifiers obtained with our bi-directional network we exceed previous state-of-the-art unsupervised adaptation results on four different benchmark datasets

Commentary on a microfluidic platform to design crosslinked hyaluronic acid nanoparticles (cHANPs) for enhanced MRI

Strategies to enhance the relaxometric properties of gadolinium (Gd)-based contrast agents (CAs) for magnetic resonance imaging (MRI), without the chemical modification of chelates, have recently had a strong impact on the diagnostic field. We have taken advantage of the interaction between Gadolinium diethylenetriamine penta-acetic acid (Gd-DTPA) and the hydrogel structure of hyaluronic acid to design cross-linked hyaluronic acid nanoparticles down to 35 nm for use in MRI applications. The proposed bioformulations enable the control of the relaxometric properties of CAs, thus boosting the relaxation rate of T1. Our results led us to identify this approach as an adjustable scenario to design intravascularly injectable hydrogel nanoparticles entrapping Gd-DTPA. This approach overcomes the general drawbacks of clinically approved CAs having poor relaxivity and toxic effects

A Microfluidic Platform to design crosslinked Hyaluronic Acid Nanoparticles (cHANPs) for enhanced MRI

Recent advancements in imaging diagnostics have focused on the use of nanostructures that entrap Magnetic Resonance Imaging (MRI) Contrast Agents (CAs), without the need to chemically modify the clinically approved compounds. Nevertheless, the exploitation of microfluidic platforms for their controlled and continuous production is still missing. Here, a microfluidic platform is used to synthesize crosslinked Hyaluronic Acid NanoParticles (cHANPs) in which a clinically relevant MRI-CAs, gadolinium diethylenetriamine penta-acetic acid (Gd-DTPA), is entrapped. This microfluidic process facilitates a high degree of control over particle synthesis, enabling the production of monodisperse particles as small as 35 nm. Furthermore, the interference of Gd-DTPA during polymer precipitation is overcome by finely tuning process parameters and leveraging the use of hydrophilic-lipophilic balance (HLB) of surfactants and pH conditions. For both production strategies proposed to design Gd-loaded cHANPs, a boosting of the relaxation rate T(1) is observed since a T(1) of 1562 is achieved with a 10 μM of Gd-loaded cHANPs while a similar value is reached with 100 μM of the relevant clinical Gd-DTPA in solution. The advanced microfluidic platform to synthesize intravascularly-injectable and completely biocompatible hydrogel nanoparticles entrapping clinically approved CAs enables the implementation of straightforward and scalable strategies in diagnostics and therapy applications

Venice as a short-term city. Between global trends and local lock-ins

This paper examines the ongoing transition of Venice towards a short-term city, posited as an urban form which accommodates the dwelling practices of temporary populations as tourists, at the expenses of a stable resident population. This shift is approached through the conceptual framework of resilience, which is also explored in its political and discursive dimensions. At the base of the emergence of a short-term city, we analyse the redistributive impacts of short-term rentals mediated by digital platforms and their influence on the housing market, but also the related entrenchments of a local policy agenda supporting the resilience of the industry itself above that of the city as a living organism. After illustrating the development of the hospitality sector in the city fabric over the last four decades and presenting the historical challenges that Venice has been facing in regard to its capacity to retain a stable population, we seek to unravel the debate on ‘the future of Venice’, which confronts local and global agents defending a ‘conservationist’ approach for Venice as an ineluctably tourist city, with social actors who claim for the defence of residence – and therefore for a ban on STR – as a necessary condition for a socially resilient alternative

On Krylov projection methods and Tikhonov regularization

In the framework of large-scale linear discrete ill-posed problems, Krylov projection methods represent an essential tool since their development, which dates back to the early 1950\u2019s. In recent years, the use of these methods in a hybrid fashion or to solve Tikhonov regularized problems has received great attention especially for problems involving the restoration of digital images. In this paper we review the fundamental Krylov-Tikhonov techniques based on Lanczos bidiagonalization and the Arnoldi algorithms. Moreover, we study the use of the unsymmetric Lanczos process that, to the best of our knowledge, has just marginally been considered in this setting. Many numerical experiments and comparisons of different methods are presented

Attachment anxiety and depressive symptoms in undergraduate medical students

Introduction Several studies report that medical students are at high risk of depression. Despite the variability in students’ vulnerability to depression, the role of individual differences in depression risk among medical students has hardly been investigated. Studies outside of medical student populations have shown that individual differences in attachment style and emotion regulation participate in vulnerability to depression. Objectives This study investigates to what extent medical students’ depressive symptoms are related to differences in students’ insecure attachment styles and their perception of reduced access to emotion regulation strategies. Methods In a cross-sectional quantitative study, undergraduate medical students at the beginning of their second academic year completed online questionnaires measuring their attachment style, difficulties in emotion regulation, and depressive symptoms. Results Out of the 390 medical students invited, 267 participated in the survey. Higher secure attachment was associated with fewer depressive symptoms. Medical students’ insecure attachment style and emotion dysregulation were significantly related to depressive symptoms. Difficulties in employing strategies to disengage from one’s own negative affect partly mediated the effects of two dimensions of insecure anxious attachment—need for approval and preoccupation with relationships—on depressive symptoms. Discussion Anxious attachment style and limited access to emotion regulation strategies participate in medical students’ depressive symptoms. These findings highlight the need for acknowledging medical students’ attachment style and students’ perceived access to emotion regulation strategies for the early identification of and intervention programs for the risk of depression

Heparanase and macrophage interplay in the onset of liver fibrosis

Abstract The heparan sulfate endoglycosidase heparanase (HPSE) is involved in tumor growth, chronic inflammation and fibrosis. Since a role for HPSE in chronic liver disease has not been demonstrated to date, the current study was aimed at investigating the involvement of HPSE in the pathogenesis of chronic liver injury. Herein, we revealed that HPSE expression increased in mouse livers after carbon tetrachloride (CCl4)-mediated chronic induction of fibrosis, but with a trend to decline during progression of the disease. In mouse fibrotic liver tissues HPSE immunostaining was restricted in necro-inflammatory areas, co-localizing with F4/80 macrophage marker and TNF-α. TNF-α treatment induced HPSE expression as well as HPSE secretion in U937 macrophages. Moreover, macrophage-secreted HPSE regulated the expression of α-SMA and fibronectin in hepatic stellate LX-2 cells. Finally, HPSE activity increased in the plasma of patients with liver fibrosis but it inversely correlated with liver stiffness. Our results suggest the involvement of HPSE in early phases of reaction to liver damage and inflammatory macrophages as an important source of HPSE. HPSE seems to play a key role in the macrophage-mediated activation of hepatic stellate cells (HSCs), thus suggesting that HPSE targeting could be a new therapeutic option in the treatment of liver fibrosis