PADDLE: Proximal Algorithm for Dual Dictionaries LEarning

Recently, considerable research efforts have been devoted to the design of methods to learn from data overcomplete dictionaries for sparse coding. However, learned dictionaries require the solution of an optimization problem for coding new data. In order to overcome this drawback, we propose an algorithm aimed at learning both a dictionary and its dual: a linear mapping directly performing the coding. By leveraging on proximal methods, our algorithm jointly minimizes the reconstruction error of the dictionary and the coding error of its dual; the sparsity of the representation is induced by an $\ell_1$-based penalty on its coefficients. The results obtained on synthetic data and real images show that the algorithm is capable of recovering the expected dictionaries. Furthermore, on a benchmark dataset, we show that the image features obtained from the dual matrix yield state-of-the-art classification performance while being much less computational intensive

Nonparametric Sparsity and Regularization

In this work we are interested in the problems of supervised learning and variable selection when the input-output dependence is described by a nonlinear function depending on a few variables. Our goal is to consider a sparse nonparametric model, hence avoiding linear or additive models. The key idea is to measure the importance of each variable in the model by making use of partial derivatives. Based on this intuition we propose and study a new regularizer and a corresponding least squares regularization scheme. Using concepts and results from the theory of reproducing kernel Hilbert spaces and proximal methods, we show that the proposed learning algorithm corresponds to a minimization problem which can be provably solved by an iterative procedure. The consistency properties of the obtained estimator are studied both in terms of prediction and selection performance. An extensive empirical analysis shows that the proposed method performs favorably with respect to the state-of-the-art

Iterative Projection Methods for Structured Sparsity Regularization

In this paper we propose a general framework to characterize and solve the optimization problems underlying a large class of sparsity based regularization algorithms. More precisely, we study the minimization of learning functionals that are sums of a differentiable data term and a convex non differentiable penalty. These latter penalties have recently become popular in machine learning since they allow to enforce various kinds of sparsity properties in the solution. Leveraging on the theory of Fenchel duality and subdifferential calculus, we derive explicit optimality conditions for the regularized solution and propose a general iterative projection algorithm whose convergence to the optimal solution can be proved. The generality of the framework is illustrated, considering several examples of regularization schemes, including l1 regularization (and several variants), multiple kernel learning and multi-task learning. Finally, some features of the proposed framework are empirically studied

Involve to improve: A participatory approach for a Decision Support System for coastal climate change impacts assessment. The North Adriatic case

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Should european countries refuse entrance to migrants that do not speak their language? A case for the right to family life

This article analyses the recent case of an ECJ ruling concerning Member States’ possibility to require third county nationals (TCNs) to pass a civic integration examination prior to family reunification. Following a description of the content of the right to family life for non-EU citizens residing in the EU, the article discusses the controversies surrounding the ECJ’s ruling, as well as the ethical and policy implication of the decision. The article argues that, while the Court’s decision is in line with the European Directive on Family Reunification, it does not take full consideration of the consequences of the Dutch policy regarding civic integration tests. In particular, the Court overlooks the fact that, while the test is hardly functional to state the capacity of integration, it acts as a form of ex-ante discrimination that contravenes Article 7 of the European Convention and Article 8 of the European Charter on the right to family life

New Insights on the Role of Vitamin D in the Progression of Renal Damage

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Rituximab in steroid-refractory immune-related pancreatitis: a case report

The use of immune checkpoint inhibitors (ICIs) for treating several types of cancer is increasing, but they may be associated with immune-related adverse events (irAEs). Pancreatitis is a rare irAE, mostly responsive to steroid treatment. There are no published data on the management of steroid-refractory ICI-induced pancreatitis. Rituximab has shown efficacy in the setting of relapsing non-ICI-induced autoimmune pancreatitis. However, its use has not been tested for treating immunotherapy-related pancreatitis. Here, we present the case of a patient with steroid-refractory immune-related pancreatitis successfully treated with rituximab as a potential strategy for irAE management

KCa3.1 channel inhibition sensitizes malignant gliomas to temozolomide treatment

Malignant gliomas are among the most frequent and aggressive cerebral tumors, characterized by high proliferative and invasive indexes. Standard therapy for patients, after surgery and radiotherapy, consists of temozolomide (TMZ), a methylating agent that blocks tumor cell proliferation. Currently, there are no therapies aimed at reducing tumor cell invasion. Ion channels are candidate molecular targets involved in glioma cell migration and infiltration into the brain parenchyma. In this paper we demonstrate that: i) blockade of the calcium-activated potassium channel KCa3.1 with TRAM-34 has co-adjuvant effects with TMZ, reducing GL261 glioma cell migration, invasion and colony forming activity, increasing apoptosis, and forcing cells to pass the G2/M cell cycle phase, likely through cdc2 de-phosphorylation; ii) KCa3.1 silencing potentiates the inhibitory effect of TMZ on glioma cell viability; iii) the combination of TMZ/TRAM-34 attenuates the toxic effects of glioma conditioned medium on neuronal cultures, through a microglia dependent mechanism since the effect is abolished by clodronate-induced microglia killing; iv) TMZ/TRAM-34 co-treatment increases the number of apoptotic tumor cells, and the mean survival time in a syngeneic mouse glioma model (C57BL6 mice implanted with GL261 cells); v) TMZ/TRAM-34 co-treatment reduces cell viability of GBM cells and cancer stem cells (CSC) freshly isolated from patients.Taken together, these data suggest a new therapeutic approach for malignant glioma, targeting both glioma cell proliferating and migration, and demonstrate that TMZ/TRAM-34 co-treatment affects both glioma cells and infiltrating microglia, resulting in an overall reduction of tumor cell progression