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

GURLS: a Toolbox for Regularized Least Squares Learning

We present GURLS, a toolbox for supervised learning based on the regularized least squares algorithm. The toolbox takes advantage of all the favorable properties of least squares and is tailored to deal in particular with multi-category/multi-label problems. One of the main advantages of GURLS is that it allows training and tuning a multi-category classifier at essentially the same cost of one single binary classifier. The toolbox provides a set of basic functionalities including different training strategies and routines to handle computations with very large matrices by means of both memory-mapped storage and distributed task execution. The system is modular and can serve as a basis for easily prototyping new algorithms. The toolbox is available for download, easy to set-up and use

Automatic modal identification of bridges based on free vibrations and advanced signal decomposition techniques

Free vibration tests are attractive because they can be performed by means of a network consisting of few sensors temporarily installed on the structure in such a way to limit duration and cost of the experimental campaign. Additionally, free vibration tests are usually performed by introducing an initial perturbation that can induce a structural response significantly higher than the ambient excitation. This, in turn, allows to reduce the noise-to-signal ratio in the final measurements and/or to consider less stringent requirements about the technical specifications of the sensors. Since free vibration tests can provide accurate estimates of the modal parameters while being rather cheap and easy to implement, they have been performed in many applications, such as the experimental dynamic characterization of base-isolated buildings, masonry towers, ancient tie-rods, and bridges. An efficient and automatic computational framework is thus presented for the modal identification of bridges based on their free vibrations. The novel procedure proposed in the current work combines advanced signal decomposition techniques and a robust approach for damping identification. Two advanced signal decomposition techniques are considered, namely the Variational Mode Decomposition and the Empirical Fourier Decomposition. Experimental applications are then illustrated for roadway and railway bridges

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

GURLS: A Least Squares Library for Supervised Learning

We present GURLS, a least squares, modular, easy-to-extend software library for efficient supervised learning. GURLS is targeted to machine learning practitioners, as well as non- specialists. It offers a number state-of-the-art training strategies for medium and large-scale learning, and routines for efficient model selection. The library is particularly well suited for multi-output problems (multi-category/multi-label). GURLS is currently available in two independent implementations: Matlab and C++. It takes advantage of the favorable properties of regularized least squares algorithm to exploit advanced tools in linear algebra. Routines to handle computations with very large matrices by means of memory-mapped storage and distributed task execution are available. The package is distributed under the BSD license and is available for download at https://github.com/LCSL/GURLS

Multi-scale analysis of lung computed tomography images

A computer-aided detection (CAD) system for the identification of lung internal nodules in low-dose multi-detector helical Computed Tomography (CT) images was developed in the framework of the MAGIC-5 project. The three modules of our lung CAD system, a segmentation algorithm for lung internal region identification, a multi-scale dot-enhancement filter for nodule candidate selection and a multi-scale neural technique for false positive finding reduction, are described. The results obtained on a dataset of low-dose and thin-slice CT scans are shown in terms of free response receiver operating characteristic (FROC) curves and discussed.Comment: 18 pages, 12 low-resolution figure

Isatuximab plus atezolizumab in patients with advanced solid tumors: results from a phase I/II, open-label, multicenter study

Atezolizumab; Isatuximab; Solid tumorsAtezolizumab; Isatuximab; Tumores sólidosAtezolizumab; Isatuximab; Tumors sòlidsBackground The anti-CD38 antibody isatuximab is approved for the treatment of relapsed/refractory multiple myeloma, but there are no data on its efficacy in solid tumors. This phase I/II study (NCT03637764) assessed the safety and activity of isatuximab plus atezolizumab (Isa + Atezo), an anti-programmed death-ligand 1 (PD-L1) antibody, in patients with immunotherapy-naive solid tumors: epithelial ovarian cancer (EOC), glioblastoma (GBM), hepatocellular carcinoma (HCC), and squamous cell carcinoma of the head and neck (SCCHN). Patients and methods Phase I assessed safety, tolerability, pharmacokinetics, pharmacodynamics, and the recommended phase II dose (RP2D) of isatuximab 10 mg/kg intravenously (i.v.) every week for 3 weeks followed by once every 3 weeks + atezolizumab 1200 mg i.v. every 3 weeks. Phase II used a Simon’s two-stage design to assess the overall response rate or progression-free survival rate at 6 months (GBM cohort). Interim analysis was carried out at 6 months following first dose of the last enrolled patient in each cohort. Pharmacodynamic biomarkers were tested for CD38, PD-L1, tumor-infiltrating immune cells, and FOXP3+ regulatory T cells (Tregs) in the tumor microenvironment (TME). Results Overall, 107 patients were treated (EOC, n = 18; GBM, n = 33; HCC, n = 27; SCCHN, n = 29). In phase I, Isa + Atezo showed an acceptable safety profile, no dose-limiting toxicities were observed, and RP2D was confirmed. Most patients experienced ≥1 treatment-emergent adverse event (TEAE), with ≤48.5% being grade ≥3. The most frequent TEAE was infusion reactions. The study did not continue to stage 2 based on prespecified targets. Tumor-infiltrating CD38+ immune cells were reduced and almost cleared after treatment. Isa + Atezo did not significantly modulate Tregs or PD-L1 expression in the TME. Conclusions Isa + Atezo had acceptable safety and tolerability. Clinical pharmacodynamic evaluation revealed efficient target engagement of isatuximab via treatment-mediated reduction of CD38+ immune cells in the TME. Based on clinical data, CD38 inhibition does not improve responsiveness to PD-L1 blockade in these patients.This work was sponsored by Sanofi (no grant number)

The Li intercalation potential of LiMPO4 and LiMSiO4 olivines with M = Fe, Mn, Co, Ni

The Li intercalation potential of LiMPO4 and LiMSiO4 compounds with M = Fe, Mn, Co, and Ni is computed with the GGA+U method. It is found that this approach is considerably more accurate than standard LDA or GGA methods. The calculated potentials for LiFePO4, LiMnPO4 and LiCoOPO4 agree to within 0.1 V with experimental results. The LiNiPO4 potential is predicted to be above 5 V. The potentials of the silicate materials are all found to be rather high, but LiFeSiO4 and LiCoSiO4 have negligible volume change upon Li extraction.Comment: 10 pages, 2 figure