The Maximum Common Subgraph Problem: A Parallel and Multi-Engine Approach

The maximum common subgraph of two graphs is the largest possible common subgraph, i.e., the common subgraph with as many vertices as possible. Even if this problem is very challenging, as it has been long proven NP-hard, its countless practical applications still motivates searching for exact solutions. This work discusses the possibility to extend an existing, very effective branch-and-bound procedure on parallel multi-core and many-core architectures. We analyze a parallel multi-core implementation that exploits a divide-and-conquer approach based on a thread pool, which does not deteriorate the original algorithmic efficiency and it minimizes data structure repetitions. We also extend the original algorithm to parallel many-core GPU architectures adopting the CUDA programming framework, and we show how to handle the heavily workload-unbalance and the massive data dependency. Then, we suggest new heuristics to reorder the adjacency matrix, to deal with “dead-ends”, and to randomize the search with automatic restarts. These heuristics can achieve significant speed-ups on specific instances, even if they may not be competitive with the original strategy on average. Finally, we propose a portfolio approach, which integrates all the different local search algorithms as component tools; such portfolio, rather than choosing the best tool for a given instance up-front, takes the decision on-line. The proposed approach drastically limits memory bandwidth constraints and avoids other typical portfolio fragility as CPU and GPU versions often show a complementary efficiency and run on separated platforms. Experimental results support the claims and motivate further research to better exploit GPUs in embedded task-intensive and multi-engine parallel applications

Boundary value problems associated with singular strongly nonlinear equations with functional terms

We study boundary value problems associated with singular, strongly nonlinear differential equations with functional terms of type $$\big(\Phi(k(t)\,x'(t))\big)' + f(t,\mathcal{G}_x(t))\,\rho(t, x'(t)) = 0$$ on a compact interval $[a,b]$. These equations are quite general due to the presence of a strictly increasing homeomorphism $\Phi$, the so-called $\Phi$-Laplacian operator, of a nonnegative function $k$, which may vanish on a set of null measure, and moreover of a functional term $\mathcal{G}_x$. We look for solutions, in a suitable weak sense, which belong to the Sobolev space $W^{1,1}([a,b])$. Under the assumptions of the existence of a well-ordered pair of upper and lower solutions and of a suitable Nagumo-type growth condition, we prove an existence result by means of fixed point arguments

Manufacture of a MoO3 coated copper made device

We describe the procedure to manufacture a model of a cylindrical RF cavity made in copper and coated with a 100 nm thick layer of molybdenum trioxide. The device is 100 mm long, has an internal diameter of 60 mm and an external diameter of 80 mm. The cylindrical device was carefully divided into four sections to make possible the coating on the internal curved surfaces polished to a roughness < 10 nm. The molybdenum trioxide has been deposed utilizing a thermal evaporation technique with a dedicated high vacuum chamber equipped with a high-temperature Alumina crucible working in the temperature range of 400° - 600° C

Manufacture of a MoO3 coated copper made device

In this report we describe the procedure to manufacture a model of a cylindrical RF cavity made in copper and coated with a 100 nm thick layer of molybdenum trioxide. The device is 100 mm long, has an internal diameter of 60 mm and an external diameter of 80 mm. The cylindrical device was carefully divided into four sections to make possible the coating on the internal curved surfaces polished to a roughness < 10 nm. The molybdenum trioxide has been deposed utilizing a thermal evaporation technique with a dedicated high vacuum chamber equipped with a high-temperature Alumina crucible working in the temperature range of 400° - 600° C

Luminescence, vibrational and XANES studies of AlN nanomaterials

Abstract The paper reports comparative studies on synthesized aluminium nitride nanotubes, nanoparticles and commercially available micron-sized AlN powder using different spectroscopic techniques: cathodoluminescence measurements (CL), X-ray absorption near edge spectroscopy (XANES) and Fourier-transform infrared spectroscopy (FTIR). Crucial distinctions in CL spectra are observed for nano- and microsized aluminium nitride powders; systematic shift of the IR absorption maximum has been detected for nanostructured aluminium nitride as compared to commercial samples. Through XANES experiments on Al K-edge structural differences between nano- and bulk AlN are revealed, intensity of features in absorption spectra has been found to be a function of wurtzite and zincblend phases amount in nanostructured samples

Characterization of Pupillary Light Response Features for the Classification of Patients with Optic Neuritis

Pupillometry is a promising technique for the potential diagnosis of several neurological pathologies. However, its potential is not fully explored yet, especially for prediction purposes and results interpretation. In this work, we analyzed 100 pupillometric curves obtained by 12 subjects, applying both advanced signal processing techniques and physics methods to extract typically collected features and newly proposed ones. We used machine learning techniques for the classification of Optic Neuritis (ON) vs. Healthy subjects, controlling for overfitting and ranking the features by random permutation, following their importance in prediction. All the extracted features, except one, turned out to have significant importance for prediction, with an average accuracy of 76%, showing the complexity of the processes involved in the pupillary light response. Furthermore, we provided a possible neurological interpretation of this new set of pupillometry features in relation to ON vs. Healthy classification

AEducaAR, Anatomical Education in Augmented Reality: A Pilot Experience of an Innovative Educational Tool Combining AR Technology and 3D Printing

Gross anatomy knowledge is an essential element for medical students in their education, and nowadays, cadaver-based instruction represents the main instructional tool able to provide three-dimensional (3D) and topographical comprehensions. The aim of the study was to develop and test a prototype of an innovative tool for medical education in human anatomy based on the combination of augmented reality (AR) technology and a tangible 3D printed model that can be explored and manipulated by trainees, thus favoring a three-dimensional and topographical learning approach. After development of the tool, called AEducaAR (Anatomical Education with Augmented Reality), it was tested and evaluated by 62 second-year degree medical students attending the human anatomy course at the International School of Medicine and Surgery of the University of Bologna. Students were divided into two groups: AEducaAR-based learning (“AEducaAR group”) was compared to standard learning using human anatomy atlas (“Control group”). Both groups performed an objective test and an anonymous questionnaire. In the objective test, the results showed no significant difference between the two learning methods; instead, in the questionnaire, students showed enthusiasm and interest for the new tool and highlighted its training potentiality in open-ended comments. Therefore, the presented AEducaAR tool, once implemented, may contribute to enhancing students’ motivation for learning, increasing long-term memory retention and 3D comprehension of anatomical structures. Moreover, this new tool might help medical students to approach to innovative medical devices and technologies useful in their future careers