SOM-based aggregation for graph convolutional neural networks

Graph property prediction is becoming more and more popular due to the increasing availability of scientific and social data naturally represented in a graph form. Because of that, many researchers are focusing on the development of improved graph neural network models. One of the main components of a graph neural network is the aggregation operator, needed to generate a graph-level representation from a set of node-level embeddings. The aggregation operator is critical since it should, in principle, provide a representation of the graph that is isomorphism invariant, i.e. the graph representation should be a function of graph nodes treated as a set. DeepSets (in: Advances in neural information processing systems, pp 3391–3401, 2017) provides a framework to construct a set-aggregation operator with universal approximation properties. In this paper, we propose a DeepSets aggregation operator, based on Self-Organizing Maps (SOM), to transform a set of node-level representations into a single graph-level one. The adoption of SOMs allows to compute node representations that embed the information about their mutual similarity. Experimental results on several real-world datasets show that our proposed approach achieves improved predictive performance compared to the commonly adopted sum aggregation and many state-of-the-art graph neural network architectures in the literature

Magnetoresistance in Thin Permalloy Film (10nm-thick and 30-200nm-wide) Nanocontacts Fabricated by e-Beam Lithography

In this paper we show spin dependent transport experiments in nanoconstrictions ranging from 30 to 200nm. These nanoconstrictions were fabricated combining electron beam lithography and thin film deposition techniques. Two types of geometries have been fabricated and investigated. We compare the experimental results with the theoretical estimation of the electrical resistance. Finally we show that the magnetoresistance for the different geometries does not scale with the resistance of the structure and obtain drops in voltage of 20mV at 20Oe.Comment: 15 pages, 4 figures. Accepted by AP

Innovative passive reinforcements for the gradual stabilization of a landslide according with the observational method

A large number of landslides occur in North-Eastern Italy during every rainy period due to the particular hydrogeological conditions of this area. Even if there are no casualties, the economic losses are often significant, and municipalities frequently do not have sufficient financial resources to repair the damage and stabilize all the unstable slopes. In this regard, the research for more economically sustainable solutions is a crucial challenge. Floating composite anchors are an innovative and low-cost technique set up for slope stabilization: it consists in the use of passive sub-horizontal reinforcements, obtained by coupling a traditional self-drilling bar with some tendons cemented inside it. This work concerns the application of this technique according to the observational method described within the Italian and European technical codes and mainly recommended for the design of geotechnical works, especially when performed in highly uncertain site conditions. The observational method prescribes designing an intervention and, at the same time, using a monitoring system in order to correct and adapt the project during realization of the works on the basis of new data acquired while on site. The case study is the landslide of Cischele, a medium landslide which occurred in 2010 after an exceptional heavy rainy period. In 2015, some floating composite anchors were installed to slow down the movement, even if, due to a limited budget, they were not enough to ensure the complete stabilization of the slope. Thanks to a monitoring system installed in the meantime, it is now possible to have a comparison between the site conditions before and after the intervention. This allows the evaluation of benefits achieved with the reinforcements and, at the same time, the assessment of additional improvements. Two stabilization scenarios are studied through an FE model: the first includes the stabilization system built in 2015, while the second evaluates a new solution proposed to further increase the slope stability

Linear graph convolutional networks

Many neural networks for graphs are based on the graph convolution operator, proposed more than a decade ago. Since then, many alternative definitions have been proposed, that tend to add complexity (and non-linearity) to the model. In this paper, we follow the opposite direction by proposing a linear graph convolution operator. Despite its simplicity, we show that our convolution operator is more theoretically grounded than many proposals in literature, and shows improved predictive performance

Nursery management in relation to root deformation, sowing and shading

The polybag is widely used for seedling production in the Philippines. Seedlings commonly have root deformation which has adverse effects as they grow and develop into mature trees. This study assessed the influence of potting technique and hardening intensity on the growth performance of seedlings in nursery and field conditions. Seedlings of bagras (Eucalyptus deglupta) and mangium (Acacia mangium) were grown in hiko trays and 4" x 6" polybags at the College of Forestry nursery, Leyte State University (LSU). Kalumpit (Terminalia microcarpa) and pellita (Eucalyptus pellita) were used for a trial in the Conalum Agroforestry Farmers Association (CAFA) nursery in Inopacan, Leyte, aimed at validating LSU results and at the same time evaluating farmers' perceptions on the use of hiko trays and polybags in seedling production. It was found that seedlings of bagras, mangium, kalumpit and pellita grown in hiko trays have smaller diameter and height compared with those in polybags at 12 weeks. Root deformation of seedlings was absent in hiko trays but high with seedlings in polybags. As perceived by both farmers and ACIAR researchers, hiko tray seedlings are of high quality exhibiting sturdy shoot, trained roots and homogenous growth

Identification of ultramodified proteins using top-down tandem mass spectra

Post-translational modifications (PTMs) play an important role in various biological processes through changing protein structure and function. Some ultramodified proteins (like histones) have multiple PTMs forming PTM patterns that define the functionality of a protein. While bottom-up mass spectrometry (MS) has been successful in identifying individual PTMs within short peptides, it is unable to identify PTM patterns spreading along entire proteins in a coordinated fashion. In contrast, top-down MS analyzes intact proteins and reveals PTM patterns along the entire proteins. However, while recent advances in instrumentation have made top-down MS accessible to many laboratories, most computational tools for top-down MS focus on proteins with few PTMs and are unable to identify complex PTM patterns. We propose a new algorithm, MS-Align-E, that identifies both expected and unexpected PTMs in ultramodified proteins. We demonstrate that MS-Align-E identifies many proteoforms of histone H4 and benchmark it against the currently accepted software tools