Implementation of the PaperRank and AuthorRank indices in the Scopus database

We implement the PaperRank and AuthorRank indices introduced in [Amodio & Brugnano, 2014] in the Scopus database, in order to highlight quantitative and qualitative information that the bare number of citations and/or the h-index of an author are unable to provide. In addition to this, the new indices can be cheaply updated in Scopus, since this has a cost comparable to that of updating the number of citations. Some examples are reported to provide insight in their potentialities, as well as possible extensions

AutoGraph: Automated Graph Neural Network

Graphs play an important role in many applications. Recently, Graph Neural Networks (GNNs) have achieved promising results in graph analysis tasks. Some state-of-the-art GNN models have been proposed, e.g., Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), etc. Despite these successes, most of the GNNs only have shallow structure. This causes the low expressive power of the GNNs. To fully utilize the power of the deep neural network, some deep GNNs have been proposed recently. However, the design of deep GNNs requires significant architecture engineering. In this work, we propose a method to automate the deep GNNs design. In our proposed method, we add a new type of skip connection to the GNNs search space to encourage feature reuse and alleviate the vanishing gradient problem. We also allow our evolutionary algorithm to increase the layers of GNNs during the evolution to generate deeper networks. We evaluate our method in the graph node classification task. The experiments show that the GNNs generated by our method can obtain state-of-the-art results in Cora, Citeseer, Pubmed and PPI datasets.Comment: Accepted by ICONIP 202

Analysis of infected human mononuclear cells by atomic force microscopy

The surfaces of the human lymphoid cells of the line H9 chronically infected with the Human Immunodeficiency Virus HIV-1, and of human monocytes acutely infected in vitro with Mycobacterium Tuberculosis (MTB) were dried, fixed and imaged with atomic force microscopy (AFM). These images were compared with those of non-infected samples. Dried and fixed samples of infected cells can be distinguished from non-infected ones by AFM technology due to their different surface structures and by the presence of pathogenic (viz al or mycobacterial) agents on the cell surface

Multitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Network

The application of deep learning to symbolic domains remains an active research endeavour. Graph neural networks (GNN), consisting of trained neural modules which can be arranged in different topologies at run time, are sound alternatives to tackle relational problems which lend themselves to graph representations. In this paper, we show that GNNs are capable of multitask learning, which can be naturally enforced by training the model to refine a single set of multidimensional embeddings $\in \mathbb{R}^d$ and decode them into multiple outputs by connecting MLPs at the end of the pipeline. We demonstrate the multitask learning capability of the model in the relevant relational problem of estimating network centrality measures, focusing primarily on producing rankings based on these measures, i.e. is vertex $v_1$ more central than vertex $v_2$ given centrality $c$?. We then show that a GNN can be trained to develop a \emph{lingua franca} of vertex embeddings from which all relevant information about any of the trained centrality measures can be decoded. The proposed model achieves $89\%$ accuracy on a test dataset of random instances with up to 128 vertices and is shown to generalise to larger problem sizes. The model is also shown to obtain reasonable accuracy on a dataset of real world instances with up to 4k vertices, vastly surpassing the sizes of the largest instances with which the model was trained ($n=128$). Finally, we believe that our contributions attest to the potential of GNNs in symbolic domains in general and in relational learning in particular.Comment: Published at ICANN2019. 10 pages, 3 Figure

Cell-type-specific 2 adrenergic receptor clusters identified using photo-activated localization microscopy are not lipid raft related, but depend on actin cytoskeleton integrity

Recent developments in the field of optical super-resolution techniques allow both a 10-fold increase in resolution as well as an increased ability to quantify the number of labeled molecules visualized in the fluorescence measurement. By using photoactivated localization microscopy (PALM) and an experimental approach based on the systematic comparison with a nonclustering peptide as a negative control, we found that the prototypical G protein-coupled receptor beta 2-adrenergic receptor is partially preassociated in nanoscale-sized clusters only in the cardiomyocytes, such as H9C2 cells, but not in other cell lines, such as HeLa and Chinese hamster ovary (CHO). The addition of the agonist for very short times or the addition of the inverse agonist did not significantly affect the organization of receptor assembly. To investigate the mechanism governing cluster formation, we altered plasma membrane properties with cholesterol removal and actin microfilament disruption. Although cholesterol is an essential component of cell membranes and it is supposed to be enriched in the lipid rafts, its sequestration and removal did not affect receptor clustering, whereas the inhibition of actin polymerization did decrease the number of clusters. Our findings are therefore consistent with a model in which beta 2 receptor clustering is influenced by the actin cytoskeleton, but it does not rely on lipid raft integrity, thus ruling out the possibility that cell type-specific beta 2 receptor clustering is associated with the raft

Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities

Graph Neural Networks (GNNs) have become the leading paradigm for learning on (static) graph-structured data. However, many real-world systems are dynamic in nature, since the graph and node/edge attributes change over time. In recent years, GNN-based models for temporal graphs have emerged as a promising area of research to extend the capabilities of GNNs. In this work, we provide the first comprehensive overview of the current stateof-the-art of temporal GNN, introducing a rigorous formalization of learning settings and tasks and a novel taxonomy categorizing existing approaches in terms of how the temporal aspect is represented and processed. We conclude the survey with a discussion of the most relevant open challenges for the field, from both research and application perspectives

Photoresponse from noble metal nanoparticles-multi walled carbon nanotube composites

In this Letter, we investigated the photo-response of multi wall carbon nanotube-based composites obtained from in situ thermal evaporation of noble metals (Au, Ag, and Cu) on the nanotube films. The metal deposition process produced discrete nanoparticles on the nanotube outer walls. The nanoparticle-carbon nanotube films were characterized by photo-electrochemical measurements in a standard three electrode cell. The photocurrent from the decorated carbon nanotubes remarkably increased with respect to that of bare multiwall tubes. With the aid of first-principle calculations, these results are discussed in terms of metal nanoparticle–nanotube interactions and electronic charge transfer at the interface.VC 2012 American Institute of Physics

Applications of three-dimensional carbon nanotube

In this paper, we show that it is possible to synthesize carbon-based three-dimensional networks by adding sulfur, as growth enhancer, during the synthesis process. The obtained material is self-supporting and consists of curved and interconnected carbon nanotubes and to lesser extent of carbon fibers. Studies on the microstructure indicate that the assembly presents a marked variability in the tube external diameter and in the inner structure. We study the relationship between the observed microscopic properties and some potential applications. In particular, we show that the porous nature of the network is directly responsible for the hydrophobic and the lipophilic behavior. Moreover, we used a cut piece of the produced carbon material as working electrode in a standard electrochemical cell and, thus, demonstrating the capability of the system to respond to incident light in the visible and near-ultraviolet region and to generate a photocurrent

Design and Experimental Characterization of a Niti-Based, High-Frequency, Centripetal Peristaltic Actuator

Development and experimental testing of a peristaltic device actuated by a single shape-memory NiTi wire are described. The actuator is designed to radially shrink a compliant silicone pipe, and must work on a sustained basis at an actuation frequency that is higher than those typical of NiTi actuators. Four rigid, aluminum-made circular sectors are sitting along the pipe circumference and provide the required NiTi wire housing. The aluminum assembly acts as geometrical amplifier of the wire contraction and as heat sink required to dissipate the thermal energy of the wire during the cooling phase. We present and discuss the full experimental investigation of the actuator performance, measured in terms of its ability to reduce the pipe diameter, at a sustained frequency of 1.5 Hz. Moreover, we investigate how the diameter contraction is affected by various design parameters as well as actuation frequencies up to 4 Hz. We manage to make the NiTi wire work at 3% in strain, cyclically providing the designed pipe wall displacement. The actuator performance is found to decay approximately linearly with actuation frequencies up to 4 Hz. Also, the interface between the wire and the aluminum parts is found to be essential in defining the functional performance of the actuator