606 research outputs found
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 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
more central than vertex given centrality ?. 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 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 ().
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
Characterisation of submarine depression trails driven by upslope migrating cyclic steps: Insights from the Ceará Basin (Brazil)
Circular to elliptical topographic depressions, isolated or organized in trails, have been observed on the modern seabed in different contexts and water depths. Such features have been alternatively interpreted as pockmarks generated by fluid flow, as sediment waves generated by turbidity currents, or as a combination of both processes. In the latter case, the dip of the slope has been hypothesized to control the formation of trails of downslope migrating pockmarks. In this study, we use high-quality 3D seismic data from the offshore Ceará Basin (Equatorial Brazil) to examine vertically stacked and upslope-migrating trails of depressions visible at the seabed and in the subsurface. Seismic reflection terminations and stratal architecture indicate that these features are formed by cyclic steps generated by turbidity currents, while internal amplitude anomalies point to the presence of fluid migration. Amplitude Versus Offset analysis (AVO) performed on partial stacks shows that the investigated anomalies do not represent hydrocarbon indicators. Previous studies have suggested that the accumulation of permeable and porous sediments in the troughs of vertically stacked cyclic steps may create vertical pathways for fluid migration, and we propose that this may have facilitated the upward migration of saline pore water due to fluid buoyancy. The results of this study highlight the importance of gravity-driven processes in shaping the morphology of the Ceará Basin slope and show how non-hydrocarbon fluids may interact with vertically stacked cyclic steps
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
- …