1,175 research outputs found
Preparation of TiO2 nanotube/nanoparticle composite particles and their applications in dye-sensitized solar cells
Efficiency of dye-sensitized solar cells [DSSCs] was enhanced by combining the use of TiO2 nanotubes [TNTs] and nanoparticles. TNTs were fabricated by a sol-gel method, and TiO2 powders were produced through an alkali hydrothermal transformation. DSSCs were constructed using TNTs and TiO2 nanoparticles at various weight percentages. TNTs and TiO2 nanoparticles were coated onto FTO glass by the screen printing method. The DSSCs were fabricated using ruthenium(II) (N-719) and electrolyte (I3/I3-) dyes. The crystalline structure and morphology were characterized by X-ray diffraction and using a scanning electron microscope. The absorption spectra were measured using an UV-Vis spectrometer. The incident photocurrent conversion efficiency was measured using a solar simulator (100 mW/cm2). The DSSCs based on TNT/TiO2 nanoparticle hybrids showed better photovoltaic performance than cells made purely of TiO2 nanoparticles
Unsupervised Hyperbolic Representation Learning via Message Passing Auto-Encoders
Most of the existing literature regarding hyperbolic embedding concentrate
upon supervised learning, whereas the use of unsupervised hyperbolic embedding
is less well explored. In this paper, we analyze how unsupervised tasks can
benefit from learned representations in hyperbolic space. To explore how well
the hierarchical structure of unlabeled data can be represented in hyperbolic
spaces, we design a novel hyperbolic message passing auto-encoder whose overall
auto-encoding is performed in hyperbolic space. The proposed model conducts
auto-encoding the networks via fully utilizing hyperbolic geometry in message
passing. Through extensive quantitative and qualitative analyses, we validate
the properties and benefits of the unsupervised hyperbolic representations.
Codes are available at https://github.com/junhocho/HGCAE
TCT-301 Lesion-specific myocardial mass: A new index for diagnosis and treatment of coronary artery disease
Abducens nerve palsy as a postoperative complication of minimally invasive thoracic spine surgery: a case report
Evaluation of Bond Properties of Reinforced Concrete with Corroded Reinforcement by Uniaxial Tension Testing
Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning
We propose a symmetric graph convolutional autoencoder which produces a
low-dimensional latent representation from a graph. In contrast to the existing
graph autoencoders with asymmetric decoder parts, the proposed autoencoder has
a newly designed decoder which builds a completely symmetric autoencoder form.
For the reconstruction of node features, the decoder is designed based on
Laplacian sharpening as the counterpart of Laplacian smoothing of the encoder,
which allows utilizing the graph structure in the whole processes of the
proposed autoencoder architecture. In order to prevent the numerical
instability of the network caused by the Laplacian sharpening introduction, we
further propose a new numerically stable form of the Laplacian sharpening by
incorporating the signed graphs. In addition, a new cost function which finds a
latent representation and a latent affinity matrix simultaneously is devised to
boost the performance of image clustering tasks. The experimental results on
clustering, link prediction and visualization tasks strongly support that the
proposed model is stable and outperforms various state-of-the-art algorithms.Comment: 10 pages, 3 figures, ICCV 2019 accepte
Class-Attentive Diffusion Network for Semi-Supervised Classification
Recently, graph neural networks for semi-supervised classification have been
widely studied. However, existing methods only use the information of limited
neighbors and do not deal with the inter-class connections in graphs. In this
paper, we propose Adaptive aggregation with Class-Attentive Diffusion (AdaCAD),
a new aggregation scheme that adaptively aggregates nodes probably of the same
class among K-hop neighbors. To this end, we first propose a novel stochastic
process, called Class-Attentive Diffusion (CAD), that strengthens attention to
intra-class nodes and attenuates attention to inter-class nodes. In contrast to
the existing diffusion methods with a transition matrix determined solely by
the graph structure, CAD considers both the node features and the graph
structure with the design of our class-attentive transition matrix that
utilizes a classifier. Then, we further propose an adaptive update scheme that
leverages different reflection ratios of the diffusion result for each node
depending on the local class-context. As the main advantage, AdaCAD alleviates
the problem of undesired mixing of inter-class features caused by discrepancies
between node labels and the graph topology. Built on AdaCAD, we construct a
simple model called Class-Attentive Diffusion Network (CAD-Net). Extensive
experiments on seven benchmark datasets consistently demonstrate the efficacy
of the proposed method and our CAD-Net significantly outperforms the
state-of-the-art methods. Code is available at
https://github.com/ljin0429/CAD-Net.Comment: Accepted to AAAI 202
VaB-AL: Incorporating Class Imbalance and Difficulty with Variational Bayes for Active Learning
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