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Deep generative architectures provide a way to model not only images but also complex, 3-dimensional objects, such as point clouds. In this work, we present a novel method to obtain meaningful representations of 3D shapes that can be used for challenging tasks including 3D points generation, reconstruction, compression, and clustering. Contrary to existing methods for 3D point cloud generation that train separate decoupled models for representation learning and generation, our approach is the first endto-end solution that allows to simultaneously learn a latent space of representation and generate 3D shape out of it. Moreover, our model is capable of learning meaningful compact binary descriptors with adversarial training conducted on a latent space. To achieve this goal, we extend a deep Adversarial Autoencoder model (AAE) to accept 3D input and create 3D output. Thanks to our end-to-end training regime, the resulting method called 3D Adversarial Autoencoder (3dAAE) obtains either binary or continuous latent space that covers a much wider portion of training data distribution. Finally, our quantitative evaluation shows that 3dAAE provides state-of-the-art results for 3D points clustering and 3D object retrieval.

Supplementary data are available at Bioinformatics online.

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Metallic nanoparticles with different physical properties have been screen printed as authentication tags on different types of paper. Gold and silver nanoparticles show unique optical signatures, including sharp emission bandwidths and long lifetimes of the printed label, even under accelerated weathering conditions. Magnetic nanoparticles show distinct physical signals that depend on the size of the nanoparticle itself. They were also screen printed on different substrates and their magnetic signals read out using a magnetic pattern recognition sensor and a vibrating sample magnetometer. The novelty of our work lies in the demonstration that the combination of nanomaterials with optical and magnetic properties on the same printed support is possible, and the resulting combined signals can be used to obtain a user-configurable label, providing a high degree of security in anti-counterfeiting applications using simple commercially-available sensors.

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In this paper, we propose a service-oriented support decision system (SOSDS) for diagnostic problems that is insensitive to the problems of the imbalanced data and missing values of the attributes, which are widely observed in the medical domain. The system is composed of distributed Web services, which implement machine-learning solutions dedicated to constructing the decision models directly from the datasets impaired by the high percentage of missing values of the attributes and imbalanced class distribution. The issue of the imbalanced data is solved by the application of a cost-sensitive support vector machine and the problem of missing values of attributes is handled by proposing the novel ensemble-based approach that splits the incomplete data space into complete subspaces that are further used to construct base learners. We evaluate the quality of the SOSDS components using three ontological datasets.