MCMT-GAN: Multi-Task Coherent Modality Transferable GAN for 3D Brain Image Synthesis

© 1992-2012 IEEE. The ability to synthesize multi-modality data is highly desirable for many computer-aided medical applications, e.g. clinical diagnosis and neuroscience research, since rich imaging cohorts offer diverse and complementary information unraveling human tissues. However, collecting acquisitions can be limited by adversary factors such as patient discomfort, expensive cost and scanner unavailability. In this paper, we propose a multi-task coherent modality transferable GAN (MCMT-GAN) to address this issue for brain MRI synthesis in an unsupervised manner. Through combining the bidirectional adversarial loss, cycle-consistency loss, domain adapted loss and manifold regularization in a volumetric space, MCMT-GAN is robust for multi-modality brain image synthesis with visually high fidelity. In addition, we complement discriminators collaboratively working with segmentors which ensure the usefulness of our results to segmentation task. Experiments evaluated on various cross-modality synthesis show that our method produces visually impressive results with substitutability for clinical post-processing and also exceeds the state-of-the-art methods

Dynamic Graph Representation Learning via Graph Transformer Networks

Dynamic graph representation learning is an important task with widespread applications. Previous methods on dynamic graph learning are usually sensitive to noisy graph information such as missing or spurious connections, which can yield degenerated performance and generalization. To overcome this challenge, we propose a Transformer-based dynamic graph learning method named Dynamic Graph Transformer (DGT) with spatial-temporal encoding to effectively learn graph topology and capture implicit links. To improve the generalization ability, we introduce two complementary self-supervised pre-training tasks and show that jointly optimizing the two pre-training tasks results in a smaller Bayesian error rate via an information-theoretic analysis. We also propose a temporal-union graph structure and a target-context node sampling strategy for efficient and scalable training. Extensive experiments on real-world datasets illustrate that DGT presents superior performance compared with several state-of-the-art baselines

Control for Ship Course-Keeping Using Optimized Support Vector Machines

Support vector machines (SVM) are proposed in order to obtain a robust controller for ship course-keeping. A cascaded system is constructed by combining the dynamics of the rudder actuator with the dynamics of ship motion. Modeling errors and disturbances are taken into account in the plant. A controller with a simple structure is produced by applying an SVM and L2-gain design. The SVM is used to identify the complicated nonlinear functions and the modeling errors in the plant. The Lagrangian factors in the SVM are obtained using on-line tuning algorithms. L2-gain design is applied to suppress the disturbances. To obtain the optimal parameters in the SVM, then particle swarm optimization (PSO) method is incorporated. The stability and robustness of the close-loop system are confirmed by Lyapunov stability analysis. Numerical simulation is performed to demonstrate the validity of the proposed hybrid controller and its superior performance over a conventional PD controller

Metal-doped polymer-derived SiOC composites with inorganic metal salt as the metal source by digital light processing 3D printing

Metal-doped polymer derived ceramics (PDCs) exhibits multifunctional properties due to the introduction of special metal element; however as one of the cheapest metal element carriers, metal inorganic salts have rarely been reported for the fabrication of metal-doped PDCs. In this article, a novel hydrophilic photosensitive γ-methacryloxypropyl trimethoxy modified polyhydroxymethylsiloxane preceramic polymer was firstly synthesised and three different metal-doped SiOC ceramic lattices with a strut thickness of 200 μm were successfully fabricated using inorganic metal salts as the metal source by digital light processing. The photosensitive coefficient, initial photocuring coefficient and the corresponding working curves were employed to evaluate the photocurable ability of preceramic polymers with different recipes. Results showed that both the metal-containing polymer lattices and the metal-doped SiOC ceramic lattices exhibited high-resolution microstructure and homogeneous metal element distribution, indicating that the inorganic metal salt is an effective metal source for the fabrication of metal-doped PDCs