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1. Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation

   https://doi.org/10.1145/3442381.3449844  

   Yu, Junliang; Yin, Hongzhi; Li, Jundong; Wang, Qinyong; Hung, Nguyen Quoc; Zhang, Xiangliang (April 2021, Proceedings of the Web Conference 2021)

   null (Ed.)

   Social relations are often used to improve recommendation quality when user-item interaction data is sparse in recommender systems. Most existing social recommendation models exploit pairwise relations to mine potential user preferences. However, real-life interactions among users are very complex and user relations can be high-order. Hypergraph provides a natural way to model high-order relations, while its potentials for improving social recommendation are under-explored. In this paper, we fill this gap and propose a multi-channel hypergraph convolutional network to enhance social recommendation by leveraging high-order user relations. Technically, each channel in the network encodes a hypergraph that depicts a common high-order user relation pattern via hypergraph convolution. By aggregating the embeddings learned through multiple channels, we obtain comprehensive user representations to generate recommendation results. However, the aggregation operation might also obscure the inherent characteristics of different types of high-order connectivity information. To compensate for the aggregating loss, we innovatively integrate self-supervised learning into the training of the hypergraph convolutional network to regain the connectivity information with hierarchical mutual information maximization. Extensive experiments on multiple real-world datasets demonstrate the superiority of the proposed model over the current SOTA methods, and the ablation study verifies the effectiveness and rationale of the multi-channel setting and the self-supervised task. The implementation of our model is available via https://github.com/Coder-Yu/RecQ. 

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2. GNTD: reconstructing spatial transcriptomes with graph-guided neural tensor decomposition informed by spatial and functional relations

   https://doi.org/10.1038/s41467-023-44017-0  

   Song, Tianci; Broadbent, Charles; Kuang, Rui (December 2023, Nature Communications)

   Abstract Spatially-resolved RNA profiling has now been widely used to understand cells’ structural organizations and functional roles in tissues, yet it is challenging to reconstruct the whole spatial transcriptomes due to various inherent technical limitations in tissue section preparation and RNA capture and fixation in the application of the spatial RNA profiling technologies. Here, we introduce a graph-guided neural tensor decomposition (GNTD) model for reconstructing whole spatial transcriptomes in tissues. GNTD employs a hierarchical tensor structure and formulation to explicitly model the high-order spatial gene expression data with a hierarchical nonlinear decomposition in a three-layer neural network, enhanced by spatial relations among the capture spots and gene functional relations for accurate reconstruction from highly sparse spatial profiling data. Extensive experiments on 22 Visium spatial transcriptomics datasets and 3 high-resolution Stereo-seq datasets as well as simulation data demonstrate that GNTD consistently improves the imputation accuracy in cross-validations driven by nonlinear tensor decomposition and incorporation of spatial and functional information, and confirm that the imputed spatial transcriptomes provide a more complete gene expression landscape for downstream analyses of cell/spot clustering for tissue segmentation, and spatial gene expression clustering and visualizations. 

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3. A Physics-based and Control-oriented Model for Dielectric Elastomer Tubular Actuator

   https://doi.org/10.23919/ACC50511.2021.9483321  

   Kaaya, Theophilus; Wang, Shengbin; Cescon, Marzia; Chen, Zheng (May 2021, 2021 American Control Conference)

   Dielectric elastomers (DEs) are electro-active polymers that deform and change their shape when an electric field is applied across them. They are used as soft actuators since they are flexible, resilient, lightweight, and durable. Many models have been proposed to describe and capture the behavior of these actuators such as circuit representation, lumped parameter modeling, and physics-based modeling. In this paper, a hybrid between the physics and lumped parameter model is presented which is used to control the actuator. The focus of this paper is on a tubular dielectric elastomer actuator (DEA). The model proposed is validated with experimental data to evaluate its approximation to the physical actuator. The physics model offers the ability to describe how the material properties and actuator's geometry affect the dynamics and behavior of the actuator under different states. The lumped parameter model accounts for physical quantities that may not be fully expressed when formulating the physics-based equations. The discussed model performance is found to have an error less than 10% for the sinusoidal signals discussed. 

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4. Collision Avoidance in Tightly-Constrained Environments without Coordination: a Hierarchical Control Approach

   https://doi.org/10.1109/ICRA48506.2021.9561417  

   Shen, Xu; Zhu, Edward L.; Sturz, Yvonne R.; Borrelli, Francesco (May 2021, 2021 IEEE International Conference on Robotics and Automation (ICRA))

   We present a hierarchical control approach for maneuvering an autonomous vehicle (AV) in tightly-constrained environments where other moving AVs and/or human driven vehicles are present. A two-level hierarchy is proposed: a high-level data-driven strategy predictor and a lower-level model-based feedback controller. The strategy predictor maps an encoding of a dynamic environment to a set of high-level strategies via a neural network. Depending on the selected strategy, a set of time-varying hyperplanes in the AV’s position space is generated online and the corresponding halfspace constraints are included in a lower-level model-based receding horizon controller. These strategy-dependent constraints drive the vehicle towards areas where it is likely to remain feasible. Moreover, the predicted strategy also informs switching between a discrete set of policies, which allows for more conservative behavior when prediction confidence is low. We demonstrate the effectiveness of the proposed data-driven hierarchical control framework in a two-car collision avoidance scenario through simulations and experiments on a 1/10 scale autonomous car platform where the strategy-guided approach outperforms a model predictive control baseline in both cases. 

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5. Transformation Elastography: Converting Anisotropy to Isotropy

   Guidetti, Martina; Klatt, Dieter; Royston, Thomas J. (April 2020, IEEE International Symposium on Biomedical Imaging (ISBI) 2020)

   Elastography refers to mapping mechanical properties in a material based on measuring wave motion in it using noninvasive optical, acoustic or magnetic resonance imaging methods. For example, increased stiffness will increase wavelength. Stiffness and viscosity can depend on both location and direction. A material with aligned fibers or layers may have different stiffness and viscosity values along the fibers or layers versus across them. Converting wave measurements into a mechanical property map or image is known as reconstruction. To make the reconstruction problem analytically tractable, isotropy and homogeneity are often assumed, and the effects of finite boundaries are ignored. But, infinite isotropic homogeneity is not the situation in most cases of interest, when there are pathological conditions, material faults or hidden anomalies that are not uniformly distributed in fibrous or layered structures of finite dimension. Introduction of anisotropy, inhomogeneity and finite boundaries complicates the analysis forcing the abandonment of analytically-driven strategies, in favor of numerical approximations that may be computationally expensive and yield less physical insight. A new strategy, Transformation Elastography (TE), is proposed that involves spatial distortion in order to make an anisotropic problem become isotropic. The fundamental underpinnings of TE have been proven in forward simulation problems. In the present paper a TE approach to inversion and reconstruction is introduced and validated based on numerical finite element simulations. 

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