ABSTRACT Cyber‐Physical‐Social System (CPSS) which refers to the complex interaction of cyber, physical and social systems, has the important purpose to provide personalized intelligent services. CPSS data, generated from every aspect of people living life, are mainly in the form of time series multimodal data with characteristic of high order and high dimension. How to efficiently process these CPSS data is one of the fundamental ways for the intelligent services. In this paper, a highly‐efficient attention driven Quantized Tensor Train Recursive Neural Network is proposed, in which the CPSS data is decomposed into the form of tensor train cores. In this way, the proposed method is composed of lightweight high‐order neural network units, which better preserves the multi‐attribute features of the original data and the correlation between different dimensions by using tensors with its calculations, and implicitly trims the dense vector‐matrix connections in the fully connected network by using the form of quantization tensor train decomposition, which greatly reduces the model parameters, shortens the training time and improves the efficiency. Also, an effective attentional feature enhancement module is constructed to assist the high‐order neural network, so that the overall model can achieve a balance between low parameter number and accuracy. The network structure proposed in this paper realizes an efficient and lossless high‐order tensor recurrent neural network model with a small number of parameters. Finally, experiments on the UCF50 action video dataset, CWRU bearing dataset, and image generation tasks are conducted. Comparative analyses with vanilla LSTM and other tensorized LSTMs in terms of training time, accuracy, error, and compression ratio validate the reliability of the proposed model.

Open tracheostomy (OT) is considered the traditional way and golden standard for treating airway obstruction patients. However, OT has many unavoidable drawbacks, including strict performing scenarios, significant scarring, and the risk of surgeon infection. Percutaneous dilation tracheostomy (PDT) emerges, with advantages including a lower cost, smaller scarring, and better protection of surgeons from inflecting by aerosol. However, the outside-in puncture manner of PDT has a risk of piercing the post-tracheal wall and the esophagus with uncontrolled force. Additionally, locating tracheal rings and determining the puncture site externally can be challenging for certain patients, such as those who are obese or have undergone neck surgery, while this procedure typically relies on palpation and the surgeon's expertise. Hence, to improve the safety and simplicity of tracheostomy, a minimally-invasive endotracheal inside-out flexible needle-driving system towards microendoscope-guided robotic tracheostomy (MERT) has been proposed in this paper. Guided by an optical coherence tomography (OCT) probe and a microendoscope, the robot inserts into the trachea and performs an inside-out puncture using a flexible needle. The robot can work through a standard endotracheal tube (ETT), and the puncture direction of the flexible needle is variable. Kinematics and statics models of the flexible needle have been derived, and the minimum position errors generated in the kinematics and statics validation experiments are$0.57 \pm 0.21 \mathbf{~ m m}$and$0.27 \pm 0.21 \mathbf{~ m m}$. Finally, a porcine trachea puncture experiment is carried out, and the feasibility of the proposed system is verified.

In situ tissue biopsy with an endoluminal catheter is an efficient approach for disease diagnosis, featuring low invasiveness and few complications. However, the endoluminal catheter struggles to adjust the biopsy direction by distal endoscope bending or proximal twisting for tissue sampling within the tortuous luminal organs, due to friction-induced hysteresis and narrow spaces. Here, we propose a pneumatically-driven robotic catheter enabling the adjustment of the sampling direction without twisting the catheter for an accurate in situ omnidirectional biopsy. The distal end of the robotic catheter consists of a pneumatic bending actuator for the catheter’s deployment in torturous luminal organs and a pneumatic rotatable biopsy mechanism (PRBM). By hierarchical airflow control, the PRBM can adjust the biopsy direction under low airflow and deploy the biopsy needle with higher airflow, allowing for rapid omnidirectional sampling of tissue in situ. This paper describes the design, modeling, and characterization of the proposed robotic catheter, including repeated deployment assessments of the biopsy needle, puncture force measurement, and validation via phantom tests. The PRBM prototype has six sampling directions evenly distributed across 360 degrees when actuated by a positive pressure of 0.3 MPa. The pneumatically-driven robotic catheter provides a novel biopsy strategy, potentially facilitating in situ multidirectional biopsies in tortuous luminal organs with minimum invasiveness.

Knowledge tracing defines the task of predicting future performance of students based on their historical interactions. Recently, some graph-based methods try to capture correspondence between questions and concepts by constructing the question-concept bipartite graph to tackle the knowledge tracing problem. However, they fail to explicitly integrate such intrinsic relations into the final answer predictor due to the sparse data. In this paper, we propose a novel Multi-relation Enhanced Graph-based Model for Knowledge Tracing (MGKT) to tackle the above problem. More specifically, MGKT constructs graph structure to explore multiple relations such as the high-order association among questions and the similarity of question’s attributes. In addition, two self-supervised training strategies, namely hypergraph contrast learning and hypergraph reconstruction, are proposed to incorporate these special correlations into question representations. Extensive experiments demonstrate that MGKT outperforms state-of-the-art knowledge tracing methods on three benchmark datasets.

Super-resolution combined with novel image synthesis is an advanced image processing method to synthesize low-resolution images into new high-resolution images. NeRF-SR is the first model to obtain decent multi-view super-resolution results with only low-resolution input images, but the super-sampling method implemented using the original Nerf’s MLP network cannot represent the complex details of the scene well. We consider that the volume density and color features obtained by the MLP network do not take into account the global geometry along the ray and the color relationship between the sampling points. To tackle this challenge, we introduce an attention-based model and auto-encoding network to synthesize high-fidelity views from low-resolution input to high-resolution output. The attention-based model mixes the pixel color information of the sampling points on each ray and supervises using ground-truth colors. At the same time, the auto-encoding network learns the global geometry along the ray. Experimental results demonstrate that our model can produce high-quality results for high-resolution new view synthesis, both on synthetic and real-world datasets.

Miniature rotation actuators have been extensively developed and utilized in optical coherence tomography (OCT) endoscopy, enabling distortion-free OCT imaging in complex and tortuous environments. However, the use of electrical-driven rotation actuators raises safety concerns. Although magnetic-driven rotation actuators have been reported in OCT endoscopy, their use can potentially interfere with other medical devices in clinical settings. Here, we propose a pneumatic miniature rotation actuator that eliminates the electricity and magnetism concerns in circumferential imaging for OCT endoscopy. The rotor of the actuator is designed as a windmill, enabling it to convert air energy into rotation energy. In addition, to maintain the stable rotation, both a sliding bearing with two supporting points and a glass spindle with a half-ball end surface are developed. The rotation speed of our pneumatic actuator can be controlled from 66 to 97 revolutions per second by adjusting the airflow rate from 3.25 to 4.00 liters per minute. By OCT imaging of the human fingers, we demonstrate the feasibility of the pneumatic actuator in electricity-free distal scanning OCT endoscopy. Our pneumatic rotation actuator has wide-ranging potential in various fiber-imaging modalities, including not only OCT but also ultrasound imaging that requires similar rotation capabilities.

Federated learning enables multiple data providers to collaborate on training models without exposing personal data. During the training process, frequent communication is required between the data provider and the central server, which puts great pressure on federated learning. To reduce the communication pressure of federated learning, we use the CP-decomposition processing model to reduce the size of data that needs to be transmitted during the communication process. In addition, we aggregate the global model based on the Shapley value, and eliminate nodes that are not beneficial to federated learning as soon as possible, which reduces the communication pressure and can stimulate the participating nodes and enhance the enthusiasm of participants in federated learning, thus improving the training results of the global model. We named the system CPSV, which stands for Federated learning of CP-decomposition models based on Shapley value aggregation. Numerous experiments on CPSV have shown that CPSV can motivate and supervise participating nodes to aggregate better global models while reducing the stress of federal learning communication.

Community detection is a well-established problem and nontrivial task in complex network analysis. The goal of community detection is to discover community structures in complex networks. In recent years, many existing works have been proposed to handle this task, particularly nonnegative matrix factorization-based method, e.g. HNMF, BNMF, which is interpretable and can learn latent features of complex data. These methods usually decompose the original matrix into two matrixes, in one matrix, each column corresponds to a representation of community and each column of another matrix indicates the membership between overall pairs of communities and nodes. Then they discover the community by updating the two matrices iteratively and learn the shallow feature of the community. However, these methods either ignore the topological structure characteristics of the community or ignore the microscopic community structure properties. In this paper, we propose a novel model, named Modularized Deep NonNegative Matrix Factorization (MDNMF) for community detection, which preserves both the topology information and the instinct community structure properties of the community. The experimental results show that our proposed models can significantly outperform state-of-the-art approaches on several well-known dataset.

Due to the joint clearance, parallel manipulators always exhibit some position and orientation errors at the mobile platform. This paper aims to provide a systematic framework for the error analysis problem of general parallel mechanisms influenced by the joint clearance. A novel and efficient method is proposed to evaluate the maximal pose errors of general spatial parallel manipulators with joint clearance.