BIDL: a brain-inspired deep learning framework for spatiotemporal processing.

Brain-inspired deep spiking neural network (DSNN) which emulates the function of the biological brain provides an effective approach for event-stream spatiotemporal perception (STP), especially for dynamic vision sensor (DVS) signals. However, there is a lack of generalized learning frameworks that can handle various spatiotemporal modalities beyond event-stream, such as video clips and 3D imaging data. To provide a unified design flow for generalized spatiotemporal processing (STP) and to investigate the capability of lightweight STP processing via brain-inspired neural dynamics, this study introduces a training platform called brain-inspired deep learning (BIDL).

Fault diagnosis of rotor based on Semi-supervised Multi-Graph Joint Embedding.

Traditional graph embedding methods only consider the pairwise relationship between fault data. But in practical applications, the relationship of high-dimensional fault data usually is multiple classes corresponding to multiple samples. Therefore, the hypergraph structure is introduced to fully portray the complex structural relationship of high-dimensional fault data.

Modeling learnable electrical synapse for high precision spatio-temporal recognition.

Bio-inspired recipes are being introduced to artificial neural networks for the efficient processing of spatio-temporal tasks. Among them, Leaky Integrate and Fire (LIF) model is the most remarkable one thanks to its temporal processing capability, lightweight model structure, and well investigated direct training methods. However, most learnable LIF networks generally take neurons as independent individuals that communicate via chemical synapses, leaving electrical synapses all behind.

Unsupervised domain adaptation of bearing fault diagnosis based on Join Sliced Wasserstein Distance.

Deep neural networks have been successfully utilized in the mechanical fault diagnosis, however, a large number of them have been based on the same assumption that training and test datasets followed the same distributions. Unfortunately, the mechanical systems are easily affected by environment noise interference, speed or load change. Consequently, the trained networks have poor generalization under various working conditions.

Learnable Heterogeneous Convolution: Learning both topology and strength.

Existing convolution techniques in artificial neural networks suffer from huge computation complexity, while the biological neural network works in a much more powerful yet efficient way. Inspired by the biological plasticity of dendritic topology and synaptic strength, our method, Learnable Heterogeneous Convolution, realizes joint learning of kernel shape and weights, which unifies existing handcrafted convolution techniques in a data-driven way. A model based on our method can converge with structural sparse weights and then be accelerated by devices of high parallelism.

The role of hippocampal niche exosomes in rat hippocampal neurogenesis after fimbria-fornix transection.

Exosomes transfer signaling molecules such as proteins, lipids, and RNAs to facilitate cell-cell communication and play an important role in the stem cell microenvironment. In previous work, we demonstrated that rat fimbria-fornix transection (FFT) enhances neurogenesis from neural stem cells (NSCs) in the subgranular zone (SGZ). However, how neurogenesis is modulated after denervation remains unknown.

A New Fault Feature Extraction Method for Rotating Machinery Based on Multiple Sensors.

During the operation of rotating machinery, the vibration signals measured by sensors are the aliasing signals of various vibration sources, and they contain strong noises. Conventional signal processing methods have difficulty separating the aliasing signals, which causes great difficulties in the condition monitoring and fault diagnosis of the equipment. The principle and method of blind source separation are introduced, and it is pointed out that the blind source separation algorithm is invalid in strong pulse noise environments.

Drug-eluting stents vs. intracoronary brachytherapy for in-stent restenosis: a meta-analysis.

Background: It has been reported that drug-eluting stents (DES) were superior to intracoronary brachytherapy (ICBT) in patients with in-stent restenosis (ISR). However, it is unknown whether there might be differences between DES and ICBT in terms of efficacy and safety in large sample size and long-term follow-up.

Hypothesis: The aim of this study was to determine whether DES implantation remains favorable in large sample size and long-term follow-up when compared with ICBT among patients with ISR.