Rejecting Unknown Gestures Based on Surface-Electromyography Using Variational Autoencoder.

The conventional surface electromyography (sEMG)-based gesture recognition systems exhibit impressive performance in controlled laboratory settings. As most systems are trained in a closed-set setting, the systems's performance may see significant deterioration when novel gestures are presented as imposter. In addition, the state-of-the-art generative and discriminative methods have achieved considerable performance on high-density sEMG signals.

Improved Network and Training Scheme for Cross-Trial Surface Electromyography (sEMG)-Based Gesture Recognition.

To enhance the performance of surface electromyography (sEMG)-based gesture recognition, we propose a novel network-agnostic two-stage training scheme, called , that produces trial-invariant representations to be aligned with corresponding hand movements via cross-modal knowledge distillation. In the first stage, an sEMG encoder is trained via cross-trial mutual information maximization using the sEMG sequences sampled from the same time step but different trials in a contrastive learning manner. In the second stage, the learned sEMG encoder is fine-tuned with the supervision of gesture and hand movements in a knowledge-distillation manner.

The complete mitochondrial genome of (Isoptera: Rhinotermitidae).

The mitochondrial genome of the is 15,913 bp in length and encodes 37 genes including 13 protein-coding genes (PCGs), 22 transfer RNA genes (tRNA), 2 ribosomal RNA genes (rRNA), and a non-coding control region (D-loop). The percentage of A/T (65.59%) is much higher than that of C/G (34.

Surface-Electromyography-Based Gesture Recognition by Multi-View Deep Learning.

Gesture recognition using sparse multichannel surface electromyography (sEMG) is a challenging problem, and the solutions are far from optimal from the point of view of muscle-computer interface. In this paper, we address this problem from the context of multi-view deep learning. A novel multi-view convolutional neural network (CNN) framework is proposed by combining classical sEMG feature sets with a CNN-based deep learning model.

MALAT1 Promotes the Proliferation and Metastasis of Osteosarcoma Cells By Activating the Rac1/JNK Pathway Via Targeting MiR-509.

Metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) is a long noncoding RNA (lncRNA) that contributes to the initiation and development of many solid tumors, including osteosarcoma (OS). In this study, we showed that MALAT1 was increased in human OS tissues and cell lines. MALAT1 knockdown suppressed OS cell growth and metastasis, induced OS cell apoptosis and delayed tumor growth in an OS xenograft model.