Skeleton-Based Gesture Recognition With Learnable Paths and Signature Features

For the skeleton-based gesture recognition, graph convolutional networks (GCNs) have achieved remarkable performance since the human skeleton is a natural graph. However, the biological structure might not be the crucial one for motion analysis. Also, spatial differential information like joint distance and angle between bones may be overlooked during the graph convolution. In this paper, we focus on obtaining meaningful joint groups and extracting their discriminative features by the path signature (PS) theory. Firstly, to characterize the constraints and dependencies of various joints, we propose three types of paths, i.e., spatial, temporal, and learnable path. Especially, a learnable path generation mechanism can group joints together that are not directly connected or far away, according to their kinematic characteristic. Secondly, to obtain informative and compact features, a deep integration of PS with few parameters are introduced. All the computational process is packed into two modules, i.e., spatial-temporal path signature module (ST-PSM) and learnable path signature module (L-PSM) for the convenience of utilization. They are plug-and-play modules available for any neural network like CNNs and GCNs to enhance the feature extraction ability. Extensive experiments have conducted on three mainstream datasets (ChaLearn 2013, ChaLearn 2016, and AUTSL). We achieved the state-of-the-art results with simpler framework and much smaller model size. By inserting our two modules into the several GCN-based networks, we can observe clear improvements demonstrating the great effectiveness of our proposed method

DFormer: Diffusion-guided Transformer for Universal Image Segmentation

This paper introduces an approach, named DFormer, for universal image segmentation. The proposed DFormer views universal image segmentation task as a denoising process using a diffusion model. DFormer first adds various levels of Gaussian noise to ground-truth masks, and then learns a model to predict denoising masks from corrupted masks. Specifically, we take deep pixel-level features along with the noisy masks as inputs to generate mask features and attention masks, employing diffusion-based decoder to perform mask prediction gradually. At inference, our DFormer directly predicts the masks and corresponding categories from a set of randomly-generated masks. Extensive experiments reveal the merits of our proposed contributions on different image segmentation tasks: panoptic segmentation, instance segmentation, and semantic segmentation. Our DFormer outperforms the recent diffusion-based panoptic segmentation method Pix2Seq-D with a gain of 3.6% on MS COCO val2017 set. Further, DFormer achieves promising semantic segmentation performance outperforming the recent diffusion-based method by 2.2% on ADE20K val set. Our source code and models will be publicly on https://github.com/cp3wan/DForme

A Novel Fault Detection and Fault Location Method for VSC-HVDC Links Based on Gap Frequency Spectrum Analysis

This paper proposes a one-end gap-based fault location method for VSC-HVDC transmission line using the fault current signal. Using the post-fault current time series, the frequency spectrum is generated for measuring the gaps between the contiguous peak frequencies. This method is able to locate the fault by analyzing the single-end fault current, which guarantees a faster response than using two-end data. The new gap-based approach is able to give accurate fault detection using any appropriate range of post-fault signal. Furthermore, the proposed method is fault resistance independent. A two-terminal VSC-HVDC system is modeled in PSCAD/EMTDC. The algorithm is verified by studying different cases of different fault resistances in various fault locations. The result shows that the proposed method gives an accurate and reliable fault location detection along DC transmission line. In addition, the proposed algorithm can be potentially used in other HVDC systems

An Optimized Channel Selection Method Based on Multifrequency CSP-Rank for Motor Imagery-Based BCI System

Background. Due to the redundant information contained in multichannel electroencephalogram (EEG) signals, the classification accuracy of brain-computer interface (BCI) systems may deteriorate to a large extent. Channel selection methods can help to remove task-independent electroencephalogram (EEG) signals and hence improve the performance of BCI systems. However, in different frequency bands, brain areas associated with motor imagery are not exactly the same, which will result in the inability of traditional channel selection methods to extract effective EEG features. New Method. To address the above problem, this paper proposes a novel method based on common spatial pattern- (CSP-) rank channel selection for multifrequency band EEG (CSP-R-MF). It combines the multiband signal decomposition filtering and the CSP-rank channel selection methods to select significant channels, and then linear discriminant analysis (LDA) was used to calculate the classification accuracy. Results. The results showed that our proposed CSP-R-MF method could significantly improve the average classification accuracy compared with the CSP-rank channel selection method

Bioelectricity generation from the decolorization of reactive blue 19 by using microbial fuel cell.

Microbial fuel cell (MFC) was compared to conventional biological techniques for decolorization of anthraquinone dye, reactive blue 19 (RB19) with simultaneous electricity generation. With 50 mg/L of RB19 in the anode chamber as a fuel, the MFC achieved 89% decolorization efficiency of RB19 within 48 h, compared with 51 and 55% decolorization efficiency achieved by aerobic and anaerobic techniques, respectively. The cyclic voltammetry results showed that RB19 could promote the electron transfer and redox reaction on the surface of anode. The RB19 decolorization process can be described by first-order kinetics, and the decolorization rate decreased with the increase of RB19 concentration. The high-throughput 16S rRNA sequencing analysis indicated significant microbial community shift in the MFC. At phylum level, the majority of sequences belong to Proteobacteria, accounting from 23 to 84% of the total reads in each bacterium community. At genus level, the MFC contained two types of microorganisms in general such as electrochemically active and decolorization bacteria. Overall, MFC is an effective method for anthraquinone dye treatment with simultaneous energy recovery. The 16S rRNA revealed that there were two major functioning microbial communities in the MFC such as electricity-producing and RB19-degrading bacteria which synergistically worked on RB19 degradation

Self-Organized Hydrodynamics with congestion and path formation in crowds

A continuum model for self-organized dynamics is numerically investigated. The model describes systems of particles subject to alignment interaction and short-range repulsion. It consists of a non-conservative hyperbolic system for the density and velocity orientation. Short-range repulsion is included through a singular pressure which becomes infinite at the jamming density. The singular limit of infinite pressure stiffness leads to phase transitions from compressible to incompressible dynamics. The paper proposes an Asymptotic-Preserving scheme which takes care of the singular pressure while preventing the breakdown of the CFL stability condition near congestion. It relies on a relaxation approximation of the system and an elliptic formulation of the pressure equation. Numerical simulations of impinging clusters show the efficiency of the scheme to treat congestions. A two-fluid variant of the model provides a model of path formation in crowds

Quantum Neuronal Sensing of Quantum Many-Body States on a 61-Qubit Programmable Superconducting Processor

Classifying many-body quantum states with distinct properties and phases of matter is one of the most fundamental tasks in quantum many-body physics. However, due to the exponential complexity that emerges from the enormous numbers of interacting particles, classifying large-scale quantum states has been extremely challenging for classical approaches. Here, we propose a new approach called quantum neuronal sensing. Utilizing a 61 qubit superconducting quantum processor, we show that our scheme can efficiently classify two different types of many-body phenomena: namely the ergodic and localized phases of matter. Our quantum neuronal sensing process allows us to extract the necessary information coming from the statistical characteristics of the eigenspectrum to distinguish these phases of matter by measuring only one qubit. Our work demonstrates the feasibility and scalability of quantum neuronal sensing for near-term quantum processors and opens new avenues for exploring quantum many-body phenomena in larger-scale systems.Comment: 7 pages, 3 figures in the main text, and 13 pages, 13 figures, and 1 table in supplementary material