From Unimodal to Multimodal: improving the sEMG-Based Pattern Recognition via deep generative models

Multimodal hand gesture recognition (HGR) systems can achieve higher recognition accuracy. However, acquiring multimodal gesture recognition data typically requires users to wear additional sensors, thereby increasing hardware costs. This paper proposes a novel generative approach to improve Surface Electromyography (sEMG)-based HGR accuracy via virtual Inertial Measurement Unit (IMU) signals. Specifically, we trained a deep generative model based on the intrinsic correlation between forearm sEMG signals and forearm IMU signals to generate virtual forearm IMU signals from the input forearm sEMG signals at first. Subsequently, the sEMG signals and virtual IMU signals were fed into a multimodal Convolutional Neural Network (CNN) model for gesture recognition. To evaluate the performance of the proposed approach, we conducted experiments on 6 databases, including 5 publicly available databases and our collected database comprising 28 subjects performing 38 gestures, containing both sEMG and IMU data. The results show that our proposed approach outperforms the sEMG-based unimodal HGR method (with increases of 2.15%-13.10%). It demonstrates that incorporating virtual IMU signals, generated by deep generative models, can significantly enhance the accuracy of sEMG-based HGR. The proposed approach represents a successful attempt to transition from unimodal HGR to multimodal HGR without additional sensor hardware

Multi-Label Continual Learning using Augmented Graph Convolutional Network

Multi-Label Continual Learning (MLCL) builds a class-incremental framework in a sequential multi-label image recognition data stream. The critical challenges of MLCL are the construction of label relationships on past-missing and future-missing partial labels of training data and the catastrophic forgetting on old classes, resulting in poor generalization. To solve the problems, the study proposes an Augmented Graph Convolutional Network (AGCN++) that can construct the cross-task label relationships in MLCL and sustain catastrophic forgetting. First, we build an Augmented Correlation Matrix (ACM) across all seen classes, where the intra-task relationships derive from the hard label statistics. In contrast, the inter-task relationships leverage hard and soft labels from data and a constructed expert network. Then, we propose a novel partial label encoder (PLE) for MLCL, which can extract dynamic class representation for each partial label image as graph nodes and help generate soft labels to create a more convincing ACM and suppress forgetting. Last, to suppress the forgetting of label dependencies across old tasks, we propose a relationship-preserving constrainter to construct label relationships. The inter-class topology can be augmented automatically, which also yields effective class representations. The proposed method is evaluated using two multi-label image benchmarks. The experimental results show that the proposed way is effective for MLCL image recognition and can build convincing correlations across tasks even if the labels of previous tasks are missing

VoxelFormer: Bird's-Eye-View Feature Generation based on Dual-view Attention for Multi-view 3D Object Detection

In recent years, transformer-based detectors have demonstrated remarkable performance in 2D visual perception tasks. However, their performance in multi-view 3D object detection remains inferior to the state-of-the-art (SOTA) of convolutional neural network based detectors. In this work, we investigate this issue from the perspective of bird's-eye-view (BEV) feature generation. Specifically, we examine the BEV feature generation method employed by the transformer-based SOTA, BEVFormer, and identify its two limitations: (i) it only generates attention weights from BEV, which precludes the use of lidar points for supervision, and (ii) it aggregates camera view features to the BEV through deformable sampling, which only selects a small subset of features and fails to exploit all information. To overcome these limitations, we propose a novel BEV feature generation method, dual-view attention, which generates attention weights from both the BEV and camera view. This method encodes all camera features into the BEV feature. By combining dual-view attention with the BEVFormer architecture, we build a new detector named VoxelFormer. Extensive experiments are conducted on the nuScenes benchmark to verify the superiority of dual-view attention and VoxelForer. We observe that even only adopting 3 encoders and 1 historical frame during training, VoxelFormer still outperforms BEVFormer significantly. When trained in the same setting, VoxelFormer can surpass BEVFormer by 4.9% NDS point. Code is available at: https://github.com/Lizhuoling/VoxelFormer-public.git

Global Burden of Aortic Aneurysm and Attributable Risk Factors from 1990 to 2017

Background: To date, our understanding of the global aortic aneurysm (AA) burden distribution is very limited. Objective: To assess a full view of global AA burden distribution and attributable risk factors from 1990 to 2017. Methods: We extracted data of AA deaths, disability-adjusted life years (DALYs), and their corresponding age-standardized rates (ASRs), in general and by age/sex from the 2017 Global Burden of Disease (GBD) study. The current AA burden distribution in 2017 and its changing trend from 1990 to 2017 were separately showed. The spatial divergence was discussed from four levels: global, five social-demographic index regions, 21 GBD regions, and 195 countries and territories. We also estimated the risk factors attributable to AA related deaths. Results: Globally, the AA deaths were 167,249 with an age-standardized death rate (ASDR) of 2.19/100,000 persons in 2017, among which the elderly and the males accounted for the majority. Although reductions in ASRs were observed in developed areas, AA remained an important health issue in those relatively underdeveloped areas and might be much more important in the near future. AA may increasingly affect the elderly and the female population. Similar patterns of AA DALYs burden were noted during the study period. AA burden attributable to high blood pressure and smoking decreased globally and there were many heterogeneities in their distribution. Discussion: AA maintained an incremental public health issue worldwide. The change pattern of AA burden was heterogeneous across locations, ages, and sexes and it is paramount to improve resource allocation for more effective and targeted prevention strategies. Also, prevention of tobacco consumption and blood pressure control should be emphasized

Prediction of high-Tc superconductivity in ternary lanthanum borohydrides

The study of superconductivity in compressed hydrides is of great interest due to measurements of high critical temperatures (Tc) in the vicinity of room temperature, beginning with the observations of LaH10 at 170-190 GPa. However, the pressures required for synthesis of these high Tc superconducting hydrides currently remain extremely high. Here we show the investigation of crystal structures and superconductivity in the La-B-H system under pressure with particle-swarm intelligence structure searches methods in combination with first-principles calculations. Structures with six stoichiometries, LaBH, LaBH3, LaBH4, LaBH6, LaBH7 and LaBH8, were predicted to become stable under pressure. Remarkably, the hydrogen atoms in LaBH8 were found to bond with B atoms in a manner that is similar to that in H3S. Lattice dynamics calculations indicate that LaBH7 and LaBH8 become dynamically stable at pressures as low as 109.2 and 48.3 GPa, respectively. Moreover, the two phases were predicted to be superconducting with a critical temperature (Tc) of 93 K and 156 K at 110 GPa and 55 GPa, respectively. Our results provide guidance for future experiments targeting new hydride superconductors with both low synthesis pressures and high Tc.Comment: 16 pages, 5 figures

Uric acid predicts recovery of left ventricular function and adverse events in heart failure with reduced ejection fraction: Potential mechanistic insight from network analyses

Background and Aims: Heart failure with reduced ejection fraction (HFrEF) still carries a high risk for a sustained decrease in left ventricular ejection fraction (LVEF) even with the optimal medical therapy. Currently, there is no effective tool to stratify these patients according to their recovery potential. We tested the hypothesis that uric acid (UA) could predict recovery of LVEF and prognosis of HFrEF patients and attempted to explore mechanistic relationship between hyperuricemia and HFrEF. Methods: HFrEF patients with hyperuricemia were selected from the National Inpatient Sample (NIS) 2016-2018 database and our Xianyang prospective cohort study. Demographics, cardiac risk factors, and cardiovascular events were identified. Network-based analysis was utilized to examine the relationship between recovery of LVEF and hyperuricemia, and we further elucidated the underlying mechanisms for the impact of hyperuricemia on HFrEF. Results: After adjusting confounding factors by propensity score matching, hyperuricemia was a determinant of HFrEF [OR 1.247 (1.172-1.328); Conclusion: Lower baseline UA value predicted the LVEF recovery and less long-term adverse events in HFrEF patients. Our results provide new insights into underlying mechanistic relationship between hyperuricemia and HFrEF

Education in inpatient children and young people’s mental health services

<p>As a chronic disease, osteoarthritis (OA) leads to the degradation of both cartilage and subchondral bone, its development being mediated by proinflammatory cytokines like interleukin-1β. In the present study, the anti-inflammatory effect of specnuezhenide (SPN) in OA and its underlying mechanism were studied in vitro and in vivo. The results showed that SPN decreases the expression of cartilage matrix-degrading enzymes and the activation of NF-κB and wnt/β-catenin signaling, and increases chondrocyte-specific gene expression in IL-1β-induced inflammation in chondrocytes. Furthermore, SPN treatment prevents the degeneration of both cartilage and subchondral bone in a rat model of OA. To the best of our knowledge, this study is the first to report that SPN decreases interleukin-1β-induced inflammation in rat chondrocytes by inhibiting the activation of the NF-κB and wnt/β-catenin pathways, and, thus, has therapeutic potential in the treatment of OA.</p

Phase transitions of alkaline-earth metal sulfides under pressure

We have systematically explored the crystal structures of alkaline-earth metal sulfides under pressure by using a swarm-intelligence structural prediction method. At low pressures we successfully reproduced their known structures and phase transition sequences. Under high pressure, MgS is predicted to transform from B28 to a β-NbP-type structure at 262 GPa. CaS and SrS present the same phase transition sequence, from B2 to a β-NbP-type structure, while BaS is predicted to transform to a Imma structure. The Imma structure is actually similar to the β-NbP-type structure, as it can be seen as a modulated distortion of the latter structure. The absence of any imaginary phonon mode for the predicted structures suggests that they are dynamically stable. The calculated electronic band structures and density of states reveal that all the predicted phases are metallic, except that MgS is a semiconductor. Subsequent electron-phonon coupling calculations suggest that Imma BaS is a superconductor with a low Tc of 1.32 K, while β-NbP MgS, CaS and SrS are not superconductors. The current study provides a comprehensive analysis of phase transitions for alkaline-earth metal sulfides up to 300 GPa and might stimulate experimental studies in the future.The work was supported by National Natural Science Foundation of China (91963115, 52022089), the PhD Foundation by Yanshan University (B970), Science and Technology Project of Hebei Education Department (Grant No. QN2021136). A.B. acknowledges financial support from the Spanish Ministry of Science and Innovation (PID2019-105488GB-I00).Peer reviewe

Muon Flux Measurement at China Jinping Underground Laboratory

China Jinping Underground Laboratory (CJPL) is ideal for studying solar-, geo-, and supernova neutrinos. A precise measurement of the cosmic-ray background would play an essential role in proceeding with the R\&D research for these MeV-scale neutrino experiments. Using a 1-ton prototype detector for the Jinping Neutrino Experiment (JNE), we detected 264 high-energy muon events from a 645.2-day dataset at the first phase of CJPL (CJPL-I), reconstructed their directions, and measured the cosmic-ray muon flux to be $(3.53\pm0.22_{\text{stat.}}\pm0.07_{\text{sys.}})\times10^{-10}$ cm$^{-2}$s$^{-1}$. The observed angular distributions indicate the leakage of cosmic-ray muon background and agree with the simulation accounting for Jinping mountain's terrain. A survey of muon fluxes at different laboratory locations situated under mountains and below mine shaft indicated that the former is generally a factor of $(4\pm2)$ larger than the latter with the same vertical overburden. This study provides a convenient back-of-the-envelope estimation for muon flux of an underground experiment