Shifting Perspective to See Difference: A Novel Multi-View Method for Skeleton based Action Recognition

Skeleton-based human action recognition is a longstanding challenge due to its complex dynamics. Some fine-grain details of the dynamics play a vital role in classification. The existing work largely focuses on designing incremental neural networks with more complicated adjacent matrices to capture the details of joints relationships. However, they still have difficulties distinguishing actions that have broadly similar motion patterns but belong to different categories. Interestingly, we found that the subtle differences in motion patterns can be significantly amplified and become easy for audience to distinct through specified view directions, where this property haven't been fully explored before. Drastically different from previous work, we boost the performance by proposing a conceptually simple yet effective Multi-view strategy that recognizes actions from a collection of dynamic view features. Specifically, we design a novel Skeleton-Anchor Proposal (SAP) module which contains a Multi-head structure to learn a set of views. For feature learning of different views, we introduce a novel Angle Representation to transform the actions under different views and feed the transformations into the baseline model. Our module can work seamlessly with the existing action classification model. Incorporated with baseline models, our SAP module exhibits clear performance gains on many challenging benchmarks. Moreover, comprehensive experiments show that our model consistently beats down the state-of-the-art and remains effective and robust especially when dealing with corrupted data. Related code will be available on https://github.com/ideal-idea/SAP

Visualization of ultrasonic wave field by stroboscopic polarization selective imaging

A stroboscopic method based on polarization selective imaging is proposed for dynamic visualization of ultrasonic waves propagating in a transparent medium. Multiple independent polarization parametric images were obtained, which enabled quantitative evaluation of the distribution of the ultrasonic pressure in quartz. In addition to the detection of optical phase differences δ in conventional photo-elastic techniques, the azimuthal angle φ and the Stokes parameter S2 of the polarized light are found to be highly sensitive to the wave-induced refraction index distribution, opening a new window on ultrasonic field visualization

Co-optimization method to improve lateral resolution in photoacoustic computed tomography

In biomedical imaging, photoacoustic computed tomography (PACT) has recently gained increased interest as this imaging technique has good optical contrast and depth of acoustic penetration. However, a spinning blur will be introduced during the image reconstruction process due to the limited size of the ultrasonic transducers (UT) and a discontinuous measurement process. In this study, a damping UT and adaptive back-projection co-optimization (CODA) method is developed to improve the lateral spatial resolution of PACT. In our PACT system, a damping aperture UT controls the size of the receiving area, which suppresses image blur at the signal acquisition stage. Then, an innovative adaptive back-projection algorithm is developed, which corrects the undesirable artifacts. The proposed method was evaluated using agar phantom and ex-vivo experiments. The results show that the CODA method can effectively compensate for the spinning blur and eliminate unwanted artifacts in PACT. The proposed method can significantly improve the lateral spatial resolution and image quality of reconstructed images, making it more appealing for wider clinical applications of PACT as a novel, cost-effective modality

10.13% Efficiency All-Polymer Solar Cells Enabled by Improving the Optical Absorption of Polymer Acceptors

The limited light absorption capacity for most polymer acceptors hinders the improvement of the power conversion efficiency (PCE) of all-polymer solar cells (all-PSCs). Herein, by simultaneously increasing the conjugation of the acceptor unit and enhancing the electron-donating ability of the donor unit, a novel narrow-bandgap polymer acceptor PF3-DTCO based on an A–D–A-structured acceptor unit ITIC16 and a carbon–oxygen (C–O)-bridged donor unit DTCO is developed. The extended conjugation of the acceptor units from IDIC16 to ITIC16 results in a red-shifted absorption spectrum and improved absorption coefficient without significant reduction of the lowest unoccupied molecular orbital energy level. Moreover, in addition to further broadening the absorption spectrum by the enhanced intramolecular charge transfer effect, the introduction of C–O bridges into the donor unit improves the absorption coefficient and electron mobility, as well as optimizes the morphology and molecular order of active layers. As a result, the PF3-DTCO achieves a higher PCE of 10.13% with a higher short-circuit current density (Jsc) of 15.75 mA cm−2 in all-PSCs compared with its original polymer acceptor PF2-DTC (PCE = 8.95% and Jsc = 13.82 mA cm−2). Herein, a promising method is provided to construct high-performance polymer acceptors with excellent optical absorption for efficient all-PSCs

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Final performance comparisons on both the publicand the industrial datasets.</p

Spatial signature of the photoelastic effect in the acoustic–plasmonic coupling revealed by space responsivity induced by polarized optical excitation

Peer reviewed: TruePublication status: PublishedAcoustic–plasmonic coupling in metallic nanoparticles can significantly alter their optical absorption and scattering characteristics. However, almost all previous investigations on acoustic–plasmonic coupling so far have been focused on the spectral response of particles in a vacuum. In this report, a spatial photon scattering mode taking count in the acoustic–plasmonic coupling of individual gold nanoparticle (GN) on a silicon substrate under ultrasonic influence was presented. The acoustic–plasmonic is visualized with parametric images with spatial scattering patterns of the particle under the excitation of polarized light along the Poincare’s equatorial trajectory. The ultrasonic sources can be sensitively extracted from the parametric sinδ images, providing clear evidence of the extremely weak influence of ultrasound wave directivity on the spatial characteristics of the scattering of the particle through acoustic–plasmonic coupling. Experiment and simulation results reveal that, in general, the coupling is the strongest, when the maximum electric field (plasmon vibration mode) aligns with the ultrasonic propagation direction. This study provides a new angle to observe and deepen the understanding of the acoustic–plasmonic effect of nanoparticles, in addition to the conventional manner of investigation on their scattering spectra. It emphasizes the possibility of determining the spatial distribution of nanoparticles via photon state scattering when they are in a weakly oscillating environment, providing valuable guidance for future potential applications exploiting the acoustic–plasmonic effect of nanostructures

Efficient Ring-Topology Decentralized Federated Learning with Deep Generative Models for Medical Data in eHealthcare Systems

By leveraging deep learning technologies, data-driven-based approaches have reached great success with the rapid increase of data generated for medical applications. However, security and privacy concerns are obstacles for data providers in many sensitive data-driven scenarios, such as rehabilitation and 24 h on-the-go healthcare services. Although many federated learning (FL) approaches have been proposed with DNNs for medical applications, these works still suffer from low usability of data due to data incompleteness, low quality, insufficient quantity, sensitivity, etc. Therefore, we propose a ring-topology-based decentralized federated learning (RDFL) scheme for deep generative models (DGM), where DGM is a promising solution for solving the aforementioned data usability issues. Our RDFL schemes provide communication efficiency and maintain training performance to boost DGMs in target tasks compared with existing FL works. A novel ring FL topology and a map-reduce-based synchronizing method are designed in the proposed RDFL to improve the decentralized FL performance and bandwidth utilization. In addition, an inter-planetary file system (IPFS) is introduced to further improve communication efficiency and FL security. Extensive experiments have been taken to demonstrate the superiority of RDFL with either independent and identically distributed (IID) datasets or non-independent and identically distributed (Non-IID) datasets

CRISPR-based nucleic acid diagnostics for pathogens

Pathogenic infections remain the primary threat for human health, especially the global COVID-19 pandemic. It is important to develop rapid, sensitive and multiplexed tools for detecting pathogens and their mutations, particularly the tailor-made strategies for point-of-care diagnosis allowing for use in resource-constrained settings. The rapidly evolving CRISPR/Cas systems have provided a powerful toolbox for pathogenic diagnostics via nucleic acid tests. In this review, we first describe the resultant promising class 2 (single, multidomain effector) and recently explored class 1 (multisubunit effector complexes) CRISPR tools. We present the diverse engineering nucleic acid diagnostics based on CRISPR/Cas systems for pathogenic viruses, bacteria and fungi, and highlight the application for detecting viral variants and drug-resistant bacteria enabled by CRISPR-based mutation profiling. Finally, we discuss the challenges such as the development of preamplification-free diagnostic assays and present the emerging CRISPR systems and CRISPR cascade that potentially enable multiplexed and preamplification-free pathogenic diagnostics

Temporal evolution of refractive index induced by short laser pulses accounting for both photoacoustic and photothermal effects

Materials such as silicon, copper, gold, and aluminum exhibit strong absorption and scattering characterization under short-pulsed laser irradiation. Due to the photoelastic effect and thermoelastic relaxation, the focal area may induce a local modulation in the refractive index, which can be detected with the intensity reflection coefficient perturbation. Normally, the thermal effect causes a weak refractive index change and is negligible, compared with the pressure-induced effect in most photoacoustic analytical systems. In this study, we present a theoretical model with the whole process of absorbed energy conversion analysis for the refractive index perturbation induced by both thermal effect and photoacoustic pressure. In this model, data analysis was carried out on the transformation of the energy absorbed by the sample into heat and stress. To prove the feasibility of this model, numerical simulation was performed for the photothermal and photoacoustic effects under different incident intensities using the finite element method. Experiment results on silicon and carbon fiber verified that the refractive index change induced by the photothermal effect can be detected and be incorporated with pressure-induced refractive index change. The simulation results showed very good agreement with the results of the experiments. The main aim of this study was to further understand the absorption and conversion process of short-pulsed light energy and the resulting photothermal and photoacoustic effects

Efficient Ring-Topology Decentralized Federated Learning with Deep Generative Models for Medical Data in eHealthcare Systems

By leveraging deep learning technologies, data-driven-based approaches have reached great success with the rapid increase of data generated for medical applications. However, security and privacy concerns are obstacles for data providers in many sensitive data-driven scenarios, such as rehabilitation and 24 h on-the-go healthcare services. Although many federated learning (FL) approaches have been proposed with DNNs for medical applications, these works still suffer from low usability of data due to data incompleteness, low quality, insufficient quantity, sensitivity, etc. Therefore, we propose a ring-topology-based decentralized federated learning (RDFL) scheme for deep generative models (DGM), where DGM is a promising solution for solving the aforementioned data usability issues. Our RDFL schemes provide communication efficiency and maintain training performance to boost DGMs in target tasks compared with existing FL works. A novel ring FL topology and a map-reduce-based synchronizing method are designed in the proposed RDFL to improve the decentralized FL performance and bandwidth utilization. In addition, an inter-planetary file system (IPFS) is introduced to further improve communication efficiency and FL security. Extensive experiments have been taken to demonstrate the superiority of RDFL with either independent and identically distributed (IID) datasets or non-independent and identically distributed (Non-IID) datasets