ECAPA-TDNN based online discussion activity-level evaluation.

With the continuous development and application of online interactive activities and network transmission technology, online interactive behaviors such as online discussion meetings and online teaching have become indispensable in people's studies and work. However, the effectiveness of working with online discussions and feedback from participants on their conference performance has been a major concern, and this is the issue examined in this post. Based on the above issues, this paper designs an online discussion activity-level evaluation system based on voiceprint recognition technology. The application system developed in this project is divided into two parts; the first part is to segment the online discussion audio into multiple independent audio segments by audio segmentation technology and train the voiceprint recognition model to predict the speaker's identity in each separate audio component. In the second part, we propose a linear normalized online meeting activity-level calculation model based on the modified main indexes by traversing and counting each participant's speaking frequency and total speaking time as the main indexes for activity-level evaluation. To make the evaluation results more objective, reasonable, and distinguishable, the activity score of each participant is calculated, and each participant's activity-level in the discussion meeting is derived by combining the fuzzy membership function. To test the system's performance, we designed an experiment with 25 participants in an online discussion meeting, with two assistants manually recording the discussion and a host moderating the meeting. The results of the experiment showed that the system's evaluation results matched those recorded by the two assistants. The system can fulfill the task of distinguishing the level of activity of participants in online discussions.

Microsaccade-inspired event camera for robotics.

Neuromorphic vision sensors or event cameras have made the visual perception of extremely low reaction time possible, opening new avenues for high-dynamic robotics applications. These event cameras' output is dependent on both motion and texture. However, the event camera fails to capture object edges that are parallel to the camera motion. This is a problem intrinsic to the sensor and therefore challenging to solve algorithmically. Human vision deals with perceptual fading using the active mechanism of small involuntary eye movements, the most prominent ones called microsaccades. By moving the eyes constantly and slightly during fixation, microsaccades can substantially maintain texture stability and persistence. Inspired by microsaccades, we designed an event-based perception system capable of simultaneously maintaining low reaction time and stable texture. In this design, a rotating wedge prism was mounted in front of the aperture of an event camera to redirect light and trigger events. The geometrical optics of the rotating wedge prism allows for algorithmic compensation of the additional rotational motion, resulting in a stable texture appearance and high informational output independent of external motion. The hardware device and software solution are integrated into a system, which we call artificial microsaccade-enhanced event camera (AMI-EV). Benchmark comparisons validated the superior data quality of AMI-EV recordings in scenarios where both standard cameras and event cameras fail to deliver. Various real-world experiments demonstrated the potential of the system to facilitate robotics perception both for low-level and high-level vision tasks.

Condition-Adaptive Graph Convolution Learning for Skeleton-Based Gait Recognition.

Graph convolutional networks have been widely applied in skeleton-based gait recognition. A key challenge in this task is to distinguish the individual walking styles of different subjects across various views. Existing state-of-the-art methods employ uniform convolutions to extract features from diverse sequences and ignore the effects of viewpoint changes. To overcome these limitations, we propose a condition-adaptive graph (CAG) convolution network that can dynamically adapt to the specific attributes of each skeleton sequence and the corresponding view angle. In contrast to using fixed weights for all joints and sequences, we introduce a joint-specific filter learning (JSFL) module in the CAG method, which produces sequence-adaptive filters at the joint level. The adaptive filters capture fine-grained patterns that are unique to each joint, enabling the extraction of diverse spatial-temporal information about body parts. Additionally, we design a view-adaptive topology learning (VATL) module that generates adaptive graph topologies. These graph topologies are used to correlate the joints adaptively according to the specific view conditions. Thus, CAG can simultaneously adjust to various walking styles and viewpoints. Experiments on the two most widely used datasets (i.e., CASIA-B and OU-MVLP) show that CAG surpasses all previous skeleton-based methods. Moreover, the recognition performance can be enhanced by simply combining CAG with appearance-based methods, demonstrating the ability of CAG to provide useful complementary information.

Advances in biosynthesis of scopoletin.

Scopoletin is a typical example of coumarins, which can be produced in plants. Scopoletin acts as a precursor for pharmaceutical and health care products, and also possesses promising biological properties, including antibacterial, anti-tubercular, anti-hypertensive, anti-inflammatory, anti-diabetic, and anti-hyperuricemic activity. Despite the potential benefits, the production of scopoletin using traditional extraction processes from plants is unsatisfactory. In recent years, synthetic biology has developed rapidly and enabled the effective construction of microbial cell factories for production of high value-added chemicals. Herein, this review summarizes the progress of scopoletin biosynthesis in artificial microbial cell factories. The two main pathways of scopoletin biosynthesis are summarized firstly. Then, synthetic microbial cell factories are reviewed as an attractive improvement strategy for biosynthesis. Emerging techniques in synthetic biology and metabolic engineering are introduced as innovative tools for the efficient synthesis of scopoletin. This review showcases the potential of biosynthesis of scopoletin in artificial microbial cell factories.

Artificial nondirectional site-specific recombination systems.

Site-specific recombination systems (SRSs) are widely used in studies on synthetic biology and related disciplines. Nondirectional SRSs can randomly trigger excision, integration, reversal, and translocation, which are effective tools to achieve large-scale genome recombination. In this study, we designed 6 new nondirectional SRSs named Vika/voxsym1-4 and Dre/roxsym1-2. All 6 artificial nondirectional SRSs were able to generate random deletion and inversion in Saccharomyces cerevisiae. Moreover, all six SRSs were orthogonal to Cre/loxPsym. The pairwise orthogonal nondirected SRSs can simultaneously initiate large-scale and independent gene recombination in two different regions of the genome, which could not be accomplished using previous orthogonal systems. These SRSs were found to be robust while working in the cells at different growth stages, as well as in the different spatial structure of the chromosome. These artificial pairwise orthogonal nondirected SRSs offer newfound potential for site-specific recombination in synthetic biology.

Parallax-Robust Surveillance Video Stitching.

This paper presents a parallax-robust video stitching technique for timely synchronized surveillance video. An efficient two-stage video stitching procedure is proposed in this paper to build wide Field-of-View (FOV) videos for surveillance applications. In the stitching model calculation stage, we develop a layered warping algorithm to align the background scenes, which is location-dependent and turned out to be more robust to parallax than the traditional global projective warping methods. On the selective seam updating stage, we propose a change-detection based optimal seam selection approach to avert ghosting and artifacts caused by moving foregrounds. Experimental results demonstrate that our procedure can efficiently stitch multi-view videos into a wide FOV video output without ghosting and noticeable seams.