GANet: Goal Area Network for Motion Forecasting

Predicting the future motion of road participants is crucial for autonomous driving but is extremely challenging due to staggering motion uncertainty. Recently, most motion forecasting methods resort to the goal-based strategy, i.e., predicting endpoints of motion trajectories as conditions to regress the entire trajectories, so that the search space of solution can be reduced. However, accurate goal coordinates are hard to predict and evaluate. In addition, the point representation of the destination limits the utilization of a rich road context, leading to inaccurate prediction results in many cases. Goal area, i.e., the possible destination area, rather than goal coordinate, could provide a more soft constraint for searching potential trajectories by involving more tolerance and guidance. In view of this, we propose a new goal area-based framework, named Goal Area Network (GANet), for motion forecasting, which models goal areas rather than exact goal coordinates as preconditions for trajectory prediction, performing more robustly and accurately. Specifically, we propose a GoICrop (Goal Area of Interest) operator to effectively extract semantic lane features in goal areas and model actors' future interactions, which benefits a lot for future trajectory estimations. GANet ranks the 1st on the leaderboard of Argoverse Challenge among all public literature (till the paper submission), and its source codes will be released

Bioinspired cilia arrays with programmable nonreciprocal motion and metachronal coordination

Coordinated nonreciprocal dynamics in biological cilia is essential to many living systems, where the emergentmetachronal waves of cilia have been hypothesized to enhance net fluid flows at low Reynolds numbers (Re). Experimental investigation of this hypothesis is critical but remains challenging. Here, we report soft miniature devices with both ciliary nonreciprocal motion and metachronal coordination and use them to investigate the quantitative relationship between metachronal coordination and the induced fluid flow. We found that only antiplectic metachronal waves with specific wave vectors could enhance fluid flows compared with the synchronized case. These findings further enable various bioinspired cilia arrays with unique functionalities of pumping and mixing viscous synthetic and biological complex fluids at low Re. Our design method and developed soft miniature devices provide unprecedented opportunities for studying ciliary biomechanics and creating cilia-inspired wireless microfluidic pumping, object manipulation and lab- and organ-on-a-chip devices, mobile microrobots, and bioengineering systems.ISSN:2375-254

Survey on Video Object Tracking Algorithms

Video object tracking is an important research content in the field of computer vision, mainly studying the tracking of objects with interest in video streams or image sequences. Video object tracking has been widely used in cameras and surveillance, driverless, precision guidance and other fields. Therefore, a comprehensive review on video object tracking algorithms is of great significance. Firstly, according to different sources of challenges, the challenges faced by video object tracking are classified into two aspects, the objects’ factors and the backgrounds’ factors, and summed up respectively. Secondly, the typical video object tracking algorithms in recent years are classified into correlation filtering video object tracking algorithms and deep learning video object tracking algorithms. And further the correlation filtering video object tracking algorithms are classified into three categories: kernel correlation filtering algorithms, scale adaptive correlation filtering algorithms and multi-feature fusion corre-lation filtering algorithms. The deep learning video object tracking algorithms are classified into two categories: video object tracking algorithms based on siamese network and based on convolutional neural network. This paper analyzes various algorithms from the aspects of research motivation, algorithm ideas, advantages and disadvantages. Then, the widely used datasets and evaluation indicators are introduced. Finally, this paper sums up the research and looks forward to the development trends of video object tracking in the future

A novel heteromorphic ensemble algorithm for hand pose recognition

Imagining recognition of behaviors from video sequences for a machine is full of challenges but meaningful. This work aims to predict students’ behavior in an experimental class, which relies on the symmetry idea from reality to annotated reality centered on the feature space. A heteromorphic ensemble algorithm is proposed to make the obtained features more aggregated and reduce the computational burden. Namely, the deep learning models are improved to obtain feature vectors representing gestures from video frames and the classification algorithm is optimized for behavior recognition. So, the symmetric idea is realized by decomposing the task into three schemas including hand detection and cropping, hand joints feature extraction, and gesture classification. Firstly, a new detector method named YOLOv4-specific tiny detection (STD) is proposed by reconstituting the YOLOv4-tiny model, which could produce two outputs with some attention mechanism leveraging context information. Secondly, the efficient pyramid squeeze attention (EPSA) net is integrated into EvoNorm-S0 and the spatial pyramid pool (SPP) layer to obtain the hand joint position information. Lastly, the D–S theory is used to fuse two classifiers, support vector machine (SVM) and random forest (RF), to produce a mixed classifier named S–R. Eventually, the synergetic effects of our algorithm are shown by experiments on self-created datasets with a high average recognition accuracy of 89.6%

Identification of the toxin components of Rhizoctonia solani AG1-IA and its destructive effect on plant cell membrane structure

Rice sheath blight is a fungal disease caused mainly by Rhizoctonia solani AG1-IA. Toxins are a major pathogenic factor of R. solani, and some studies have reported their toxin components; however, there is no unified conclusion. In this study, we reported the toxin components and their targets that play a role in R. solani AG1-IA. First, toxins produced by R. solani AG1-IA were examined. Several important phytotoxins, including benzoic acid (BZA), 5-hydroxymethyl-2-furanic aid (HFA), and catechol (CAT), were identified by comparative analysis of secondary metabolites from AG1-IA, AG1-IB, and healthy rice. Follow-up studies have shown that the toxin components of this fungus can rapidly disintegrate the biofilm structure while maintaining the content of host plant membrane components, thereby affecting the organelles, which may also explain the lack of varieties highly resistant to sheath blight

Diagnostic Performance of Neurofilaments in Chinese Patients With Amyotrophic Lateral Sclerosis: A Prospective Study

Several studies have attempted to reduce diagnostic delay and identify biomarkers for drug development in amyotrophic lateral sclerosis (ALS). In this study, we aimed to evaluate the diagnostic accuracy for ALS of cerebrospinal fluid (CSF) neurofilament (Nf), Tau protein, and inflammatory factors such as interleukin (IL)-2, IL-6, IL-10, IL-15, and granulocyte-macrophage colony-stimulating factor (GMCSF) in Chinese patients. Our prospective study measured the concentration of phosphorylated Nf heavy chain (pNfH), Nf light chain (NfL), Tau, pTau, and inflammatory factors in the CSF of 85 patients. Detailed clinical data and laboratory, neuroimaging, and neurophysiological findings were recorded. The concentrations of pNfH and NfL were higher in the ALS group than in the control group. At the 1104 pg/mL pNfH cutoff, the specificity was 68.8%, the sensitivity 100%, and the area under the curve (AUC) 0.907. At the 1,139 pg/mL NfL cutoff, the specificity was 56.3%, the sensitivity 96.2%, and the AUC 0.775. There was no significant difference in the concentrations of Tau, pTau, IL-2, IL-6, IL-10, IL-15, and GMCSF between the ALS and control groups (p > 0.05). In the ALS group, the concentration of pNfH in the CSF was correlated with disease duration (r = −0.475, p < 0.001). This is the first prospective study to confirm the diagnostic value of Nf for ALS in Chinese patients