View Vertically: A Hierarchical Network for Trajectory Prediction via Fourier Spectrums

Understanding and forecasting future trajectories of agents are critical for behavior analysis, robot navigation, autonomous cars, and other related applications. Previous methods mostly treat trajectory prediction as time sequence generation. Different from them, this work studies agents' trajectories in a "vertical" view, i.e., modeling and forecasting trajectories from the spectral domain. Different frequency bands in the trajectory spectrums could hierarchically reflect agents' motion preferences at different scales. The low-frequency and high-frequency portions could represent their coarse motion trends and fine motion variations, respectively. Accordingly, we propose a hierarchical network V$^2$-Net, which contains two sub-networks, to hierarchically model and predict agents' trajectories with trajectory spectrums. The coarse-level keypoints estimation sub-network first predicts the "minimal" spectrums of agents' trajectories on several "key" frequency portions. Then the fine-level spectrum interpolation sub-network interpolates the spectrums to reconstruct the final predictions. Experimental results display the competitiveness and superiority of V$^2$-Net on both ETH-UCY benchmark and the Stanford Drone Dataset.Comment: Accepted to ECCV 202

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

Association between change in high density lipoprotein cholesterol and cardiovascular disease morbidity and mortality: systematic review and meta-regression analysis

Objective To investigate the association between treatment induced change in high density lipoprotein cholesterol and total death, coronary heart disease death, and coronary heart disease events (coronary heart disease death and non-fatal myocardial infarction) adjusted for changes in low density lipoprotein cholesterol and drug class in randomised trials of lipid modifying interventions

Molecular epidemiology of measles viruses in China, 1995–2003

This report describes the genetic characterization of 297 wild-type measles viruses that were isolated in 24 provinces of China between 1995 and 2003. Phylogenetic analysis of the N gene sequences showed that all of the isolates belonged to genotype H1 except 3 isolates, which were genotype A. The nucleotide sequence and predicted amino acid homologies of the 294-genotype H1 strains were 94.7%–100% and 93.3%–100%, respectively. The genotype H1 isolates were divided into 2 clusters, which differed by approximately 2.9% at the nucleotide level. Viruses from both clusters were distributed throughout China with no apparent geographic restriction and multiple co-circulating lineages were present in many provinces. Even though other measles genotypes have been detected in countries that border China, this report shows that genotype H1 is widely distributed throughout the country and that China has a single, endemic genotype. This important baseline data will help to monitor the progress of measles control in China

Mechanically active materials in three-dimensional mesostructures

Complex, three-dimensional (3D) mesostructures that incorporate advanced, mechanically active materials are of broad, growing interest for their potential use in many emerging systems. The technology implications range from precision-sensing microelectromechanical systems, to tissue scaffolds that exploit the principles of mechanobiology, to mechanical energy harvesters that support broad bandwidth operation. The work presented here introduces strategies in guided assembly and heterogeneous materials integration as routes to complex, 3D microscale mechanical frameworks that incorporatemultiple, independently addressable piezoelectric thin-film actuators for vibratory excitation and precise control. The approach combines transfer printing as a scheme formaterials integrationwith structural buckling as ameans for 2D-to-3D geometric transformation, for designs that range from simple, symmetric layouts to complex, hierarchical configurations, on planar or curvilinear surfaces. Systematic experimental and computational studies reveal the underlying characteristics and capabilities, including selective excitation of targeted vibrational modes for simultaneous measurements of viscosity and density of surrounding fluids. The results serve as the foundations for unusual classes of mechanically active 3D mesostructures with unique functions relevant to biosensing, mechanobiology, energy harvesting, and others

Sirtuin 6 maintains epithelial STAT6 activity to support intestinal tuft cell development and type 2 immunity

Dynamic regulation of intestinal epithelial cell (IEC) differentiation is crucial for both homeostasis and the response to helminth infection. SIRT6 belongs to the NAD+-dependent deacetylases and has established diverse roles in aging, metabolism and disease. Here, we report that IEC Sirt6 deletion leads to impaired tuft cell development and type 2 immunity in response to helminth infection, thereby resulting in compromised worm expulsion. Conversely, after helminth infection, IEC SIRT6 transgenic mice exhibit enhanced epithelial remodeling process and more efficient worm clearance. Mechanistically, Sirt6 ablation causes elevated Socs3 expression, and subsequently attenuated tyrosine 641 phosphorylation of STAT6 in IECs. Notably, intestinal epithelial overexpression of constitutively activated STAT6 (STAT6vt) in mice is sufficient to induce the expansion of tuft and goblet cell linage. Furthermore, epithelial STAT6vt overexpression remarkedly reverses the defects in intestinal epithelial remodeling caused by Sirt6 ablation. Our results reveal a novel function of SIRT6 in regulating intestinal epithelial remodeling and mucosal type 2 immunity in response to helminth infection

MSN: Multi-Style Network for Trajectory Prediction

Trajectory prediction aims at forecasting agents' possible future locations considering their observations along with the video context. It is strongly required by a lot of autonomous platforms like tracking, detection, robot navigation, self-driving cars, and many other computer vision applications. Whether it is agents' internal personality factors, interactive behaviors with the neighborhood, or the influence of surroundings, all of them might represent impacts on agents' future plannings. However, many previous methods model and predict agents' behaviors with the same strategy or the ``single'' feature distribution, making them challenging to give predictions with sufficient style differences. This manuscript proposes the Multi-Style Network (MSN), which utilizes style hypothesis and stylized prediction two sub-networks, to give agents multi-style predictions in a novel categorical way adaptively. We use agents' end-point plannings and their interaction context as the basis for the behavior classification, so as to adaptively learn multiple diverse behavior styles through a series of style channels in the network. Then, we assume one by one that the target agents will plan their future behaviors according to each of these categorized styles, thus utilizing different style channels to give a series of predictions with significant style differences in parallel. Experiments show that the proposed MSN outperforms current state-of-the-art methods up to 10\% - 20\% quantitatively on two widely used datasets, and presents better multi-style characteristics qualitatively

Organic field-effect transistors with a sandwich structure from inserting 2,2′,2″-(1,3,5-benzenetriyl)tris[1-phenyl-1H-benzimidazole] in the pentacene active layer

Organic field-effect transistors (OFETs) with a pentacene/TPBi/pentacene sandwich structure were realized and characterized. Compared with the single pentacene layer transistors, these devices not only showed an obvious reduction of threshold voltage but also presented a significant enhancement of field-effect mobilities. The performance improvement was attributed to the high conductivity of the sandwich active layers, which were analyzed by applying the transfer line method. Meanwhile, the morphologies of pentacene films with and without the TPBi interlayer were characterized by atomic force microscopy, the smoother surface of the upper pentacene film was observed in pentacene/TPBi/pentacene. Moreover, by applying X-ray diffraction, a higher-order growth phase of the pentacene thin film was observed

Nighttime Image Dehazing Based on Point Light Sources

Images routinely suffer from quality degradation in fog, mist, and other harsh weather conditions. Consequently, image dehazing is an essential and inevitable pre-processing step in computer vision tasks. Image quality enhancement for special scenes, especially nighttime image dehazing is extremely well studied for unmanned driving and nighttime surveillance, while the vast majority of dehazing algorithms in the past were only applicable to daytime conditions. After observing a large number of nighttime images, artificial light sources have replaced the position of the sun in daytime images and the impact of light sources on pixels varies with distance. This paper proposed a novel nighttime dehazing method using the light source influence matrix. The luminosity map can well express the photometric difference value of the picture light source. Then, the light source influence matrix is calculated to divide the image into near light source region and non-near light source region. Using the result of two regions, the two initial transmittances obtained by dark channel prior are fused by edge-preserving filtering. For the atmospheric light term, the initial atmospheric light value is corrected by the light source influence matrix. Finally, the final result is obtained by substituting the atmospheric light model. Theoretical analysis and comparative experiments verify the performance of the proposed image dehazing method. In terms of PSNR, SSIM, and UQI, this method improves 9.4%, 11.2%, and 3.3% over the existed night-time defogging method OSPF. In the future, we will explore the work from static picture dehazing to real-time video stream dehazing detection and will be used in detection on potential applications

Intelligent Rolling Bearing Fault Diagnosis Method Using Symmetrized Dot Pattern Images and CBAM-DRN

Rolling bearings are a vital component of mechanical equipment. It is crucial to implement rolling bearing fault diagnosis research to guarantee the stability of the long-term action of mechanical equipment. Conversion of rolling bearing vibration signals into images for fault diagnosis research has been a practical diagnostic approach. The current paper presents a rolling bearing fault diagnosis method using symmetrized dot pattern (SDP) images and a deep residual network with convolutional block attention module (CBAM-DRN). The rolling bearing vibration signal is first visualized and transformed into an SDP image with distinct fault characteristics. Then, CBAM-DRN is utilized to derive characteristics directly and detect faults from the input SDP images. In order to prevent conventional time-frequency images from being limited by their inherent flaws and avoid missing the fault features, the SDP technique is employed to convert vibration signals into images for visualization. DRN enables adequate extraction of rolling bearing fault characteristics and prevents training difficulties and gradient vanishing in deep level networks. CBAM assists the diagnostic model in concentrating on the image’s more distinctive parts and preventing the interference of non-featured parts. Finally, the method’s validity was tested with a composite fault dataset of motor bearings containing multiple loads and fault diameters. The experimental results reflect that the presented approach can attain a diagnostic precision of over 99% and good stability and generalization