AMENet: Attentive Maps Encoder Network for Trajectory Prediction

Trajectory prediction is critical for applications of planning safe future movements and remains challenging even for the next few seconds in urban mixed traffic. How an agent moves is affected by the various behaviors of its neighboring agents in different environments. To predict movements, we propose an end-to-end generative model named Attentive Maps Encoder Network (AMENet) that encodes the agent's motion and interaction information for accurate and realistic multi-path trajectory prediction. A conditional variational auto-encoder module is trained to learn the latent space of possible future paths based on attentive dynamic maps for interaction modeling and then is used to predict multiple plausible future trajectories conditioned on the observed past trajectories. The efficacy of AMENet is validated using two public trajectory prediction benchmarks Trajnet and InD.Comment: Accepted by ISPRS Journal of Photogrammetry and Remote Sensin

MCENET: Multi-Context Encoder Network for Homogeneous Agent Trajectory Prediction in Mixed Traffic

Trajectory prediction in urban mixed-traffic zones (a.k.a. shared spaces) is critical for many intelligent transportation systems, such as intent detection for autonomous driving. However, there are many challenges to predict the trajectories of heterogeneous road agents (pedestrians, cyclists and vehicles) at a microscopical level. For example, an agent might be able to choose multiple plausible paths in complex interactions with other agents in varying environments. To this end, we propose an approach named Multi-Context Encoder Network (MCENET) that is trained by encoding both past and future scene context, interaction context and motion information to capture the patterns and variations of the future trajectories using a set of stochastic latent variables. In inference time, we combine the past context and motion information of the target agent with samplings of the latent variables to predict multiple realistic trajectories in the future. Through experiments on several datasets of varying scenes, our method outperforms some of the recent state-of-the-art methods for mixed traffic trajectory prediction by a large margin and more robust in a very challenging environment. The impact of each context is justified via ablation studies.Comment: 8 pages, 5 figures, code is available on https://github.com/haohao11/MCENE

Measuring information exposure attacks on interest management

For a scalable Massively Multiplayer Online Game (MMOG), interest management (IM) is an essential component to reduce unnecessary network traffic. As Area-Of-Interest (AOI) defines each player's interests, an entity normally maintains a subscriber list of players whose AOIs cover the position of the entity. To maintain the subscriber list, players are required to send AOI updates. Unfortunately, AOI update is vulnerable to information exposure (IE) attack especially on P2P infrastructure. Sensitive information, such as player's position, can be revealed during AOI update without owner's authorization and attention. This eventually results in an unfair game. In this paper, we demonstrate that such IE attack on MMOG can help cheaters gain unauthorized benefits. Notably, we present a Monte Carlo based simulator to quantitatively measure the impact of IE attack when different IM schemes are applied. Three P2P schemes are assessed and a Client/Server scheme is also employed for comparison. In addition, we also evaluate IE attack when a group of players collude with each other to share information. Experimental data obtained from simulation are analyzed and explained. Practical suggestions are also given for choosing an IM scheme for P2P gaming

Electrochemical Deposition of Bismuth on Graphite Felt Electrodes: Influence on Negative Half-Cell Reactions in Vanadium Redox Flow Batteries

In this paper, bismuth (Bi) was successfully deposited on graphite felts to improve the electrochemical performances of vanadium redox flow batteries. Modified graphite felts with different Bi particle loadings were obtained through electrochemical deposition at voltages of 0.8 V, 1.2 V and 1.6 V in 0.1 M BiCl3 solution for 10 min. The optimal Bi particle loading was confirmed by scanning electron microscopy (SEM), single cells and electrochemical tests. The SEM images revealed the deposition of granular Bi particles on the fiber surface. The Bi-modified felts which were electro-chemically deposited at 1.2 V (Bi/TGF-1.2V) showed excellent electrochemical performances in cyclic voltammetry curves and impedance spectroscopy. Meanwhile, the single cells assembled with Bi/TGF-1.2V as negative electrodes exhibited higher voltage efficiencies than the others. The optimized Bi particle loading induced better catalysis of the V3+/V2+ reaction and hence significantly improved the cell performances. In addition, the prepared Bi-modified felts showed stable cell performances and slower charge–discharge capacity declines than the other electrodes at current densities between 20 mA/cm2 and 80 mA/cm2. Compared with the pristine felt, the voltage efficiency of the vanadium redox flow battery assembled with Bi/TGF-1.2V graphite felt was 9.47% higher at the current density of 80 mA/cm2. The proposed method has considerable potential and guiding significance for the future modification of graphite felt for redox flow batteries

MCENET: Multi-Context Encoder Network for Homogeneous Agent Trajectory Prediction in Mixed Traffic

Trajectory prediction in urban mixed-traffic zones (a.k. a. shared spaces) is critical for many intelligent transportation systems, such as intent detection for autonomous driving. However, there are many challenges to predict the trajectories of heterogeneous road agents (pedestrians, cyclists and vehicles) at a microscopical level. For example, an agent might be able to choose multiple plausible paths in complex interactions with other agents in varying environments. To this end, we propose an approach named Multi-Context Encoder Network (MCENET) that is trained by encoding both past and future scene context, interaction context and motion information to capture the patterns and variations of the future trajectories using a set of stochastic latent variables. In inference time, we combine the past context and motion information of the target agent with samplings of the latent variables to predict multiple realistic trajectories in the future. Through experiments on several datasets of varying scenes, our method outperforms some of the recent state-of-the-art methods for mixed traffic trajectory prediction by a large margin and more robust in a very challenging environment. The impact of each context is justified via ablation studies

AMENet: Attentive Maps Encoder Network for trajectory prediction

Trajectory prediction is critical for applications of planning safe future movements and remains challenging even for the next few seconds in urban mixed traffic. How an agent moves is affected by the various behaviors of its neighboring agents in different environments. To predict movements, we propose an end-to-end generative model named Attentive Maps Encoder Network (AMENet) that encodes the agent's motion and interaction information for accurate and realistic multi-path trajectory prediction. A conditional variational auto-encoder module is trained to learn the latent space of possible future paths based on attentive dynamic maps for interaction modeling and then is used to predict multiple plausible future trajectories conditioned on the observed past trajectories. The efficacy of AMENet is validated using two public trajectory prediction benchmarks Trajnet and InD

Pleiotropic Modulation of Chitooligosaccharides on Inflammatory Signaling in LPS-Induced Macrophages

Chitooligosaccharide (COS) is a green and non-toxic cationic carbohydrate that has attracted wide attention in recent years due to its anti-inflammatory activity. However, the anti-inflammatory mechanism of COS remains unclear. In this study, RNA-seq was used to investigate the integrated response of COS to LPS-induced damage in macrophages. The results showed that the experimental group with COS had 2570 genes with significant differences compared to the model group, and that these genes were more enriched in inflammatory and immune pathways. The KEGG results showed that COS induces the pleiotropic modulation of classical inflammatory pathways, such as the Toll-like receptor signaling pathway, NF-κB, MAPK, etc. Based on the RNA-seq data and the RT-qPCR, as well as the WB validation, COS can significantly upregulate the expression of membrane receptors, such as Tlr4, Tlr5, and MR, and significantly inhibits the phosphorylation of several important proteins, such as IκB and JNK. Overall, this study offers deep insights into the anti-inflammatory mechanism and lays the foundation for the early application of COS as an anti-inflammatory drug

Exploring dynamic context for multi-path trajectory prediction

To accurately predict future positions of different agents in traffic scenarios is crucial for safely deploying intelligent autonomous systems in the real-world environment. However, it remains a challenge due to the behavior of a target agent being affected by other agents dynamically and there being more than one socially possible paths the agent could take. In this paper, we propose a novel framework, named Dynamic Context Encoder Network (DCENet). In our framework, first, the spatial context between agents is explored by using self-attention architectures. Then, the two-stream encoders are trained to learn temporal context between steps by taking the respective observed trajectories and the extracted dynamic spatial context as input. The spatial-temporal context is encoded into a latent space using a Conditional Variational Auto-Encoder (CVAE) module. Finally, a set of future trajectories for each agent is predicted conditioned on the learned spatial-temporal context by sampling from the latent space, repeatedly. DCENet is evaluated on one of the most popular challenging benchmarks for trajectory forecasting Trajnet and reports a new state-of-the-art performance. It also demonstrates superior performance evaluated on the benchmark inD for mixed traffic at intersections. A series of ablation studies is conducted to validate the effectiveness of each proposed modul

Preparation and Antioxidant Activity of Chitosan Dimers with Different Sequences

As a popular marine saccharide, chitooligosaccharides (COS) has been proven to have good antioxidant activity. Its antioxidant effect is closely related to its degree of polymerization, degree of acetylation and sequence. However, the specific structure–activity relationship remains unclear. In this study, three chitosan dimers with different sequences were obtained by the separation and enzymatic method, and the antioxidant activity of all four chitosan dimers were studied. The effect of COS sequence on its antioxidant activity was revealed for the first time. The amino group at the reducing end plays a vital role in scavenging superoxide radicals and in the reducing power of the chitosan dimer. At the same time, we found that the fully deacetylated chitosan dimer DD showed the strongest DPPH scavenging activity. When the amino groups of the chitosan dimer were acetylated, it showed better activity in scavenging hydroxyl radicals. Research on COS sequences opens up a new path for the study of COS, and is more conducive to the investigation of its mechanism