Understanding the Robustness of Skeleton-based Action Recognition under Adversarial Attack

Action recognition has been heavily employed in many applications such as autonomous vehicles, surveillance, etc, where its robustness is a primary concern. In this paper, we examine the robustness of state-of-the-art action recognizers against adversarial attack, which has been rarely investigated so far. To this end, we propose a new method to attack action recognizers that rely on 3D skeletal motion. Our method involves an innovative perceptual loss that ensures the imperceptibility of the attack. Empirical studies demonstrate that our method is effective in both white-box and black-box scenarios. Its generalizability is evidenced on a variety of action recognizers and datasets. Its versatility is shown in different attacking strategies. Its deceitfulness is proven in extensive perceptual studies. Our method shows that adversarial attack on 3D skeletal motions, one type of time-series data, is significantly different from traditional adversarial attack problems. Its success raises serious concern on the robustness of action recognizers and provides insights on potential improvements.Comment: Accepted in CVPR 2021. arXiv admin note: substantial text overlap with arXiv:1911.0710

Ubiquitin ligase RNF125 targets PD-L1 for ubiquitination and degradation

As a critical immune checkpoint molecule, PD-L1 is expressed at significantly higher levels in multiple neoplastic tissues compared to normal ones. PD-L1/PD-1 axis is a critical target for tumor immunotherapy, blocking the PD-L1/PD-1 axis is recognized and has achieved unprecedented success in clinical applications. However, the clinical efficacy of therapies targeting the PD-1/PD-L1 pathway remains limited, emphasizing the need for the mechanistic elucidation of PD-1/PD-L1 expression. In this study, we found that RNF125 interacted with PD-L1 and regulated PD-L1 protein expression. Mechanistically, RNF125 promoted K48-linked polyubiquitination of PD-L1 and mediated its degradation. Notably, MC-38 and H22 cell lines with RNF125 knockout, transplanted in C57BL/6 mice, exhibited a higher PD-L1 level and faster tumor growth than their parental cell lines. In contrast, overexpression of RNF125 in MC-38 and H22 cells had the opposite effect, resulting in lower PD-L1 levels and delayed tumor growth compared with parental cell lines. In addition, immunohistochemical analysis of MC-38 tumors with RNF125 overexpression showed significantly increased infiltration of CD4+, CD8+ T cells and macrophages. Consistent with these findings, analyses using The Cancer Genome Atlas (TCGA) public database revealed a positive correlation of RNF125 expression with CD4+, CD8+ T cell and macrophage tumor infiltration. Moreover, RNF125 expression was significantly downregulated in several human cancer tissues, and was negatively correlated with the clinical stage of these tumors, and patients with higher RNF125 expression had better clinical outcomes. Our findings identify a novel mechanism for regulating PD-L1 expression and may provide a new strategy to increase the efficacy of immunotherapy

Modeling for wind-thermal combined bidding considering bilateral tail information

The stochastic output of wind power will lead to the penalty of bidding deviation in the spot market and bring bidding risk, restricting the participation of wind power in market competition. The combined bidding strategy for multiple power generation stakeholders can help alleviate the output uncertainty of wind power, reducing the bidding deviation penalty. This paper aims at the modeling method for wind-thermal combined bidding in the spot power market. Conditional value at risk (CVaR) is used to describe the bidding risk of the combined bidding. To better reflect the bidding risk, the bilateral tail information of the upper and lower deviations is modeled according to the revenue function. The uncertainties of wind power output and the clearing price in the day-ahead market are considered, and the risk decision-making model for wind-thermal combined bidding is established. The model is solved by the chaotic particle swarm optimization algorithm with constraint handling (EDFC-PSO). The results show that the proposed modeling method can describe the risk indicators in detail, providing a better reference for market bidding. Also, the combination of the wind farm and the thermal power unit can reduce the risk of wind power in bidding and improve its profitability in the day-ahead market. Besides, the risk speculation of the wind farm can be found when facing the inevitable increased risks. It means the behaviors of the market participants can be better explained using the proposed method in this paper

Motion Control of a Gecko-like Robot Based on a Central Pattern Generator

To solve the problem of the motion control of gecko-like robots in complex environments, a central pattern generator (CPG) network model of motion control was designed. The CPG oscillation model was first constructed using a sinusoidal function, resulting in stable rhythm control signals for each joint of the gecko-like robot. Subsequently, the gecko-like robot successfully walked, crossed obstacles and climbed steps in the vertical plane, based on stable rhythm control signals. Both simulations and experiments validating the feasibility of the proposed CPG motion control model are presented

Distribution Network Congestion Dispatch Considering Time-Spatial Diversion of Electric Vehicles Charging

With the popularization of electric vehicles, free charging behaviors of electric vehicle owners can lead to uncertainty about charging in both time and space. A time-spatial dispatching strategy for the distribution network guided by electric vehicle charging fees is proposed in this paper, which aims to solve the network congestion problem caused by the unrestrained and free charging behaviors of large numbers of electric vehicles. In this strategy, congestion severity of different lines is analyzed and the relationship between the congested lines and the charging stations is clarified. A price elastic matrix is introduced to reflect the degree of owners’ response to the charging prices. A pricing scheme for optimal real-time charging fees for multiple charging stations is designed according to the congestion severity of the lines and the charging power of the related charging stations. Charging price at different charging station at different time is different, it can influence the charging behaviors of vehicle owners. The simulation results confirmed that the proposed congestion dispatching strategy considers the earnings of the operators, charging cost to the owners and the satisfaction of the owners. Moreover, the strategy can influence owners to make judicious charging plans that help to solve congestion problems in the network and improve the safety and economy of the power grid

Sample Entropy Based Net Load Tracing Dispatch of New Energy Power System

The high-proportion of renewable energies is gradually becoming one of the main power supply sources and bringing strong uncertainties to the power grid. In this paper, a sample entropy (SampEn) based net load tracing dispatch strategy with a specific thermal generating mode is proposed. In this strategy, renewable energies are fully and preferentially consumed by electric loads, turned to net loads, to maximize the utilization of renewable energies. SampEn theory is utilized to evaluate the complexity of net load time series, based on which, the traditional power generators trace the complexity of the net load flexibly. According to the SampEn, a specific generating model of thermal generators is determined and the cooperation between thermal generators and pumped storage is realized, aiming at reducing the ramp power of thermal generators and increasing the throughput of pumped storage. The experiment simulation is developed on the 10-unit test system. Results show that the ramping power of the thermal generators are reduced 43% and 13% in the two cases together with the throughput of pumped storage is increased 44% and 27% on the premise that the economy of the system is maintained and renewable energies are fully consumed. Therefore, the efficiency and reasonability of the proposed dispatch strategy are confirmed

A Real-Time Safety Helmet Wearing Detection Approach Based on CSYOLOv3

In the practical scenario of construction sites with extremely complicated working environment and numerous personnel, it is challenging to detect safety helmet wearing (SHW) in real time on the premise of ensuring high precision performance. In this paper, a novel SHW detection model on the basis of improved YOLOv3 (named CSYOLOv3) is presented to heighten the capability of target detection on the construction site. Firstly, the backbone network of darknet53 is improved by applying the cross stage partial network (CSPNet), which reduces the calculation cost and improves the training speed. Secondly, the spatial pyramid pooling (SPP) structure is employed in the YOLOv3 model, and the multi-scale prediction network is improved by combining the top-down and bottom-up feature fusion strategies to realize the feature enhancement. Finally, the safety helmet wearing detection dataset containing 10,000 images is established using the construction site cameras, and the manual annotation is required for the model training. Experimental data and contrastive curves demonstrate that, compared with YOLOv3, the novel method can largely ameliorate mAP by 28% and speed is improved by 6 fps

Hypolipidemic Effect of Rice Bran Oil Extract Tocotrienol in High-Fat Diet-Induced Hyperlipidemia Zebrafish (Danio Rerio) Induced by High-Fat Diet

In recent years, the potent influence of tocotrienol (T3) on diminishing blood glucose and lipid concentrations in both Mus musculus (rats) and Homo sapiens (humans) has been established. However, the comprehensive exploration of tocotrienol’s hypolipidemic impact and the corresponding mechanisms in aquatic species remains inadequate. In this study, we established a zebrafish model of a type 2 diabetes mellitus (T2DM) model through high-fat diet administration to zebrafish. In the T2DM zebrafish, the thickness of ocular vascular walls significantly increased compared to the control group, which was mitigated after treatment with T3. Additionally, our findings demonstrate the regulatory effect of T3 on lipid metabolism, leading to the reduced synthesis and storage of adipose tissue in zebrafish. We validated the expression patterns of genes relevant to these processes using RT-qPCR. In the T2DM model, there was an almost two-fold upregulation in pparγ and cyp7a1 mRNA levels, coupled with a significant downregulation in cpt1a mRNA (p Pparγ and Rxrα exhibited a two-fold elevation in the T2DM group relative to the control. In the T3-treated group, Pparγ and Rxrα protein expression levels consistently exhibited a two-fold decrease compared to the model group. Lipid metabolomics showed that T3 could affect the metabolic pathways of zebrafish lipid regulation, including lipid synthesis and decomposition. We provided experimental evidence that T3 could mitigate lipid accumulation in our zebrafish T2DM model. Elucidating the lipid-lowering effects of T3 could help to minimize the detrimental impacts of overfeeding in aquaculture