The high-quality generation results of conditional diffusion models have brought about concerns regarding privacy and copyright issues. As a possible technique for preventing the abuse of diffusion models, the adversarial attack against diffusion models has attracted academic attention recently. In this work, utilizing the phenomenon that diffusion models are highly sensitive to the mean value of the input noise, we propose the Mean Fluctuation Attack (MFA) to introduce mean fluctuations by shifting the mean values of the estimated noises during the reverse process. In addition, we reveal that the vulnerability of different reverse steps against adversarial attacks actually varies significantly. By modeling the step vulnerability and using it as guidance to sample the target steps for generating adversarial examples, the effectiveness of adversarial attacks can be substantially enhanced. Extensive experiments show that our algorithm can steadily cause the mean shift of the predicted noises so as to disrupt the entire reverse generation process and degrade the generation results significantly. We also demonstrate that the step vulnerability is intrinsic to the reverse process by verifying its effectiveness in an attack method other than MFA. Code and Supplementary is available at https://github.com/yuhongwei22/MFA

Circular RNA (circRNA) is closely associated with human diseases. Accordingly, identifying the associations between human diseases and circRNA can help in disease prevention, diagnosis and treatment. Traditional methods are time consuming and laborious. Meanwhile, computational models can effectively predict potential circRNA-disease associations (CDAs), but are restricted by limited data, resulting in data with high dimension and imbalance. In this study, we propose a model based on automatically selected meta-path and contrastive learning, called the MPCLCDA model. First, the model constructs a new heterogeneous network based on circRNA similarity, disease similarity and known association, via automatically selected meta-path and obtains the low-dimensional fusion features of nodes via graph convolutional networks. Then, contrastive learning is used to optimize the fusion features further, and obtain the node features that make the distinction between positive and negative samples more evident. Finally, circRNA-disease scores are predicted through a multilayer perceptron. The proposed method is compared with advanced methods on four datasets. The average area under the receiver operating characteristic curve, area under the precision-recall curve and F1 score under 5-fold cross-validation reached 0.9752, 0.9831 and 0.9745, respectively. Simultaneously, case studies on human diseases further prove the predictive ability and application value of this method.

Abstract 3D human pose and shape estimation is the foundation of analyzing human motion. However, estimating accurate and temporally consistent 3D human motion from a video remains a challenge. By now, most of the video‐based methods for estimating 3D human pose and shape rely on unidirectional temporal features and lack more comprehensive information. To solve this problem, we propose a novel model “bidirectional temporal feature for human motion recovery” (BTMR), which consists of a human motion generator and a discriminator. The transformer‐based generator effectively captures the forward and reverse temporal features to enhance the temporal correlation between frames and reduces the loss of spatial information. The motion discriminator based on Bi‐LSTM can distinguish whether the generated pose sequences are consistent with the realistic sequences of the AMASS dataset. In the process of continuous generation and discrimination, the model can learn more realistic and accurate poses. We evaluate our BTMR on 3DPW and MPI‐INF‐3DHP datasets. Without the training set of 3DPW, BTMR outperforms VIBE by 2.4 mm and 14.9 mm/s2 in PA‐MPJPE and Accel metrics and outperforms TCMR by 1.7 mm in PA‐MPJPE metric on 3DPW. The results demonstrate that our BTMR produces better accurate and temporal consistent 3D human motion.

Increasing evidences show that the occurrence of human complex diseases is closely related to microRNA (miRNA) variation and imbalance. For this reason, predicting disease-related miRNAs is essential for the diagnosis and treatment of complex human diseases. Although some current computational methods can effectively predict potential disease-related miRNAs, the accuracy of prediction should be further improved. In our study, a new computational method via deep forest ensemble learning based on autoencoder (DFELMDA) is proposed to predict miRNA-disease associations. Specifically, a new feature representation strategy is proposed to obtain different types of feature representations (from miRNA and disease) for each miRNA-disease association. Then, two types of low-dimensional feature representations are extracted by two deep autoencoders for predicting miRNA-disease associations. Finally, two prediction scores of the miRNA-disease associations are obtained by the deep random forest and combined to determine the final results. DFELMDA is compared with several classical methods on the The Human microRNA Disease Database (HMDD) dataset. Results reveal that the performance of this method is superior. The area under receiver operating characteristic curve (AUC) values obtained by DFELMDA through 5-fold and 10-fold cross-validation are 0.9552 and 0.9560, respectively. In addition, case studies on colon, breast and lung tumors of different disease types further demonstrate the excellent ability of DFELMDA to predict disease-associated miRNA-disease. Performance analysis shows that DFELMDA can be used as an effective computational tool for predicting miRNA-disease associations.

Abstract In view of the fact that the existing public opinion propagation aspects are mostly based on single-layer propagation network, these works rarely consider the double-layer network structure and the negative opinion evolution. This paper proposes a new susceptible-infected-vaccinated-susceptible negative opinion information propagation model with preventive vaccination by constructing double-layer network topology. Firstly, the continuous-time Markov chain is used to simulate the negative public opinion information propagation process and the nonlinear dynamic equation of the model is derived; secondly, the steady state condition of the virus propagation in the model is proposed and mathematically proved; finally, Monte Carlo method is applied in the proposed model. The parameters of simulation model have an effect on negative public opinion information propagation, the derivation results are verified by computer simulation. The simulation results show that the proposed model has a larger threshold of public opinion information propagation and has more effective control of the scale of negative public opinion; it also can reduce the density of negative public opinion information propagation and suppress negative public opinion information compared with the traditional susceptible infected susceptible model. It also can provide the scientific method and research approach based on probability statistics for the study of negative public opinion information propagation in complex networks.

With the rapid development of cloud computing, major cloud providers have launched various cloud services with different functions to meet customer's needs. Therefore, flexibility is extremely important when developers use these cloud services. However, APIs of cloud services change dozens of times annually without backward compatibility. It means developers have to adapt these clouds with manual efforts. Such efforts make the multi-cloud collaboration extremely complex and cannot meet the demand of flexibility. This paper describes a configuration-based multi-cloud collaboration framework, which can support new clouds with comprehensible configurations. Meanwhile, if cloud APIs are updated without backward compatibility, it can restore services during runtime with minimized configuration. The main technologies used in this article include automatic discovery, unified abstraction, dynamic mapping and incremental update. We tested the virtual machine and container services of seven well-known cloud providers. The system can support heterogeneous clouds well. When the APIs are updated, the system can restore services in less than 200 milliseconds. At the same time, the extra cost of our framework is acceptable to cloud users.

The elimination of leakage current is of first importance for transformerless grid-tied photovoltaic (PV) inverters. A unified leakage current analytical model for single phase inverters, which unifies topologies of both transformer and transformerless, is presented in this paper. It is suitable for the analysis of leakage current elimination mechanism for all the bridge topologies, which are systematically analyzed and categorized. After that, the topologies of H5, Heric and a H6-type configuration are optimized. The leakage current of these optimized inverters can be better eliminated by changing the topology configuration and substituting the suppressor way of changing the common impedance with the way of constant common voltage. On the other hand, the inverters can operate in any power factors by means of the optimization of the modulation strategies. A grid-tied photovoltaic inverter experiment is conducted with a 5kW optimized Heric prototype, and the theoretical analysis is verified.