Anti-CRISPR proteins play a crucial role in bacterial-phage interactions by inhibiting the CRISPR/Cas system and thus enhancing phage survival. Accurately predicting these proteins is essential for understanding phage-host immune interactions and progressing CRISPR/ Cas-based technologies. Current approaches primarily analyze proteins individually, which may overlook the intrinsic similarities and potential connections among protein sequences. This study introduces PACRGNN, a graph neural network framework that creates a heterogeneous protein network by integrating sequence and structural similarities, wherein nodes represent proteins and edges signify their relationships. By combining Graph Attention (GAT) and Graph Sample and Aggregation (GraphSAGE) layers, PACRGNN captures both local and global topological dependencies, while incorporating six protein feature categories to enrich node representations. PACRGNN achieves an accuracy of 0.9577, an F1-Score of 0.9572, and a PRAUC of 0.9876 on the validation set. The model demonstrated superior performance to existing methods on the independent test set derived from NCBI database (Jan.-Oct. 2024).

This brief designs a distributed stochastic annealing algorithm for nonconvex cooperative aggregative games, whose players' cost functions not only depend on players' own decision variables but also rely on the sum of players' decision variables. To seek the social optimum of cooperative aggregative games, a distributed stochastic annealing algorithm is proposed, where the local cost functions are nonconvex and the communication topology between players is time-varying. The weak convergence to the social optimum of the algorithm is further analyzed. A numerical example is finally given to illustrate the effectiveness of the proposed algorithm.

Neural Architecture Search (NAS) aims to automatically find optimal neural network architectures in an efficient way. Zero-Shot NAS is a promising technique that leverages proxies to predict the accuracy of candidate architectures without any training. However, we have observed that most existing proxies do not consistently perform well across different search spaces, and are less concerned with generalization. Recently, the gradient signal-to-noise ratio (GSNR) was shown to be correlated with neural network generalization performance. In this paper, we not only explicitly give the probability that larger GSNR at network initialization can ensure better generalization, but also theoretically prove that GSNR can ensure better convergence. Then we design the ξ-based gradient signal-to-noise ratio (ξ-GSNR) as a Zero-Shot NAS proxy to predict the network accuracy at initialization. Extensive experiments in different search spaces demonstrate that ξ-GSNR provides superior ranking consistency compared to previous proxies. Moreover, ξ-GSNR-based Zero-Shot NAS also achieves outstanding performance when directly searching for the optimal architecture in various search spaces and datasets. The source code is available at https://github.com/Sunzh1996/Xi-GSNR.

Most GAN(Generative Adversarial Network)-based approaches towards high-fidelity waveform generation heavily rely on discriminators to improve their performance.However, GAN methods introduce much uncertainty into the generation process and often result in mismatches of pitch and intensity, which is fatal when it comes to sensitive use cases such as singing voice synthesis(SVS).To address this problem, we propose RefineGAN, a highfidelity neural vocoder focused on the robustness, pitch and intensity accuracy, and high-speed full-band audio generation.We applyed a pitch-guided refine architecture with a multi-scale spectrogram-based loss function to help stabilize the training process and maintain the robustness of the neural vocoder while using the GAN-based training method.Audio generated using this method shows a better performance in subjective tests when compared with the groundtruth audio.This result shows that the fidelity is even improved during the waveform reconstruction by eliminating defects produced by recording procedures.Moreover, it shows that models trained on a specified type of data can perform on totally unseen language and unseen speaker identically well.Generated sample pairs are provided on https://timedomaintech.github.io/refinegan/ .