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Nash Equilibrium (NE) is the canonical solution concept of game theory, which provides an elegant tool to understand the rationalities. Though mixed strategy NE exists in any game with finite players and actions, computing NE in two- or multi-player general-sum games is PPAD-Complete. Various alternative solutions, e.g., Correlated Equilibrium (CE), and learning methods, e.g., fictitious play (FP), are proposed to approximate NE. For convenience, we call these methods as ``inexact solvers'', or ``solvers'' for short. However, the alternative solutions differ from NE and the learning methods generally fail to converge to NE. Therefore, in this work, we propose REinforcement Nash Equilibrium Solver (RENES), which trains a single policy to modify the games with different sizes and applies the solvers on the modified games where the obtained solution is evaluated on the original games. Specifically, our contributions are threefold. i) We represent the games as alpha-rank response graphs and leverage graph neural network (GNN) to handle the games with different sizes as inputs; ii) We use tensor decomposition, e.g., canonical polyadic (CP), to make the dimension of modifying actions fixed for games with different sizes; iii) We train the modifying strategy for games with the widely-used proximal policy optimization (PPO) and apply the solvers to solve the modified games, where the obtained solution is evaluated on original games. Extensive experiments on large-scale normal-form games show that our method can further improve the approximation of NE of different solvers, i.e., alpha-rank, CE, FP and PRD, and can be generalized to unseen games. 

Policy-Space Response Oracles (PSRO) as a general algorithmic framework has achieved state-of-the-art performance in learning equilibrium policies of two-player zero-sum games. However, the hand-crafted hyperparameter value selection in most of the existing works requires extensive domain knowledge, forming the main barrier to applying PSRO to different games. In this work, we make the first attempt to investigate the possibility of self-adaptively determining the optimal hyperparameter values in the PSRO framework. Our contributions are three-fold: (1) Using several hyperparameters, we propose a parametric PSRO that unifies the gradient descent ascent (GDA) and different PSRO variants. (2) We propose the self-adaptive PSRO (SPSRO) by casting the hyperparameter value selection of the parametric PSRO as a hyperparameter optimization (HPO) problem where our objective is to learn an HPO policy that can self-adaptively determine the optimal hyperparameter values during the running of the parametric PSRO. (3) To overcome the poor performance of online HPO methods, we propose a novel offline HPO approach to optimize the HPO policy based on the Transformer architecture. Experiments on various two-player zero-sum games demonstrate the superiority of SPSRO over different baselines. 

Eukaryotic elongation factors (eEFs) are protein factors that mediate the extension of peptide chain, among which eukaryotic elongation factor 1 alpha (eEF1A) is one of the most abundant protein synthesis factors. Previously we showed that the P3 protein of Soybean mosaic virus (SMV), one of the most destructive and successful viral pathogens of soybean, targets a component of the soybean translation elongation complex to facilitate its pathogenesis. Here, we conducted a systematic analyses of the soybeaneEF(GmeEF) gene family in soybean and examinedits role in virus resistance. In this study, GmeEF family members were identified and characterized based on sequence analysis. The 42 members, which were unevenly distributed across the 15 chromosomes, were renamed according to their chromosomal locations. The GmeEF members were further divided into 12 subgroups based on conserved motif, gene structure, and phylogenetic analyses. Analysis of the promoter regions showed conspicuous presence of myelocytomatosis (MYC) and ethylene-responsive (ERE) cis-acting elements, which are typically involved in drought and phytohormone response, respectively, and thereby in plant stress response signaling. Transcriptome data showed that the expression of 15GmeEFgene family members changed significantly in response to SMV infection. To further examine EF1A function in pathogen response, three different Arabidopsis mutants carrying T-DNA insertions in orthologous genes were analyzed for their response to Turnip crinkle virus (TCV) and Cucumber mosaic virus (CMV). Results showed that there was no difference in viral response between the mutants and the wild type plants. This study provides a systematic analysis of theGmeEFgene family through analysis of expression patterns and predicted protein features. Our results lay a foundation for understanding the role ofeEFgene in soybean anti-viral response. 

A self-assembling peptide nanofiber was developed to sense the microenvironmental pH change associated with bacterial growth. Using a near-infrared probe, a strong correlation was observed between the local pH reduction of bacterial colonies with the degree of peptide disassembly, which led to their enhanced antimicrobial activity against anaerobic bacteria.