Multi-Agent Game Abstraction via Graph Attention Neural Network

In large-scale multi-agent systems, the large number of agents and complex game relationship cause great difficulty for policy learning. Therefore, simplifying the learning process is an important research issue. In many multi-agent systems, the interactions between agents often happen locally, which means that agents neither need to coordinate with all other agents nor need to coordinate with others all the time. Traditional methods attempt to use pre-defined rules to capture the interaction relationship between agents. However, the methods cannot be directly used in a large-scale environment due to the difficulty of transforming the complex interactions between agents into rules. In this paper, we model the relationship between agents by a complete graph and propose a novel game abstraction mechanism based on two-stage attention network (G2ANet), which can indicate whether there is an interaction between two agents and the importance of the interaction. We integrate this detection mechanism into graph neural network-based multi-agent reinforcement learning for conducting game abstraction and propose two novel learning algorithms GA-Comm and GA-AC. We conduct experiments in Traffic Junction and Predator-Prey. The results indicate that the proposed methods can simplify the learning process and meanwhile get better asymptotic performance compared with state-of-the-art algorithms.Comment: Accepted by AAAI202

Manifold Learning Side-Channel Attacks against Masked Cryptographic Implementations

Masking, as a common countermeasure, has been widely utilized to protect cryptographic implementations against power side-channel attacks. It significantly enhances the difficulty of attacks, as the sensitive intermediate values are randomly partitioned into multiple parts and executed on different times. The adversary must amalgamate information across diverse time samples before launching an attack, which is generally accomplished by feature extraction (e.g., Points-Of-Interest (POIs) combination and dimensionality reduction). However, traditional POIs combination methods, machine learning and deep learning techniques are often too time consuming, and necessitate a significant amount of computational resources. In this paper, we undertake the first study on manifold learning and their applications against masked cryptographic implementations. The leaked information, which manifests as the manifold of high-dimensional power traces, is mapped into a low-dimensional space and achieves feature extraction through manifold learning techniques like ISOMAP, Locally Linear Embedding (LLE), and Laplacian Eigenmaps (LE). Moreover, to reduce the complexity, we further construct explicit polynomial mappings for manifold learning to facilitate the dimensionality reduction. Compared to the classical machine learning and deep learning techniques, our schemes built from manifold learning techniques are faster, unsupervised, and only require very simple parameter tuning. Their effectiveness has been fully validated by our detailed experiments

From Few to More: Large-scale Dynamic Multiagent Curriculum Learning

A lot of efforts have been devoted to investigating how agents can learn effectively and achieve coordination in multiagent systems. However, it is still challenging in large-scale multiagent settings due to the complex dynamics between the environment and agents and the explosion of state-action space. In this paper, we design a novel Dynamic Multiagent Curriculum Learning (DyMA-CL) to solve large-scale problems by starting from learning on a multiagent scenario with a small size and progressively increasing the number of agents. We propose three transfer mechanisms across curricula to accelerate the learning process. Moreover, due to the fact that the state dimension varies across curricula,, and existing network structures cannot be applied in such a transfer setting since their network input sizes are fixed. Therefore, we design a novel network structure called Dynamic Agent-number Network (DyAN) to handle the dynamic size of the network input. Experimental results show that DyMA-CL using DyAN greatly improves the performance of large-scale multiagent learning compared with state-of-the-art deep reinforcement learning approaches. We also investigate the influence of three transfer mechanisms across curricula through extensive simulations.Comment: Accepted by AAAI202

Elevated atmospheric CO2 concentration triggers redistribution of nitrogen to promote tillering in rice

Elevated atmospheric CO2 concentration (eCO2) often reduces nitrogen (N) content in rice plants and stimulates tillering. However, there is a general consensus that reduced N would constrain rice tillering. To resolve this contradiction, we investigated N distribution and transcriptomic changes in different rice plant organs after subjecting them to eCO2 and different N application rates. Our results showed that eCO2 significantly promoted rice tillers (by 0.6, 1.1, 1.7, and 2.1 tillers/plant at 0, 75, 150, and 225 kg N ha−1 N application rates, respectively) and more tillers were produced under higher N application rates, confirming that N availability constrained tillering in the early stages of growth. Although N content declined in the leaves (−11.0 to −20.7 mg g−1) and sheaths (−9.8 to −28.8 mg g−1) of rice plants exposed to eCO2, the N content of newly emerged tillers on plants exposed to eCO2 equaled or exceeded the N content of tillers produced under ambient CO2 conditions. Apparently, the redistribution of N within the plant per se was a critical adaptation strategy to the eCO2 condition. Transcriptomic analysis revealed that eCO2 induced less extensive alteration of gene expression than did N application. Most importantly, the expression levels of multiple N-related transporters and receptors such as nitrate transporter NRT2.3a/b and NRT1.1a/b were differentially regulated in leaf and shoot apical meristem, suggesting that multiple genes were involved in sensing the N signal and transporting N metabolites to adapt to eCO2. The redistribution of N in different organs could be a universal adaptation strategy of terrestrial plants to eCO2

Emerging roles of i-motif in gene expression and disease treatment

As non-canonical nucleic acid secondary structures consisting of cytosine-rich nucleic acids, i-motifs can form under certain conditions. Several i-motif sequences have been identified in the human genome and play important roles in biological regulatory functions. Due to their physicochemical properties, these i-motif structures have attracted attention and are new targets for drug development. Herein, we reviewed the characteristics and mechanisms of i-motifs located in gene promoters (including c-myc, Bcl-2, VEGF, and telomeres), summarized various small molecule ligands that interact with them, and the possible binding modes between ligands and i-motifs, and described their effects on gene expression. Furthermore, we discussed diseases closely associated with i-motifs. Among these, cancer is closely associated with i-motifs since i-motifs can form in some regions of most oncogenes. Finally, we introduced recent advances in the applications of i-motifs in multiple areas

SPI1-induced downregulation of FTO promotes GBM progression by regulating pri-miR-10a processing in an m6A-dependent manner

As one of the most common post-transcriptional modifications of mRNAs and noncoding RNAs, N6-methyladenosine (m6A) modification regulates almost every aspect of RNA metabolism. Evidence indicates that dysregulation of m6A modification and associated proteins contributes to glioblastoma (GBM) progression. However, the function of fat mass and obesity-associated protein (FTO), an m6A demethylase, has not been systematically and comprehensively explored in GBM. Here, we found that decreased FTO expression in clinical specimens correlated with higher glioma grades and poorer clinical outcomes. Functionally, FTO inhibited growth and invasion in GBM cells in vitro and in vivo. Mechanistically, FTO regulated the m6A modification of primary microRNA-10a (pri-miR-10a), which could be recognized by reader HNRNPA2B1, recruiting the microRNA microprocessor complex protein DGCR8 and mediating pri-miR-10a processing. Furthermore, the transcriptional activity of FTO was inhibited by the transcription factor SPI1, which could be specifically disrupted by the SPI1 inhibitor DB2313. Treatment with this inhibitor restored endogenous FTO expression and decreased GBM tumor burden, suggesting that FTO may serve as a novel prognostic indicator and therapeutic molecular target of GBM.publishedVersio

The dual role of glioma exosomal microRNAs: glioma eliminates tumor suppressor miR-1298-5p via exosomes to promote immunosuppressive effects of MDSCs

Clear evidence shows that tumors could secrete microRNAs (miRNAs) via exosomes to modulate the tumor microenvironment (TME). However, the mechanisms sorting specific miRNAs into exosomes are still unclear. In order to study the biological function and characterization of exosomal miRNAs, we performed whole-transcriptome sequencing in 59 patients’ whole-course cerebrospinal fluid (CSF) small extracellular vesicles (sEV) and matched glioma tissue samples. The results demonstrate that miRNAs could be divided into exosome-enriched miRNAs (ExomiRNAs) and intracellular-retained miRNAs (CLmiRNAs), and exosome-enriched miRNAs generally play a dual role. Among them, miR-1298-5p was enriched in CSF exosomes and suppressed glioma progression in vitro and vivo experiments. Interestingly, exosomal miR-1298-5p could promote the immunosuppressive effects of myeloid-derived suppressor cells (MDSCs) to facilitate glioma. Therefore, we found miR-1298-5p had different effects on glioma cells and MDSCs. Mechanically, downstream signaling pathway analyses showed that miR-1298-5p plays distinct roles in glioma cells and MDSCs via targeting SETD7 and MSH2, respectively. Moreover, reverse verification was performed on the intracellular-retained miRNA miR-9-5p. Thus, we confirmed that tumor-suppressive miRNAs in glioma cells could be eliminated through exosomes and target tumor-associated immune cells to induce tumor-promoting phenotypes. Glioma could get double benefit from it. These findings uncover the mechanisms that glioma selectively sorts miRNAs into exosomes and modulates tumor immunity.publishedVersio

Early Cretaceous anuran from China.

39 pages : illustrations (some color), map ; 26 cm.Based on 12 well-preserved skeletons of postmetamorphic individuals, a new crown-group frog taxon is named and described from the Lower Cretaceous Guanghua (upper part of Longjiang) Formation (stratigraphic equivalent of the world-famed Yixian Formation) exposed in Dayangshu Basin, Hulunbuir, in the far northeast of Inner Mongolia, China. The new taxon, Genibatrachus baoshanensis, documents another early Cretaceous anuran having reduction of the presacral vertebrae to eight in number, similar to several frog taxa of roughly the same age from Spain and Brazil. The new frog also displays several features that are ontogenetically and phylogenetically informative, including ontogenetic fusion of the palatine to the sphenethmoid, and ontogenetic fusion of ribs to the diapophyses of the posterior trunk vertebrae. In addition, the new discovery extends the geographic range of early Cretaceous frogs of the Jehol Biota northward to near the 50th parallel north in East Asia