Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games

Many artificial intelligence (AI) applications often require multiple intelligent agents to work in a collaborative effort. Efficient learning for intra-agent communication and coordination is an indispensable step towards general AI. In this paper, we take StarCraft combat game as a case study, where the task is to coordinate multiple agents as a team to defeat their enemies. To maintain a scalable yet effective communication protocol, we introduce a Multiagent Bidirectionally-Coordinated Network (BiCNet ['bIknet]) with a vectorised extension of actor-critic formulation. We show that BiCNet can handle different types of combats with arbitrary numbers of AI agents for both sides. Our analysis demonstrates that without any supervisions such as human demonstrations or labelled data, BiCNet could learn various types of advanced coordination strategies that have been commonly used by experienced game players. In our experiments, we evaluate our approach against multiple baselines under different scenarios; it shows state-of-the-art performance, and possesses potential values for large-scale real-world applications.Comment: 10 pages, 10 figures. Previously as title: "Multiagent Bidirectionally-Coordinated Nets for Learning to Play StarCraft Combat Games", Mar 201

New insights into the reaction of tricalcium silicate (C3S) with solutions to the end of the induction period

Although dissolution theory is widely used, in certain circumstance, it seems to be unable to explain the hydration of C3S. In this article, more attention is paid to the nucleation of hydration products. We find that the precipitation of C-S-H is a nonclassical nucleation process. It starts with nucleation of primary particles and then grows by particle attachment. A sharp increase in the reaction rate after induction period may come from the accelerating growth rate of C-S-H instead of dissolution of etch pits. The duration of induction period relates to the size of primary floc. Potassium salts influence the primary globule floc size and mitigate the effect from Al. The pH impacts ion species in solution to affect the dissolution and precipitation. A hypothesis regarding the dissolution of C3S and nucleation of C-S-H within the near-surface region may narrow the gap between dissolution theory and protective layer theory

Adapting Segment Anything Model for Change Detection in HR Remote Sensing Images

Vision Foundation Models (VFMs) such as the Segment Anything Model (SAM) allow zero-shot or interactive segmentation of visual contents, thus they are quickly applied in a variety of visual scenes. However, their direct use in many Remote Sensing (RS) applications is often unsatisfactory due to the special imaging characteristics of RS images. In this work, we aim to utilize the strong visual recognition capabilities of VFMs to improve the change detection of high-resolution Remote Sensing Images (RSIs). We employ the visual encoder of FastSAM, an efficient variant of the SAM, to extract visual representations in RS scenes. To adapt FastSAM to focus on some specific ground objects in the RS scenes, we propose a convolutional adaptor to aggregate the task-oriented change information. Moreover, to utilize the semantic representations that are inherent to SAM features, we introduce a task-agnostic semantic learning branch to model the semantic latent in bi-temporal RSIs. The resulting method, SAMCD, obtains superior accuracy compared to the SOTA methods and exhibits a sample-efficient learning ability that is comparable to semi-supervised CD methods. To the best of our knowledge, this is the first work that adapts VFMs for the CD of HR RSIs

Automatic Definition and Application of Similarity Measures for Self-operation of Network

Self-operation concept is proposed to learn the past experiences of network operations and apply the learned operation experiences to solve new but similar problems. It works based upon the observation that actions appropriate for achieving an objective resemble each other in similar network contexts. Plenty of such similarities exist at the level of network elements, functions, and their relations. Similarity measure definition and application are essential components for the self-operation to apply the learned operation experiences. This paper provides a solution for self-operation to define and apply two types of similarity measures for two self-operation use cases. The first use case answers how to select a best suitable function to achieve any given objective. The second use case tells how the selected function should be configured with the most optimal parameter values so that the given objective could be achieved. This solution is realized on a demonstrator implementing the self-operation concept. Corresponding experiments are made with the demonstrator. The experimental results show that the self-operation solution works well.Peer reviewe

Coherent perfect absorber and laser induced by directional emissions in the non-Hermitian photonic crystals

In this study, we propose the application of non-Hermitian photonic crystals (PCs) with anisotropic emissions. Unlike a ring of exceptional points (EPs) in isotropic non-Hermitian PCs, the EPs of anisotropic non-Hermitian PCs appear as lines symmetrical about the $\Gamma$ point. The non-Hermitian Hamiltonian indicates that the formation of EPs is related to the non-Hermitian strength. The real spectrum appears in the $\Gamma$Y direction and has been validated as the complex conjugate medium (CCM) by effective medium theory (EMT). But for the $\Gamma$X direction, EMT indicates that the effective refractive index has a large imaginary part, which forms an evanescent wave inside the PCs. Thence, coherent perfect absorber (CPA) and laser effects can be achieved in the directional emission of the $\Gamma$Y. The outgoing wave in the $\Gamma$X direction is weak, which can significantly reduce the losses and electromagnetic interference caused by the leakage waves. Furthermore, the non-Hermitian PCs enable many fascinating applications such as signal amplification, collimation, and angle sensors.Comment: 11 pages, 11 figure

Adaptive Pairwise Encodings for Link Prediction

Link prediction is a common task on graph-structured data that has seen applications in a variety of domains. Classically, hand-crafted heuristics were used for this task. Heuristic measures are chosen such that they correlate well with the underlying factors related to link formation. In recent years, a new class of methods has emerged that combines the advantages of message-passing neural networks (MPNN) and heuristics methods. These methods perform predictions by using the output of an MPNN in conjunction with a "pairwise encoding" that captures the relationship between nodes in the candidate link. They have been shown to achieve strong performance on numerous datasets. However, current pairwise encodings often contain a strong inductive bias, using the same underlying factors to classify all links. This limits the ability of existing methods to learn how to properly classify a variety of different links that may form from different factors. To address this limitation, we propose a new method, LPFormer, which attempts to adaptively learn the pairwise encodings for each link. LPFormer models the link factors via an attention module that learns the pairwise encoding that exists between nodes by modeling multiple factors integral to link prediction. Extensive experiments demonstrate that LPFormer can achieve SOTA performance on numerous datasets while maintaining efficiency

Identification of Hydrolyzable Tannins (Punicalagin, Punicalin and Geraniin) as Novel inhibitors of Hepatitis B Virus Covalently Closed Circular DNA

The development of new agents to target HBV cccDNA is urgently needed because of the limitations of current available drugs for treatment of hepatitis B. By using a cell-based assay in which the production of HBeAg is in a cccDNA-dependent manner, we screened a compound library derived from Chinese herbal remedies for inhibitors against HBV cccDNA. Three hydrolyzable tannins, specifically punicalagin, punicalin and geraniin, emerged as novel anti-HBV agents. These compounds significantly reduced the production of secreted HBeAg and cccDNA in a dose-dependent manner in our assay, without dramatic alteration of viral DNA replication. Furthermore, punicalagin did not affect precore/core promoter activity, pgRNA transcription, core protein expression, or HBsAg secretion. By employing the cell-based cccDNA accumulation and stability assay, we found that these tannins significantly inhibited the establishment of cccDNA and modestly facilitated the degradation of preexisting cccDNA. Collectively, our results suggest that hydrolyzable tannins inhibit HBV cccDNA production via a dual mechanism through preventing the formation of cccDNA and promoting cccDNA decay, although the latter effect is rather minor. These hydrolyzable tannins may serve as lead compounds for the development of new agents to cure HBV infection

Towards Understanding the Adoption and Social Experience of Digital Wallet Systems

For millions around the globe, digital wallets are replacing cash and credit cards. These services support user-to-user payments, and add a social component to transactions. However, there is little understanding of the key factors behind digital wallets’ rapid growth in US (Venmo) and China (WeChat Pay). What are the factors that led to their success? How social relationships play a role in their adoption? We conduct a mixed methods study, using a comprehensive survey (N=879) and semi-structured interviews (N=41) to explore the interplay of the two roles of these digital wallets, i.e., a payment system and a social platform. Our analysis suggests that the network effect does benefit their adoption and retention, but through different mechanisms. In return, transaction activities performed in digital wallets help strengthen existing social ties. We also present design implications for future social payment services