MADiff: Offline Multi-agent Learning with Diffusion Models

Diffusion model (DM), as a powerful generative model, recently achieved huge success in various scenarios including offline reinforcement learning, where the policy learns to conduct planning by generating trajectory in the online evaluation. However, despite the effectiveness shown for single-agent learning, it remains unclear how DMs can operate in multi-agent problems, where agents can hardly complete teamwork without good coordination by independently modeling each agent's trajectories. In this paper, we propose MADiff, a novel generative multi-agent learning framework to tackle this problem. MADiff is realized with an attention-based diffusion model to model the complex coordination among behaviors of multiple diffusion agents. To the best of our knowledge, MADiff is the first diffusion-based multi-agent offline RL framework, which behaves as both a decentralized policy and a centralized controller, which includes opponent modeling and can be used for multi-agent trajectory prediction. MADiff takes advantage of the powerful generative ability of diffusion while well-suited in modeling complex multi-agent interactions. Our experiments show the superior performance of MADiff compared to baseline algorithms in a range of multi-agent learning tasks.Comment: 17 pages, 7 figures, 4 table

Will Cooperation Help Content Creators Grow? Empirical Evidence from Twitch.tv

The increasingly popular live streaming platforms have attracted many content creators through various revenue sharing and incentive mechanisms. Despite intense competition, many content creators actively cooperate with other competitors, even at the risk of losing their audience. In this study, we develop an integrated theoretical framework to examine the drivers for different types of cooperative behaviors and explore their impacts on market performance. By analyzing monthly activities of 412 cooperative pairs of channels on Twitch, we found a positive impact of specialty homophily, reciprocity, and social influence on content creators’ cooperative behaviors, although such impacts differ across different types of cooperative activities. Moreover, our results show that spreading behavior has a positive impact on content creators’ market performance, while supporting behavior has a negative impact. This negative effect becomes weaker when content creators are more popular. Our results offer important implications to foster a sustainable growth of UGC platforms

Maximizing the benefit of technology for language learning

Since the advent of the information age, ongoing technological developments have significantly changed our lives. In educational settings, the prevalence of technology is also expected to bring about a revolution in learning and teaching. Governments and policymakers have injected significant amounts of resources, and support to promote the use of technology in schools. The use of information and communications technology (ICT) in learning and teaching processes is believed to benefit learners and learning in various ways and in a whole range of curriculum areas. This belief still persists although it is also known that some teachers are reluctant to use modern technology for teaching purposes and for some, ICT usage tends to be superficial (Yeung et al. 2012b). In this chapter, we focus on the use of ICT in language learning. We first identify critical issues related to the use of ICT in language learning and teaching, and then attempt to suggest possible ways to maximise the benefit of ICT application for language learning

Dynamic Evolution and Quantitative Characterization of Fractures in Coal at the Eastern Edge of Ordos Basin under Axial Loading

Understanding the evolution of pore-fracture networks in coal during loading is of paramount importance for coalbed methane exploration. To shed light on these dynamic changes, this study undertook uniaxial compression experiments on coal samples collected from the eastern edge of the Ordos Basin, complemented by μ-CT scanning to obtain a 3D visualization of the crack network model. The compression process was divided into three stages, namely, micro-crack compaction, linear elasticity, and peak failure. An increase in stress resulted in greater concentration and unevenness in fractal dimensions, illustrating the propagation of initial cleats and micro-cracks in the dominant crack direction and the ensuing process of crack merging. These results provide valuable insights into the internal structure and behavior of coal under stress, informing more efficient strategies for coalbed methane extraction

Subsampling Spectral Clustering for Large-Scale Social Networks

Online social network platforms such as Twitter and Sina Weibo have been extremely popular over the past 20 years. Identifying the network community of a social platform is essential to exploring and understanding the users' interests. However, the rapid development of science and technology has generated large amounts of social network data, creating great computational challenges for community detection in large-scale social networks. Here, we propose a novel subsampling spectral clustering algorithm to identify community structures in large-scale social networks with limited computing resources. More precisely, spectral clustering is conducted using only the information of a small subsample of the network nodes, resulting in a huge reduction in computational time. As a result, for large-scale datasets, the method can be realized even using a personal computer. Specifically, we introduce two different sampling techniques, namely simple random subsampling and degree corrected subsampling. The methodology is applied to the dataset collected from Sina Weibo, which is one of the largest Twitter-type social network platforms in China. Our method can very effectively identify the community structure of registered users. This community structure information can be applied to help Sina Weibo promote advertisements to target users and increase user activity

Sorafenib regulates c-CBL gene-mediated chemoresistance in acute myeloid leukemia cells

ABSTRACTChemotherapeutic regimens containing sorafenib are widely used in salvage treatment for patients with relapsed and refractory acute leukemia, especially those with FLT3-ITD mutations. However, the therapeutic effects in individuals are heterogeneous, and the effective maintenance period is relatively short. Our clinical analysis showed patients with high c-kit (CD117) expression in leukemia cells generally had a better response to sorafenib, but the reason for this finding was not clear. c-kit (CD117) is a receptor tyrosine kinase, and its signal inactivation and hydrolytic metabolism are regulated by the CBL protein, a Ring finger E3 ubiquitin ligase, encoded by the c-CBL gene. And we also found that the c-CBL gene expression in refractory and relapsed patients was significantly lower than that in healthy hematopoietic stem cell donors. Therefore, we assumed that there is a relationship among c-CBL gene function, high expression of c-kit (CD117) and a better clinical response to sorafenib. To confirm this hypothesis, we packaged interfering lentiviruses and overexpressed adenoviruses targeting the c-CBL gene respectively, and infected leukemia cell lines with these viruses to regulate the expression of the c-CBL gene, and observed the subsequent changes of these cells in various biological behaviors. Our results showed when the c-CBL gene was silenced, the cells proliferation was accelerated, drug sensitivity to cytarabine or sorafenib was decreased, and apoptosis ratio was decreased. And all these phenomena were reversed when the gene was overexpressed, which confirmed the expression of c-CBL gene was related to drug resistance in leukemia cells. At last, we explored the possible molecular mechanisms underlying these phenomena

Design of the Ship-Borne Multi-Wavelength Polarization Ocean Lidar System and Measurement of Seawater Optical Properties

A ship-borne multi-wavelength polarization ocean lidar system LOOP (Lidar for Ocean Optics Profiler) is introduced in detail, aiming to obtain high-precision vertical profiles of seawater optical characteristics. Based on Monte-Carlo simulation, the receiving telescope is designed with a variable field of view, producing system attenuation coefficient (Klidar) approximating the optical parameters of seawater under a different field of view and water body conditions. At first, a sea trial was conducted in Jiaozhou Bay, and the measured diffuse attenuation coefficient (Kd) of seawater was 0.3m−1, being in good agreement compared with the results measured by field instrument TriOS. Then a field campaign was organized in the South China Sea. The measurement of the seawater diffuse attenuation (Kd) was 0.035m−1. These results support the prospects that lidar, as an effective tool supplement to traditional passive ocean color remote sensing, can provide the vertical distributions of optical properties in the upper ocean