In recent years, live streaming develops rapidly, attracting an increasing number of users. Providing personalized live streaming recommendations is crucial for platform improvement in enhancing user experience and increasing profitability. In live streaming scenarios, users often enter the same live streaming room multiple times, send chat messages and give virtual gifts repeatedly. However, existing recommendation models fail to effectively model the complex multiple types of repeated behaviors of users in live streaming scenarios, thus failing to achieve optimal recommendation results. To address this issue, we propose a novel live recommendation model called MRB4LS based on multiple types of repeated behaviors data. Specifically, we first construct three bipartite graphs to better capture the effects of multiple types of users’ repeated behaviors, including enter, chat, and gift. Second, we introduce a graph attention network named RepGAT, which explicitly learns from users’ repeated behaviors. RepGAT incorporates the number of repeated interactions between nodes when computing normalized attention coefficients, enabling a deeper exploration of users’ preferences and the heterogeneous strength of the interaction relationship between users and live streaming rooms. Then, we design two embedding fusion strategies, namely concatenation-based and attention-based methods, to integrate node representations generated by different repeated behaviors. Finally, we adopt a multi-task learning approach to enhance the prediction effectiveness of gift behavior by leveraging predictions from enter behavior and chat behavior. To validate our approach, we construct two live streaming datasets from a large-scale game live streaming platform. Extensive experiments on two real-world datasets with different scales show that our method can significantly outperform various baseline approaches.

Colitis is a commonly encountered inflammatory disease in colon tissue, which can be triggered by various causes. Although a few ingredients in traditional Chinese medicine (TCM) have been identified as effective for the treatment of colitis, it remains a great challenge to discover the potential therapeutic bioactive constituents and their modes of action among thousands of ingredients in TCM prescriptions. To address this issue, we propose a pipeline that combines deep neural network (DNN) with network pharmacology to discover bioactive constituents. By integrating the herbal information network of 9,845 nodes and 161,950 edges, which includes detailed information on bioactive molecules and protein targets, with a prescription list expanded through a novel data augmentation strategy, the DNN can recommend diverse herbal combinations. Network pharmacology study revealed that the 10 most frequent constituents in recommended prescriptions were associated with multiple inflammatory signaling pathways. To verify the bioactive constituents in the recommended prescriptions, 5 selected constituents were administrated to BALB/c mice with colitis. Suppressive effects of disease progression and pro-inflammatory factors comparable to sulfasalazin were observed with these compounds, revealing the effectiveness of our artificial intelligence strategy in discovering bioactive constituents from TCM prescriptions.

The field of neuromorphic computing is approaching an era of rapid adoption driven by the urgent need of a substitute for the von Neumann computing architecture. NEUROTEC I: “Neuro-inspired Artificial Intelligence Technologies for the Elec-tronics of the Future” project is an initiative sponsored by the German Federal Ministry of Education and Research (BMBF for its initials in German), that aims to effectively advance the foundations for the utilization and exploitation of neuromorphic computing. NEUROTEC I stands at its successful “final stage” driven by the collaboration from more than 8 institutes from the Jiilich Research Center and the RWTH Aachen University, as well as collaboration from several high-tech industry partners. The NEUROTEC I project considers the field interplay among materials, circuits, design and simulation tools. This paper provides an overview of the project's overall structure and discusses the scientific achievements of its individual activities.

In this paper, we explore an alternative form of volunteer work, PledgeWork, where individuals, rather than working directly for a charity, make indirect donations by completing tasks provided by a third party task provider. PledgeWork poses novel research questions on issues of user acceptance of on-line volunteerism, on quality and quantity of work performed as a volunteer, and on the benefits low-barrier volunteerism might provide to charities. To evaluate these questions, we conduct a mixed methods study that compares the quality and quantity of work between volunteer workers and paid workers and user attitudes toward PledgeWork, including perceived benefits and drawbacks. We find that PledgeWork can improve the quality of simple tasks and that the vast majority of our participants expressed interest in using our PledgeWork platform to contribute to a charity. Our interview also reveals current problems with volunteering and online donations, thus highlighting additional strengths of PledgeWork.

Surface wave is the most energetic form of motions in the ocean and is crucially important to navigation safety and climate change. High-resolution global wave model plays a key role in accurate surface wave forecasting. However, operational forecasting systems are still not in high-resolution due to entailed high demand for large computation, as well as low parallel efficiency barrier. Here breakthroughs encompassing the design and application of irregular quasi-rectangular domain decomposition, master-slave cooperative computing workflow and pipelining scheme were applied to a global wave model, which has been used in several operational forecasting systems and earth system models. Our realistic surface wave simulations on Sunway TaihuLight Supercomputer demonstrated that our model had outstanding scalability and achieved 45.43 PFlops in ultra-high resolution of (1/100)°, using full-scale supercomputer with 10,649,600 cores. That provides a highly effective solution for accurate surface wave forecasting and climate change prediction.