A script, which describes the evolutionary path of events, is a structured event sequence. Script event prediction aims to predict the next event from a sequence of historical events. Current studies favor modeling macroscale information, including event sequence, event unit, and event argument, while ignoring the smallest unit of event, i.e., argument vocabulary, which we refer to as microscale information. To fuse event information from different scales, we propose a staged encoder-decoder architecture (SEDA) for script event prediction. SEDA aggregates microscale information to enhance the representation of event arguments and event units in the event-enhancement stage, and extracts the context of event sequence to predict the most relevant candidate event in the context-extraction stage. Both stages of SEDA adopt an efficient and scalable encoder-decoder architecture. The experimental results demonstrate that the accuracy of SEDA on the MCNC task surpasses that of the current SOTA baseline. Additionally, we empoly a method based on the Shapley value to calculate the importance of different event units in an event sequence, providing a quantitative analysis of the prediction results.

Multi-agent reinforcement learning (MARL) algorithms with sequential decision-making strategies have achieved great success in cooperation tasks recently. To overcome the non-stationarity problem, these methods design a centralized controller that takes global observation as input and chooses actions for each agent in sequence. However, in most scenarios, global information is only available at training time, while agents act synchronously with their local observation at execution time, which prevents agents from leveraging more information in cooperation. In this paper, based on actor-critic architecture, we propose the actor-predicts-critic (APC) algorithm, in which the actor learns to predict the global representations of centralized critic from local observation. During the training, the actor not only receives the estimated state values, but also takes the critic’s representations that are extracted from global information as the prediction targets. Since these global representations are closely related to agents’ goals and rewards, agents can achieve better cooperation on MARL tasks utilizing the predicted representations. To prove the validity of APC, we evaluate the algorithm on StarCraft2, Google Research Football, and MultiAgent Mujoco benchmarks. The results show that APC significantly outperforms the strong baselines in centralized training and decentralized execution (CTDE) framework, including MATDec, MAPPO, and fine-tuned QMIX.

Summary To identify recyclable garbage via the garbage classification is an effective countermeasure for protecting the environment. An automatic classification system supported by image recognition technologies is able to significantly reduce huge human labors of recycling tasks. However, performing the real‐time and accurate garbage detection is not a trivial task. In this article, we present an edge‐cloud framework equipped with deep learning model for recyclable garbage detection. Specifically, we propose to use the deep convolutional neural network for garbage images classification, and thus design the collaborative mechanism between edge devices and the cloud server. As a result, we design and develop a novel recyclable garbage detection system, where scanning garbage images and thus detecting recyclable ones can be completed in real‐time. We validate the performances of the proposed recyclable garbage detection system on 1000 real‐life household garbage images. Experimental results show the overall accuracy of our system reaches nearly 90%, and the time for detection is less than 500 ms.

Long noncoding RNAs (lncRNAs) are a large family of noncoding RNAs that play a critical role in various normal bioprocesses as well as tumorigenesis. However, the expression patterns and biological functions of lncRNAs in acute leukemia have not been well studied. Here, we performed transcriptome-wide lncRNA expression profiling of acute myeloid leukemia (AML) patient samples, along with non-leukemia control hematopoietic samples. We found that lncRNAs were differentially expressed in AML samples relative to control samples. Notably, we identified that lncRNAs upregulated in AML (relative to the control samples) are associated with a lower degree of DNA methylation and a higher ratio of being bound by transcription factors such as SP1, STAT4, ATF-2 and ELK-1 compared with those downregulated in AML. Moreover, an enrichment of H3K4me3 and a depletion of H3K27me3 were observed in upregulated lncRNAs in AML. Expression patterns of three types of lncRNAs (antisense, enhancer and intergenic lncRNAs) have previously been characterized. Of the identified lncRNAs, we found that high expression level lncRNA LOC285758 is associated with the poor prognosis in AML patients. Furthermore, we found that LOC285758 regulates proliferation of AML cell lines by enhancing the expression of HDAC2, a key factor in carcinogenesis. Collectively, our study depicts a landscape of important lncRNAs in AML and provides novel potential therapeutic targets and prognostic markers for AML treatment.