An End-to-End Multi-Task Learning to Link Framework for Emotion-Cause Pair Extraction

Emotion-cause pair extraction (ECPE), as an emergent natural language processing task, aims at jointly investigating emotions and their underlying causes in documents. It extends the previous emotion cause extraction (ECE) task, yet without requiring a set of pre-given emotion clauses as in ECE. Existing approaches to ECPE generally adopt a two-stage method, i.e., (1) emotion and cause detection, and then (2) pairing the detected emotions and causes. Such pipeline method, while intuitive, suffers from two critical issues, including error propagation across stages that may hinder the effectiveness, and high computational cost that would limit the practical application of the method. To tackle these issues, we propose a multi-task learning model that can extract emotions, causes and emotion-cause pairs simultaneously in an end-to-end manner. Specifically, our model regards pair extraction as a link prediction task, and learns to link from emotion clauses to cause clauses, i.e., the links are directional. Emotion extraction and cause extraction are incorporated into the model as auxiliary tasks, which further boost the pair extraction. Experiments are conducted on an ECPE benchmarking dataset. The results show that our proposed model outperforms a range of state-of-the-art approaches.Comment: 7 pages, 3 figures, 5 table

MA2CL:Masked Attentive Contrastive Learning for Multi-Agent Reinforcement Learning

Recent approaches have utilized self-supervised auxiliary tasks as representation learning to improve the performance and sample efficiency of vision-based reinforcement learning algorithms in single-agent settings. However, in multi-agent reinforcement learning (MARL), these techniques face challenges because each agent only receives partial observation from an environment influenced by others, resulting in correlated observations in the agent dimension. So it is necessary to consider agent-level information in representation learning for MARL. In this paper, we propose an effective framework called \textbf{M}ulti-\textbf{A}gent \textbf{M}asked \textbf{A}ttentive \textbf{C}ontrastive \textbf{L}earning (MA2CL), which encourages learning representation to be both temporal and agent-level predictive by reconstructing the masked agent observation in latent space. Specifically, we use an attention reconstruction model for recovering and the model is trained via contrastive learning. MA2CL allows better utilization of contextual information at the agent level, facilitating the training of MARL agents for cooperation tasks. Extensive experiments demonstrate that our method significantly improves the performance and sample efficiency of different MARL algorithms and outperforms other methods in various vision-based and state-based scenarios. Our code can be found in \url{https://github.com/ustchlsong/MA2CL

In situ-constructed LixMoS2 with highly exposed interface boosting high-loading and long-life cathode for all-solid-state Li–S batteries

As the persistent concerns regarding sluggish reaction kinetics and insufficient conductivities of sulfur cathodes in all-solid-state Li–S batteries (ASSLSBs), numerous carbon additives and solid-state electrolytes (SSEs) have been incorporated into the cathode to facilitate ion/electron pathways around sulfur. However, this has resulted in a reduced capacity and decomposition of SSEs. Therefore, it is worth exploring neotype sulfur hosts with electronic/ionic conductivity in the cathode. Herein, we present a hybrid cathode composed of few-layered S/MoS2/C nanosheets (<5 layers) that exhibits high-loading and long-life performance without the need of additional carbon additives in advanced ASSLSBs. The multifunctional MoS2/C host exposes the abundant surface for intimate contacting sites, in situ-formed LixMoS2 during discharging as mixed ion/electron conductive network improves the S/Li2S conversion, and contributes extra capacity for the part of active materials. With a high active material content (S + MoS2/C) of 60 wt% in the S/MoS2/C/Li6PS5Cl cathode composite (the carbon content is only ~3.97 wt%), the S/MoS2/C electrode delivers excellent electrochemical performance, with a high reversible discharge capacity of 980.3 mAh g−1 (588.2 mAh g−1 based on the whole cathode weight) after 100 cycles at 100 mA g−1. The stable cycling performance is observed over 3500 cycles with a Coulombic efficiency of 98.5% at 600 mA g−1, while a high areal capacity of 10.4 mAh cm−2 is achieved with active material loading of 12.8 mg cm−2

Validation of the GALAD model and establishment of a new model for HCC detection in Chinese patients

BackgroundGALAD model is a statistical model used to estimate the possibility of hepatocellular carcinoma (HCC) in patients with chronic liver disease. Many studies with other ethnic populations have shown that it has high sensitivity and specificity. However, whether this model can be used for Chinese patients remains to be determined. Our study was conducted to verify the performance of GALAD model in a Chinese cohort and construct a new model that is more appropriately for Chinese populations.MethodsThere are total 512 patients enrolled in the study, which can be divided into training set and validation set. 80 patients with primary liver cancer, 139 patients with chronic liver disease and 87 healthy people were included in the training set. Through the ROC(receiver operating characteristic) curve analysis, the recognition performance of GALAD model for liver cancer was evaluated, and the GAADPB model was established by logistic regression, including gender, age, AFP, DCP, total protein, and total bilirubin. The validation set (75 HCC patients and 130 CLD patients) was used to evaluate the performance of the GAADPB model.ResultThe GALAD and GAADPB achieved excellent performance (area under the receiver operating characteristic curve [AUC], 0.925, 0.945), and were better than GAAP, Doylestown, BALAD-2, aMAP, AFP, AFP-L3%, DCP and combined detection of AFP, AFP-L3 and DCP (AUCs: 0.894, 0.870, 0.648, 0.545, 0.879, 0.782, 0.820 and 0.911) for detecting HCC from CLD in the training set. As for early stage of HCC (BCLC 0/A), GAADPB had the best sensitivity compared to GALAD, ADP and DCP (56.3%, 53.1%, 40.6%, 50.0%). GAADPB had better performance than GALAD in the test set, AUC (0.896 vs 0.888).ConclusionsThe new GAADPB model was powerful and stable, with better performance than the GALAD and other models, and it also was promising in the area of HCC prognosis prediction. Further study on the real-world HCC patients in China are needed

Different mechanisms for the extremely hot central-eastern China in July–August 2022 from a Eurasian large-scale circulation perspective

In July and August of 2022, unprecedented and long-lasting heatwaves attacked central and eastern China (CEC); and the most affected area was in the Yangtze River (YR) basin. The extreme heatwaves and associated drought and wildfire had significant social impacts, but the underlying mechanisms remain unknown. Observational analysis indicates that the heatwaves were regulated by anomalous anticyclone in the mid-upper troposphere over northern CEC. Specifically, the easterly anomalies at the southern flank of the anticyclone caused air isentropic sliding and transported low moist enthalpy (cold and dry) air to the YR basin, contributing to anomalous sinking motions and extreme heatwaves. In comparison, heatwaves were more serious in August than in July due to stronger upper-level anomalous anticyclone and associated easterlies. Importantly, different mechanisms were responsible for the heatwaves in the two months. In July, the relatively weaker anticyclonic anomaly over northern CEC was dominated by the forcing of diabatic heating over northwestern South Asia (NWSA), corresponding with the record-breaking rainfall in and around Pakistan. In August, a powerful anticyclonic condition for the CEC heatwaves originated from an extreme silk road pattern (SRP), superposing the effect of NWSA diabatic heating due to persistent downpour. We notice that another upstream anticyclonic node in the SRP also created heatwaves in Europe. Therefore, the CEC extreme heat was actually associated with other concurrent extremes over the Eurasian continent through large-scale atmospheric teleconnections in 2022

A Survey of Controllable Text Generation using Transformer-based Pre-trained Language Models

Controllable Text Generation (CTG) is emerging area in the field of natural language generation (NLG). It is regarded as crucial for the development of advanced text generation technologies that are more natural and better meet the specific constraints in practical applications. In recent years, methods using large-scale pre-trained language models (PLMs), in particular the widely used transformer-based PLMs, have become a new paradigm of NLG, allowing generation of more diverse and fluent text. However, due to the lower level of interpretability of deep neural networks, the controllability of these methods need to be guaranteed. To this end, controllable text generation using transformer-based PLMs has become a rapidly growing yet challenging new research hotspot. A diverse range of approaches have emerged in the recent 3-4 years, targeting different CTG tasks which may require different types of controlled constraints. In this paper, we present a systematic critical review on the common tasks, main approaches and evaluation methods in this area. Finally, we discuss the challenges that the field is facing, and put forward various promising future directions. To the best of our knowledge, this is the first survey paper to summarize CTG techniques from the perspective of PLMs. We hope it can help researchers in related fields to quickly track the academic frontier, providing them with a landscape of the area and a roadmap for future research

A Deadline and Budget Constrained Cost-Time Optimization Algorithm for Scheduling Dependent Tasks in Grid Computing

Abstract. Computational grid has a promising future in large-scale computing, because it enables the sharing of widely distributed computing resources. Good managements with excellent scheduling algorithms are in great demand to take full advantage of it. Many scheduling algorithms in grid computing are for independent tasks. However, communications are very common in scientific computing programs. In this paper, we will propose an easy-implemented algorithm to schedule the tasks with some communications. Our algorithm is suitable for a large proportion of scientific computing programs, and is based on Binary Integer Programming. It is able to meet the users ’ quality of service (QoS) requirements, and to minimize the combination of costs and time consumed by the users ’ programs. We will give an example of scheduling a typical scientific computing task to show the power of our algorithm. In our experiment, the grid resource consists of an SGI Onyx 3900 supercomputer, four SGI Octane workstations, four Intel P4-2.0GHz PCs and four Intel P4-1.8GHz PCs.