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Large language models have shown astonishing performance on a wide range of reasoning tasks. In this paper, we investigate whether they could reason about real-world events and help improve the prediction performance of event sequence models. We design ...

Neural Temporal Point Processes (TPPs) are the prevalent paradigm for modeling continuous-time event sequences, such as user activities on the web and financial transactions. In real-world applications, event data is typically received in a streaming ...

Recently, models for user representation learning have been widely applied in click-through-rate (CTR) and conversion-rate (CVR) prediction. Usually, the model learns a universal user representation as the input for subsequent scenario-specific models. ...

The rapid rise in cloud computing has resulted in an alarming increase in data centers' carbon emissions, which now account for >3% of global greenhouse gas emissions, necessitating immediate steps to combat their mounting strain on the global climate. An ...

We consider a sequential decision making problem where the agent faces the environment characterized by the stochastic discrete events and seeks an optimal intervention policy such that its long-term reward is maximized. This problem exists ubiquitously ...

Video-language pre-training for text-based video retrieval tasks is vitally important. Previous pre-training methods suffer from semantic misalignments. The reason is that these methods ignore sequence alignments but focus on critical token alignment. To ...

Slot filling, to extract entities for specific types of information (slot), is a vitally important modular of dialogue systems for automatic diagnosis. Doctor responses can be regarded as the weak supervision of patient queries. In this way, a ...

In this paper, we tackle the important yet under-investigated problem of making long-horizon prediction of event sequences. Existing state-of-the-art models do not perform well at this task due to their autoregressive structure. We propose HYPRO, a ...

Predictive autoscaling (autoscaling with workload forecasting) is an important mechanism that supports autonomous adjustment of computing resources in accordance with fluctuating workload demands in the Cloud. In recent works, Reinforcement Learning (RL) ...

Accurate traffic forecasting is important to enable intelligent transportation systems in a smart city. This problem is challenging due to the complicated spatial, short-term temporal and long-term periodical dependencies. Existing approaches have ...

In UAV-enabled mmWave networks, the locations of UAVs are usually modeled by a Poisson point process or a Poisson cluster process in an infinite area. However, some typical scenarios merely deploy a fixed number of UAVs in a finite area such as hotspot ...

The efficiency of task scheduling under cloud computing is related to the effectiveness of users. Aiming at the problems of long scheduling time, high cost consumption, and large virtual machine load in cloud computing task scheduling, an improved ...

Because of the problem of the low accuracy and slow speed of the traditional semantic segmentation model, making it difficult to actually use. In response to this problem, this paper focuses on the method to improve the precision and speed of the ...

The purpose of this study was to observe the impact of a programme of teaching about the Nature of Science on high school students and teachers, and consider the implications of the findings for school science education in China. Since 2018, there ...

Developers frequently use inefficient code sequences that could be fixed by simple patches. These inefficient code sequences can cause significant performance degradation and resource waste, referred to as performance bugs. Meager increases in single ...

This paper proposes a head pose identification method using nose's relative position information in a face region and develops a game system based on visual head pose identification techniques to control a virtual robot walking in a virtual maze ...