Emotional intelligence in large language models (LLMs) is of great importance in Natural
Language Processing. However, the previous research mainly focus on basic sentiment …

Graph neural networks (GNNs) have shown ad-vantages in graph-based analysis tasks.
However, most existing methods have the homogeneity assumption and show poor …

Low-bit quantization has become widespread for compressing image super-resolution (SR)
models for edge deployment, which allows advanced SR models to enjoy compact low-bit …

Emotion cognition in large language models (LLMs) is crucial for enhancing performance
across various applications, such as social media, human-computer interaction, and mental …

Accurate exchange rate prediction is fundamental to financial stability and international
trade, positioning it as a critical focus in economic and financial research. Traditional …

The map matching of cellular data reconstructs real trajectories of users by exploiting the
sequential connections between mobile devices and cell towers. The difficulty in obtaining …

Crafting personalized recommendations for users with minimal interaction histories, a
prevalent challenge in user cold-start recommendation within recommendation systems …

Both Transformer and Graph Neural Networks (GNNs) have been employed in the domain
of learning to rank (LTR). However, these approaches adhere to two distinct yet …

Understanding the creation, evolution, and dissemination of scientific knowledge is crucial
for bridging diverse subject areas and addressing complex global challenges such as …

Learning to rank (LTR) is widely employed in web searches to prioritize pertinent webpages
from retrieved content based on input queries. However, traditional LTR models encounter …