5 accepted papers
Recent advances in time series forecasting (TSF) leverage large language models (LLMs) to provide semantic priors, enabling more robust forecasting under limited training data. However, directly fusing semantic signals into temporal features often induces modality entanglement and obscures local tem
In this paper, two technologies are proposed to deal with the problem of flight safty of multiple unmanned aerial vehicles (UAVs) in unknown environments. One technology is to optimize the front-end path generated by traditional path planning methods in order to better match the dynamics of UAVs to
Current end-to-end multi-modal models utilize different encoders and decoders to process input and output information. This separation hinders the joint representation learning of various modalities. To unify multi-modal processing, we propose a novel architecture called MDM (Multi-modal Diffusion M…
Aspect-level sentiment classification (ASC) is a significant problem in fine-grained sentiment analysis, which automatically predicts the sentiment polarity of a given aspect in a sentence. Dependency tree-based graph convolutional networks have been widely studied for their ability to effectively c…
The paper presents a passivity-based control law for an antagonistic tendon-driven mechanism. It is proven, by using the passivity theorem, that the proposed control law is able to achieve two properties such as the passivity of interconnected subsystems when the external torque is applied and the g…