To address these challenges, we propose AffiGrapher, a physics-driven graph neural network that integrates a physics-informed graph architecture with contrastive learning. Incorporating multiple RNA ...

Thermally induced phase separation (TIPS) is a convenient method to prepare polymer micro/nanoparticles from synthesized polymers. However, the mapping relationship between the phase separation ...

SuperFlow is introduced to harness consecutive LiDAR-camera pairs for establishing spatiotemporal pretraining objectives. It stands out by integrating two key designs: 1) a dense-to-sparse consistency ...

Methods: In this work, we propose a novel graph neural network-based architecture with dual contrastive learning and syntax label enhancement. Specifically, a contrastive learning-based contextual ...

We propose a new UDA architecture for point cloud classification that benefits from multimodal contrastive learning to get better class separation in both domains individually. Further, the use of ...

Unsupervised 3D shape clustering is emerging as a promising research topic in multimedia and computer vision field. Considering the flexibility of acquiring multiple views for 3D shapes, this paper ...

The pervasive law of equi-separation consistently prevails across diverse datasets, learning rates, and class imbalances, as illustrated in Fig. 3. Additionally, SI Appendix, Fig. S6 demonstrates its ...

Starting with the PLMs, our second insight directly addresses the fine-grained specificity problem in our architecture by using the “Con” (Contrastive learning) part: a protein-anchored contrastive ...

In this paper, we explore data-efficient learning for 3D point cloud. As a first step towards this direction, we propose Contrastive Scene Contexts, a 3D pre-training method that makes use of both ...

It employs contrastive learning for social recommendation for the first time, which takes recommendation and contrastive learning as the primary task and auxiliary task, respectively.