ConKI: Contrastive Knowledge Injection for Multimodal Sentiment Analysis

Multimodal Sentiment Analysis leverages multimodal signals to detect the sentiment of a speaker. Previous approaches concentrate on performing multimodal fusion and representation learning based on general knowledge obtained from pretrained models, which neglects the effect of domain-specific knowledge. In this paper, we propose Contrastive Knowledge Injection (ConKI) for multimodal sentiment analysis, where specific-knowledge representations for each modality can be learned together with general knowledge representations via knowledge injection based on an adapter architecture. In addition, ConKI uses a hierarchical contrastive learning procedure performed between knowledge types within every single modality, across modalities within each sample, and across samples to facilitate the effective learning of the proposed representations, hence improving multimodal sentiment predictions. The experiments on three popular multimodal sentiment analysis benchmarks show that ConKI outperforms all prior methods on a variety of performance metrics.Comment: Accepted by ACL Findings 202

Surface Adsorption-Mediated Ultrahigh Efficient Peptide Encapsulation with a Precise Ratiometric Control for Type 1 and 2 Diabetic Therapy

A surface adsorption strategy is developed to enable the engineering of microcomposites featured with ultrahigh loading capacity and precise ratiometric control of co-encapsulated peptides. In this strategy, peptide molecules (insulin, exenatide, and bivalirudin) are formulated into nanoparticles and their surface is decorated with carrier polymers. This polymer layer blocks the phase transfer of peptide nanoparticles from oil to water and, consequently, realizes ultrahigh peptide loading degree (up to 78.9%). After surface decoration, all three nanoparticles are expected to exhibit the properties of adsorbed polymer materials, which enables the co-encapsulation of insulin, exenatide, and bivalirudin with a precise ratiometric control. After solidification of this adsorbed polymer layer, the release of peptides is synchronously prolonged. With the help of encapsulation, insulin achieves 8 days of glycemic control in type 1 diabetic rats with one single injection. The co-delivery of insulin and exenatide (1:1) efficiently controls the glycemic level in type 2 diabetic rats for 8 days. Weekly administration of insulin and exenatide co-encapsulated microcomposite effectively reduces the weight gain and glycosylated hemoglobin level in type 2 diabetic rats. The surface adsorption strategy sets a new paradigm to improve the pharmacokinetic and pharmacological performance of peptides, especially for the combination of peptides.Peer reviewe