The automatic and accurate extraction of diverse biomedical relations from literature constitutes the core elements of medical knowledge graphs, which are indispensable for healthcare artificial intelligence. Currently, fine-tuning through stacking various neural networks on pre-trained language models (PLMs) represents a common framework for end-to-end resolution of the biomedical relation extraction (RE) problem. Nevertheless, sequence-based PLMs, to a certain extent, fail to fully exploit the connections between semantics and the topological features formed by these connections. In this study, we presented a graph-driven framework named BioGSF for RE from the literature by integrating shortest dependency paths (SDP) with entity-pair graph through the employment of the graph neural network model. Initially, we leveraged dependency relationships to obtain the SDP between entities and incorporated this information into the entity-pair graph. Subsequently, the graph attention network was utilized to acquire the topological information of the entity-pair graph. Ultimately, the obtained topological information was combined with the semantic features of the contextual information for relation classification. Our method was evaluated on two distinct datasets, namely S4 and BioRED. The outcomes reveal that BioGSF not only attains the superior performance among previous models with a micro-F1 score of 96.68% (S4) and 96.03% (BioRED), but also demands the shortest running times. BioGSF emerges as an efficient framework for biomedical RE.

This article studies two robust adaptive dynamic programming (ADP) approaches for uncertain discrete-time (DT) nonlinear systems. Since the uncertainty is implicit in the traditional Hamilton-Jacobi–Bellman (HJB) equation, it is difficult to deal with the uncertainty. In this article, the Taylor series approximation technique is utilized to convert the traditional HJB equation into an explicit form of the uncertainty. In virtue of the first-order Taylor series approximation technique, a robust first-order approximate HJB equation is established. To further improve the approximation accuracy, a robust second-order approximate HJB equation is exploited by using the Hessian matrix of the value function. It is shown that the second-order approximate HJB equation could be extended to the uncertain DT linear systems. Aiming at obtaining the solutions of the two robust approximate HJB equations, we propose two corresponding policy iteration (PI) algorithms. More importantly, the convergence and optimality of the designed PI algorithms are clarified. Finally, a numerical case is conducted to test the validity of the designed robust DT PI ADP approaches.

Group conversations often shift quickly from topic to topic, leaving a small window of time for participants to contribute. AAC users often miss this window due to the speed asymmetry between using speech and using AAC devices. AAC users may take over a minute longer to contribute, and this speed difference can cause mismatches between the ongoing conversation and the AAC user’s response. This results in misunderstandings and missed opportunities to participate. We present COMPA, an add-on tool for online group conversations that seeks to support conversation partners in achieving common ground. COMPA uses a conversation’s live transcription to enable AAC users to mark conversation segments they intend to address (Context Marking) and generate contextual starter phrases related to the marked conversation segment (Phrase Assistance) and a selected user intent. We study COMPA in 5 different triadic group conversations, each composed by a researcher, an AAC user and a conversation partner (n=10) and share findings on how conversational context supports conversation partners in achieving common ground.