Neural control for constrained human-robot interaction with human motion intention estimation and impedance learning

In this paper, an impedance control strategy is proposed for a rigid robot collaborating with human by considering impedance learning and human motion intention estimation. The least square method is used in human impedance identification, and the robot can adjust its impedance parameters according to human impedance model for guaranteeing compliant collaboration. Neural networks (NNs) are employed in human motion intention estimation, so that the robot follows the human actively and human partner costs less control effort. On the other hand, the full-state constraints are considered for operational safety in human-robot interactive processes. Neural control is presented in the control strategy to deal with the dynamic uncertainties and improve the system robustness. Simulation results are carried out to show the effectiveness of the proposed control design

Asiatic acid attenuates malignancy of human metastatic ovarian cancer cells via inhibition of epithelial-tomesenchymal transition

Purpose: To investigate the anticancer effects of asiatic acid on human metastatic ovarian cancer cells.Methods: Human metastatic ovarian cancer cell line SKOV-3 was treated with various concentrations of asiatic acid for 24 and 48 h. Cell proliferation, migration, invasion and morphology were analyzed by CCK-8, Transwell and immunofluorescence assays, respectively. Epithelial-to-mesenchymal transitionrelated gene and protein expressions were analyzed by quantitative polymerase chain reaction (qPCR) and Western blotting.Results: Asiatic acid (10 μM) significantly suppressed SKOV-3 cell migration and invasion (both p < 0.01). Moreover, epithelial markers (E-cad and KRT-7/14/19) were elevated, while mesenchymal markers (vimetin, N-cad and ZEB1/2) were suppressed after asiatic acid treatment, at both mRNA and protein levels. Inhibition of epithelial-to-mesenchymal transition was further evidenced by immunofluorescence staining of pan-cytokeratin and F-actin.Conclusion: Asiatic acid attenuates the malignancy of human metastatic ovarian cancer cells via epithelial-to-mesenchymal transition inhibition, and thus, is a therapeutic agent for ovarian cancer management.Keywords: Asiatic acid, Ovarian cancer, Metastasis, Epithelial-to-mesenchymal transition, Vometi

Metabolic fingerprinting of Angelica sinensis during growth using UPLC-TOFMS and chemometrics data analysis

BACKGROUND: The radix of Angelica sinensis is widely used as a medicinal herbal and metabolomics research of this plant during growth is necessary. RESULTS: Principal component analysis of the UPLC-QTOFMS data showed that these 27 samples could be separated into 4 different groups. The chemical markers accounting for these separations were identified from the PCA loadings plot. These markers were further verified by accurate mass tandem mass and retention times of available reference standards. The study has shown that accumulation of secondary metabolites of Angelica sinensis is closely related to the growth periods. CONCLUSIONS: The UPLC-QTOFMS based metabolomics approach has great potential for analysis of the alterations of secondary metabolites of Angelica sinensis during growth

Safe RLHF: Safe Reinforcement Learning from Human Feedback

With the development of large language models (LLMs), striking a balance between the performance and safety of AI systems has never been more critical. However, the inherent tension between the objectives of helpfulness and harmlessness presents a significant challenge during LLM training. To address this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly decouples human preferences regarding helpfulness and harmlessness, effectively avoiding the crowdworkers' confusion about the tension and allowing us to train separate reward and cost models. We formalize the safety concern of LLMs as an optimization task of maximizing the reward function while satisfying specified cost constraints. Leveraging the Lagrangian method to solve this constrained problem, Safe RLHF dynamically adjusts the balance between the two objectives during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we demonstrate a superior ability to mitigate harmful responses while enhancing model performance compared to existing value-aligned algorithms. Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with collected human preferences, significantly improving its helpfulness and harmlessness according to human evaluations

LOREN: Logic-Regularized Reasoning for Interpretable Fact Verification

Given a natural language statement, how to verify its veracity against a large-scale textual knowledge source like Wikipedia? Most existing neural models make predictions without giving clues about which part of a false claim goes wrong. In this paper, we propose LOREN, an approach for interpretable fact verification. We decompose the verification of the whole claim at phrase-level, where the veracity of the phrases serves as explanations and can be aggregated into the final verdict according to logical rules. The key insight of LOREN is to represent claim phrase veracity as three-valued latent variables, which are regularized by aggregation logical rules. The final claim verification is based on all latent variables. Thus, LOREN enjoys the additional benefit of interpretability -- it is easy to explain how it reaches certain results with claim phrase veracity. Experiments on a public fact verification benchmark show that LOREN is competitive against previous approaches while enjoying the merit of faithful and accurate interpretability. The resources of LOREN are available at: https://github.com/jiangjiechen/LOREN.Comment: Accepted to AAAI 202