Generating Persona Consistent Dialogues by Exploiting Natural Language Inference

Consistency is one of the major challenges faced by dialogue agents. A human-like dialogue agent should not only respond naturally, but also maintain a consistent persona. In this paper, we exploit the advantages of natural language inference (NLI) technique to address the issue of generating persona consistent dialogues. Different from existing work that re-ranks the retrieved responses through an NLI model, we cast the task as a reinforcement learning problem and propose to exploit the NLI signals from response-persona pairs as rewards for the process of dialogue generation. Specifically, our generator employs an attention-based encoder-decoder to generate persona-based responses. Our evaluator consists of two components: an adversarially trained naturalness module and an NLI based consistency module. Moreover, we use another well-performed NLI model in the evaluation of persona-consistency. Experimental results on both human and automatic metrics, including the model-based consistency evaluation, demonstrate that the proposed approach outperforms strong generative baselines, especially in the persona-consistency of generated responses.Comment: AAAI20. Update code link

PD-L1 aptamer-functionalized degradable hafnium oxide nanoparticles for near infrared-II diagnostic imaging and radiosensitization

Immune checkpoint blockade is now recognized as a paradigm-shifting cancer therapeutic strategy, whereas there remains difficulty in accurately predicting immunotherapy efficacy by PD-L1 expression. In addition, radiotherapy for cancer patients faces the problem of insufficient dose of radiotherapy at the tumor site while which have been not tolerated by normal tissues. In this study, we created PD-L1 aptamer-anchored spherical nucleic acids (SNAs) with a shell made of PD-L1 aptamer and indocyanine green (ICG) embedded in a mesoporous hafnium oxide nanoparticle core (Hf@ICG-Apt). Upon low pH irradiation in the tumor sites, the nano-system enabled the release of ICG in the high PD-L1 expression tumor to develop a high tumor-to-background ratio of 7.97 ± 0.76 and enhanced the ICG tumor retention to more than 48 h. Moreover, Hf@ICG-Apt improved radiation therapy (RT) when combined with radiation. Notably, Hf@ICG-Apt showed scarcely any systemic toxicity in vivo. Overall, this research offered a novel approach for applying reliable monitoring of PD-L1 expression and localization and robust RT sensitization against cancer with good biosafety

Closing the Gap Between the Upper Bound and the Lower Bound of Adam's Iteration Complexity

Recently, Arjevani et al. [1] established a lower bound of iteration complexity for the first-order optimization under an $L$-smooth condition and a bounded noise variance assumption. However, a thorough review of existing literature on Adam's convergence reveals a noticeable gap: none of them meet the above lower bound. In this paper, we close the gap by deriving a new convergence guarantee of Adam, with only an $L$-smooth condition and a bounded noise variance assumption. Our results remain valid across a broad spectrum of hyperparameters. Especially with properly chosen hyperparameters, we derive an upper bound of the iteration complexity of Adam and show that it meets the lower bound for first-order optimizers. To the best of our knowledge, this is the first to establish such a tight upper bound for Adam's convergence. Our proof utilizes novel techniques to handle the entanglement between momentum and adaptive learning rate and to convert the first-order term in the Descent Lemma to the gradient norm, which may be of independent interest.Comment: NeurIPS 2023 Accep

The long noncoding RNA THRIL knockdown protects hypoxia-induced injuries of H9C2 cells through regulating miR-99a

Background: Myocardial infarction (MI) is a leading cause of disease with high morbidity and mortality worldwide. Recent studies have revealed that long non-coding RNAs (lncRNAs) are involved inheart disease pathogenesis. This study aimed to investigate the effect and the molecular basis of THRIL on hypoxia-injured H9C2 cells. Methods: THRIL, miR-99a and Brahma-related gene 1 (Brg1) expressions in H9C2 cells were altered by transient transfections. The cells were subjected to hypoxia for 4 h, and then the levels of THRIL, miR-99a and Brg1 were investigated. Cell viability, migration and invasion, and apoptotic cells were respectively measured by trypan blue exclusion assay, transwell migration assay and flow cytometry assay. Dual luciferase reporter assay was conducted to verify the interaction between miR-99a and THRIL. Furthermore, levels of apoptosis-, PI3K/AKT and mTOR pathways-related factors were measured by western blotting. Results: Hypoxia induced an increase of THRIL but a reduction of miR-99a and Brg1. THRIL inhibition significantly attenuated hypoxia-induced cell injuries, as increased cell viability, migration and invasion, and decreased cell apoptosis. THRIL negatively regulated miR-99a expression through sponging with miR-99a binding site, and miR-99a inhibition abolished the protective effects of THRIL knockdown against hypoxia-induced injury in H9C2 cells. Furthermore, miR-99a positively regulated the expression of Brg1. Brg1 inhibition promoted hypoxia-induced cell injuries, while Brg1 overexpression alleviated hypoxia-induced cell injuries. Moreover, Brg1 overexpression activated PI3K/AKT and mTOR pathways. Conclusions: This study demonstrated that THRIL inhibition represented a protective effect againsthypoxia-induced injuries in H9C2 cells by up-regulating miR-99a expression

Bayesian active learning line sampling with log-normal process for rare-event probability estimation

Line sampling (LS) stands as a powerful stochastic simulation method for structural reliability analysis, especially for assessing small failure probabilities. To further improve the performance of traditional LS, a Bayesian active learning idea has recently been pursued. This work presents another Bayesian active learning alternative, called ‘Bayesian active learning line sampling with log-normal process’ (BAL-LS-LP), to traditional LS. In this method, we assign an LP prior instead of a Gaussian process prior over the distance function so as to account for its non-negativity constraint. Besides, the approximation error between the logarithmic approximate distance function and the logarithmic true distance function is assumed to follow a zero-mean normal distribution. The approximate posterior mean and variance of the failure probability are derived accordingly. Based on the posterior statistics of the failure probability, a learning function and a stopping criterion are developed to enable Bayesian active learning. In the numerical implementation of the proposed BAL-LS-LP method, the important direction can be updated on the fly without re-evaluating the distance function. Four numerical examples are studied to demonstrate the proposed method. Numerical results show that the proposed method can estimate extremely small failure probabilities with desired efficiency and accuracy