Don't Lose Yourself! Empathetic Response Generation via Explicit Self-Other Awareness

As a critical step to achieve human-like chatbots, empathetic response generation has attained increasing interests. Previous attempts are incomplete and not sufficient enough to elicit empathy because they only focus on the initial aspect of empathy to automatically mimic the feelings and thoughts of the user via other-awareness. However, they ignore to maintain and take the own views of the system into account, which is a crucial process to achieve the empathy called self-other awareness. To this end, we propose to generate Empathetic response with explicit Self-Other Awareness (EmpSOA). Specifically, three stages, self-other differentiation, self-other modulation and self-other generation, are devised to clearly maintain, regulate and inject the self-other aware information into the process of empathetic response generation. Both automatic and human evaluations on the benchmark dataset demonstrate the superiority of EmpSOA to generate more empathetic responses

Numerical Simulation of Compressible Reactive Flows

Numerical simulation has been widely employed to investigate the compressible flows since it is difficult to carry out the experimental measurements, especially in the reactive flows. The shock-wave capturing scheme will be necessary for resolving the compressible flows, and moreover the careful treatments of chemical reaction should be considered for proceeding numerical simulation stable and fast. Recently, the present authors have tried to establish a high-resolution numerical solver for computing the compressible reactive flows. This chapter presents the numerical procedures acquired in this solver for computing the fluxes using weighted essentially non-oscillatory (WENO) scheme, dealing with chemical stiffness problems, and tracing droplets and their interaction with the compressible fluids. As examples, the Taylor-Green vortex (TGV) problem considering the passive scalar mixing, the spatially developing reactive mixing layer flows taken gas-phase hydrogen, and liquid n-decane as fuel are simulated, respectively. Many important characteristics of flow, flame, and ignition are analyzed under the supersonic condition

A LES Study on Passive Mixing in Supersonic Shear Layer Flows Considering Effects of Baffle Configuration

Under the background of dual combustor ramjet (DCR), a numerical investigation of supersonic mixing layer was launched, focused on the mixing enhancement method of applying baffles with different geometric configurations. Large eddy simulation with high order schemes, containing a fifth-order hybrid WENO compact scheme for the convective flux and sixth-order compact one for the viscous flux, was utilized to numerically study the development of the supersonic mixing layer. The supersonic cavity flow was simulated and the cavity configuration could influence the mixing characteristics, since the impingement process of large scale structures formed inside the cavity could raise the vorticity and promote the mixing. The effect of baffle's configurations on the mixing process was analyzed by comparing the flow properties, mixing efficiency, and total pressure loss. The baffle could induce large scale vortexes, promote the mixing layer to lose its stability easily, and then lead to the mixing efficiency enhancement. However, the baffle could increase the total pressure loss. The present investigation could provide guidance for applying new passive mixing enhancement methods for the supersonic mixing

Robust Statistical Inference for Large-dimensional Matrix-valued Time Series via Iterative Huber Regression

Matrix factor model is drawing growing attention for simultaneous two-way dimension reduction of well-structured matrix-valued observations. This paper focuses on robust statistical inference for matrix factor model in the ``diverging dimension" regime. We derive the convergence rates of the robust estimators for loadings, factors and common components under finite second moment assumption of the idiosyncratic errors. In addition, the asymptotic distributions of the estimators are also derived under mild conditions. We propose a rank minimization and an eigenvalue-ratio method to estimate the pair of factor numbers consistently. Numerical studies confirm the iterative Huber regression algorithm is a practical and reliable approach for the estimation of matrix factor model, especially under the cases with heavy-tailed idiosyncratic errors . We illustrate the practical usefulness of the proposed methods by two real datasets, one on financial portfolios and one on the macroeconomic indices of China

Is ChatGPT Equipped with Emotional Dialogue Capabilities?

This report presents a study on the emotional dialogue capability of ChatGPT, an advanced language model developed by OpenAI. The study evaluates the performance of ChatGPT on emotional dialogue understanding and generation through a series of experiments on several downstream tasks. Our findings indicate that while ChatGPT's performance on emotional dialogue understanding may still lag behind that of supervised models, it exhibits promising results in generating emotional responses. Furthermore, the study suggests potential avenues for future research directions

Increased electrical conductivity in fine-grained (Zr,Hf)NiSn based thermoelectric materials with nanoscale precipitates

Grain refinement has been conducted to reduce the thermal conductivity and improve the thermoelectric performance of the (Zr,Hf)NiSn based half-Heusler alloys. Nanoscale in situ precipitates were found embedded in the matrix with submicron grains. The lattice thermal conductivity was decreased due to the enhanced boundary scattering of phonons. The increased carrier concentration and electrical conductivity were observed compared to the coarse-grained alloys, which is discussed in relation to the existence of nanoscale precipitates, the effect of antisite defects, and composition change. It is suggested that the nanoscale precipitates play a significant role in the observed electrical conductivity increase

An Improved SMOTE Algorithm Based on Genetic Algorithm for Imbalanced Data Collection

Classification of imbalanced data has been recognized as a crucial problem in machine learning and data mining. In an imbalanced dataset, minority class instances are likely to be misclassified. When the synthetic minority over-sampling technique (SMOTE) is applied in imbalanced dataset classification, the same sampling rate is set for all samples of the minority class in the process of synthesizing new samples, this scenario involves blindness. To overcome this problem, an improved SMOTE algorithm based on genetic algorithm (GA), namely, GASMOTE was proposed. First, GASMOTE set different sampling rates for different minority class samples. A combination of the sampling rates corresponded to an individual in the population. Second, the selection, crossover, and mutation operators of GA were iteratively applied to the population to obtain the best combination of sampling rates when the stopping criteria were met. Lastly, the best combination of sampling rates was used in SMOTE to synthetize new samples. Experimental results on 10 typical imbalanced datasets show that GASMOTE increases the F-measure value by 5.9% and the G-mean value by 1.6% compared with the SMOTE algorithm. Meanwhile, GASMOTE increases the F-measure value by 3.7% and the G-mean value by 2.3% compared with the borderline-SMOTE algorithm. GASMOTE can be utilized as a new over-sampling technique to address the problem of imbalanced dataset classification. The GASMOTE algorithm can be then adopted in a practical engineering application, namely, prediction of rockburst in VCR rockburst datasets. The experimental results indicate that the GASMOTE algorithm can accurately predict the rockburst occurrence and thus provides guidance to the design and construction of safe deep-mining engineering structures

Transcriptional up-regulation of relaxin-3 by Nur77 attenuates β-adrenergic agonist-induced apoptosis in cardiomyocytes.

The relaxin family peptides have been shown to exert several beneficial effects on the heart, including anti-apoptosis, anti-fibrosis, and anti-hypertrophy activity. Understanding their regulation might provide new opportunities for therapeutic interventions, but the molecular mechanism(s) coordinating relaxin expression in the heart remain largely obscured. Previous work demonstrated a role for the orphan nuclear receptor Nur77 in regulating cardiomyocyte apoptosis. We therefore investigated Nur77 in the hopes of identifying novel relaxin regulators. Quantitative real-time PCR (qRT-PCR) and enzyme-linked immunosorbent assay (ELISA) data indicated that ectopic expression of orphan nuclear receptor Nur77 markedly increased the expression of latexin-3 (RLN3), but not relaxin-1 (RLN1), in neonatal rat ventricular cardiomyocytes (NRVMs). Furthermore, we found that the -adrenergic agonist isoproterenol (ISO) markedly stimulated RLN3 expression, and this stimulation was significantly attenuated in Nur77 knockdown cardiomyocytes and Nur77 knockout hearts. We showed that Nur77 significantly increased RLN3 promoter activity via specific binding to the RLN3 promoter, as demonstrated by electrophoretic mobility shift assay (EMSA) and chromatin immuno-precipitation (ChIP) assays. Furthermore, we found that Nur77 overexpression potently inhibited ISO-induced cardiomyocyte apoptosis, whereas this protective effect was significantly attenuated in RLN3 knockdown cardiomyocytes, suggesting that Nur77-induced RLN3 expression is an important mediator for the suppression of cardiomyocyte apoptosis. These findings show that Nur77 regulates RLN3 expression, therefore suppressing apoptosis in the heart, and suggest that activation of Nur77 may represent a useful therapeutic strategy for inhibition of cardiac fibrosis and heart failure. © 2018 You et al