ValueNet: A New Dataset for Human Value Driven Dialogue System

Building a socially intelligent agent involves many challenges, one of which is to teach the agent to speak guided by its value like a human. However, value-driven chatbots are still understudied in the area of dialogue systems. Most existing datasets focus on commonsense reasoning or social norm modeling. In this work, we present a new large-scale human value dataset called ValueNet, which contains human attitudes on 21,374 text scenarios. The dataset is organized in ten dimensions that conform to the basic human value theory in intercultural research. We further develop a Transformer-based value regression model on ValueNet to learn the utility distribution. Comprehensive empirical results show that the learned value model could benefit a wide range of dialogue tasks. For example, by teaching a generative agent with reinforcement learning and the rewards from the value model, our method attains state-of-the-art performance on the personalized dialog generation dataset: Persona-Chat. With values as additional features, existing emotion recognition models enable capturing rich human emotions in the context, which further improves the empathetic response generation performance in the EmpatheticDialogues dataset. To the best of our knowledge, ValueNet is the first large-scale text dataset for human value modeling, and we are the first one trying to incorporate a value model into emotionally intelligent dialogue systems. The dataset is available at https://liang-qiu.github.io/ValueNet/.Comment: Paper accepted by AAAI 202

Development of attractants and repellents for Tuta absoluta based on plant volatiles from tomato and eggplant

IntroductionTuta absoluta is currently considered one of the most devastating invasive pests of solanaceous plants worldwide, causing severe damage to the tomato industry. Insects use volatile organic compounds (VOCs) to locate host plant for feeding and oviposition. Those VOCs could be developed as lures for pest monitoring and control.MethodsIn this study, the differentially accumulated VOCs between the preferred host (tomato) and non-preferred host (eggplant) were analyzed by GC–MS method, and their roles on female T. absoluta host selection and egg laying behaviors were investigated using electroantennography (EAG), olfactometer and cage experiments.ResultsA total of 39 differentially accumulated VOCs were identified in tomato and eggplant. Strong EAG signals were obtained in 9 VOCs, including 5 VOCs highly accumulated in tomato and 4 VOCs highly accumulated in eggplant. Further olfactometer bioassays showed that 4 compounds (1-nonanol, ethyl heptanoate, ethyl octanoate and o-nitrophenol) were attractive to T. absoluta females, while 5 compounds (2-phenylethanol, 2-pentylfuran, trans,trans-2,4-nonadienal, 2-ethyl-5-methylpyrazine and trans-2-nonenal) were repellent, indicating that VOCs from host plants play important roles in host plant preferences. The attractive activities of 1-nonanol and ethyl octanoate, as well as the repellent activities of trans,trans-2,4-nonadienal and trans-2-nonenal, were further confirmed in cage experiments.DiscussionIn this study, two attractants and two repellents for T. absoluta were developed from plant released VOCs. Our results could be useful to enhance the development of eco-friendly and sustainable pest management strategies for T. absoluta

Fasting plasma glucose is an independent predictor for severity of H1N1 pneumonia

<p>Abstract</p> <p>Background</p> <p>The pandemic influenza A (H1N1) virus emerged during 2009 and has spread worldwide. This virus can cause injuries to the lungs, liver, and heart. However, data regarding whether this influenza virus can affect pancreatic islets are limited. We investigated the effects of influenza A (H1N1) pneumonia on fasting plasma glucose (FPG) and islet function, and evaluated possible correlations between biochemical test results and the severity of H1N1 pneumonia.</p> <p>Methods</p> <p>We performed a retrospective analysis of patients either diagnosed with or suspected of having H1N1 pneumonia who were admitted to our hospital in 2009. Possible associations between FPG levels and H1N1 virus infection were assessed by logistic regression. Correlation and regression analyses were used to assess relationships between FPG and biochemical test results. Associations between admission days and significant data were assessed by single factor linear regression. To evaluate effects of H1N1 on pancreatic β-cell function, results of a resistance index (homa-IR), insulin function index (homa-β), and insulin sensitivity index (IAI) were compared between a H1N1 group and a non-H1N1 group by t-tests.</p> <p>Results</p> <p>FPG was significantly positively associated with H1N1 virus infection (OR = 1.377, 95%CI: 1.062-1.786; p = 0.016). FPG was significantly correlated with AST (r = 0.215; p = 0.039), LDH (r = 0.400; p = 0.000), BUN (r = 0.28; p = 0.005), and arterial Oxygen Saturation (SaO₂; r = -0.416; p = 0.000) in the H1N1 group. H1N1 patients who were hypoxemic (SaO₂<93%) had higher FPG levels than those who were not hypoxic (9.82 ± 4.14 vs. 6.64 ± 1.78; p < 0.05). FPG was negatively correlated with SaO₂in the H1N1 group with hypoxia (SaO₂<93; r = -0.497; p = 0.041). SaO₂levels in patients with high FPG levels (≥7 mmol/L) were significantly lower than those of H1N1 patients with low FPG levels (<5.6 mmol/L). There were no significant differences in homa-IR, homa-β, or IAI between the H1N1 and non-H1N1 groups after adjusting for age, sex, and BMI.</p> <p>Conclusions</p> <p>FPG on admission could be an independent predictor for the severity of H1N1 pneumonia. Elevated FPG induced by H1N1 pneumonia is not a result of direct damage to pancreatic β-cells, but arises from various factors' combinations caused by H1N1 virus infection.</p

XLF and APLF bind Ku80 at two remote sites to ensure DNA repair by non-homologous end joining

International audienceThe Ku70-Ku80 (Ku) heterodimer binds rapidly and tightly to the ends of DNA double-strand breaks and recruits factors of the non-homologous end-joining (NHEJ) repair pathway through molecular interactions that remain unclear. We have determined crystal structures of the Ku-binding motifs (KBM) of the NHEJ proteins APLF (A-KBM) and XLF (X-KBM) bound to a Ku-DNA complex. The two KBM motifs bind remote sites of the Ku80 alpha/beta domain. The X-KBM occupies an internal pocket formed by an unprecedented large outward rotation of the Ku80 alpha/beta domain. We observe independent recruitment of the APLF-interacting protein XRCC4 and of XLF to laser-irradiated sites via binding of A- and X-KBMs, respectively, to Ku80. Finally, we show that mutation of the X-KBM and A-KBM binding sites in Ku80 compromises both the efficiency and accuracy of end joining and cellular radiosensitivity. A- and X-KBMs may represent two initial anchor points to build the intricate interaction network required for NHEJ

A nearly optimal multigrid method for general unstructured grids

a

Deformation monitoring and prediction for residential areas in the Panji mining area based on an InSAR time series analysis and the GM-SVR model

Underground coal mining activities often cause ground subsidence and damage to surface construction, which seriously threatens the lives and property of residents in mining areas. In this paper, the deformation of the Yang Juzhuang village, which is a residential area in the Huainan mining area (China), was monitored through an interferometric synthetic aperture radar (InSAR) time series analysis. The vertical displacements were detected using thirteen Sentinel-1A images that were acquired between December 2016 and May 2017. The validity and applicability of the method are verified by comparing the acquired images with the GPS measurement results. Because of the deformation characteristics of the mining area, a prediction model that is combined with a grey support vector machine regression (GM-SVR) is proposed, and the practical effects of the model are verified using the deformation monitoring results of the study area. The combination of this model and SBAS-InSAR provides rapid dynamic monitoring and enables the issuance of disaster warnings in the region

The Effect of Forest Tenure on Forest Composition in a Miao Area of Guizhou, China

Guizhou Province is one of the most expansive and important forest regions in China. Traditional Miao people have inhabited the mountains and managed the forest resources of this region for more than 1000 years. In the 1980s, the Chinese government transferred many collectively owned forests to individual household control. Since then, there have been 3 forest tenure types in Miao areas: state forests, collective forests, and household forests. The Collective Forest Tenure Reform was implemented nationwide starting in 2005, and in Guizhou in 2007, to develop and stabilize the forest tenure institutions. To investigate the effect of tenure type on the floristic composition and size structure of local forests, we conducted inventories in replicate forests under each type of tenure in 3 traditional Miao villages in Leishan County. Results showed that tree species richness and diversity were significantly higher in state and collective forests than in household forests; no significant differences were detected among villages. Cunninghamia lanceolata, an important local timber species, was most abundant in household forests, while higher proportions of associated broadleaf and pine species were recorded in state and collective forests. The lack of significant differences between state and collective forests for most measurement variables suggests that the inherent similarities between these 2 tenure types created by long-term use and management by the Miao have largely overshadowed the effects of more recent management efforts by the state. Each tenure regime offers different benefits, and a portfolio including all 3 tenure types would best provide the ecosystem services and economic opportunities required by forest-dependent communities