SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers

This paper aims to improve the performance of text-to-SQL parsing by exploring the intrinsic uncertainties in the neural network based approaches (called SUN). From the data uncertainty perspective, it is indisputable that a single SQL can be learned from multiple semantically-equivalent questions.Different from previous methods that are limited to one-to-one mapping, we propose a data uncertainty constraint to explore the underlying complementary semantic information among multiple semantically-equivalent questions (many-to-one) and learn the robust feature representations with reduced spurious associations. In this way, we can reduce the sensitivity of the learned representations and improve the robustness of the parser. From the model uncertainty perspective, there is often structural information (dependence) among the weights of neural networks. To improve the generalizability and stability of neural text-to-SQL parsers, we propose a model uncertainty constraint to refine the query representations by enforcing the output representations of different perturbed encoding networks to be consistent with each other. Extensive experiments on five benchmark datasets demonstrate that our method significantly outperforms strong competitors and achieves new state-of-the-art results. For reproducibility, we release our code and data at https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/sunsql.Comment: Accepted at COLING 202

Plasma Protein and MicroRNA Biomarkers of Insulin Resistance: A Network-Based Integrative -Omics Analysis

Although insulin resistance (IR) is a key pathophysiologic condition underlying various metabolic disorders, impaired cellular glucose uptake is one of many manifestations of metabolic derangements in the human body. To study the systems-wide molecular changes associated with obesity-dependent IR, we integrated information on plasma proteins and microRNAs in eight obese insulin-resistant (OIR, HOMA-IR > 2.5) and nine lean insulin-sensitive (LIS, HOMA-IR < 1.0) normoglycemic males. Of 374 circulating miRNAs we profiled, 65 species increased and 73 species decreased in the OIR compared to the LIS subjects, suggesting that the overall balance of the miRNA secretome is shifted in the OIR subjects. We also observed that 40 plasma proteins increased and 4 plasma proteins decreased in the OIR subjects compared to the LIS subjects, and most proteins are involved in metabolic and endocytic functions. We used an integrative -omics analysis framework called iOmicsPASS to link differentially regulated miRNAs with their target genes on the TargetScan map and the human protein interactome. Combined with tissue of origin information, the integrative analysis allowed us to nominate obesity-dependent and obesity-independent protein markers, along with potential sites of post-transcriptional regulation by some of the miRNAs. We also observed the changes in each -omics platform that are not linked by the TargetScan map, suggesting that proteins and microRNAs provide orthogonal information for the progression of OIR. In summary, our integrative analysis provides a network of elevated plasma markers of OIR and a global shift of microRNA secretome composition in the blood plasma

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Machine Users Detection on Sina Weibo Platform

In recent years, the rapid development of Sina Weibo has made it the representative of many Weibo platforms in China. Sina Weibo has attracted large numbers of users in China because of its fast speed of information dissemination, simple use and many star users. More and more Chinese people get news and share information through Sina Weibo. In addition to the normal users, Sina Weibo also appeared on some machine users, these users are generated in order to create false sentiment, which seriously affected the good order of the Sina Weibo platform. By studying normal users and machine users, this paper extracts eight features, they are the number of followings, the number of followers, the number of Weibos, the number of years using Sina Weibo, Sunshine credit, the number of Weibos you like, the proportion of following others by recommending and the ratio of followings and followers. Naive Bayes classification approach, KNN classification approach and SVC classification approach are used for experiment. The experimental results show that the recall rate of the machine users detection is above 96% and the accuracy rate is above 98%, which validates the validity of the features extracted in this paper

Machine Users Detection on Sina Weibo Platform

In recent years, the rapid development of Sina Weibo has made it the representative of many Weibo platforms in China. Sina Weibo has attracted large numbers of users in China because of its fast speed of information dissemination, simple use and many star users. More and more Chinese people get news and share information through Sina Weibo. In addition to the normal users, Sina Weibo also appeared on some machine users, these users are generated in order to create false sentiment, which seriously affected the good order of the Sina Weibo platform. By studying normal users and machine users, this paper extracts eight features, they are the number of followings, the number of followers, the number of Weibos, the number of years using Sina Weibo, Sunshine credit, the number of Weibos you like, the proportion of following others by recommending and the ratio of followings and followers. Naive Bayes classification approach, KNN classification approach and SVC classification approach are used for experiment. The experimental results show that the recall rate of the machine users detection is above 96% and the accuracy rate is above 98%, which validates the validity of the features extracted in this paper

Mathematical Modeling of Growth of \u3ci\u3eSalmonella\u3c/i\u3e in Raw Ground Beef under Isothermal Conditions from 10 to 45° C

The objective of this study was to develop primary and secondary models to describe the growth of Salmonella in raw ground beef. Primary and secondary models can be integrated into a dynamic model that can predict the microbial growth under varying environmental conditions. Growth data of Salmonella at nine different isothermal conditions — 10,15, 20, 25, 28, 32, 35, 42, and 45 °C were first fitted into primary models, namely the logistic, modified Gompertz, Baranyi, and Huang models. Performances of these models were evaluated by using various statistical criteria, namely mean square error (MSE), pseudo-R2, −2 log likelihood, Akaike\u27s and Bayesian\u27s information criteria. All the chosen models fitted well to the growth data of Salmonella based on these criteria. The results of statistical analysis showed that there was no significant difference in the performances of the four primary models, suggesting that the models were equally suitable for describing isothermal bacterial growth. The specific growth rates derived from each model was fitted to the Modified Ratkowsky equation, relating the specific growth rate to growth temperatures. It was also observed that the lag phase duration was an inverse function of specific growth rates. These models, if validated, can be used to construct dynamic models to predict potential Salmonella growth in raw ground beef