Unscrupulous merchants would sell cheap, low-nutrition formula milk powder as pasteurised milk, or use it as a raw material in dairy products, for the purpose of making a profit. Currently, biochemical methods are utilised to identify the type of processed milk, which could involve chemical reagents, sample preparation and costly instruments. This paper investigates the utility of electrical impedance spectroscopy (EIS) in distinguishing different types of processed milk. Ultra-high temperature sterilised milk, pasteurised milk and formula milk powder were studied. The one-factor analysis of variance revealed that milk type had a significant influence on electrical properties, whereas sensory analysis, pH and total soluble solids showed poor discrimination of milk type. Recognition models were developed using Support Vector Classification, Partial Least Squares-Discriminant Analysis (PLS-DA), Gaussian Naive Bayes and Random Forest (RF). The nested cross-validation and the external validation set (N = 45) indicated the RF model had the highest predicting accuracy of 97.8%; whereas PLS-DA had a misclassification rate of 33.3% on positive samples and was, therefore, unsuitable for the authentication. The results demonstrated that the EIS data was featured enough for a small sample modelling (N = 75), and the established method was simple, fast and effective for liquid food classification.
Recommender systems are seen as an effective tool to address information overload, but it is widely known that the presence of various biases makes direct training on large-scale observational data result in sub-optimal prediction performance. In contrast, unbiased ratings obtained from randomized controlled trials or A/B tests are considered to be the golden standard, but are costly and small in scale in reality. To exploit both types of data, recent works proposed to use unbiased ratings to correct the parameters of the propensity or imputation models trained on the biased dataset. However, the existing methods fail to obtain accurate predictions in the presence of unobserved confounding or model misspecification. In this paper, we propose a theoretically guaranteed model-agnostic balancing approach that can be applied to any existing debiasing method with the aim of combating unobserved confounding and model misspecification. The proposed approach makes full use of unbiased data by alternatively correcting model parameters learned with biased data, and adaptively learning balance coefficients of biased samples for further debiasing. Extensive real-world experiments are conducted along with the deployment of our proposal on four representative debiasing methods to demonstrate the effectiveness.
CCS CONCEPTS• Information systems → Recommender systems.
As a new market in 2015, the Chinese options market has the characteristics of information asymmetry. Therefore, searching for a general investment pattern is important. This article analyzes all Chinese stock index options data and reveals the statistical law of the calendar effect in the option market. The article conducts regression analysis on unreasonable volatility, building delta hedge and straddle option strategies, and using regression analysis on options yield to verify that the market makers neglect the influence of the difference between expiration date and purchase date. The pricing difference caused by the calendar effect of options does exist, but unlike the US options market, investors cannot just use simple strategies to obtain a robust positive return in the Chinese market through the calendar effect.
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