Dataset vs Reality: Understanding Model Performance from the Perspective of Information Need

Deep learning technologies have brought us many models that outperform human beings on a few benchmarks. An interesting question is: can these models well solve real-world problems with similar settings (e.g., identical input/output) to the benchmark datasets? We argue that a model is trained to answer the same information need for which the training dataset is created. Although some datasets may share high structural similarities, e.g., question-answer pairs for the question answering (QA) task and image-caption pairs for the image captioning (IC) task, they may represent different research tasks aiming for answering different information needs. To support our argument, we use the QA task and IC task as two case studies and compare their widely used benchmark datasets. From the perspective of information need in the context of information retrieval, we show the differences in the dataset creation processes, and the differences in morphosyntactic properties between datasets. The differences in these datasets can be attributed to the different information needs of the specific research tasks. We encourage all researchers to consider the information need the perspective of a research task before utilizing a dataset to train a model. Likewise, while creating a dataset, researchers may also incorporate the information need perspective as a factor to determine the degree to which the dataset accurately reflects the research task they intend to tackle.Comment: 19 pages, 5 figure

Nutrient content of 122 kinds of retail handcrafted milk tea products in Shanghai

BackgroundThe retail milk tea industry is in a period of rapid development, but there is little research on its nutrient content, which restricts the nutritional guidance of milk tea. ObjectiveTo determine the levels of nutrients in best-selling handcrafted milk tea in Shanghai and analyze the nutritional characteristics. MethodsIn 2018 and 2021, a total of 13 handcrafted milk tea brands with ≥3 branch stores in Shanghai were selected by searching for milk tea on Meituan and Ele.me food delivery platforms, and a total of 122 types of handcrafted milk tea products were collected from the top three sales [milk tea (including all sweetness levels available), milk cover tea, and fruit tea]. National standard methods were used to detect energy, protein, fat, carbohydrate, sugar, trans fatty acid, calcium, caffeine, and tea polyphenol. ResultsThe median energy of the milk tea samples was 310 kJ (per 100 g sample). The main sources of energy were carbohydrate and fat. The levels of energy, protein, and fat in milk cover tea and milk tea were significantly higher than those in fruit tea (P＜0.05), and there was no significant difference in carbohydrate among them. The total sugar, fructose, and glucose levels in milk tea were significantly lower than those in milk cover tea and fruit tea, and the lactose level in fruit tea was significantly lower than those in milk tea and milk cover tea (P＜0.05). Themedian trans fat acid level in milk cover tea was higher than that in milk tea (P＜0.05). The median levels of caffeine and tea polyphenol were higher in milk tea than in milk cover tea (P＜0.05). The levels of energy, carbohydrate, sucrose, total sugar, and calcium in milk tea were positively correlated with the number of ingredients added (0-3) (r=0.386, 0.371, 0.238, 0.698, 0.466, respectively, P < 0.05). The levels of energy, carbohydrate, and total sugar tended to increase with increasing sweetness (P<0.05), and total sugar was mainly sucrose, followed by fructose and glucose. The total sugar levels of the samples labeled sugar free, light sugar, half sugar, less sugar, and regular sugar were 3.40 (2.20, 4.9), 4.97 (4.25, 5.97), 5.80 (4.31, 6.88), 6.59 (5.17, 8.53), and 7.96 (6.82, 9.20) g, respectively; the proportions of the samples containing more than 0.5 g of total sugar were 93.3% for sugar free milk tea, 47.4% for light sugar milk tea, and 94.0% for regular sugar milk tea; the proportion of the regular sugar samples with sugar content greater than 10 g was 18.0% (all samples with nominal sugar content were measured per 100 g). ConclusionThe retail handcrafted milk tea in Shanghai is characterized by high energy, high added sugar, high fat, and low protein. It is necessary to standardize the added sugar content and sweetness labeling, strengthen the nutrition education of milk tea, and guide residents to limit its intake

Analysis of genetic and chemical variability of five Curcuma species based on DNA barcoding and HPLC fingerprints

The rhizomes of Curcuma species have a long medicinal history in Asia. In China, Curcuma species mainly be utilized to make pharmaceutical products, including C. phaecocaulis, C. aromatica, C. wenyujin, C. kwangsiensis and C. longa. In this study, twenty-four samples were selected to study the genetic and chemical variability among five Curcuma species. The ITS2 and trnK intron gene fragment were used to identify the five Curcuma species, the differences in chemical composition were computed using the Euclidean distance based on the data of HPLC characteristic peak areas and the content of six key components, and agronomic characteristics were analyzed including morphological and volatile oil characteristics. The ITS2 and trnK intron gene fragment could distinguish the five Curcuma species clearly. The genetic distance between Curcuma species ranged from 0.0085 to 0.0767 based on the data of ITS2 gene sequences with 32 variation sites, and the genetic distance between Curcuma species ranged from 0.0003 to 0.0194 based on the data of trnK intron gene sequences with 39 variation sites. Five Curcuma species showed otherness chemical composition characteristics, with the Euclidean distance ranging from 3.373 to 6.998. The C. longa showed the biggest variation compared with other species, with the Euclidean distance above 6.239. Among the samples of the original plants of Ezhu, the volatile oil yield of W1 was the highest, reached to 105.75 mL per single plant. Among all the samples, J6 showed the highest yield of volatile oil, reached to 149.42 mL per single plant. The results showed that chemical composition similarity of the medicinal plants was the primary proof for the selection of the original plants of the Curcuma medicinal materials. The genetic distance and chemical variability were important references for discovering new medicinal plant resources

Analysis of Key Aroma Components of Three Representative Oolong Tea Varieties by Stir Bar Sorptive Extraction Combined with Gas Chromatography-Olfactory-Mass Spectrometry

Stir bar sorptive extraction (SBSE) combined with gas chromatography-olfactory-mass spectrometry (GC-O-MS) was used to identify and describe the key aroma components of three representative oolong tea varieties, Huangdan, Tieguanyin and Jinguanyin. Comparative analysis was conducted in terms of odor activity value (OAV), aroma character impact (ACI) value and sensory evaluation. The sensory evaluation showed that each variety showed obvious aroma characteristics. Huangdan oolong tea had an obvious floral aroma as well as a slight milky aroma. Tieguanyin oolong tea had a strong floral aroma. Jinguanyin oolong tea had a sweet fruity aroma as well as a slight woody aroma. According to the results of OAV and GC-O-MS analysis, geraniol, phytol, methyl jasmonate, trans-nerol tertiary alcohol, 2-nonone, and phenyl ethanol were identified as key aroma components in Huangdan oolong tea, which provided it with clean and high floral aroma and obvious milky aroma characteristics. In Tieguanyin oolong tea, linalool, 3,5-octylodiene-2-one, linalool oxide, cis-jasmonone, dehydrolinalool, and α-terpineol showed diverse floral aromas, which were closely related to the characteristic aroma of Tieguanyin oolong tea. The key aroma components identified in Jinguanyin oolong tea included linalool, canalaldehyde, geranyl acetone, cis-jasmonone and isoeugenol, which were responsible for the characteristic sweet floral and woody aromas of Jinguanyin oolong tea