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Abstract Ficus carica L. (FC) is rich in phenolic compounds such as phenolic acids, flavonoids, and anthocyanins. Phenolic compounds in FC show remarkable health benefits and applications. The composition of phenolic compounds in FC can vary with some factors, such as variety, ripeness, geographical location, extraction methods and processing methods. New techniques such as supercritical fluid extraction, subcritical water extraction and ionic liquid extraction should be further developed and combined with traditional techniques to obtain higher quality sources of phenolic compounds. Numerous in vivo and in vitro studies reported healthy benefits of phenolic compounds in FC, such as anti‐oxidant, anti‐inflammatory, anti‐diabetic, anti‐obesity, anti‐cancer and anti‐Alzheimer's. Further works are required to reveal mechanisms and structure‐activity relationships, such as targeted metabolomics and pharmacological studies. In terms of applications, phenolic compounds in FC as prebiotics on the intestinal flora need to be further studied to expand their applicability in food fermentation. In a word, this review highlights the remarkable health benefits and application value of phenolic compounds in FC.

Abstract Recently, ithas been observed that {0,±1}‐ternary codes, which are simply generated from deep features by hard thresholding, tend to outperform {−1,1}‐binary codes in image retrieval. To obtain better ternary codes, the authors for the first time propose to jointly learn the features with the codes by appending a smoothed function to the networks. During training, the function could evolve into a non‐smoothed ternary function by a continuation method, and then generate ternary codes. The method circumvents the difficulty of directly training discrete functions and reduces the quantization errors of ternary codes. Experiments show that the proposed joint learning indeed could produce better ternary codes. For the first time, the authors propose to generate ternary hash codes by jointly learning the codes with deep features via a continuation method. Experiments show that the proposed method outperforms existing methods.