Ultrasonication improves the structures and physicochemical properties of Cassava starch films containing acetic acid

Cassava starch films are fabricated with acetic acid treatment and ultrasonication. Different ultrasound power levels from 200 to 750 W are used and the effects of ultrasonication on the morphology, microstructures, and properties of the starch–acetic acid films are investigated. Scanning electron microscopy shows a cohesive and compact structure of the films resulting from ultrasonication. X‐ray diffraction analysis reveals that the crystalline index is decreased by acid treatment and increased by ultrasonication. The tensile strength and elongation at break of the films first increase and then decrease with increasing ultrasound power level. Ultrasonication also results in higher opacity, higher water barrier performance, and lower water adsorption of the films. Thus, the results show that ultrasonication can be used as a simple and efficient way to modify the morphology, microstructure, and performance of starch–acetic acid films to better meet the application needs

Open-Vocabulary Object Detection with Meta Prompt Representation and Instance Contrastive Optimization

Classical object detectors are incapable of detecting novel class objects that are not encountered before. Regarding this issue, Open-Vocabulary Object Detection (OVOD) is proposed, which aims to detect the objects in the candidate class list. However, current OVOD models are suffering from overfitting on the base classes, heavily relying on the large-scale extra data, and complex training process. To overcome these issues, we propose a novel framework with Meta prompt and Instance Contrastive learning (MIC) schemes. Firstly, we simulate a novel-class-emerging scenario to help the prompt learner that learns class and background prompts generalize to novel classes. Secondly, we design an instance-level contrastive strategy to promote intra-class compactness and inter-class separation, which benefits generalization of the detector to novel class objects. Without using knowledge distillation, ensemble model or extra training data during detector training, our proposed MIC outperforms previous SOTA methods trained with these complex techniques on LVIS. Most importantly, MIC shows great generalization ability on novel classes, e.g., with $+4.3\%$ and $+1.9\% \ \mathrm{AP}$ improvement compared with previous SOTA on COCO and Objects365, respectively.Comment: BMVC 202

Your Contrastive Learning Is Secretly Doing Stochastic Neighbor Embedding

Contrastive learning, especially self-supervised contrastive learning (SSCL), has achieved great success in extracting powerful features from unlabeled data. In this work, we contribute to the theoretical understanding of SSCL and uncover its connection to the classic data visualization method, stochastic neighbor embedding (SNE), whose goal is to preserve pairwise distances. From the perspective of preserving neighboring information, SSCL can be viewed as a special case of SNE with the input space pairwise similarities specified by data augmentation. The established correspondence facilitates deeper theoretical understanding of learned features of SSCL, as well as methodological guidelines for practical improvement. Specifically, through the lens of SNE, we provide novel analysis on domain-agnostic augmentations, implicit bias and robustness of learned features. To illustrate the practical advantage, we demonstrate that the modifications from SNE to $t$-SNE can also be adopted in the SSCL setting, achieving significant improvement in both in-distribution and out-of-distribution generalization.Comment: Accepted by ICLR 202

Understanding the digestibility and nutritional functions of rice starch subjected to heat-moisture treatment

In this study, rice starch with well-controlled digestion resistibility achieved by heat-moisture treatment (HMT) was chosen as a supplementary diet for high-fat-diet-fed mice. Then, the nutritional functions of HMT-modified rice starch were evaluated by the physiological and biochemical indices, proliferation and distribution of intestinal microflora, and functional diversity by putative metagenomes analysis. Compared with the native-rice-starch mice (DM) group, the blood glucose, serum lipid, oxidative stress, and liver function metabolic levels/indices of the HMT-rice-starch mice (HMT-DM) group were worse due to the declined level of slowly digestible starch (SDS) in HMT-modified rice starch. Meanwhile, the species diversity index was observed to be higher in the DM group and Bifidobacteria was identified as a type of bacteria related to the relatively higher content of RS in HMT-modified rice starch. Overall, our results provide important information for the rational design of rice starch-based health-promoting foods with nutritional functions