奶啤的研究进展

奶啤是一种以牛乳作为主要原料，经过乳酸菌和酵母菌发酵后所得到的一种含二氧化碳的低醇发酵型乳酸菌饮品。该文系统的介绍了奶啤的发展历程、风味变化、原料组成、发酵过程和工艺流程中的质量控制的现状，对奶啤的未来进行了展望，为奶啤产业的深入研究提供理论参考。     

气相色谱—质谱联用技术在食品分析方面的应用进展

气相色谱—质谱联用技术综合了气相色谱高效快速和质谱精准鉴别的优势。近些年气相色谱—质谱联用技术发展迅速，在食品中挥发性组分的分离分析方面应用广泛。研究综述了气质联用技术在食品分析方面的应用进展，介绍了气相色谱的分离机理，气相色谱柱类别以及检测器分类；并结合具体应用实例对气相色谱—质谱联用技术在食品营养、食品安全和食品溯源方面的应用进行了详细阐述。     

超高压处理对奶油奶酪质构、风味及货架期的影响

目的:确定超高压(HHP)处理奶酪的最适条件。方法:以自制奶油奶酪为研究对象，探究超高压处理压力对奶油奶酪质构、风味及货架期的影响。通过质构仪、固相微萃取—气相色谱质谱联用仪及菌落计数法分别对奶酪的质构、风味物质、微生物等进行测定。结果:经HHP处理后，奶油奶酪的硬度、黏性、耐咀性呈降低趋势；当处理压力为300 MPa时，奶酪的弹性达到最高，比未处理奶酪增加了14.0%(P<0.05);当处理压力≥400 MPa时，奶酪挥发性物质的含量和种类明显降低，且200,300 MPa处理奶酪与未处理奶酪均位于第一主成分区域；HHP处理人工染菌后的奶油奶酪，其菌落数显著下降(P<0.05),且处理压力越大，杀菌效果越好；当HHP处理压力≥300 MPa时，奶酪的货架期从7 d延长至21 d。结论:经HHP处理的奶油奶酪有良好的质地和风味，且其货架期有效延长。     

DCAM: Disturbed class activation maps for weakly supervised semantic segmentation

In the field of weakly supervised semantic segmentation (WSSS), Class Activation Maps (CAM) are typically adopted to generate pseudo masks. Yet, we find that the crux of the unsatisfactory pseudo masks is the incomplete CAM. Specifically, as convolutional neural networks tend to be dominated by the specific regions in the high-confidence channels of feature maps during prediction, the extracted CAM contains only parts of the object. To address this issue, we propose the Disturbed CAM (DCAM), a simple yet effective method for WSSS. Following CAM, we adopt a binary cross-entropy (BCE) loss to train a multi-label classification model. Then, we disturb the feature map with retraining to enhance the high-confidence channels. In addition, a softmax cross-entropy (SCE) loss branch is employed to increase the model attention to the target classes. Once converged, we extract DCAM in the same way as in CAM. The evaluation on both PASCAL VOC and MS COCO shows that DCAM not only generates high-quality masks (6.2% and 1.4% higher than the benchmark models), but also enables more accurate activation in object regions. The code is available at https://github.com/gyyang23/DCAM.     

A secure fingerprint template generation mechanism using visual secret sharing with inverse halftoning

Fingerprints are the most popular and widely practiced biometric trait for human recognition and authentication. Due to the wide approval, reliable fingerprint template generation and secure saving of the generated templates are highly vital. Since fingers are permanently connected to the human body, loss of fingerprint data is irreversible. Cancelable fingerprint templates are used to overcome this problem. This paper introduces a novel cancelable fingerprint template generation mechanism using Visual Secret Sharing (VSS), data embedding, inverse halftoning, and super-resolution. During the fingerprint template generation, VSS shares with some hidden information are formulated as the secure cancelable template. Before authentication, the secret fingerprint image is reconstructed back from the VSS shares. The experimental results show that the proposed cancelable templates are simple, secure, and fulfill all the properties of the ideal cancelable templates, such as security, accuracy, non-invertibility, diversity, and revocability. The experimental analysis shows that the reconstructed fingerprint images are similar to the original fingerprints in terms of visual parameters and matching error rates.     

Fast saliency prediction based on multi-channels activation optimization

The saliency prediction precision has improved rapidly with the development of deep learning technology, but the inference speed is slow due to the continuous deepening of networks. Hence, this paper proposes a fast saliency prediction model. Concretely, the siamese network backbone based on tailored EfficientNetV2 accelerates the inference speed while maintaining high performance. The shared parameters strategy further curbs parameter growth. Furthermore, we add multi-channel activation maps to optimize the fine features considering different channels and low-level visual features, which improves the interpretability of the model. Extensive experiments show that the proposed model achieves competitive performance on the standard benchmark datasets, and prove the effectiveness of our method in striking a balance between prediction accuracy and inference speed. Moreover, the small model size allows our method to be applied in edge devices. The code is available at: https://github.com/lscumt/fast-fixation-prediction.     

流式细胞术在乳品微生物检测中的应用

阐述了流式细胞仪的工作原理、结构及流式细胞术在乳品微生物检测中的方法和应用，并展望了未来的研究方向。     

Person re-identification based on improved attention mechanism and global pooling method

Deep network has become a new favorite for person re-identification (Re-ID), whose research focus is how to effectively extract the discriminative feature representation for pedestrians. In the paper, we propose a novel Re-ID network named as improved ReIDNet (iReIDNet), which can effectively extract the local and global multi-granular feature representations of pedestrians by a well-designed spatial feature transform and coordinate attention (SFTCA) mechanism together with improved global pooling (IGP) method. SFTCA utilizes channel adaptability and spatial location to infer a 2D attention map and can help iReIDNet to focus on the salient information contained in pedestrian images. IGP makes iReIDNet capture more effectively the global information of the whole human body. Besides, to boost the recognition accuracy, we develop a weighted joint loss to guide the training of iReIDNet. Comprehensive experiments demonstrate the availability and superiority of iReIDNet over other Re-ID methods. The code is available at https://github.com/XuRuyu66/ iReIDNet.     

超临界流体色谱在食品质量与安全检测中的应用进展

现代超临界流体色谱技术具有更好的稳定性和耐用性，并可与质谱联用。超临界流体色谱具有高选择性、高灵敏度和短时性等特点，既适合于高通量分析，又适合于复杂样品中的微量化合物的分析。文章针对超临界流体色谱在食品质量和安全检测方面的研究进行了论述，并对其未来发展方向进行了展望。