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Automatic person recognition using finger-vein has been extensively investigated in recent years. Mostly the published research on biometrics are based on the hand-crafted feature descriptors to describe the appearance of a finger-vein image, which is prone to be affected by irregular shading, irrelevant background, varying illumination and non-rigid deformation. To improve the recognition performance of hand-crafted feature-based methods, many researchers have contributed substantially in designing enhancement methods for texture descriptors. However, hand-crafted algorithms are highly targeted with weak generalization ability with respect to the emerging data samples. Nowadays, deep convolutional neural networks (CNN) is emerging as a powerful technology to extract multi-level feature representation instead of traditionally designed features from raw data. These methods are achieving unprecedented performance in the field of computer vision. In context to biometrics modalities, finger-vein recognition using CNN is still in its primary stage. In this paper, we proposed a simple yet effective multi-scale arc-fusion approach to optimize the feature embedding discrimination power. An exhaustive comparative experiment is conducted on four publicly available databases: HKPU, FV-USM, SDUMLA and UTFVP, to demonstrate that the proposed approach is superior to the existing state-of-the-art (SOTA) methods. Also, the experimental result shows that the concept is actually scalable and can be migrated to other deep neural network architectures. [ABSTRACT FROM AUTHOR]

In the field of natural language processing, sentiment analysis via deep learning has a excellent performance by using large labeled datasets. Meanwhile, labeled data are insufficient in many sentiment analysis tasks, and obtaining these data is time-consuming and laborious. Prompt learning devotes to resolving the data deficiency by reformulating downstream tasks with the help of prompt. The model performance of this method depends on the quality of the prompt. This paper proposes an adaptive prompting (AP) construction strategy using seq2seq-attention structure to acquire the semantic information of the input sequence. Our method of dynamically constructing adaptive prompts can not only improve the quality of prompt, but also can effectively generalize to other fields by constructing a pre-trained prompt with existing public labeled data. The experimental results on FewCLUE datasets demonstrate that the proposed method AP can effectively construct appropriate adaptive prompt regardless of the quality of hand-crafted prompt and outperform the state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

Palmprint biometrics has a broad application prospect owing to non-intrusiveness, ease of image acquisition and stable textural pattern. Hand-crafted approaches are vulnerable to non-ideal palmprint images caused by uneven illumination, motion blur and noise contamination. Many researchers have designed excellent texture descriptors or/and advanced image pre-processing algorithms. Nevertheless, they are highly targeted at some specific data, less adaptable to the emerging data. In this paper, a semi-pretrained contactless palmprint recognition deep network is developed to achieve high accuracy and robustness. Semi-CPRN is composed of underlying network structure from ResNet-152 and the proposed assistant layers dedicated to high-performance palmprint recognition. The well-designed assistant layers enhance convolutional neural network to steadily extract the real palmprint features even from the degraded images without being deceived by the degradation factors. Besides, to better carry out the research on palmprint recognition in the open environment, we established a new contactless database HIT-NIST-V1 under natural scene. The comparative experiments on CASIA, IITD, PolyU3D/2D, Tongji and HIT-NIST-V1 illustrate that Semi-CPRN is comparable and superior to previously published state-of-the-art approaches. Simultaneously, CNN-based palmprint biometrics methods show significant robustness to motion blur, Gaussian noise, and salt and pepper noise. [ABSTRACT FROM AUTHOR]

Finger-vein has been used for automatic personal recognition systems for over a decade. Its size and characteristics against presentation attacks make it suitable for various commercial and governmental applications. As with other biometric traits, the majority of finger-vein recognition methods employ hand-crafted feature for image classification. Lately, there are some convolutional neural network (CNN) models are designed for finger-vein identification and verification. Whereas, the CNN-based approaches tend to set up increasing layers and more parameters, which incur equipment memory issue and algorithm execution speed issue. Besides, many researchers have made great efforts to enhance hand-crafted feature-based method by using image enhancement algorithms. Hence, it is significant to explore high-performance finger-vein recognition without image enhancement in deep learning world. In this work, two different dimension issues are investigated: lightweight CNN model, the effect of image enhancement on finger-vein biometrics in deep feature space. Transfer learning plays an important role in extracting informative and representative features by information transfer from the pre-trained network to a novice via fine-tuning. The experimental results on four benchmark databases HKPU, FV-USM, SDUMLA and UTFVP demonstrate the proposed lightweight model 'LightFVN' outperforms the existing published competition winners. Furthermore, image enhancement turns out not to be essential for CNN-based finger-vein biometrics. [ABSTRACT FROM AUTHOR]

Imidacloprid is a well-known pesticide and it is timely to evaluate its toxicity to earthworms ( Eisenia fetida). In the present study, the effect of imidacloprid on reproduction, growth, acetylcholinesterase (AChE) and DNA damage in earthworms was assessed using an artificial soil medium. The median lethal concentration (LC) and the median number of hatched cocoons (EC) of imidacloprid to earthworms was 3.05 and 0.92 mg/kg respectively, the lowest observed effect concentration of imidacloprid about hatchability, growth, AChE activity and DNA damage was 0.02, 0.5, 0.1 and 0.5 mg/kg, respectively. [ABSTRACT FROM AUTHOR]