Analysis of Audit Failure of Yu Diamond by Asia Pacific Certified Public Accountants

Since the 18th National Congress of the Communist Party of China, China has always adhered to and deepened its unique financial roadmap to ensure the efficient operation and high-quality development of the financial industry. At the same time, we are also constantly improving our financial supervision and management system. With the establishment of the whole process registration system, the need for the legal construction and the later monitoring and control restrictions of the capital market has become more urgent. Because the capital market is established based on the openness and transparency of information, accounting firms play key roles in this field.—As “gatekeepers” to ensure the accuracy and integrity of financial information, which is of vital significance to maintaining the stable and healthy development of the capital market. This paper takes the audit case of Zhengzhou Huajing diamond by Asia-Pacific Accounting firm as the research object, according to the defects in the audit, and puts forward countermeasures and suggestions for the audit, aiming to provide certain reference value for reducing the audit failure

DIAGNOSIS OF THE COENOSIA MOLLICULA-GROUP (DIPTERA: MUSCIDAE), WITH DESCRIPTIONS OF FIVE NEW SPECIES FROM CHINA

The Coenosia mollicula-group from China is studied, and five new species are described: Coenosia albifronta Xue et Wang sp. n., Coenosia adrohalter Xue et Wang sp. n., Coenosia deciseta Xue et Wang sp. n., Coenosia latiaedeaga Xue et Wang sp. n. and Coenosia nigriceps Xue et Wang sp. n. A key for the identification of males of the 16 Chinese species are given

One-Shot Image Classification by Learning to Restore Prototypes

One-shot image classification aims to train image classifiers over the dataset with only one image per category. It is challenging for modern deep neural networks that typically require hundreds or thousands of images per class. In this paper, we adopt metric learning for this problem, which has been applied for few- and many-shot image classification by comparing the distance between the test image and the center of each class in the feature space. However, for one-shot learning, the existing metric learning approaches would suffer poor performance because the single training image may not be representative of the class. For example, if the image is far away from the class center in the feature space, the metric-learning based algorithms are unlikely to make correct predictions for the test images because the decision boundary is shifted by this noisy image. To address this issue, we propose a simple yet effective regression model, denoted by RestoreNet, which learns a class agnostic transformation on the image feature to move the image closer to the class center in the feature space. Experiments demonstrate that RestoreNet obtains superior performance over the state-of-the-art methods on a broad range of datasets. Moreover, RestoreNet can be easily combined with other methods to achieve further improvement.Comment: Published as a conference paper in AAAI 202

Improving Cross-domain Few-shot Classification with Multilayer Perceptron

Cross-domain few-shot classification (CDFSC) is a challenging and tough task due to the significant distribution discrepancies across different domains. To address this challenge, many approaches aim to learn transferable representations. Multilayer perceptron (MLP) has shown its capability to learn transferable representations in various downstream tasks, such as unsupervised image classification and supervised concept generalization. However, its potential in the few-shot settings has yet to be comprehensively explored. In this study, we investigate the potential of MLP to assist in addressing the challenges of CDFSC. Specifically, we introduce three distinct frameworks incorporating MLP in accordance with three types of few-shot classification methods to verify the effectiveness of MLP. We reveal that MLP can significantly enhance discriminative capabilities and alleviate distribution shifts, which can be supported by our expensive experiments involving 10 baseline models and 12 benchmark datasets. Furthermore, our method even compares favorably against other state-of-the-art CDFSC algorithms.Comment: 5pages, 4 figure

The effects of timbre on neural responses to musical emotion

Timbre is an important factor that affects the perception of emotion in music. To date, little is known about the effects of timbre on neural responses to musical emotion. To address this issue, we used ERPs to investigate whether there are different neural responses to musical emotion when the same melodies are presented in different timbres. With a cross-modal affective priming paradigm, target faces were primed by affectively congruent or incongruent melodies without lyrics presented in violin, flute, and the voice. Results showed a larger P3 and a larger left anterior distributed LPC in response to affectively incongruent versus congruent trials in the voice version. For the flute version, however, only the LPC effect was found, which was distributed over centro-parietal electrodes. Unlike the voice and flute versions, an N400 effect was observed in the violin version. These findings revealed different patterns of neural responses to emotional processing of music when the same melodies were presented in different timbres, and provide evidence to confirm the hypothesis that there are specialized neural responses to the human voice