Micro-expressions, as fleeting facial expressions, are very important for judging people’s true emotions, thus can provide an essential behavioral clue for lie and dangerous demeanor detection. From embodied accounts of cognition, we derived a novel hypothesis that facial feedback from upper and lower facial regions has differential effects on micro-expression recognition. This hypothesis was tested and supported across three studies. Specifically, the results of Study 1 showed that people became better judges of intense micro-expressions with a duration of 450 ms (...) when the facial feedback from upper face was enhanced via a restricting gel. Additional results of Study 2 showed that the recognition accuracy of subtle micro-expressions was significantly impaired under all duration conditions (50 ms, 150 ms, 333 ms, and 450 ms) when facial feedback from lower face was enhanced. In addition, the results of Study 3 also revealed that blocking the facial feedback of lower face, significantly boosted the recognition accuracy of subtle and intense micro-expressions under all duration conditions (150 ms and 450 ms). Together, these results highlight the role of facial feedback in judging the subtle movements of micro-expressions. (shrink)

Through the analysis of facial feature extraction technology, this paper designs a lightweight convolutional neural network. The LW-CNN model adopts a separable convolution structure, which can propose more accurate features with fewer parameters and can extract 3D feature points of a human face. In order to enhance the accuracy of feature extraction, a face detection method based on the inverted triangle structure is used to detect the face frame of the images in the training set before the model extracts the (...) features. Aiming at the problem that the feature extraction algorithm based on the difference criterion cannot effectively extract the discriminative information, the Generalized Multiple Maximum Dispersion Difference Criterion and the corresponding feature extraction algorithm are proposed. The algorithm uses the difference criterion instead of the entropy criterion to avoid the “small sample” problem, and the use of QR decomposition can extract more effective discriminative features for facial recognition, while also reducing the computational complexity of feature extraction. Compared with traditional feature extraction methods, GMMSD avoids the problem of “small samples” and does not require preprocessing steps on the samples; it uses QR decomposition to extract features from the original samples and retains the distribution characteristics of the original samples. According to different change matrices, GMMSD can evolve into different feature extraction algorithms, which shows the generalized characteristics of GMMSD. Experiments show that GMMSD can effectively extract facial identification features and improve the accuracy of facial recognition. (shrink)

Elucidating distinct morphological atrophy patterns of Alzheimer’s disease and its prodromal stage, namely, mild cognitive impairment helps to improve early diagnosis and medical intervention of AD. On that account, we aimed to obtain distinct patterns of voxel-wise morphological atrophy and its further perturbation on structural covariance network in AD and MCI compared with healthy controls. T1-weighted anatomical images of matched AD, MCI, and HCs were included in this study. Gray matter volume was obtained using voxel-based morphometry and compared among three (...) groups. In addition, structural covariance network of identified brain regions exhibiting morphological difference was constructed and compared between pairs of three groups. Thus, patients with AD have a reduced hippocampal volume and an increased rate of atrophy compared with MCI and HCs. MCI exhibited a decreased trend in bilateral hippocampal volume compared with HCs and the accelerated right hippocampal atrophy rate than HCs. In AD, the hippocampus further exhibited increased structural covariance connected to reward related brain regions, including the anterior cingulate cortex, the putamen, the caudate, and the insula compared with HCs. In addition, the patients with AD exhibited increased structural covariance of left hippocampus with the bilateral insula, the inferior frontal gyrus, the superior temporal gyrus, and the cerebellum than MCI. These results reveal distinct patterns of morphological atrophy in AD and MCI, providing new insights into pathology of AD. (shrink)

In multimodal multiobjective optimization problems, multiple Pareto optimal sets, even some good local Pareto optimal sets, should be reserved, which can provide more choices for decision-makers. To solve MMOPs, this paper proposes an evolutionary algorithm with clustering-based assisted selection strategy for multimodal multiobjective optimization, in which the addition operator and deletion operator are proposed to comprehensively consider the diversity in both decision and objective spaces. Specifically, in decision space, the union population is partitioned into multiple clusters by using a density-based (...) clustering method, aiming to assist the addition operator to strengthen the population diversity. Then, a number of weight vectors are adopted to divide population into N subregions in objective space. Moreover, in the deletion operator, the solutions in the most crowded subregion are first collected into previous clusters, and then the worst solution in the most crowded cluster is deleted until there are N solutions left. Our algorithm is compared with other multimodal multiobjective evolutionary algorithms on the well-known benchmark MMOPs. Numerical experiments report the effectiveness and advantages of our proposed algorithm. (shrink)

ABSTRACT We are concerned with the theorem proving in annotated logics. By using annotated polynomials to express knowledge, we develop an inference rule superposition. A proof procedure is thus presented, and an improvement named M- strategy is mainly described. This proof procedure uses single overlaps instead of multiple overlaps, and above all, both the proof procedure and M-strategy are refutationally complete.

Electroencephalography is the measurement of neuronal activity in different areas of the brain through the use of electrodes. As EEG signal technology has matured over the years, it has been applied in various methods to EEG emotion recognition, most significantly including the use of convolutional neural network. However, these methods are still not ideal, and shortcomings have been found in the results of some models of EEG feature extraction and classification. In this study, two CNN models were selected for the (...) extraction and classification of preprocessed data, namely, common spatial patterns- CNN and wavelet transform- CNN. Using the CSP-CNN, we first used the common space model to reduce dimensionality and then applied the CNN directly to extract and classify the features of the EEG; while, with the WT-CNN model, we used the wavelet transform to extract EEG features, thereafter applying the CNN for classification. The EEG classification results of these two classification models were subsequently analyzed and compared, with the average classification accuracy of the CSP-CNN model found to be 80.56%, and the average classification accuracy of the WT-CNN model measured to 86.90%. Thus, the findings of this study show that the average classification accuracy of the WT-CNN model was 6.34% higher than that of the CSP-CNN. (shrink)

Entity linking involves mapping ambiguous mentions in documents to the correct entities in a given knowledge base. Most existing methods failed to link when a mention appears multiple times in a document, since the conflict of its contexts in different locations may lead to difficult linking. Sentence representation, which has been studied based on deep learning approaches recently, can be used to resolve the above issue. In this paper, an effective entity linking model is proposed to capture the semantic meaning (...) of the sentences and reduce the noise introduced by different contexts of the same mention in a document. This model first uses the symmetry of the Siamese network to learn the sentence similarity. Then, the attention mechanism is added to improve the interaction between input sentences. To show the effectiveness of our sentence representation model combined with attention mechanism, named ELSR, extensive experiments are conducted on two public datasets. Results illustrate that our model outperforms the baselines and achieves the superior performance. (shrink)

In practice, more and more companies are using warmth appeals in their advertisements, but not all warmth appeals can bring the expected results. Grounded in social perception, we propose that consumers’ inferences and behavioral intentions stemming from warmth appeals in advertising are moderated by brand concepts. Specifically, warmth appeal decreases competence inferences and, in turn, behavioral intentions toward the self-enhancement brands. However, it increases warmth inferences and, in turn, behavioral intentions toward self-transcendence brands. We tested our hypotheses through two experimental (...) studies. Experiment 1 demonstrated that for self-enhancement brands, warmth appeals in advertisements decreased brand attitudes and purchase intentions; for self-transcendence brands, warmth appeals in advertisements increased brand attitudes and purchase intentions. Experiment 2 showed further evidence to the proposed effect and tested the mediating effects of warmth perception and competence perception. This research provides significant implications for advertising strategies. (shrink)

The study aimed to examine the psychometric properties of the coronavirus anxiety scale during the coronavirus disease 2019 delta epidemic. A total of 2,116 participants on the Chinese mainland completed the online survey. We employed exploratory factor analysis and confirmatory factor analysis to investigate the factor structure. The findings showed that the one-factor model of the CAS Chinese version fitted perfectly with the data. The multigroup CFAs showed the measurement invariance across gender and age groups. We also examined the CAS’s (...) internal consistency and convergent and concurrent validity. The results demonstrated that the one-factor model had good reliability and convergent and concurrent validity. Overall, according to our findings, the CAS Chinese version was reliable for measuring coronavirus anxiety during the COVID-19 delta outbreak. (shrink)

The adverse effects of life stress on social networking sites addiction are increasingly recognized, but so far there is little evidence on how and which specific types of life stress are conducive to the addictive behavior. Interpersonal relationship stress being the main source of stress for undergraduates, the purpose of the current paper is thus to delve into whether perceived stress in interpersonal relationships significantly leads to WeChat addiction and, if so, how this type of stress drives the excessive use (...) of WeChat. The data was collected from self-report questionnaires completed by 463 Chinese undergraduate students and then analyzed with structural equation modeling. The results revealed that the positive association between WeChat users’ interpersonal relationship stress and addictive behavior is fully and sequentially mediated by WeChat use intensity and social interaction. More specifically, accumulation of stress in interpersonal relationships gives rise to the intensity of WeChat use, which in turn fuels rising addiction to WeChat both directly and indirectly via social interaction on WeChat. These findings contribute to a more refined understanding of the pathological use of WeChat. (shrink)

This study investigates the Proteus effect from the first-person perspective and during avatar embodiment in actual exercise. In addition to the immediate measurements of the Proteus effect, prolonged effects such as next-day perception and exercise-related outcomes are also explored. We theorized the Proteus effect as altered perceived self-concept and explored the association between virtual reality avatar manipulation and self-concept in the exercise context. While existing studies have mainly investigated the Proteus effect in a non-VR environment or after VR embodiment, we (...) aim to contribute to the literature by addressing this concern to explore how the Proteus effect works in actual VR exercise. Through a 2 × 2 between-subject experiment, the results partially support the Proteus effect. Regarding actual physical activity, embodying an avatar with a six pack during exercise creates fewer body movements. No significant effect was found for perceived exertion. We also explored the role of sex as a potential moderator in the association of the Proteus effect on exercise outcomes. The Proteus effect was supported by immediate and next-day self-efficacy for core-muscle exercise only among female participants. The between-subject design allowed us to probe how avatar manipulation of muscular body shape with a six pack as opposed to normal body shape influences participants’ self-concept and exercise outcomes, as limited VR studies have employed within-subject comparisons. This also contributes to the literature by providing an upward comparison as opposed to the previous downward comparison regarding body fitness. The overall results supported the Proteus effect in the context of core-muscle exercise when comparing normal and ideal body shape avatars. However, the Proteus effect as an altered self-concept and its effects on self-efficacy for exercise were supported among females but not males. Whereas the female participants who embodied avatars with a six pack associated themselves more with the muscular concept than other people, the male participants who embodied avatars with a six pack perceived themselves as more normal than others. Theoretical and practical implications are discussed. (shrink)

Lung nodules are an early symptom of lung cancer. The earlier they are found, the more beneficial it is for treatment. However, in practice, Chinese doctors are likely to cause misdiagnosis. Therefore, deep learning is introduced, an improved target detection network is used, and public datasets are used to diagnose and identify lung nodules. This paper selects the Mask-RCNN network and uses the dense block structure of Densenet and the channel shuffle convolution method to improve the Mask-RCNN network. The experimental (...) results prove that proposed algorithm is extremely effective. (shrink)

摘编分"唯物史观指引历史学成为真正的科学","唯物史观的若干基本概念和基本原理","有关实证历史研究的理论和方法论的部分论述"三大部分--摘编说明.

本书收《祖坟——叛逆与创作》、《绍兴闲话》、《寰宇觅知音》、《油画的联想》、《漓江》等百余篇文章。.

Business ethics education is a cornerstone of preparing future leaders to navigate the complex ethical dilemmas inherent in modern management. Existing curricula often fall short of equipping students with actionable tools to address these challenges, frequently adopting reactive approaches rather than fostering proactive ethical reasoning. This paper addresses the gaps by creating a framework that applies normative ethics—consequentialism, deontology, and virtue ethics. We further propose a teaching protocol for an in-class activity in which student groups can apply their understanding of (...) the framework in real-world scenarios. (shrink)

In the era of the digital economy, the important issue for enterprises is how to achieve optimal performance by balancing the development of digital technology and undertaking digital responsibility. Based on optimal distinctiveness theory, we explore how enterprises balance the tension between political strategy and competitive strategy from the perspective of corporate digital responsibility to achieve optimal firm performance. Observing data such as annual reports, patent application information, and operating conditions of Chinese A-share listed companies from 2009 to 2021 reveals (...) the impact of corporate digital responsibility, substantive and symbolic digital technology on firm performance. The research results show that substantive digital technology innovation is superior to symbolic digital technology innovation in promoting firm performance. Mechanism analysis shows that symbolic digital technology innovation satisfies the firm's political strategy and positively impacts firm performance through management empowerment. In contrast, substantive digital technology innovation satisfies the firm's competitive strategy and improves performance by empowering the workforce. Further analysis find that corporate digital responsibility positively moderates the impact of substantive digital technology innovation on corporate performance and negatively moderates the relationship between symbolic digital technology innovation and corporate performance. This study incorporates digital technology innovation and digital responsibility into a unified research framework, which provides a new interpretative path for improving firm performance and contributes important practical references for firms to achieve a “win-win” situation that improves competitiveness and social value. (shrink)

The main purpose of this paper is to bring out some significant humanistic characteristics of Chinese religious thought. My account is limited to what is originally and typically Chinese. That is to say, it will exclude what has been influenced by Buddhism from India or Christianity from the Western world. Some of the theses of this paper are based on scholarly works, while others are drawn from the author's primary experience.

该书收入其论著6种, 主要包括《仪礼奭固》, 《礼器图》, 《周政三图》, 《天文图考》, 《经脉分图》等.