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"Yang, Yifan"
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Telling the EU's story by others : the Jean Monnet Programme and European Union public diplomacy
China is one of the first few non-EU member states to be covered by the Jean Monnet Programme. By studying its implementation in China through interviews with EU officials, Chinese professors, and college students who were and are involved in the program, this book enables a better understanding of why and how it works in the Chinese context. Furthermore, this book on the role of the Jean Monnet Programme in EU public diplomacy adds first-hand empirical material to the existing literature on public diplomacy implementation through educational programmes.
Book
Floating mora affixation in Huozhou diminutive subtraction
This paper argues for an item-and-arrangement approach by demonstrating that the diminutive form in Huozhou Chinese results from the affixation of a floating mora. Diminutives in Huozhou Chinese are formed by deleting the final non-vocalic segment of a syllable, along with some subsegmental changes (e.g., [pʰɑŋ 35 ] ‘plate’, [pʰaː 35 ] ‘plate.DIM’). This paper provides a detailed description of these patterns and proposes that the underlying representation of the diminutive morpheme is a floating mora, with subtraction serving as a repair strategy for this floating mora affix. This paper makes two main contributions. First, it introduces new data on subtractive morphology. Second, it provides a formal analysis and further supports this proposal from a typological perspective, thereby supporting the item-and-arrangement approach to morphology. Overall, by combining a case study of Huozhou Chinese with a typological discussion, this analysis shows that nonconcatenative morphology can be interpreted as additive, leading to a more economical grammar and more restrictive predictions.
Journal Article
How Does Internet Use Improve Mental Health among Middle-Aged and Elderly People in Rural Areas in China? A Quasi-Natural Experiment Based on the China Health and Retirement Longitudinal Study (CHARLS)
One of the most significant public health issues in rural China is how to improve the mental health of middle-aged and older individuals. Using 2013, 2015, and 2018 CHARLS panel data, this paper properly examined the effects of Internet use on the mental health of middle-aged and elderly people in rural China based on the difference-in-differences method. The findings are as follows: (1) Internet use effectively improves the mental health status of middle-aged and elderly people in rural China; (2) compared to the middle-aged group, Internet use has a more obvious effect on the mental health of the elderly; (3) further analysis showed that reading news, watching videos, and playing games online could significantly improve the mental health status of middle-aged and elderly people in rural China, while chatting online and other Internet activities cannot significantly improve mental health status; and (4) playing games, watching videos, and reading news have different effects on the mental health of middle-aged and elderly people in rural China. The results indicate that playing games have a better effect on depression levels than watching videos. In contrast, watching news had the lowest effect on depression levels among middle-aged and elderly people in rural China. The results of this study also show the latest evidence that Internet use can help China’s rural middle-aged and elderly populations to reduce social isolation, establish new social connections, gain social support, and, ultimately, achieve active ageing. Therefore, promoting multiple forms of interaction is an effective path to prevent loneliness, which has become the new policy direction of the government to create an age-friendly Internet environment using various measures in the future to eliminate the barriers to Internet access affecting the middle-aged and elderly in rural China.
Journal Article
Deep Learning Approaches on Defect Detection in High Resolution Aerial Images of Insulators
By detecting the defect location in high-resolution insulator images collected by unmanned aerial vehicle (UAV) in various environments, the occurrence of power failure can be timely detected and the caused economic loss can be reduced. However, the accuracies of existing detection methods are greatly limited by the complex background interference and small target detection. To solve this problem, two deep learning methods based on Faster R-CNN (faster region-based convolutional neural network) are proposed in this paper, namely Exact R-CNN (exact region-based convolutional neural network) and CME-CNN (cascade the mask extraction and exact region-based convolutional neural network). Firstly, we proposed an Exact R-CNN based on a series of advanced techniques including FPN (feature pyramid network), cascade regression, and GIoU (generalized intersection over union). RoI Align (region of interest align) is introduced to replace RoI pooling (region of interest pooling) to address the misalignment problem, and the depthwise separable convolution and linear bottleneck are introduced to reduce the computational burden. Secondly, a new pipeline is innovatively proposed to improve the performance of insulator defect detection, namely CME-CNN. In our proposed CME-CNN, an insulator mask image is firstly generated to eliminate the complex background by using an encoder-decoder mask extraction network, and then the Exact R-CNN is used to detect the insulator defects. The experimental results show that our proposed method can effectively detect insulator defects, and its accuracy is better than the examined mainstream target detection algorithms.
Journal Article
Optimization of Aircraft Flight Trajectory Combined with Thinking Navigation Algorithm
In order to improve the optimization effect of the flight trajectory of the aircraft, this paper combines the thinking navigation algorithm to optimize the flight trajectory of the aircraft and analyzes the flight trajectory of the aircraft through the intelligent model. By processing the original satellite clock error data by the first-order difference method, the modeling data can be more suitable for nonlinear characteristics. Moreover, this paper chooses a simple network structure and uses the MEA to select the optimal initial parameters of the model for the BP neural network, which can avoid the local optimization of the BP neural network results. In addition, this paper conducts experimental analysis on the MEA-BP model through fitting data of different lengths. The simulation test results show that the thinking navigation algorithm proposed in this paper has a very obvious effect on the optimization of the flight trajectory of the aircraft.
Journal Article
Sex-specific age-related differences in cerebrospinal fluid clearance assessed by resting-state functional magnetic resonance imaging
•Changes in CSF clearance-related infra-slow (< 0.1 Hz) dynamics during aging.•The CSF clearance-related processes remain stable before age 55 and then decrease.•The processes decline more abruptly in females, likely related to menopause.
Cerebrospinal fluid (CSF) flow may assist the clearance of brain wastes, such as amyloid-β (Aβ) and tau, and thus play an important role in aging and dementias. However, a lack of non-invasive tools to assess the CSF dynamics-related clearance in humans hindered the understanding of the relevant changes in healthy aging. The global infra-slow (<0.1 Hz) brain activity measured by the global mean resting-state fMRI signal (gBOLD) was recently found to be coupled by large CSF movements. This coupling has been found to correlate with various pathologies of Alzheimer's disease (AD), particularly Aβ pathology, linking it to waste clearance. Using resting-state fMRI data from a group of 719 healthy aging participants, we examined the sex-specific differences of the gBOLD-CSF coupling over a wide age range between 36–100 years of age. We found that this coupling index remains stable before around age 55 and then starts to decline afterward, particularly in females. Menopause may contribute to the accelerated decline in females.
Journal Article
The importance of pitch in conveying meaning in English
Pitch, as a basic property of sounds and an important suprasegmental feature of speech, exerts its effect on many linguistic aspects at different levels. The current study mainly focuses on its pragmatic meaning realized by phonetic forms, namely the accent placement and tune. By observing the pitch curves of various renderings of utterances from the phonetic tool Praat and analyzing these typical instances, it is concluded that pitch is of great importance to listeners’ grasp of various contexts and meanings.
Journal Article
Matching patients to clinical trials with large language models
Patient recruitment is challenging for clinical trials. We introduce TrialGPT, an end-to-end framework for zero-shot patient-to-trial matching with large language models. TrialGPT comprises three modules: it first performs large-scale filtering to retrieve candidate trials (TrialGPT-Retrieval); then predicts criterion-level patient eligibility (TrialGPT-Matching); and finally generates trial-level scores (TrialGPT-Ranking). We evaluate TrialGPT on three cohorts of 183 synthetic patients with over 75,000 trial annotations. TrialGPT-Retrieval can recall over 90% of relevant trials using less than 6% of the initial collection. Manual evaluations on 1015 patient-criterion pairs show that TrialGPT-Matching achieves an accuracy of 87.3% with faithful explanations, close to the expert performance. The TrialGPT-Ranking scores are highly correlated with human judgments and outperform the best-competing models by 43.8% in ranking and excluding trials. Furthermore, our user study reveals that TrialGPT can reduce the screening time by 42.6% in patient recruitment. Overall, these results have demonstrated promising opportunities for patient-to-trial matching with TrialGPT.
Patient recruitment is challenging for clinical trials. Here, the authors introduce TrialGPT, an end-to-end framework for zero-shot patient-to-trial matching with large language models.
Journal Article
Adversarial prompt and fine-tuning attacks threaten medical large language models
The integration of Large Language Models (LLMs) into healthcare applications offers promising advancements in medical diagnostics, treatment recommendations, and patient care. However, the susceptibility of LLMs to adversarial attacks poses a significant threat, potentially leading to harmful outcomes in delicate medical contexts. This study investigates the vulnerability of LLMs to two types of adversarial attacks–prompt injections with malicious instructions and fine-tuning with poisoned samples–across three medical tasks: disease prevention, diagnosis, and treatment. Utilizing real-world patient data, we demonstrate that both open-source and proprietary LLMs are vulnerable to malicious manipulation across multiple tasks. We discover that while integrating poisoned data does not markedly degrade overall model performance on medical benchmarks, it can lead to noticeable shifts in fine-tuned model weights, suggesting a potential pathway for detecting and countering model attacks. This research highlights the urgent need for robust security measures and the development of defensive mechanisms to safeguard LLMs in medical applications, to ensure their safe and effective deployment in healthcare settings.
Large language models hold significant potential in healthcare settings. This study exposes their vulnerability in medical applications and demonstrates the inadequacy of existing safeguards, highlighting the need for future studies to develop reliable methods for detecting and mitigating these risks.
Journal Article
Improving model fairness in image-based computer-aided diagnosis
Deep learning has become a popular tool for computer-aided diagnosis using medical images, sometimes matching or exceeding the performance of clinicians. However, these models can also reflect and amplify human bias, potentially resulting inaccurate missed diagnoses. Despite this concern, the problem of improving model fairness in medical image classification by deep learning has yet to be fully studied. To address this issue, we propose an algorithm that leverages the marginal pairwise equal opportunity to reduce bias in medical image classification. Our evaluations across four tasks using four independent large-scale cohorts demonstrate that our proposed algorithm not only improves fairness in individual and intersectional subgroups but also maintains overall performance. Specifically, the relative change in pairwise fairness difference between our proposed model and the baseline model was reduced by over 35%, while the relative change in AUC value was typically within 1%. By reducing the bias generated by deep learning models, our proposed approach can potentially alleviate concerns about the fairness and reliability of image-based computer-aided diagnosis.
Deep learning models can reflect and amplify human bias, potentially resulting inaccurate missed diagnoses. Here, the authors show that by leveraging the marginal pairwise equal opportunity, their model reduces bias in medical image classification by over 35% compared to baseline models, with minimal impact on AUC values.
Journal Article