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Background Building a large-scale medical knowledge graphs needs to automatically extract the relations between entities from electronic medical records (EMRs) . The main challenges are the scarcity of available labeled corpus and the identification of complexity semantic relations in text of Chinese EMRs. A hybrid method based on semi-supervised learning is proposed to extract the medical entity relations from small-scale complex Chinese EMRs. Methods The semantic features of sentences are extracted by a residual network and the long dependent information is captured by bidirectional gated recurrent unit. Then the attention mechanism is used to assign weights for the extracted features respectively, and the output of two attention mechanisms is integrated for relation prediction. We adjusted the training process with manually annotated small-scale relational corpus and bootstrapping semi-supervised learning algorithm, and continuously expanded the datasets during the training process. Results We constructed a small corpus of Chinese EMRs relation extraction based on the EMR datasets released at the China Conference on Knowledge Graph and Semantic Computing. The experimental results show that the best F1-score of the proposed method on the overall relation categories reaches 89.78%, which is 13.07% higher than the baseline CNN.

With the rise in piano teaching in recent years, many people have joined the ranks of piano learners. However, the high cost of traditional manual instruction and the exclusive one-on-one teaching model have made learning the piano an extravagant endeavor. Most existing approaches, based on the audio modality, aim to evaluate piano players’ skills. Unfortunately, these methods overlook the information contained in videos, resulting in a one-sided and simplistic evaluation of the piano player’s skills. More recently, multimodal-based methods have been proposed to assess the skill level of piano players by using both video and audio information. However, existing multimodal approaches use shallow networks to extract video and audio features, which limits their ability to extract complex spatio-temporal and time-frequency characteristics from piano performances. Furthermore, the fingering and pitch-rhythm information of the piano performance is embedded within the spatio-temporal and time-frequency features, respectively. Therefore, we propose a ResNet-based audio-visual fusion model that is able to extract both the visual features of the player’s finger movement track and the auditory features, including pitch and rhythm. The joint features are then obtained through the feature fusion technique by capturing the correlation and complementary information between video and audio, enabling a comprehensive and accurate evaluation of the player’s skill level. Moreover, the proposed model can extract complex temporal and frequency features from piano performances. Firstly, ResNet18-3D is used as the backbone network for our visual branch, allowing us to extract feature information from the video data. Then, we utilize ResNet18-2D as the backbone network for the aural branch to extract feature information from the audio data. The extracted video features are then fused with the audio features, generating multimodal features for the final piano skill evaluation. The experimental results on the PISA dataset show that our proposed audio-visual fusion model, with a validation accuracy of 70.80% and an average training time of 74.02 s, outperforms the baseline model in terms of performance and operational efficiency. Furthermore, we explore the impact of different layers of ResNet on the model’s performance. In general, the model achieves optimal performance when the ratio of video features to audio features is balanced. However, the best performance achieved is 68.70% when the ratio differs significantly.

Owing to rapid development of the Internet and the rise of the big data era, microblog has become the main means for people to spread and obtain information. If people can accurately predict the development trend of a microblog event, it will be of great significance for the government to carry out public relations activities on network event supervision and guide the development of microblog event reasonably for network crisis. This paper presents effective solutions to deal with trend prediction of microblog events’ popularity. Firstly, by selecting the influence factors and quantifying the weight of each factor with an information entropy algorithm, the microblog event popularity is modeled. Secondly, the singular spectrum analysis is carried out to decompose and reconstruct the time series of the popularity of microblog event. Then, the box chart method is used to divide the popularity of microblog event into various trend spaces. In addition, this paper exploits the Bi-LSTM model to deal with trend prediction with a sequence to label model. Finally, the comparative experimental analysis is carried out on two real data sets crawled from Sina Weibo platform. Compared to three comparative methods, the experimental results show that our proposal improves F1-score by up to 39%.

Owing to frequent traffic accidents and casualties nowadays, the ability to predict the number of traffic accidents in a period is significant for the transportation department to make decisions scientifically. However, owing to many variables affecting traffic accidents in the road traffic system, there are two critical challenges in traffic accident prediction. The first issue is how to evaluate the weight of each variable’s impact on the accident. The second issue is how to model the prediction process for multiple interrelated variables. Aiming to solve these two problems, we propose effective solutions to deal with traffic accident prediction. Firstly, for the first issue, we exploit the grey correlation analysis to measure the correlation of factors to accident occurrence. Then, for the second issue, we select the main factors by correlation analysis to establish a multivariable grey model—MGM(1,N) for prediction process modeling. Further, we explore the collinearity between variables and better optimize the predictive model. The experimental results show that our approach achieves best performance than four general-purpose comparative algorithms in traffic accident prediction task.

Multi-view subspace clustering (MVC) intends to separate out samples via integrating the complementary information from diverse views. In MVC, since the structural information in the graph is crucial to the graph learning, most of the existing algorithms construct the superficial graph from the original data by directly measuring the similarity between the first-order complementary nearest neighbors. However, the information provided by the superficial graph structure would be influenced by contaminated or absent samples. To address this problem, in the proposed method, the higher-order complementary neighbor graphs are exploited to discover the latent structural information between the samples, and fusing the latent structural information across different orders to achieve the MVC. Specifically, the higher-order neighbor graphs under different views are leveraged to estimate the missing samples. Then, to integrate the neighbor graphs of different orders, the multi-order neighbor diffusion fusion is proposed. Nevertheless, the above problem of diffusion fusion is an intractable non-convex issue. Thus, to address it, the multi-order neighbor diffusion fusion is considered as a combination problem of the solution under different order, and the heuristic algorithm is leveraged to solve it. In this way, not only the data representation under different view and also the neighbor structure under different order can be diffused under a joint optimization framework, thus the consistency and integral information among various perspectives and orders can be utilized effectively and simultaneously. Experiments on both incomplete and complete multi-view dataset demonstrate the convincingness of the high-order neighborhood structure based subspace clustering scheme by comparing with the existing approaches.

Premise of the Study Nutrient resorption is essential for plant nutrient conservation. Large‐bodied plants potentially have large nutrient sink pools and high nutrient flux. Whether and how nutrient resorption can be regulated by plant size and biomass allocation are yet unknown. Methods Using the herbaceous plant Amaranthus mangostanus in greenhouse experiments for two consecutive years, we measured plant biomass, height, and stem diameter and calculated the root to shoot biomass ratio (R/S ratio) and nutrient resorption efficiency (NuRE) to assess the effects of plant body size and biomass allocation on NuRE. NuRE was calculated as the percentage reduction in leaf nutrient concentration from green leaf to senesced leaf. Key Results NuRE increased with plant biomass, height, and stem diameter, suggesting that the individuals with larger bodies, which led to a larger nutrient pool, tended to resorb proportionally more nutrients from the senescing leaves. NuRE decreased with increasing root to shoot ratio, which might have reflected the nutrient acquisition trade‐offs between resorption from the senescent leaves and absorption from the soil. Increased root biomass allocation increased the proportion of nutrient acquisition through absorption more than through resorption. Conclusions This study presented the first experimental evidence of how NuRE is linked to plant size (indicated by biomass, height, and stem diameter) and biomass allocation, suggesting that nutrient acquisition could be modulated by the size of the nutrient sink pool and its partitioning in plants, which can improve our understanding of a conservation mechanism for plant nutrients. The body size and root to shoot ratio effects might also partly explain previous inconsistent reports on the relationships between environmental nutrient availability and NuRE.

The Analytic hierarchy process (AHP) is a widely used multi-criteria decision theory, and most AHP relies on the judgments of experts to derive priority scales. However, the judgments of experts may be subjective. Using machine learning algorithms for decision-making can be more objective, but machine learning algorithms are strongly related to the collected data and not being flexible enough. This paper tries to combine experts’ judgments with algorithmic judgments to improve the bias of experts’ judgments while still making decision-making flexible. In this paper, the authors introduce the ordered pair of normalized real numbers (OPNs) into the AHP method for the first time and propose the fuzzy analytic hierarchy process with the OPNs (OFAHP). The OFAHP uses OPNs to combine experts’ judgments with those of machine learning algorithms and then make decisions by OPNs. Experiments on real data sets show that the proposed method can get reasonable decision results. Moreover, when the experts’ judgments are wrong or invalid, the judgments given by the machine learning algorithm can correct the experts’ judgments to obtain a reasonable decision-making result.

Spectral clustering is one of the most common unsupervised learning algorithms in machine learning and plays an important role in data science. Fair spectral clustering has also become a hot topic with the extensive research on fair machine learning in recent years. Current iterations of fair spectral clustering methods are based on the concepts of group and individual fairness. These concepts act as mechanisms to mitigate decision bias, particularly for individuals with analogous characteristics and groups that are considered to be sensitive. Existing algorithms in fair spectral clustering have made progress in redistributing resources during clustering to mitigate inequities for certain individuals or subgroups. However, these algorithms still suffer from an unresolved problem at the global level: the resulting clusters tend to be oversized and undersized. To this end, the first original research on scale fairness is presented, aiming to explore how to enhance scale fairness in spectral clustering. We define it as a cluster attribution problem for uncertain data points and introduce entropy to enhance scale fairness. We measure the scale fairness of clustering by designing two statistical metrics. In addition, two scale fair spectral clustering algorithms are proposed, the entropy weighted spectral clustering (EWSC) and the scale fair spectral clustering (SFSC). We have experimentally verified on several publicly available real datasets of different sizes that EWSC and SFSC have excellent scale fairness performance, along with comparable clustering effects.

Abstract In recent years, a great number of top conferences and workshops on artificial intelligence (AI) were held in China, showing Chinese AI plays an important role in the world. Meanwhile, Chinese government announced an ambitious scheme, “New Generation Artificial Intelligence Development Plan,” for the country to become a world leader in AI technologies by 2030. The AI research in China has covered various aspects, ranging from chips to algorithms. This chapter attempts to give an overview of the recent advances of AI research and development in China, as well as some perspectives on the future development of AI in China.

BackgroundCurative resection of hilar cholangiocarcinoma (HC) is typically carried out using open surgery. In the present study, we examined the safety (postoperative complication) and effectiveness (resection margin status and patient survival) of minimally invasive surgery (MIS) for HC.MethodsThis retrospective analysis included 158 patients receiving MIS for HC at 10 participating centers between December 2013 and November 2019. Patient demographics, surgical outcomes, and oncological outcomes were retrospectively analyzed.ResultsClinical information obtained from 10 different clinical centers did not show any evident cohort-bias clustering. One hundred and twenty-six (79.7%) patients underwent LRHC, 12 (7.6%) patients underwent RARHC, conversion to an open procedure occurred in 20 (12.7%) patients. The operation time and estimated blood loss were 410.8 ± 128.9 min and 477.8 ± 706.3 mL, respectively. The surgical radicality of the 158 patients was R0, 129 (81.6%); R1, 20 (18.4%) and R2, 9 (5.7%). Grades I–II complications was occurred in 68 (43.0%) patients. Severe morbidity (grade III–V) occurred in 14 (8.7%) patients. The median overall survival in whole cohort was 25.4 months. The overall survival rate was 67.6% at year 1, 28.8% at year 3, and 19.2% at year 5. Comparing the first half of MISHC performed by each center with the following cases, the operation time and postoperative hospital stay does not decrease with the increasing cases. On literature review, MISHC is non-inferior to open surgery at least in perioperative period.ConclusionsIn this Chinese MIS for HC multicenter study, the largest to date, long-term overall survival rates after MIS appear comparable to those reported in current open series. Further randomized controlled trials are necessary to assess the global impact of MISHC.