Rapid Maxillary Anterior Teeth Retraction En Masse by Bone Compression: A Canine Model

The present study sought to establish an animal model to study the feasibility and safety of rapid retraction of maxillary anterior teeth en masse aided by alveolar surgery in order to reduce orthodontic treatment time.Extraction of the maxillary canine and alveolar surgery were performed on twelve adult beagle dogs. After that, the custom-made tooth-borne distraction devices were placed on beagles' teeth. Nine of the dogs were applied compression at 0.5 mm/d for 12 days continuously. The other three received no force as the control group. The animals were killed in 1, 14, and 28 days after the end of the application of compression.The tissue responses were assessed by craniometric measurement as well as histological examination. Gross alterations were evident in the experimental group, characterized by anterior teeth crossbite. The average total movements of incisors within 12 days were 4.63±0.10 mm and the average anchorage losses were 1.25±0.12 mm. Considerable root resorption extending into the dentine could be observed 1 and 14 days after the compression. But after consolidation of 28 days, there were regenerated cementum on the dentine. There was no apparent change in the control group. No obvious tooth loosening, gingival necrosis, pulp degeneration, or other adverse complications appeared in any of the dogs.This is the first experimental study for testing the technique of rapid anterior teeth retraction en masse aided by modified alveolar surgery. Despite a preliminary animal model study, the current findings pave the way for the potential clinical application that can accelerate orthodontic tooth movement without many adverse complications.It may become a novel method to shorten the clinical orthodontic treatment time in the future

The Expressive Power of Graph Neural Networks: A Survey

Graph neural networks (GNNs) are effective machine learning models for many graph-related applications. Despite their empirical success, many research efforts focus on the theoretical limitations of GNNs, i.e., the GNNs expressive power. Early works in this domain mainly focus on studying the graph isomorphism recognition ability of GNNs, and recent works try to leverage the properties such as subgraph counting and connectivity learning to characterize the expressive power of GNNs, which are more practical and closer to real-world. However, no survey papers and open-source repositories comprehensively summarize and discuss models in this important direction. To fill the gap, we conduct a first survey for models for enhancing expressive power under different forms of definition. Concretely, the models are reviewed based on three categories, i.e., Graph feature enhancement, Graph topology enhancement, and GNNs architecture enhancement

Fusion weighted features and BiLSTM-attention model for argument mining of EFL writing

Argument mining (AM), an emerging field in natural language processing (NLP), aims to automatically extract arguments and the relationships between them in texts. In this study, we propose a new method for argument mining of argumentative essays. The method generates dynamic word vectors with BERT (Bidirectional Encoder Representations from Transformers), encodes argumentative essays, and obtains word-level and essay-level features with BiLSTM (Bi-directional Long Short-Term Memory) and attention training, respectively. By integrating these two levels of features we obtain the full-text features so that the content in the essay is annotated according to Toulmin’s argument model. The proposed method was tested on a corpus of 180 argumentative essays, and the precision of automatic annotation reached 69%. The experimental results show that our model outperforms existing models in argument mining. The model can provide technical support for the automatic scoring system, particularly on the evaluation of the content of argumentative essays

Ensembled CTR Prediction via Knowledge Distillation

Recently, deep learning-based models have been widely studied for click-through rate (CTR) prediction and lead to improved prediction accuracy in many industrial applications. However, current research focuses primarily on building complex network architectures to better capture sophisticated feature interactions and dynamic user behaviors. The increased model complexity may slow down online inference and hinder its adoption in real-time applications. Instead, our work targets at a new model training strategy based on knowledge distillation (KD). KD is a teacher-student learning framework to transfer knowledge learned from a teacher model to a student model. The KD strategy not only allows us to simplify the student model as a vanilla DNN model but also achieves significant accuracy improvements over the state-of-the-art teacher models. The benefits thus motivate us to further explore the use of a powerful ensemble of teachers for more accurate student model training. We also propose some novel techniques to facilitate ensembled CTR prediction, including teacher gating and early stopping by distillation loss. We conduct comprehensive experiments against 12 existing models and across three industrial datasets. Both offline and online A/B testing results show the effectiveness of our KD-based training strategy.Comment: Published in CIKM'202

What is in a name?: The development of cross-cultural differences in referential intuitions

Past work has shown systematic differences between Easterners' and Westerners' intuitions about the reference of proper names. Understanding when these differences emerge in development will help us understand their origins. In the present study, we investigate the referential intuitions of English- and Chinese-speaking children and adults in the U.S. and China. Using a truth-value judgment task modeled on Kripke's classic Gödel case, we find that the cross-cultural differences are already in place at age seven. Thus, these differences cannot be attributed to later education or enculturation. Instead, they must stem from differences that are present in early childhood. We consider alternate theories of reference that are compatible with these findings and discuss the possibility that the cross-cultural differences reflect differences in perspective-taking strategies

Development status and application of neuronavigation system

The neuronavigation system is a combination of navigation technology and neurosurgery. It can be used to assist in neurosurgery through three-dimensional reconstruction of medical image data, extraction of lesions, optimal surgical path planning, tracking and positioning of surgical instruments, and real-time intraoperative display. Accurate and maximal treatment of lesions, while effectively avoiding secondary injuries to patients during surgery. Therefore, the development and application of neuronavigation systems are reviewed

Hydrocarbon Detection Based on Phase Decomposition in Chaoshan Depression, Northern South China Sea

Located in the northern South China Sea, Chaoshan Depression is mainly a residual Mesozoic depression, with a construction of Meso-Cenozoic strata over 7000m thick and good hydrocarbon accumulation conditions. Amplitude attribute of -90°phase component derived by phase decomposition is employed to detect Hydrocarbon in the zone of interest (ZOI) in Chaoshan Depression. And it is found that there are evident amplitude anomalies occurring around ZOI. Phase decomposition is applied to forward modeling results of the ZOI, and high amplitudes occur on the -90°phase component more or less when ZOI is charged with hydrocarbon, which shows that the amplitude abnormality in ZOI is probably caused by oil and gas accumulation

Characteristics of the Temporal Variation in Temperature and Precipitation in China’s Lower Yellow River Region

We analyzed the spatial and temporal distributions of temperature and precipitation in China’s Yellow River Region between 1960 and 2001 by compiling meteorological data using anomalies, climate trend rate, linear regression, trend analysis, spline functions, and other methods. The results show that the average temperatures in the Region have an upward trend at a rate of 0.19°C every 10 years. There are no significant changes in the Region’s summers, but the winters have become visibly warmer, with the temperatures significantly increasing from the 1980s. The average annual precipitation rate has shown a downwards trend at a rate of −11.7 mm every 10 years. Even though the precipitation rate shows variations, the amount of precipitation is inconsistent with the most significant decrease in precipitation rates being seen during summer followed by autumn, while the rates actually slightly increased during spring and winter. Over the 42 years, the Region as a whole showed a trend of climate warming and drying with 77% of the total sites studied showing these combined trends. Before the 1980s, mainly a drying and cooling trend was observed. In the mid-to-late 80s the temperatures rose, resulting in the change to a warming and drying trend