Processing and Prospect of Electronic Nose

Recently, a Novel Coronavirus Pneumonia (NCP) swept the globe. This kind of new virus has extremely high infection efficiency, which has brought great disaster to human's production and daily life. However, it is reported that lung cancer and other diseases with a high incidence will lead to a very high mortality rate if they are not diagnosed and treated in time. Therefore, it is urgent to design an accurate and convenient new diagnostic equipment. Due to the increasing innovation of sensor module in the field of Information and Communication Technology (ICT), electronic nose emerges as the times require. The device can use sensors to analyze Volatile Organic Compounds exhaled by people with certain diseases and identify chemical components of various odor and flavor. In addition, many other types of sensors and classification methods such as machine learning (ML) algorithms are also mentioned in this paper. At the end of the paper, the author attached a related experiment and some shortcomings and prospects of electronic nose as well

Named Entity Recognition via Machine Reading Comprehension: A Multi-Task Learning Approach

Named Entity Recognition (NER) aims to extract and classify entity mentions in the text into pre-defined types (e.g., organization or person name). Recently, many works have been proposed to shape the NER as a machine reading comprehension problem (also termed MRC-based NER), in which entity recognition is achieved by answering the formulated questions related to pre-defined entity types through MRC, based on the contexts. However, these works ignore the label dependencies among entity types, which are critical for precisely recognizing named entities. In this paper, we propose to incorporate the label dependencies among entity types into a multi-task learning framework for better MRC-based NER. We decompose MRC-based NER into multiple tasks and use a self-attention module to capture label dependencies. Comprehensive experiments on both nested NER and flat NER datasets are conducted to validate the effectiveness of the proposed Multi-NER. Experimental results show that Multi-NER can achieve better performance on all datasets

Neural Collapse Inspired Federated Learning with Non-iid Data

One of the challenges in federated learning is the non-independent and identically distributed (non-iid) characteristics between heterogeneous devices, which cause significant differences in local updates and affect the performance of the central server. Although many studies have been proposed to address this challenge, they only focus on local training and aggregation processes to smooth the changes and fail to achieve high performance with deep learning models. Inspired by the phenomenon of neural collapse, we force each client to be optimized toward an optimal global structure for classification. Specifically, we initialize it as a random simplex Equiangular Tight Frame (ETF) and fix it as the unit optimization target of all clients during the local updating. After guaranteeing all clients are learning to converge to the global optimum, we propose to add a global memory vector for each category to remedy the parameter fluctuation caused by the bias of the intra-class condition distribution among clients. Our experimental results show that our method can improve the performance with faster convergence speed on different-size datasets.Comment: 11 pages, 5 figure

Effect of Two Kinds of Bone Replacement Materials on Bone Formation in Repairing Bone Defects Around Mandibular Posterior Area: a Case Study of Bone Defects Around Mandibular Posterior Area Caused by Boxing

Objective: To investigate the effect of two kinds of bone replacement materials on bone formation in repairing bone defect around mandibular posterior area. Methods: A total of 60 patients with Bone defects around mandibular posterior area caused by boxing were selected from a hospital from January 2020 to June 2020. They were divided into Perio Glas (group P) and Bone Plant (group B) by random number table method, with 30 patients in each group. Perio Glas Bone graft was used in group P and Bone Plant graft was used in group B. The vertical height and buccal lingual bone plate width of the two groups were observed at baseline and after treatment, and the success rate of implants was compared between the two groups. Results: The success rate of implant in group P was significantly lower than that in group B (P < 0.05). The vertical height and buccal lingual bone plate width in group P were significantly lower than those in group B (P < 0.05). Conclusion: Compared with Perio Glas, Bone Plant can better maintain the vertical height and buccal lingual Bone plate width of patients with Bone defects around mandibular posterior area caused by boxing, and has better effect of inducing Bone regeneration and osteogenesis

Effect of Two Kinds of Bone Replacement Materials on Bone Formation in Repairing Bone Defects around Mandibular Posterior Area: A Case Study of Bone Defects around Mandibular Posterior Area Caused by Boxing

Objective: To investigate the effect of two kinds of bone replacement materials on bone formation in repairing bone defect around mandibular posterior area. Methods: A total of 60 patients with Bone defects around mandibular posterior area caused by boxing were selected from a hospital from January 2020 to June 2020. They were divided into Perio Glas (group P) and Bone Plant (group B) by random number table method, with 30 patients in each group. Perio Glas Bone graft was used in group P and Bone Plant graft was used in group B. The vertical height and buccal lingual bone plate width of the two groups were observed at baseline and after treatment, and the success rate of implants was compared between the two groups. Results: The success rate of implant in group P was significantly lower than that in group B (P < 0.05). The vertical height and buccal lingual bone plate width in group P were significantly lower than those in group B (P < 0.05). Conclusion: Compared with Perio Glas, Bone Plant can better maintain the vertical height and buccal lingual Bone plate width of patients with Bone defects around mandibular posterior area caused by boxing, and has better effect of inducing Bone regeneration and osteogenesis

Application of Nanotechnology in Oral Implantation Based on Exercise-Induced Tooth Injury

With the development of sports in various countries, more and more people participate in sports, and the rate of sports injuries to the oral and maxillofacial system also increases, especially the dental injuries caused by boxing are more common. At the same time, with the improvement of modern production, people's basic needs have been continuously met, and more and more people have begun to pay attention to their physical health and appearance characteristics. Among them, dental plastic correction and restoration have received great attention. In addition, with the application of nanotechnology in various fields in recent years, scholars at home and abroad have also tried or have continued to introduce nanomaterials into oral clinics, in order to improve and develop the application of oral materials, so that the oral clinic can be continuously improved and developed. Therefore, this paper reviews the current situation of sports injury of the oral and maxillofacial system, the mechanical analysis of tooth injury, the current situation of tooth restoration and the application of nanotechnology in the field of oral implantation

General Rotation Invariance Learning for Point Clouds via Weight-Feature Alignment

Compared to 2D images, 3D point clouds are much more sensitive to rotations. We expect the point features describing certain patterns to keep invariant to the rotation transformation. There are many recent SOTA works dedicated to rotation-invariant learning for 3D point clouds. However, current rotation-invariant methods lack generalizability on the point clouds in the open scenes due to the reliance on the global distribution, \ie the global scene and backgrounds. Considering that the output activation is a function of the pattern and its orientation, we need to eliminate the effect of the orientation.In this paper, inspired by the idea that the network weights can be considered a set of points distributed in the same 3D space as the input points, we propose Weight-Feature Alignment (WFA) to construct a local Invariant Reference Frame (IRF) via aligning the features with the principal axes of the network weights. Our WFA algorithm provides a general solution for the point clouds of all scenes. WFA ensures the model achieves the target that the response activity is a necessary and sufficient condition of the pattern matching degree. Practically, we perform experiments on the point clouds of both single objects and open large-range scenes. The results suggest that our method almost bridges the gap between rotation invariance learning and normal methods.Comment: 4 figure