Adaptive Edge-to-Edge Interaction Learning for Point Cloud Analysis

Recent years have witnessed the great success of deep learning on various point cloud analysis tasks, e.g., classification and semantic segmentation. Since point cloud data is sparse and irregularly distributed, one key issue for point cloud data processing is extracting useful information from local regions. To achieve this, previous works mainly extract the points' features from local regions by learning the relation between each pair of adjacent points. However, these works ignore the relation between edges in local regions, which encodes the local shape information. Associating the neighbouring edges could potentially make the point-to-point relation more aware of the local structure and more robust. To explore the role of the relation between edges, this paper proposes a novel Adaptive Edge-to-Edge Interaction Learning module, which aims to enhance the point-to-point relation through modelling the edge-to-edge interaction in the local region adaptively. We further extend the module to a symmetric version to capture the local structure more thoroughly. Taking advantage of the proposed modules, we develop two networks for segmentation and shape classification tasks, respectively. Various experiments on several public point cloud datasets demonstrate the effectiveness of our method for point cloud analysis.Comment: Technical Repor

Investigation and Research on Physical Education and Health Curriculum of K-12 School in Guizhou Province

The purpose of this study is to investigate the current situation of physical education and health curriculum in primary and secondary schools in Guizhou Province, and to provide reference for promoting the better implementation of physical education and health curriculum in Guizhou Province. In the form of questionnaires, 1549 parents\u27 questionnaires and 254 teachers\u27 questionnaires were collected and statistically analyzed in Guizhou Province, China. Use Excel to summarize and analyze the collected questionnaires. The results found the teaching content could basically meet the needs of students. The satisfaction of primary school students, junior high school students and senior high school students with physical education and health curriculum evaluation was 71.6%, 68.4% and 63.6%, respectively. Students\u27 satisfaction with the content of physical education and health curriculum in senior high school decreased; both students and teachers believed that all students had the opportunity to participate in sports activities in physical education and health classes, but the time for skill learning and physical training in PE classes in primary and secondary schools was less than 20 minutes. The intensity of classroom exercise in 60% of primary and secondary schools was less than 75%. 94.1% of teachers control exercise load according to experience, and only 3.9% of schools use intelligent monitoring devices to monitor. 50.9% of primary and junior high school physical education classes did not meet the required number of class hours. 69.6% of the students were satisfied with the elective items in the physical education courses offered, but their satisfaction with the senior high school dropped to 61.6%. Primary and secondary schools in Guizhou Province should continue to increase the construction and investment of physical education and health curriculum venues, equipment and facilities, and optimize the use and development of existing physical education curriculum resources. Physical education teachers should constantly update teaching concepts, improve teaching methods and improve course teaching ability. Schools and teachers should carry out physical education and health courses according to the requirements of physical Education and Health Curriculum Standards, and actively promote the Chinese Health physical Education Curriculum Model put forward by JI Liu professor to ensure a certain exercise load and exercise density

Adaptive Pattern Extraction Multi-Task Learning for Multi-Step Conversion Estimations

Multi-task learning (MTL) has been successfully used in many real-world applications, which aims to simultaneously solve multiple tasks with a single model. The general idea of multi-task learning is designing kinds of global parameter sharing mechanism and task-specific feature extractor to improve the performance of all tasks. However, challenge still remains in balancing the trade-off of various tasks since model performance is sensitive to the relationships between them. Less correlated or even conflict tasks will deteriorate the performance by introducing unhelpful or negative information. Therefore, it is important to efficiently exploit and learn fine-grained feature representation corresponding to each task. In this paper, we propose an Adaptive Pattern Extraction Multi-task (APEM) framework, which is adaptive and flexible for large-scale industrial application. APEM is able to fully utilize the feature information by learning the interactions between the input feature fields and extracted corresponding tasks-specific information. We first introduce a DeepAuto Group Transformer module to automatically and efficiently enhance the feature expressivity with a modified set attention mechanism and a Squeeze-and-Excitation operation. Second, explicit Pattern Selector is introduced to further enable selectively feature representation learning by adaptive task-indicator vectors. Empirical evaluations show that APEM outperforms the state-of-the-art MTL methods on public and real-world financial services datasets. More importantly, we explore the online performance of APEM in a real industrial-level recommendation scenario.Comment: 18 pages, 9 figure

Advances in oncolytic herpes simplex virus and adenovirus therapy for recurrent glioma

Recurrent glioma treatment is challenging due to molecular heterogeneity and treatment resistance commonly observed in these tumors. Researchers are actively pursuing new therapeutic strategies. Oncolytic viruses have emerged as a promising option. Oncolytic viruses selectively replicate within tumor cells, destroying them and stimulating the immune system for an enhanced anticancer response. Among Oncolytic viruses investigated for recurrent gliomas, oncolytic herpes simplex virus and oncolytic adenovirus show notable potential. Genetic modifications play a crucial role in optimizing their therapeutic efficacy. Different generations of replicative conditioned oncolytic human adenovirus and oncolytic HSV have been developed, incorporating specific modifications to enhance tumor selectivity, replication efficiency, and immune activation. This review article summarizes these genetic modifications, offering insights into the underlying mechanisms of Oncolytic viruses’ therapy. It also aims to identify strategies for further enhancing the therapeutic benefits of Oncolytic viruses. However, it is important to acknowledge that additional research and clinical trials are necessary to establish the safety, efficacy, and optimal utilization of Oncolytic viruses in treating recurrent glioblastoma