Education distillation:getting student models to learn in shcools

Knowledge distillation is one of the methods for model compression, and existing knowledge distillation techniques focus on how to improve the distillation algorithm so as to enhance the distillation efficiency. This paper introduces dynamic incremental learning into knowledge distillation and proposes a distillation strategy for education distillation. Specifically, it is proposed to take fragmented student models divided from the complete student model as lower-grade models. As the grade level rises, fragmented student models deepen in conjunction with designed teaching reference layers, while learning and distilling from more teacher models. By moving from lower to higher grades, fragmented student models were gradually integrated into a complete target student model, and the performance of the student models gradually improved from lower to higher grades of the stage. Education distillation strategies combined with distillation algorithms outperform the results of single distillation algorithms on the public dataset CIFAR100,Caltech256, Food-101 dataset

Progress in developments of dry coal beneficiation

Abstract China’s energy supply heavily relies on coal and China’s coal resource and water resource has a reverse distribution. The problem of water shortages restricts the applications of wet coal beneficiation technologies in drought regions. The present situation highlights the significance and urgency of developing dry beneficiation technologies of coal. Besides, other countries that produce large amounts of coal also encounter serious problem of lack of water for coal beneficiation, such as American, Australia, Canada, South Africa, Turkey and India. Thus, dry coal beneficiation becomes the research hot-points in the field of coal cleaning worldwide in recent years. This paper systematically reviewed the promising research efforts on dry coal beneficiation reported in literature in last 5 years and discussed the progress in developments of dry coal beneficiation worldwide. Finally, we also elaborated the prospects and the challenges of the development of dry coal beneficiation

Restoration of Critical-Sized Defects in the Rabbit Mandible Using Autologous Bone Marrow Stromal Cells Hybridized with Nano- β

Nano-β-tricalcium phosphate/collagen (n-β-TCP/Col) is considered with good osteoconductivity. However, the therapeutic effectiveness of n-β-TCP/Col scaffolds in combination with autologous bone marrow stromal cells (BMSCs) remains to be elucidated for the repair of critical-sized bone defects. In this study, we found that n-β-TCP/Col scaffolds exhibited high biocompatibility in vitro. The introduction of BMSCs expanded in vitro to the scaffolds dramatically enhanced their efficiency to restore critical-sized bone defects, especially during the initial stage after implantation. Collectively, these results suggest that autologous BMSCs in n-β-TCP/Col scaffolds have the potential to be applied in bone tissue engineering

Space advanced technology demonstration satellite

The Space Advanced Technology demonstration satellite (SATech-01), a mission for low-cost space science and new technology experiments, organized by Chinese Academy of Sciences (CAS), was successfully launched into a Sun-synchronous orbit at an altitude of similar to 500 km on July 27, 2022, from the Jiuquan Satellite Launch Centre. Serving as an experimental platform for space science exploration and the demonstration of advanced common technologies in orbit, SATech-01 is equipped with 16 experimental payloads, including the solar upper transition region imager (SUTRI), the lobster eye imager for astronomy (LEIA), the high energy burst searcher (HEBS), and a High Precision Magnetic Field Measurement System based on a CPT Magnetometer (CPT). It also incorporates an imager with freeform optics, an integrated thermal imaging sensor, and a multi-functional integrated imager, etc. This paper provides an overview of SATech-01, including a technical description of the satellite and its scientific payloads, along with their on-orbit performance

A Plagiarism Detection Algorithm based on Extended Winnowing

Plagiarism is a common problem faced by academia and education. Mature commercial plagiarism detection system has the advantages of comprehensive and high accuracy, but the expensive detection costs make it unsuitable for real-time, lightweight application environment such as the student assignments plagiarism detection. This paper introduces the method of extending classic Winnowing plagiarism detection algorithm, expands the algorithm in functionality. The extended algorithm can retain the text location and length information in original document while extracting the fingerprints of a document, so that the locating and marking for plagiarism text fragment are much easier to achieve. The experimental results and several years of running practice show that the expansion of the algorithm has little effect on its performance, normal hardware configuration of PC will be able to meet small and medium-sized applications requirements. Based on the characteristics of lightweight, high efficiency, reliability and flexibility of Winnowing, the extended algorithm further enhances the adaptability and extends the application areas

A Plagiarism Detection Algorithm based on Extended Winnowing

Plagiarism is a common problem faced by academia and education. Mature commercial plagiarism detection system has the advantages of comprehensive and high accuracy, but the expensive detection costs make it unsuitable for real-time, lightweight application environment such as the student assignments plagiarism detection. This paper introduces the method of extending classic Winnowing plagiarism detection algorithm, expands the algorithm in functionality. The extended algorithm can retain the text location and length information in original document while extracting the fingerprints of a document, so that the locating and marking for plagiarism text fragment are much easier to achieve. The experimental results and several years of running practice show that the expansion of the algorithm has little effect on its performance, normal hardware configuration of PC will be able to meet small and medium-sized applications requirements. Based on the characteristics of lightweight, high efficiency, reliability and flexibility of Winnowing, the extended algorithm further enhances the adaptability and extends the application areas

Myocardial Priority Promotes Cardiovascular Recovery for Acute Type A Aortic Dissection Combined with Coronary Artery Disease Undergoing Aortic Arch Surgery

The optimal surgical strategy for acute type A aortic dissection (ATAAD) with coronary artery disease (CAD) remains unclear. The goal of this study was to investigate the cardiovascular protective effects of the myocardial priority (MP) strategy or traditional selective cerebral perfusion (SCP) in ATAAD with CAD. A total of 214 adults were analyzed retrospectively, of which 80 underwent the MP strategy intraoperatively. Seventy-nine pairs were propensity-score-matched and divided into SCP and MP groups. The follow-up period ranged from 6 to 36 months. The MP group had a significantly shorter myocardial ischemic time, higher perfusion flow, higher radial artery pressure, and lower incidence of NIRS decrease >20% of the base value, but a longer lower limb circulatory arrest and bypass time than the SCP group. Although similar adverse cardiac and cerebrovascular events were observed in both groups, a shorter posthospital stay, less blood loss and transfusion, higher postoperative hemoglobin, lower creatinine, and higher PaO2/FiO2 were observed in the MP group. Subgroup analysis showed that when the TIMI Risk Score was <4, the MP group had a lower incidence of low cardiac output and lower postoperative cTnI level. The follow-up patients had similar morbidities between the two groups. The novel MP strategy is associated with a shortened myocardial ischemic time, better maintained perfusion of vital organs, and postoperative recovery after surgery for ATAAD combined with non-severe CAD

Fish Face Identification Based on Rotated Object Detection: Dataset and Exploration

At present, fish farming still uses manual identification methods. With the rapid development of deep learning, the application of computer vision in agriculture and farming to achieve agricultural intelligence has become a current research hotspot. We explored the use of facial recognition in fish. We collected and produced a fish identification dataset with 3412 images and a fish object detection dataset with 2320 images. A rotating box is proposed to detect fish, which avoids the problem where the traditional object detection produces a large number of redundant regions and affects the recognition accuracy. A self-SE module and a fish face recognition network (FFRNet) are proposed to implement the fish face identification task. The experiments proved that our model has an accuracy rate of over 90% and an FPS of 200