ABSTRACT
Tensor networks have been recognized as a powerful numerical tool;they are applied in various fields, including physics, computer science, and more. The idea of a tensor network originates from quantum physics as an efficient representation of quantum many-body states and their operations. Matrix product states (MPS) form one of the simplest tensor networks and have been applied to machine learning for image classification. However, MPS has certain limitations when processing two-dimensional images, meaning that it is preferable for an projected entangled pair states (PEPS) tensor network with a similar structure to the image to be introduced into machine learning. PEPS tensor networks are significantly superior to other tensor networks on the image classification task. Based on a PEPS tensor network, this paper constructs a multi-layered PEPS (MLPEPS) tensor network model for image classification. PEPS is used to extract features layer by layer from the image mapped to the Hilbert space, which fully utilizes the correlation between pixels while retaining the global structural information of the image. When performing classification tasks on the Fashion-MNIST dataset, MLPEPS achieves a classification accuracy of 90.44%, exceeding tensor network models such as the original PEPS. On the COVID-19 radiography dataset, MLPEPS has a test set accuracy of 91.63%, which is very close to the results of GoogLeNet. Under the same experimental conditions, the learning ability of MLPEPS is already close to that of existing neural networks while having fewer parameters. MLPEPS can be used to build different network models by modifying the structure, and as such it has great potential in machine learning.
ABSTRACT
The arrival of COVID-19 has changed the way traditional classes are conducted, and online teaching has never been more popular. While there are many advantages to online teaching, there are also extremely obvious disadvantages, one of which is the tendency to lack concentration. For this reason, this study uses video images from the DAiSee dataset, a new sampling script, deep learning neural networks, and a new PAD emotion model to systematically assess student concentration. Our test set uses 21 short videos from the DAISee dataset, sampling a total of 1,866 frames. The final results showed that the accuracy of the neural network was approximately 80%. The results of the test set on the PAD model showed that the percentage of attentive listeners was 65.9%, while the percentage of highly inattentive listeners was 6.2%. This study constructed a complete concentration monitoring system for online classrooms centred on smart education which can provide the information of students' concentration in real time. © 2022 ACM.
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This study focuses on developing an effective online teaching strategy to improve students' cognition engagement and application ability by applying design thinking and case readings on current economic issues for private university students in Taiwan during the COVID-19 pandemic. The teaching method combines design thinking and the reading comprehension process by the two principles of divergence and convergence. The design thinking method provides stepwise guidance for building understanding and analyzing competence on current economic issues. The reading comprehension process strengthens students' reading skills and learning capability. This combination increases student engagement and concentration in economic case readings during online learning. The research participants comprised 189 first-year students studying economics courses. After implementing the innovative teaching strategies, the results show that the more students are involved in frequent readings, the better are their economics semester grade. The findings revealed that their post-quiz scores improved significantly, and the semester grade increased by 3.44 points. Increasing the reading engagement on current economic issues cases also affected the learning outcomes for absentees. Using design thinking to introduce case reading comprehension, empathy has been suggested as an essential factor affecting the effectiveness of reading learning. This theoretical model can offer directional insights and guidance on developing an effective strategy in online economics education. © 2022 Conscientia Beam. All Rights Reserved.
ABSTRACT
Emotion classification of COVID-19 Chinese microblogs helps analyze the public opinion triggered by COVID-19. Existing methods only consider the features of the microblog itself, without combining the semantics of emotion categories for modeling. Emotion classification of microblogs is a process of reading the content of microblogs and combining the semantics of emotion categories to understand whether it contains a certain emotion. Inspired by this, we propose an emotion classification model based on the emotion category description for COVID-19 Chinese microblogs. Firstly, we expand all emotion categories into formalized category descriptions. Secondly, based on the idea of question answering, we construct a question for each microblog in the form of 'What is the emotion expressed in the text XT and regard all category descriptions as candidate answers. Finally, we construct a question-and-answer pair and use it as the input of the BERT model to complete emotion classification. By integrating rich contextual and category semantics, the model can better understand the emotion of microblogs. Experiments on the COVID-19 Chinese microblog dataset show that our approach outperforms many existing emotion classification methods, including the BERT baseline. © 2021 China National Conference on Computational Linguistics Published under Creative Commons Attribution 4.0 International License
ABSTRACT
Introduction: Deficits in nephrin and other podocyte components are known to result in congenital nephrotic and familial FSGS syndromes. Weins et. al. recently described acquired anti-nephrin antibody localizing in glomerular podocytes of patients with minimal change disease. Case Description: A 16 year old male referred for new onset nephrotic syndrome progressive over 2 weeks was found to have serum albumin 1.2 gm/dL, UPCR 3.1, and elevated lipids with BP 160/100 mm Hg. Hepatitis B/C, HIV, SLE screens were negative. Renal biopsy demonstrated focal collapsing lesions with diffuse podocyte effacement. Immunofluorescence showed punctate IgG, kappa and lambda light chain staining in podocytes, but no albumin. Anti-human IgG colocalized with nephrin in the granular staining. ParvoB19 and COVID-19 titers were negative. Creatinine rose from 0.65 to 1.65 and UPCR to 10.3 but improved rapidly with high dose prednisone and ACEi. Serology for circulating anti-nephrin 2 weeks into treatment was negative, consistent with previous finding that circulating antibody levels quickly drop to low or undetectable with partial clinical remission. Discussion: This case strengthens evidence that anti-nephrin antibodies cause disruption of the slit pore diaphragm which appears to be readily responsive to immune therapy. Anti-nephrin mediated podocytopathy may present with a spectrum of glomerular histopathology, which on the background of other susceptibility factors, can lead to more severe presentations such as collapsing FSGS.
ABSTRACT
Emotion classification of COVID-19 Chinese microblogs helps analyze the public opinion triggered by COVID-19. Existing methods only consider the features of the microblog itself, without combining the semantics of emotion categories for modeling. Emotion classification of microblogs is a process of reading the content of microblogs and combining the semantics of emotion categories to understand whether it contains a certain emotion. Inspired by this, we propose an emotion classification model based on the emotion category description for COVID-19 Chinese microblogs. Firstly, we expand all emotion categories into formalized category descriptions. Secondly, based on the idea of question answering, we construct a question for each microblog in the form of ‘What is the emotion expressed in the text X?’ and regard all category descriptions as candidate answers. Finally, we construct a question-and-answer pair and use it as the input of the BERT model to complete emotion classification. By integrating rich contextual and category semantics, the model can better understand the emotion of microblogs. Experiments on the COVID-19 Chinese microblog dataset show that our approach outperforms many existing emotion classification methods, including the BERT baseline. © 2021, Springer Nature Switzerland AG.
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This article discusses the state and public reactions on the internet to the death of Dr Li Wenliang, the whistle-blower who warned of the COVID-19 outbreak in China. Contrary to findings in existing literature, this study argues that the society-state interaction over the internet is far more dynamic and complex, and that autocratic states have the capability and resilience to manage and even control the internet. While the internet could help rally waves of public outpourings and protests, the state, in response, broadcast its tactical concessions to ease widespread public anger. However, the state has been able to portray its overall highly effective control of the epidemic, thereby claiming political credits and legitimacy for governance.
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Introduction: The aim of the present study was to investigate a relatively convenient, safe, and sensitive sampling method in the nucleic acid detection of the 2019-novel coronavirus (2019-nCoV). Materials and method: The nasopharyngeal swab samples of patients admitted to the 13 inpatient areas of the Tumor Center, Xiehe Hospital of the affiliated Tongji Medical College of Huazhong University of Science and Technology (temporarily transformed into a designated hospital for critical patients with COVID-19) from February 21 to 23, 2020 were used for the nucleic acid detection analysis of 2019-nCoV. The nasopharyngeal swab samples in the present inpatient area were obtained by a standardized sampling method. Results: A total of 663 samples were collected from the tumor center with 125 positive ones. Among these samples, 33 samples were collected from the present inpatient area, and 11 cases (33%) were positive. A further 630 samples were collected from other inpatient areas, in which 114 samples (18%) were positive. The difference in the positivity between the present inpatient area and other areas was statistically significant. Conclusion: The standardized nasopharyngeal swabs sampling had a high positive detection rate in the nucleic acid detection of 2019-nCoV and was safer and more convenient for medical staff and worthy of wider clinical use.
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OBJECTIVES: To understand SARS-Co-V-2 infection and transmission in UK nursing homes in order to develop preventive strategies for protecting the frail elderly residents. METHODS: An outbreak investigation involving 394 residents and 70 staff, was carried out in 4 nursing homes affected by COVID-19 outbreaks in central London. Two point-prevalence surveys were performed one week apart where residents underwent SARS-CoV-2 testing and had relevant symptoms documented. Asymptomatic staff from three of the four homes were also offered SARS-CoV-2 testing. RESULTS: Overall, 26% (95% CI 22-31) of residents died over the two-month period. All-cause mortality increased by 203% (95% CI 70-336) compared with previous years. Systematic testing identified 40% (95% CI 35-46) of residents as positive for SARS-CoV-2, and of these 43% (95% CI 34-52) were asymptomatic and 18% (95% CI 11-24) had only atypical symptoms; 4% (95% CI -1 to 9) of asymptomatic staff also tested positive. CONCLUSIONS: The SARS-CoV-2 outbreak in four UK nursing homes was associated with very high infection and mortality rates. Many residents developed either atypical or had no discernible symptoms. A number of asymptomatic staff members also tested positive, suggesting a role for regular screening of both residents and staff in mitigating future outbreaks.