Attention Diversification for Domain Generalization

Convolutional neural networks (CNNs) have demonstrated gratifying results at learning discriminative features. However, when applied to unseen domains, state-of-the-art models are usually prone to errors due to domain shift. After investigating this issue from the perspective of shortcut learning, we find the devils lie in the fact that models trained on different domains merely bias to different domain-specific features yet overlook diverse task-related features. Under this guidance, a novel Attention Diversification framework is proposed, in which Intra-Model and Inter-Model Attention Diversification Regularization are collaborated to reassign appropriate attention to diverse task-related features. Briefly, Intra-Model Attention Diversification Regularization is equipped on the high-level feature maps to achieve in-channel discrimination and cross-channel diversification via forcing different channels to pay their most salient attention to different spatial locations. Besides, Inter-Model Attention Diversification Regularization is proposed to further provide task-related attention diversification and domain-related attention suppression, which is a paradigm of "simulate, divide and assemble": simulate domain shift via exploiting multiple domain-specific models, divide attention maps into task-related and domain-related groups, and assemble them within each group respectively to execute regularization. Extensive experiments and analyses are conducted on various benchmarks to demonstrate that our method achieves state-of-the-art performance over other competing methods. Code is available at https://github.com/hikvision-research/DomainGeneralization.Comment: ECCV 2022. Code available at https://github.com/hikvision-research/DomainGeneralizatio

Mortality and excess life-years lost in patients with schizophrenia under community care: a 5-year follow-up cohort study

Objective: Mortality rate is a general indicator which can be used to measure care and management of schizophrenia. This cohort study evaluated the standardized mortality ratios (SMRs) of all-cause mortality and life-years lost (LYLs) in patients with schizophrenia under a community care program in China. Methods: Data were obtained from the National Community Care Program System for Severe Mental Disorders. A total of 99,214 patients diagnosed with schizophrenia were enrolled before December 2014 and followed between 2015 and 2019. A total of 9,483 patients died. Crude mortality rates (CMRs) and SMRs were then stratified by natural vs. unnatural causes, and major groups of death were standardized according to the 2010 National Population SMRs. The corresponding LYLs at birth were also calculated by gender and age. Results: The SMRs of patients with schizophrenia were significantly elevated during the study period, with an overall SMR of 4.98 (95%CI 2.67-7.32). Neoplasms, cardiovascular diseases, cerebrovascular diseases, external injuries, and poisonings were the most significant causes of death among patients with schizophrenia compared to the general population. The mean LYLs of patients with schizophrenia were 15.28 (95%CI 13.26-17.30). Males with schizophrenia lost 15.82 life-years (95%CI 13.48-18.16), and females lost 14.59 life-years (95%CI 13.12-16.06). Conclusions: Patients with schizophrenia under community care had a high mortality rate in our study, even though mental health services have been integrated into the general healthcare system in China to narrow treatment gaps in mental health for > 10 years. In terms of mortality outcome indicators, effective and quality mental health services still have a long way to go. The current study demonstrates the potential for improved prevention and treatment of individuals with schizophrenia under community care

Label-Free Fluorescent Poly(amidoamine) Dendrimer for Traceable and Controlled Drug Delivery

Poly(amidoamine) dendrimer (PAMAM) is well-known for its high efficiency as a drug delivery vehicle. However, the intrinsic cytotoxicity and lack of a detectable signal to facilitate tracking have impeded its practical applications. Herein, we have developed a novel label-free fluorescent and biocompatible PAMAM derivative by simple surface modification of PAMAM using acetaldehyde. The modified PAMAM possessed a strong green fluorescence, which was generated by the C=N bonds of the resulting Schiff Bases via n-?∗ transition, while the intrinsic cytotoxicity of PAMAM was simultaneously ameliorated. Through further PEGylation, the fluorescent PAMAM demonstrated excellent intracellular tracking in human melanoma SKMEL28 cells. In addition, our PEGylated fluorescent PAMAM derivative achieved enhanced loading and delivery efficiency of the anticancer drug doxorubicin (DOX) compared to the original PAMAM. Importantly, the accelerated kinetics of DOX-encapsulated fluorescent PAMAM nanocomposites in an acidic environment facilitated intracellular drug release, which demonstrated comparable cytotoxicity to that of the free-form doxorubicin hydrochloride (DOX·HCl) against melanoma cells. Overall, our label free fluorescent PAMAM derivative offers a new opportunity of traceable and controlled delivery for DOX and other drugs of potential clinical importance

The Influence of E-commerce Live Broadcasting on Consumers\u27 Purchase Intention in Online Celebrity Economy

With the rise of online celebrity\u27s economy and the improvement of webcast technology, China\u27s new Internet economy is also showing a diversified development trend. Affected by the 2020 epidemic, the purchasing power of domestic consumers in China also pushed the live delivery of goods in online celebrity to its peak, making it a hot spot of consumption today. This paper will start with the livestream marketing in online celebrity, based on the ABC attitude theory as the theoretical framework, explore which factors have an impact on consumers\u27 purchase intention and further explore the influence effects of different influencing factors. The conclusion provides data support and decision-making for further market segmentation and differentiated marketing

Short-Term Load Forecasting with Tensor Partial Least Squares-Neural Network

Short-term load forecasting is very important for power systems. The load is related to many factors which compose tensors. However, tensors cannot be input directly into most traditional forecasting models. This paper proposes a tensor partial least squares-neural network model (TPN) to forecast the power load. The model contains a tensor decomposition outer model and a nonlinear inner model. The outer model extracts common latent variables of tensor input and vector output and makes the residuals less than the threshold by iteration. The inner model determines the relationship between the latent variable matrix and the output by using a neural network. This model structure can preserve the information of tensors and the nonlinear features of the system. Three classical models, partial least squares (PLS), least squares support vector machine (LSSVM) and neural network (NN), are selected to compare the forecasting results. The results show that the proposed model is efficient for short-term load and daily load peak forecasting. Compared to PLS, LSSVM and NN, the TPN has the best forecasting accuracy

Application of University Campus Noise Map Based on Noise Propagation Model: A Case in Guangxi University

Considering the characteristics of a campus environment and the rules that govern outdoor sound propagation, this paper identifies traffic noise as the dominant noise source of the campus environment based on the measurement of the noise environment. A noise propagation model that is suitable for university campuses was developed and used it was to create a noise map of the ambient area of the teaching building on the campus of Guangxi University. This noise map was then utilized to analyze the noise environment. The results revealed that for a given teaching building, the noise disturbance on high-rise classrooms is more significant compared to the impact on low-rise classrooms. Attention should then be paid to noise control in the high-rise classroom of the building. By appropriately increasing the distance between the building and the main traffic road or by adopting a judicious soundscape design that considers the shape of the building, it is possible to effectively reduce the interference of noise during teaching activities in a building and improve the sound quality of the campus environment. The results of this study provide a theoretical framework for the governance of the campus acoustic environment

Application of University Campus Noise Map Based on Noise Propagation Model: A Case in Guangxi University

Considering the characteristics of a campus environment and the rules that govern outdoor sound propagation, this paper identifies traffic noise as the dominant noise source of the campus environment based on the measurement of the noise environment. A noise propagation model that is suitable for university campuses was developed and used it was to create a noise map of the ambient area of the teaching building on the campus of Guangxi University. This noise map was then utilized to analyze the noise environment. The results revealed that for a given teaching building, the noise disturbance on high-rise classrooms is more significant compared to the impact on low-rise classrooms. Attention should then be paid to noise control in the high-rise classroom of the building. By appropriately increasing the distance between the building and the main traffic road or by adopting a judicious soundscape design that considers the shape of the building, it is possible to effectively reduce the interference of noise during teaching activities in a building and improve the sound quality of the campus environment. The results of this study provide a theoretical framework for the governance of the campus acoustic environment