Assessing and Enhancing Robustness of Deep Learning Models with Corruption Emulation in Digital Pathology

Deep learning in digital pathology brings intelligence and automation as substantial enhancements to pathological analysis, the gold standard of clinical diagnosis. However, multiple steps from tissue preparation to slide imaging introduce various image corruptions, making it difficult for deep neural network (DNN) models to achieve stable diagnostic results for clinical use. In order to assess and further enhance the robustness of the models, we analyze the physical causes of the full-stack corruptions throughout the pathological life-cycle and propose an Omni-Corruption Emulation (OmniCE) method to reproduce 21 types of corruptions quantified with 5-level severity. We then construct three OmniCE-corrupted benchmark datasets at both patch level and slide level and assess the robustness of popular DNNs in classification and segmentation tasks. Further, we explore to use the OmniCE-corrupted datasets as augmentation data for training and experiments to verify that the generalization ability of the models has been significantly enhanced

Spatial Temporal Analysis of Traffic Patterns during the COVID-19 Epidemic by Vehicle Detection Using Planet Remote-Sensing Satellite Images

The spread of the COVID-19 since the end of 2019 has reached an epidemic level and has quickly become a global public health crisis. During this period, the responses for COVID-19 were highly diverse and decentralized across countries and regions. Understanding the dynamics of human mobility change at high spatial temporal resolution is critical for assessing the impacts of non-pharmaceutical interventions (such as stay-at-home orders, regional lockdowns and travel restrictions) during the pandemic. However, this requires collecting traffic data at scale, which is time-consuming, cost-prohibitive and often not available (e.g., in underdeveloped countries). Therefore, spatiotemporal analysis through processing periodical remote-sensing images is very beneficial to enable efficient monitoring at the global scale. In this paper, we present a novel study that utilizes high temporal Planet multispectral images (from November 2019 to September 2020, on average 7.1 days of frequency) to detect traffic density in multiple cities through a proposed morphology-based vehicle detection method and evaluate how the traffic data collected in such a manner reflect mobility pattern changes in response to COVID-19. Our experiments at city-scale detection, demonstrate that our proposed vehicle detection method over this 3 m resolution data is able to achieve a detection level at an accuracy of 68.26% in most of the images, and the observations’ trends coincide with existing public data of where available (lockdown duration, traffic volume, etc.), further suggesting that such high temporal Planet data with global coverage (although not with the best resolution), with well-devised detection algorithms, can sufficiently provide traffic details for trend analysis to better facilitate informed decision making for extreme events at the global level

A hybrid spectral clustering simulated annealing algorithm for the street patrol districting problem

Abstract Reasonable districting plays an important role in the patrolling process. In this paper, workload attributes are considered, and a mixed integer programming model is developed to solve the street patrol districting problem (SPDP). The improved spectral clustering algorithm named spectral clustering algorithm based on the road network (SCRn) and simulated annealing algorithm (SA) are combined. This results in a hybrid algorithm called SCRn-SA. The SCRn-SA algorithm is tested on small examples and real instances in Zhengzhou, China. The experimental results show that the proposed algorithm is effective for solving SPDP. It has better performance when compared to other advanced algorithms

FDI, Technology Spillovers, and Green Innovation: Theoretical Analysis and Evidence from China

Foreign direct investment (FDI) technology spillovers play an increasingly important role in a host country’s development. Evaluating the positive effect of FDI inflows on green innovation is essential for correct city design. Based on the panel data of 262 cities in China from 2004 to 2018, we first analyzed the impact of FDI technology spillovers on green innovation in Chinese cities and then tested the threshold effect in four absorptive capacity factors: environmental regulation, economic growth, human capital, and industry size. Finally, we compared the time and space of two types of cities crossing the threshold from the perspective of innovative and non-innovative cities. The results show that FDI can significantly promote green innovation in Chinese cities and the promoting effect of FDI on green innovation has nonlinear characteristics, namely, such effects only make sense when absorptive capacity is above the threshold points. Additionally, among the four absorptive capacity factors, the development degrees of innovative cities are ahead of non-innovative cities; in particular, there is a significant difference between them in terms of economic growth. Local governments should develop reasonable policy combination tools according to the absorptive capacity characteristics of different cities to effectively promote the technology spillover effect of FDI and achieve coordinated ecological and economic development

FDI, Technology Spillovers, and Green Innovation: Theoretical Analysis and Evidence from China

Foreign direct investment (FDI) technology spillovers play an increasingly important role in a host country’s development. Evaluating the positive effect of FDI inflows on green innovation is essential for correct city design. Based on the panel data of 262 cities in China from 2004 to 2018, we first analyzed the impact of FDI technology spillovers on green innovation in Chinese cities and then tested the threshold effect in four absorptive capacity factors: environmental regulation, economic growth, human capital, and industry size. Finally, we compared the time and space of two types of cities crossing the threshold from the perspective of innovative and non-innovative cities. The results show that FDI can significantly promote green innovation in Chinese cities and the promoting effect of FDI on green innovation has nonlinear characteristics, namely, such effects only make sense when absorptive capacity is above the threshold points. Additionally, among the four absorptive capacity factors, the development degrees of innovative cities are ahead of non-innovative cities; in particular, there is a significant difference between them in terms of economic growth. Local governments should develop reasonable policy combination tools according to the absorptive capacity characteristics of different cities to effectively promote the technology spillover effect of FDI and achieve coordinated ecological and economic development

Intensive care unit nurses' perceptions and practices regarding clinical alarms: A descriptive study

Abstract Aim To describe the frequencies of physiologic monitor clinical alarms and to investigate nurses' perceptions and practices regarding clinical alarms in ICUs. Design A descriptive study. Methods A 24‐h continuous nonparticipant observation study was conducted in ICU. Observers observed and recorded the occurrence time, detail information when electrocardiogram monitor alarms triggered. And a cross‐sectional study was conducted among ICU nurses by convenience sampling, using the general information questionnaire and the Chinese version of clinical alarms survey questionnaire for medical devices. Data analysis was performed using SPSS 23. Results A total of 13,829 physiologic monitor clinical alarms were recorded in 14‐day observation and 1191 ICU nurses responded to the survey. Most nurses agreed or strongly agreed the sensitivity to alarms and responded quickly (81.28%), smart alarm systems (74.56%), alarm notification systems (72.04%) and set up alarm administrators (59.45%) were useful to improve alarm management, while frequent nuisance alarms disrupted patients care (62.47%) and reduced nurses' trust in alarms (49.03%), environmental noise interfered with nurses' recognition of the alarms (49.12%) and not everyone received education of alarm systems (64.65%). Conclusions Physiological monitor alarms occur frequently in ICU, and it is necessary to formulate or further optimize alarm management measures. It is recommended to use smart medical devices and alarm notification systems, formulate and implement standardized alarm management policies and norms, and strengthen alarm management education and training, so as to improve the nursing quality and patient safety. Patient or Public Contribution The patients in the observation study included all patients admitted to the ICU during the observation period. The nurses in the survey study were conveniently selected through an online survey