Passion amid the pandemic: Applying a person-centered approach to examine cross-domain multi-passion profiles during a crisis

We examine whether having cross-domain passion (i.e., harmonious and obsessive passion for work and for non-work activities) during the COVID-19 pandemic can help individuals fare better amid the crisis. Drawing from work-family boundary framework, we develop a provisional theory of cross-domain multi-passion, and in two studies, we use latent profile analysis to uncover five passion profiles - Dispassionate at Work and Play;Dispassionate at Work, Ambidextrous at Play;Harmonious at Work, Ambidextrous at Play;Harmonious at Work and Play;and Moderately Harmonious at Work and Play. In Study 1, we inductively explore these profiles and their relationships with life satisfaction. In Study 2, we replicate the number and content of these profiles, and test whether segmentation-integration preferences and work and non-work constraints predict the probability of individuals belonging to a certain profile. Overall, these profiles reveal how individuals can co-host multiple forms of passion simultaneously, and how doing so relate to their life satisfaction during the pandemic. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Spectrum and Style Transformation Framework for Omni-Domain COVID-19 Diagnosis

Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic and profoundly affects almost all people around the world. Thus, many automatic diagnosis methods based on computed tomography (CT) images have been proposed to reduce the workload of radiologists. Most of the existing methods focus on the in-domain predictions, i.e., the training and testing have similar distributions, which is impractical in real-world situations, since the CT images can be collected from different devices and in different hospitals. To improve the diagnosis performance of COVID-19 for both in-domain and out-of-domain data, this paper proposes a spectrum and style transformation framework for omni-domain COVID-19 diagnosis. To achieve this, we first present a spectrum transform module, which helps to discover the discriminating features of each domain to recognize the in-domain data. Then, we formulate a cross-domain stylization module, which learns the cross-domain knowledge to enhance the model generalization capability to deal with out-of-domain data. Moreover, our framework is a plug-and-play module that can be easily integrated into existing deep models. We evaluate our framework on four COVID-19 datasets and show our method consistently improves the diagnosis performance of various methods on both in-domain and out-of-domain data.

Analyzing Extreme Events in Power Systems: An Open, Cross-Domain Data-Driven Approach

Over the past several years the electric power sector has been challenged by a number of extreme events around the globe. Significant societal and economic shocks were due to the rapid spread of COVID-19 around the world. In addition to the pandemic, there have been several extreme weather and societal disruptions to the electricity sector, such as the February 2021 Texas power outage and the 9 p.m. nine-minute blackout event in India. © 2003-2012 IEEE.

Multi Layered Deep Neural Network for Feature Extraction in Cross Domain Crowd Counting

Automated crowd density monitoring is an emerging area of research. It is a vital technology that assists during recent disease outbreaks in preserving social distancing, crowd management and other widespread applications in public security and traffic control. Modern methods to count people in crowded scenes mainly rely on Convolutional Neural Network (CNN) based models. But the model's ability to adapt for different domains which is referred to as cross domain crowd counting is a challenging task. To remedy this difficulty, many researchers used Spatial Fully Convolutional Network (SFCN) based crowd counting models with synthetic crowd scene data. They covered many image domains with few-shot learning to reduce the domain adaptation gap between source and target image domains. In this paper, we propose a new multi-layered model architecture instead of SFCN single-layered model architecture. The proposed model extracts more meaningful features in image scenes along with large scale variations to increase the accuracy in cross domain crowd counting. Furthermore, with extensive experiments using four real-world datasets and analysis, we show that the proposed multi-layered architecture performs well with synthetic image data and few-shot learning in reducing domain shifts. © 2022 IEEE.

Short-term power load forecasting during the COVID-19 pandemic using XGBoost and Copula theory

The global coronavirus disease (COVID-19) has brought great challenges to the power systems due to its limitations on social, economic and productive activities. This paper proposes a short-term load forecasting method during COVID-19 pandemic based on copula theory and eXtreme Gradient Boosting (XGBoost). In this method, the coupling relationship among the cross-domain meteorological, public health, and mobility time-series data are fully analyzed based on copula theory, which is used for the short-term power load forecasting based on multi-factor fusion XGBoost algorithm. The proposed method has been fully evaluated and benchmarked on available cross-domain open-access United States data to demonstrate its effectiveness and superiority on short-term load forecasting of COVID-19. © 2021 IEEE

A Survey of Multi-Agent Cross Domain Cooperative Perception

Intelligent unmanned systems for ground, sea, aviation, and aerospace application are important research directions for the new generation of artificial intelligence in China. Intelligent unmanned systems are also important carriers of interactive mapping between physical space and cyberspace in the process of the digitization of human society. Based on the current domestic and overseas development status of unmanned systems for ground, sea, aviation, and aerospace application, this paper reviewed the theoretical problems and research trends of multi-agent cross-domain cooperative perception. The scenarios of multi-agent cooperative perception tasks in different areas were deeply investigated and analyzed, the scientific problems of cooperative perception were analyzed, and the development direction of multi-agent cooperative perception theory research for solving the challenges of the complex environment, interactive communication, and cross-domain tasks was expounded.

A Cross-Domain Approach to Analyzing the Short-Run Impact of COVID-19 on the US Electricity Sector.

The novel coronavirus disease (COVID-19) has rapidly spread around the globe in 2020, with the US becoming the epicenter of COVID-19 cases since late March. As the US begins to gradually resume economic activity, it is imperative for policymakers and power system operators to take a scientific approach to understanding and predicting the impact on the electricity sector. Here, we release a first-of-its-kind cross-domain open-access data hub, integrating data from across all existing US wholesale electricity markets with COVID-19 case, weather, mobile device location, and satellite imaging data. Leveraging cross-domain insights from public health and mobility data, we rigorously uncover a significant reduction in electricity consumption that is strongly correlated with the number of COVID-19 cases, degree of social distancing, and level of commercial activity.