Geographic Research on Hate Crimes and Incidents: Approaches for Advancing Inclusive Practices

COVID-19, originally reported in China, has brought an increase in anti-Asian and Asian American hate incidents and crimes in the United States. However, research on hate incidents and crimes are relatively new in the field of geography. To provide better ways to investigate hate crime incidents against Asians and Asian Americans during COVID-19, this article draws on various research methods from existing studies on hate crimes. Geographers have focused attention on minority groups linked to different geographic scales, and non-geographic studies have focused mainly on psychological symptoms and impacts on health. Even though existing studies have helped broaden the knowledge of the subject, the geographic aspects of the issue require further examination. This article suggests that geographers should pay more attention to four aspects of research in hate crimes and incidents for future research: avoiding oversimplified concepts, reconsidering relational aspects within the local community, identifying intersectionality and everydayness of people, and engaging more with the practice of the law enforcement and the local communities

TimeKit: A Time-series Forecasting-based Upgrade Kit for Collaborative Filtering

Recommender systems are a long-standing research problem in data mining and machine learning. They are incremental in nature, as new user-item interaction logs arrive. In real-world applications, we need to periodically train a collaborative filtering algorithm to extract user/item embedding vectors and therefore, a time-series of embedding vectors can be naturally defined. We present a time-series forecasting-based upgrade kit (TimeKit), which works in the following way: it i) first decides a base collaborative filtering algorithm, ii) extracts user/item embedding vectors with the base algorithm from user-item interaction logs incrementally, e.g., every month, iii) trains our time-series forecasting model with the extracted time-series of embedding vectors, and then iv) forecasts the future embedding vectors and recommend with their dot-product scores owing to a recent breakthrough in processing complicated time-series data, i.e., neural controlled differential equations (NCDEs). Our experiments with four real-world benchmark datasets show that the proposed time-series forecasting-based upgrade kit can significantly enhance existing popular collaborative filtering algorithms.Comment: Accepted at IEEE BigData 202

Relationship Bonds and Service Provider’s Emotional Labor: Moderating Effects of Collectivism

Since service providers directly conduct emotional labor to customers, it is important to identify the factors influencing emotional labor of service providers. Even though the studies identifying the predisposing factors influencing emotional labor are taking place, there is no empirical evidence confirming how relationship bonds, which have been established between corporations and service providers, are related to emotional labor. This study examined the influences of relationship bonds on emotional labor through person-organization fit (P-O fit) and the moderating effects of collectivism between P-O fit and emotional labor. Analysis was conducted by performing questionnaire surveys targeting 350 employees in the financial industry. As a result of the analysis, it has been found that financial bonds, social bonds, and structural bonds enhanced P-O fit and P-O fit improved deep acting. In addition, this study identified that collectivism of service providers strengthened the influence of P-O fit toward deep acting. This study not only suggested the empirical evidence identifying the process of relationship bonds influencing emotional labor but also expanded the scope of study by examining moderating roles of collectivism in cultural psychology aspect

An Effective Algorithm to Find a Cost Minimizing Gateway Deployment for Node-Replaceable Wireless Sensor Networks

In this paper, we present an efficient way to find a gateway deployment for a given sensor network topology. We assume that the expired sensors and gateways can be replaced and the locations of the gateways are chosen among the given sensor nodes. The objective is to find a gateway deployment that minimizes the cost per unit time, which consists of the maintenance and installation costs. The proposed algorithm creates a cost reference and uses it to find the optimal deployment via a divide and conquer algorithm. Comparing all cases is the most reliable way to find the optimal gateway deployment, but this is practically impossible to calculate, since its computation time increases exponentially as the number of nodes increases. The method we propose increases linearly, and so is suitable for large scale networks. Additionally, compared to stochastic algorithms such as the genetic algorithm, this methodology has advantages in computational speed and accuracy for a large number of nodes. We also verify our methodology through several numerical experiments

Actin remodelling factors control ciliogenesis by regulating YAP/TAZ activity and vesicle trafficking

Primary cilia exert a profound impact on cell signalling and cell cycle progression. Recently, actin cytoskeleton destabilization has been recognized as a dominant inducer of ciliogenesis, but the exact mechanisms regulating ciliogenesis remain poorly understood. Here we show that the actin cytoskeleton remodelling controls ciliogenesis by regulating transcriptional coactivator YAP/TAZ as well as ciliary vesicle trafficking. Cytoplasmic retention of YAP/TAZ correlates with active ciliogenesis either in spatially confined cells or in cells treated with an actin filament destabilizer. Moreover, knockdown of YAP/TAZ is sufficient to induce ciliogenesis, whereas YAP/TAZ hyperactivation suppresses serum starvation-mediated ciliogenesis. We also identify actin remodelling factors LIMK2 and TESK1 as key players in the ciliogenesis control network in which YAP/TAZ and directional vesicle trafficking are integral components. Our work provides new insights for understanding the link between actin dynamics and ciliogenesis. © 2015 Macmillan Publishers Limited138451sciescopu

Discovery of GSK3β Inhibitors through In Silico Prediction-and-Experiment Cycling Strategy, and Biological Evaluation

Direct inhibitors of glycogen synthase kinase 3β (GSK3β) have been investigated and reported for the past 20 years. In the search for novel scaffold inhibitors, 3000 compounds were selected through structure-based virtual screening (SBVS), and then high-throughput enzyme screening was performed. Among the active hit compounds, pyrazolo [1,5-a]pyrimidin-7-amine derivatives showed strong inhibitory potencies on the GSK3β enzyme and markedly activated Wnt signaling. The result of the molecular dynamics (MD) simulation, enhanced by the upper-wall restraint, was used as an advanced structural query for the SBVS. In this study, strong inhibitors designed to inhibit the GSK3β enzyme were discovered through SBVS. Our study provides structural insights into the binding mode of the inhibitors for further lead optimization