Robust geographical detector

Geographical detector (GD) is a method to measure spatial associations using a power of determinant (PD) value that compares the variance of data within spatial zones and in the whole study area. Recent studies have implemented GD in diverse fields, such as environmental and socio-economic issues. Spatial data discretization is an essential stage for determining zones using explanatory variables. However, the spatial data discretization process has been sensitive to the GD results. To address this issue, this article proposes a Robust Geographical Detector (RGD) to overcome the limitations of the sensitivity in spatial data discretization and estimate robust PD values of explanatory variables using a B-value. The RGD determines spatial zones with numerical interval breaks using an optimization algorithm of variance-based change point detection. In this study, RGD is implemented in a nationwide case study exploring potential factors of nitrogen dioxide (NO2) density in industrial regions across Australia, where data on both NO2 and potential factors are sourced from satellite images and remote sensing products using Google Earth Engine. Results show that RGD can effectively explore the maximum PD values of spatial associations between response and explanatory variables due to the optimization algorithm-based spatial zones. In addition, RGD-based PD values are generally higher, more robust, and more stable than GD-based PD values since RGD can guarantee the increment of PD values with the increase of interval numbers, which is a challenge in previous GD models. Finally, RGD could provide a more reliable interpretation of PD as RGD finds optimal intervals-based spatial zones determined by potential factors. This study demonstrates that the developed RGD model can provide robust and reliable solutions to explore spatial associations and identify geographical factors

Student engagement and learning outcomes:an empirical study applying a four-dimensional framework

Introduction: This study applies Reeve’s four-dimensional student engagement framework to a medical education context to elucidate the relationship between behavioral, emotional, cognitive, and agentic engagement and learning outcomes. Meanwhile, we categorize learning outcomes in knowledge and skills, and added taxonomies to the cognitive education objectives for the knowledge part, including memorization, comprehension, and application. Methods: We used the China Medical Student Survey to investigate student engagement, and combined it with the Clinical Medicine Proficiency Test for Medical Schools results as a standardized measurement of learning outcomes. We performed multivariate regression analyses to delve into the effectiveness of different types of student engagement. Moreover, we evaluated the moderating roles of gender and the National College Entrance Examination (NCEE) within the relationships between student engagement and learning outcomes. Results: We observed that emotional engagement is most effective in promoting learning outcomes in basic medical knowledge and basic clinical skills. Emotional engagement and cognitive engagement could effectively contribute to learning outcomes in all three aspects of basic medical knowledge. In contrast, behavioral and agentic engagement showed negative effects on learning outcomes. Besides, we found that the results of the NCEE played a positive moderating role. Conclusion: This study provides robust evidence for the effectiveness of emotional engagement and cognitive engagement in promoting learning outcomes. Whereas behavioral and agentic engagement may not be good predictors of learning outcomes in macro-level general competence tests. We suggest a combined effort by students and institutions to promote student engagement and bridge the distance between general competency tests and daily learning activities.</p

Does Eye Gaze Uniquely Trigger Spatial Orienting to Socially Relevant Information? A Behavioral and ERP Study

Using behavioral and event-related potential (ERP) measures, the present study examined whether eye gaze triggers a unique form of attentional orienting toward threat-relevant targets. A threatening or neutral target was presented after a non-predictive gaze or an arrow cue. In Experiment 1, reaction times indicated that eye gaze and arrow cues triggered different attention orienting towards threatening targets, which was confirmed by target-elicited P3b latency in Experiment 2. Specifically, for targets preceded by arrow and gaze cues, P3b peak latency was shorter for neutral targets than threatening targets. However, the latency differences were significantly smaller for gaze cues than for arrow cues. Moreover, target-elicited N2 amplitude indicated a significantly stronger cue validity effect of eye gaze than that of arrows. These findings suggest that eye gaze uniquely triggers spatial attention orienting to socially threatening information.Fundamental Research Funds for the Central UniversitiesSun Yat-sen UniversityNatural Science Foundation of ChinaSun Yat-sen UniversityPeer Reviewe

Adaptive Resource Allocation for Workflow Containerization on Kubernetes

In a cloud-native era, the Kubernetes-based workflow engine enables workflow containerized execution through the inherent abilities of Kubernetes. However, when encountering continuous workflow requests and unexpected resource request spikes, the engine is limited to the current workflow load information for resource allocation, which lacks the agility and predictability of resource allocation, resulting in over and under-provisioning resources. This mechanism seriously hinders workflow execution efficiency and leads to high resource waste. To overcome these drawbacks, we propose an adaptive resource allocation scheme named ARAS for the Kubernetes-based workflow engines. Considering potential future workflow task requests within the current task pod's lifecycle, the ARAS uses a resource scaling strategy to allocate resources in response to high-concurrency workflow scenarios. The ARAS offers resource discovery, resource evaluation, and allocation functionalities and serves as a key component for our tailored workflow engine (KubeAdaptor). By integrating the ARAS into KubeAdaptor for workflow containerized execution, we demonstrate the practical abilities of KubeAdaptor and the advantages of our ARAS. Compared with the baseline algorithm, experimental evaluation under three distinct workflow arrival patterns shows that ARAS gains time-saving of 9.8% to 40.92% in the average total duration of all workflows, time-saving of 26.4% to 79.86% in the average duration of individual workflow, and an increase of 1% to 16% in CPU and memory resource usage rate

Learning from Future: A Novel Self-Training Framework for Semantic Segmentation

Self-training has shown great potential in semi-supervised learning. Its core idea is to use the model learned on labeled data to generate pseudo-labels for unlabeled samples, and in turn teach itself. To obtain valid supervision, active attempts typically employ a momentum teacher for pseudo-label prediction yet observe the confirmation bias issue, where the incorrect predictions may provide wrong supervision signals and get accumulated in the training process. The primary cause of such a drawback is that the prevailing self-training framework acts as guiding the current state with previous knowledge, because the teacher is updated with the past student only. To alleviate this problem, we propose a novel self-training strategy, which allows the model to learn from the future. Concretely, at each training step, we first virtually optimize the student (i.e., caching the gradients without applying them to the model weights), then update the teacher with the virtual future student, and finally ask the teacher to produce pseudo-labels for the current student as the guidance. In this way, we manage to improve the quality of pseudo-labels and thus boost the performance. We also develop two variants of our future-self-training (FST) framework through peeping at the future both deeply (FST-D) and widely (FST-W). Taking the tasks of unsupervised domain adaptive semantic segmentation and semi-supervised semantic segmentation as the instances, we experimentally demonstrate the effectiveness and superiority of our approach under a wide range of settings. Code will be made publicly available.Comment: Accepted to NeurIPS 202

Genetic immunization with Hantavirus vaccine combining expression of G2 glycoprotein and fused interleukin-2

In this research, we developed a novel chimeric HTNV-IL-2-G2 DNA vaccine plasmid by genetically linking IL-2 gene to the G2 segment DNA and tested whether it could be a candidate vaccine. Chimeric gene was first expressed in eukaryotic expression system pcDNA3.1 (+). The HTNV-IL-2-G2 expressed a 72 kDa fusion protein in COS-7 cells. Meanwhile, the fusion protein kept the activity of its parental proteins. Furthermore, BALB/c mice were vaccinated by the chimeric gene. ELISA, cell microculture neutralization test in vitro were used to detect the humoral immune response in immunized BALB/c mice. Lymphocyte proliferation assay was used to detect the cellular immune response.- The results showed that the chimeric gene could simultaneously evoke specific antibody against G2 glycoprotein and IL-2. And the immunized mice of every group elicited neutralizing antibodies with different titers. Lymphocyte proliferation assay results showed that the stimulation indexes of splenocytes of chimeric gene to G2 and IL-2 were significantly higher than that of other groups. Our results suggest that IL-2-based HTNV G2 DNA can induce both humoral and cellular immune response specific for HTNV G2 and can be a candidate DNA vaccine for HTNV infection