What makes a good teacher? : the artist\u27s engagement in public art education

This thesis is written in a language other than the author’s own, so its content as she developed an understanding of ideas in English may reveal both inaccuracies of grammar and indeed some misunderstanding of context and concept. This being said, the author uses the thesis investigation to reflect on her art education in China and also to report on encounters with teachers she has met in Providence, Rhode Island. So, the thesis has been opportunity to wonder about the significant role that various types of teacher (First Teacher to Fourth Teacher) play in cultivating students’ artistic ability. The author has come to appreciate, as a result of this investigation, that while teachers have a decisive influence on learning in art that the teacher’s personality and thoughts are as important as instructional content

Linking visionary leadership to creativity at multiple levels:The role of goal-related processes

Conceptualizing visionary leadership as a multilevel phenomenon that is manifested as dual-level (i.e., team and individual level) visionary leadership, we explore how it influences multilevel creativity through multilevel goal-related processes. Using two-wave data collected from 272 employees and 75 corresponding supervisors, we find that visionary leadership affects creativity through multilevel pathways. Specifically, team-level visionary leadership influences team creativity via team goal commitment, while individual-level visionary leadership influences employee creativity by fostering leader-follower goal congruence. The results also support two cross-level effects, i.e., team-level visionary leadership strengthens the relationship between individual-level visionary leadership and leader-follower congruence, and team goal commitment amplifies the effects of leader-follower goal congruence on employee creativity. This study thus sets the stage for further theoretical research on dual-level visionary leadership and how it functions at multiple levels to increase creativity.</p

Semi-Supervised Learning for Mars Imagery Classification and Segmentation

With the progress of Mars exploration, numerous Mars image data are collected and need to be analyzed. However, due to the imbalance and distortion of Martian data, the performance of existing computer vision models is unsatisfactory. In this paper, we introduce a semi-supervised framework for machine vision on Mars and try to resolve two specific tasks: classification and segmentation. Contrastive learning is a powerful representation learning technique. However, there is too much information overlap between Martian data samples, leading to a contradiction between contrastive learning and Martian data. Our key idea is to reconcile this contradiction with the help of annotations and further take advantage of unlabeled data to improve performance. For classification, we propose to ignore inner-class pairs on labeled data as well as neglect negative pairs on unlabeled data, forming supervised inter-class contrastive learning and unsupervised similarity learning. For segmentation, we extend supervised inter-class contrastive learning into an element-wise mode and use online pseudo labels for supervision on unlabeled areas. Experimental results show that our learning strategies can improve the classification and segmentation models by a large margin and outperform state-of-the-art approaches.Comment: Accepted by ACM Trans. on Multimedia Computing Communications and Applications (TOMM

Can Team Resilience Boost Team Creativity Among Undergraduate Students? A Sequential Mediation Model of Team Creative Efficacy and Team Trust

Although recent literature has highlighted the critical role of resilience in creativity literature, existing findings have failed to indicate the processes through which resilience contributes to creativity at the graduate level. The current study fills this gap by hypothesizing the influence of team resilience on team creativity through a sequential mediating mechanism. A time lagged research study was conducted, and a sample of 201 undergraduate students and their teacher filled out questionnaires at three different time points (with 2-week intervals). After aggregating the data at the team level, we employed the PROCESS macro in SPSS to analyze data and test all the hypotheses through performing a sequential mediation analysis. We found that (a) team resilience would predict team creativity; and (b) team efficacy and team trust sequentially mediated the relation between team resilience and team creativity. The results in our study advance the emergent literature on linking resilience and creativity for the practical applications of resilience and creativity in education settings

S5^{5}Mars: Semi-Supervised Learning for Mars Semantic Segmentation

Deep learning has become a powerful tool for Mars exploration. Mars terrain semantic segmentation is an important Martian vision task, which is the base of rover autonomous planning and safe driving. However, there is a lack of sufficient detailed and high-confidence data annotations, which are exactly required by most deep learning methods to obtain a good model. To address this problem, we propose our solution from the perspective of joint data and method design. We first present a newdataset S5Mars for Semi-SuperviSed learning on Mars Semantic Segmentation, which contains 6K high-resolution images and is sparsely annotated based on confidence, ensuring the high quality of labels. Then to learn from this sparse data, we propose a semi-supervised learning (SSL) framework for Mars image semantic segmentation, to learn representations from limited labeled data. Different from the existing SSL methods which are mostly targeted at the Earth image data, our method takes into account Mars data characteristics. Specifically, we first investigate the impact of current widely used natural image augmentations on Mars images. Based on the analysis, we then proposed two novel and effective augmentations for SSL of Mars segmentation, AugIN and SAM-Mix, which serve as strong augmentations to boost the model performance. Meanwhile, to fully leverage the unlabeled data, we introduce a soft-to-hard consistency learning strategy, learning from different targets based on prediction confidence. Experimental results show that our method can outperform state-of-the-art SSL approaches remarkably. Our proposed dataset is available at https://jhang2020.github.io/S5Mars.github.io/

DRL-GAN: dual-stream representation learning GAN for low-resolution image classification in UAV applications.

Identifying tiny objects from extremely low resolution (LR) UAV-based remote sensing images is generally considered as a very challenging task, because of very limited information in the object areas. In recent years, there have been very limited attempts to approach this problem. These attempts intend to deal with LR image classification by enhancing either the poor image quality or image representations. In this paper, we argue that the performance improvement in LR image classification is affected by the inconsistency of the information loss and learning priority on Low-Frequency (LF) components and High-Frequency (HF) components. To address this LF-HF inconsistency problem, we propose a Dual-Stream Representation Learning Generative Adversarial Network (DRL-GAN).The core idea is to produce super image representations optimal for LR recognition by simultaneously recovering the missing information in LF and HF components, respectively, under the guidance of high-resolution (HR) images. We evaluate the performance of DRL-GAN on the challenging task of LR image classification. A comparison of the experimental results on the LR benchmark, namely HRSC and CIFAR-10, and our newly collected “WIDER-SHIP” dataset demonstrates the effectiveness of our DRL-GAN, which significantly improves the classification performance, with up to 10% gain on average

Development of novel AMP-based absorbents for efficient CO2 capture with low energy consumption through modifying the electrostatic potential

The global deployment of aqueous amine absorbents for carbon dioxide (CO2) capture is hindered by their high energy consumption. A potential solution to this challenge lies in the utilization of non-aqueous amine systems, which offer energy-efficient alternatives. However, they are prone to form precipitation during CO2 absorption process, which limits their application. Combining experimental and theoretical studies, we found that the electrostatic potential of carbamate, instead of van der Waals force, is a major factor controlling the precipitation, and hydrogen bonds can effectively reduce the electrostatic potential of carbamate and prevent precipitation. Single solvent screening experiments have also demonstrated that the absorption rate is closely related to the viscosity of the organic solvent and the affinity of the functional group for CO2. The polar solvents (Dimethylformamide (DMF), Dimethyl sulfoxide (DMSO), and N-Methylformamide (NMF)) exhibit higher absorption rates, but suffer from issues of precipitation. Hydroxyl group riched solvents (Ethylene glycol (EG) and Glycerol) exhibit lower absorption rate, but they don’t have the issue of precipitation. Based on these findings, several novel 2-Amino-2-methyl-1-propanol (AMP)-based non-aqueous absorbents have been developed aiming at reducing the energy penalty, and improving CO2 absorption and desorption performance. Among these absorbents, AMP-EG-DMF (4–3) exhibits maximum CO2 absorption rate and absorption capacity of 9.91 g-CO2/(kg-soln.·min.) and 122 g-CO2/(kg-soln.), respectively, which are 64.1% and 28.4% higher than those of 30 wt% AMP aqueous solution, respectively. Additionally, compared to 30 wt% MEA, the energy consumption of AMP-EG-DMF (4–3) shows 46.30% reduction. The addition of EG effectively improves the electrostatic solubility of AMP-carbamate by increasing the number and strength of hydrogen bonds, thus avoiding the generation of precipitation. The final product species and reaction mechanism were analysed by using 13C and 1H NMR, In-situ ATR-FTIR, and quantum chemical calculation. The combination of theoretical and experimental results indicates that bi-solvent AMP-based absorbents can serve as a promising alternative for low-energy CO2 capture

16S Next-generation sequencing and quantitative PCR reveal the distribution of potential pathogens in the Liaohe Estuary

The existence of potentially pathogenic bacteria seriously threatens aquatic animals and human health. Estuaries are closely related to human activities, and the detection of pathogens is important for aquaculture and public health. However, monitoring only indicator microorganisms and pathogens is not enough to accurately and comprehensively estimate water pollution. Here, the diversity of potentially pathogenic bacteria in water samples from the Liaohe estuary was profiled using 16S next-generation sequencing (16S NGS) and quantitative polymerase chain reaction (qPCR) analysis. The results showed that the dominant genera of environmental pathogens were Pseudomonas, Vibrio, Mycobacterium, Acinetobacter, Exiguobacterium, Sphingomonas, and Legionella, and the abundance of enteric pathogens was significantly less than the environmental pathogens, mainly, Citrobacter, Enterococcus, Escherichia-Shigella, Enterobacter, Bacteroides. The qPCR results showed that the 16S rRNA genes of Vibrio were the most abundant, with concentrations between 7.06 and 9.48 lg copies/L, followed by oaa gene, fliC gene, trh gene, and uidA gene, and the temperature and salinity were the main factors affecting its abundance. Variance partitioning analysis (VPA) analysis of spatial factors on the potential pathogen’s distribution (19.6% vs 5.3%) was greater than environmental factors. In addition, the co-occurrence analysis of potential pathogens in the estuary revealed significant co-occurrence among the opportunistic pathogens Testosteronemonas, Brevimonas vesicularis, and Pseudomonas putida. Our findings provide an essential reference for monitoring and occurrence of potentially pathogenic bacteria in estuaries