China and climate change: just transition and social inclusion

China aims to transition from a carbon‐intensive economy to carbon neutrality before 2060. Although climate change policies commenced in 2007, this goal remains extremely challenging. Reporting on China’s progress, the articles in this issue refer to three concepts. Ecological civilization is a political construct framing China’s policy response to climate change and environmental degradation; its “thin” version refers to sustainable development and modernisation, but it also describes a higher form of civilization to replace industrial society. Environmental authoritarianism describes a top‐down system of governance or policy implementation that engages in minimal public participation; several of the articles report China’s green policies to be of this type. Just transition is a multifaceted evaluative concept employed in most of the articles to comment on the process or outcome of China’s climate change policies. The policy context is explained, before reviewing results from authors’ application of these concepts and offering a summary conclusion

Transitions and Conflicts: Reexamining Impacts of Migration on Young Women’s Status and Gender Practice in Rural Shanxi

This article explores impacts of migration on young women’s status and gender practice in rural northern China. Based on ethnographic fieldwork in a village in Shanxi Province, it suggests that rural-urban migration has served partially to reconstruct the traditional gender-based roles and norms in migration families. This reconstructive force arises mainly from the changes of the patrilocal residence pattern and rural women’s acquisition of subjectivity during the course of migration. However, after migrant women return to their home villages, they usually reassume their roles as care providers and homemakers, which is vividly expressed by a phrase referring to one’s wife as ‘the person inside my home’ (wo jiali de). Meanwhile, although migrant women’s capacity and confidence have greatly increased consequent upon working out of the countryside, their participation in village governance and in the public sphere has been decreasing. Further examination suggests that the reinforcement of gender inequality and the transformation of gender relations result from the continuous interplay of local power relations, market dominance, and unchallenged patrilocal institutions. Through adopting a life course perspective, it challenges too strict a differentiation between migrant and left behind women in existing literature

Driver behaviour characterization using artificial intelligence techniques in level 3 automated vehicle.

Brighton, James L. - Associate SupervisorAutonomous vehicles free drivers from driving and allow them to engage in some non-driving related activities. However, the engagement in such activities could reduce their awareness of the driving environment, which could bring a potential risk for the takeover process in the current automation level of the intelligent vehicle. Therefore, it is of great importance to monitor the driver's behaviour when the vehicle is in automated driving mode. This research aims to develop a computer vision-based driver monitoring system for autonomous vehicles, which characterises driver behaviour inside the vehicle cabin by their visual attention and hand movement and proves the feasibility of using such features to identify the driver's non-driving related activities. This research further proposes a system, which employs both information to identify driving related activities and non-driving related activities. A novel deep learning- based model has been developed for the classification of such activities. A lightweight model has also been developed for the edge computing device, which compromises the recognition accuracy but is more suitable for further in-vehicle applications. The developed models outperform the state-of-the-art methods in terms of classification accuracy. This research also investigates the impact of the engagement in non-driving related activities on the takeover process and proposes a category method to group the activities to improve the extendibility of the driving monitoring system for unevaluated activities. The finding of this research is important for the design of the takeover strategy to improve driving safety during the control transition in Level 3 automated vehicles.PhD in Manufacturin

Contrastive Object-level Pre-training with Spatial Noise Curriculum Learning

The goal of contrastive learning based pre-training is to leverage large quantities of unlabeled data to produce a model that can be readily adapted downstream. Current approaches revolve around solving an image discrimination task: given an anchor image, an augmented counterpart of that image, and some other images, the model must produce representations such that the distance between the anchor and its counterpart is small, and the distances between the anchor and the other images are large. There are two significant problems with this approach: (i) by contrasting representations at the image-level, it is hard to generate detailed object-sensitive features that are beneficial to downstream object-level tasks such as instance segmentation; (ii) the augmentation strategy of producing an augmented counterpart is fixed, making learning less effective at the later stages of pre-training. In this work, we introduce Curricular Contrastive Object-level Pre-training (CCOP) to tackle these problems: (i) we use selective search to find rough object regions and use them to build an inter-image object-level contrastive loss and an intra-image object-level discrimination loss into our pre-training objective; (ii) we present a curriculum learning mechanism that adaptively augments the generated regions, which allows the model to consistently acquire a useful learning signal, even in the later stages of pre-training. Our experiments show that our approach improves on the MoCo v2 baseline by a large margin on multiple object-level tasks when pre-training on multi-object scene image datasets. Code is available at https://github.com/ChenhongyiYang/CCOP

Adversarial Attack and Defense on Graph Data: A Survey

Deep neural networks (DNNs) have been widely applied to various applications including image classification, text generation, audio recognition, and graph data analysis. However, recent studies have shown that DNNs are vulnerable to adversarial attacks. Though there are several works studying adversarial attack and defense strategies on domains such as images and natural language processing, it is still difficult to directly transfer the learned knowledge to graph structure data due to its representation challenges. Given the importance of graph analysis, an increasing number of works start to analyze the robustness of machine learning models on graph data. Nevertheless, current studies considering adversarial behaviors on graph data usually focus on specific types of attacks with certain assumptions. In addition, each work proposes its own mathematical formulation which makes the comparison among different methods difficult. Therefore, in this paper, we aim to survey existing adversarial learning strategies on graph data and first provide a unified formulation for adversarial learning on graph data which covers most adversarial learning studies on graph. Moreover, we also compare different attacks and defenses on graph data and discuss their corresponding contributions and limitations. In this work, we systemically organize the considered works based on the features of each topic. This survey not only serves as a reference for the research community, but also brings a clear image researchers outside this research domain. Besides, we also create an online resource and keep updating the relevant papers during the last two years. More details of the comparisons of various studies based on this survey are open-sourced at https://github.com/YingtongDou/graph-adversarial-learning-literature.Comment: In submission to Journal. For more open-source and up-to-date information, please check our Github repository: https://github.com/YingtongDou/graph-adversarial-learning-literatur

Plug and Play Active Learning for Object Detection

Annotating data for supervised learning is expensive and tedious, and we want to do as little of it as possible. To make the most of a given "annotation budget" we can turn to active learning (AL) which aims to identify the most informative samples in a dataset for annotation. Active learning algorithms are typically uncertainty-based or diversity-based. Both have seen success in image classification, but fall short when it comes to object detection. We hypothesise that this is because: (1) it is difficult to quantify uncertainty for object detection as it consists of both localisation and classification, where some classes are harder to localise, and others are harder to classify; (2) it is difficult to measure similarities for diversity-based AL when images contain different numbers of objects. We propose a two-stage active learning algorithm Plug and Play Active Learning (PPAL) that overcomes these difficulties. It consists of (1) Difficulty Calibrated Uncertainty Sampling, in which we used a category-wise difficulty coefficient that takes both classification and localisation into account to re-weight object uncertainties for uncertainty-based sampling; (2) Category Conditioned Matching Similarity to compute the similarities of multi-instance images as ensembles of their instance similarities. PPAL is highly generalisable because it makes no change to model architectures or detector training pipelines. We benchmark PPAL on the MS-COCO and Pascal VOC datasets using different detector architectures and show that our method outperforms the prior state-of-the-art. Code is available at https://github.com/ChenhongyiYang/PPA

Adjusting the structure combinations of plant communities in urban greenspace reduced the maintenance energy consumption and GHG emissions

Maintaining urban greenspace results in energy use and GHG emissions. To understand the change of the annual maintenance energy consumption and GHG emissions in varying combinations of plant structures (plant density or proportion of area covered) in urban greenspace, this study investigated 34 urban plant communities as sample plots (20×20 m), and divided them into woodland, shrub, herbaceous and grassland layers. The average energy use and GHG emissions in the woodland layer were 18.64 MJ/tree/y–1 and 0.23 kg/CO2-e/tree/y–1, respectively. In the shrub, herbaceous, and grassland layers, the average energy consumption was 3.73, 2.27, 7.23 MJ/m2/y–1, and the average GHG emissions were 0.06, 0.02, 0.09 kg/CO2-e/m2/y–1, respectively. The energy use and GHG emission curves had parabolic trends as the plant density in the woodland layer increased and increasing curves with two peaks as the plant area proportions of the shrub, herbaceous, and grassland layers increased. The annual maintenance of urban greenspace can divide into low, average and high levels of energy consumption and GHG emissions due to the change in the plant structure combinations. Furthermore, city managers and landscape designers can refer to the energy consumption and GHG emissions trends to understand the environmental impact of maintenance tasks. The future plant structures in greenspace can be better designed to improve ecosystem services based on limiting the maintenance environmental impacts

Joint Embedding of Structural and Functional Brain Networks with Graph Neural Networks for Mental Illness Diagnosis

Multimodal brain networks characterize complex connectivities among different brain regions from both structural and functional aspects and provide a new means for mental disease analysis. Recently, Graph Neural Networks (GNNs) have become a de facto model for analyzing graph-structured data. However, how to employ GNNs to extract effective representations from brain networks in multiple modalities remains rarely explored. Moreover, as brain networks provide no initial node features, how to design informative node attributes and leverage edge weights for GNNs to learn is left unsolved. To this end, we develop a novel multiview GNN for multimodal brain networks. In particular, we regard each modality as a view for brain networks and employ contrastive learning for multimodal fusion. Then, we propose a GNN model which takes advantage of the message passing scheme by propagating messages based on degree statistics and brain region connectivities. Extensive experiments on two real-world disease datasets (HIV and Bipolar) demonstrate the effectiveness of our proposed method over state-of-the-art baselines.Comment: Accepted to ICML 2021 Workshop on Computational Approaches to Mental Healt