Livelihood adaptation and life satisfaction among land-lost farmers: Critiquing China’s urbanisation-driven land appropriation

Large-scale rural land appropriation and displacement, driven by the unprecedented urban growth currently experienced in China, has created millions of land-lost peasants who live in the city but remain culturally, socially and institutionally rural. The situation has attracted growing attention in the literature because of its negative social impact, but relatively few studies have addressed how land-lost farmers adapt to urban ways of life and what factors influence their life satisfaction. In this paper, we investigate the predictors of livelihood adaptation and life satisfaction of land-lost farmers from a land appropriation case in the city of Changchun, Northeast China. The results show that, five years after the  appropriation, livelihood adaptation remained very difficult and life satisfaction was poor among the resettlers. Furthermore, marginalised groups, such as those  who were older, less educated and from smaller families, and those with lower pre-displacement income were less likely to have a higher income level after resettlement, resulting in a lower level of life satisfaction. Women also had lower life satisfaction than men. The study highlights an urgent need to improve China’s unjust land appropriation policy with a particular focus on attending to the needs of marginalised groups

Hidden Path Selection Network for Semantic Segmentation of Remote Sensing Images

Targeting at depicting land covers with pixel-wise semantic categories, semantic segmentation in remote sensing images needs to portray diverse distributions over vast geographical locations, which is difficult to be achieved by the homogeneous pixel-wise forward paths in the architectures of existing deep models. Although several algorithms have been designed to select pixel-wise adaptive forward paths for natural image analysis, it still lacks theoretical supports on how to obtain optimal selections. In this paper, we provide mathematical analyses in terms of the parameter optimization, which guides us to design a method called Hidden Path Selection Network (HPS-Net). With the help of hidden variables derived from an extra mini-branch, HPS-Net is able to tackle the inherent problem about inaccessible global optimums by adjusting the direct relationships between feature maps and pixel-wise path selections in existing algorithms, which we call hidden path selection. For the better training and evaluation, we further refine and expand the 5-class Gaofen Image Dataset (GID-5) to a new one with 15 land-cover categories, i.e., GID-15. The experimental results on both GID-5 and GID-15 demonstrate that the proposed modules can stably improve the performance of different deep structures, which validates the proposed mathematical analyses

Convolutional Neural Networks-Based MRI Image Analysis for the Alzheimer’s Disease Prediction From Mild Cognitive Impairment

Mild cognitive impairment (MCI) is the prodromal stage of Alzheimer’s disease (AD). Identifying MCI subjects who are at high risk of converting to AD is crucial for effective treatments. In this study, a deep learning approach based on convolutional neural networks (CNN), is designed to accurately predict MCI-to-AD conversion with magnetic resonance imaging (MRI) data. First, MRI images are prepared with age-correction and other processing. Second, local patches, which are assembled into 2.5 dimensions, are extracted from these images. Then, the patches from AD and normal controls (NC) are used to train a CNN to identify deep learning features of MCI subjects. After that, structural brain image features are mined with FreeSurfer to assist CNN. Finally, both types of features are fed into an extreme learning machine classifier to predict the AD conversion. The proposed approach is validated on the standardized MRI datasets from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) project. This approach achieves an accuracy of 79.9% and an area under the receiver operating characteristic curve (AUC) of 86.1% in leave-one-out cross validations. Compared with other state-of-the-art methods, the proposed one outperforms others with higher accuracy and AUC, while keeping a good balance between the sensitivity and specificity. Results demonstrate great potentials of the proposed CNN-based approach for the prediction of MCI-to-AD conversion with solely MRI data. Age correction and assisted structural brain image features can boost the prediction performance of CNN

Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learning

In this work, we tackle the challenging problem of arbitrary image style transfer using a novel style feature representation learning method. A suitable style representation, as a key component in image stylization tasks, is essential to achieve satisfactory results. Existing deep neural network based approaches achieve reasonable results with the guidance from second-order statistics such as Gram matrix of content features. However, they do not leverage sufficient style information, which results in artifacts such as local distortions and style inconsistency. To address these issues, we propose to learn style representation directly from image features instead of their second-order statistics, by analyzing the similarities and differences between multiple styles and considering the style distribution. Specifically, we present Contrastive Arbitrary Style Transfer (CAST), which is a new style representation learning and style transfer method via contrastive learning. Our framework consists of three key components, i.e., a multi-layer style projector for style code encoding, a domain enhancement module for effective learning of style distribution, and a generative network for image style transfer. We conduct qualitative and quantitative evaluations comprehensively to demonstrate that our approach achieves significantly better results compared to those obtained via state-of-the-art methods. Code and models are available at https://github.com/zyxElsa/CAST_pytorchComment: Accepted by SIGGRAPH 202

ProSpect: Expanded Conditioning for the Personalization of Attribute-aware Image Generation

Personalizing generative models offers a way to guide image generation with user-provided references. Current personalization methods can invert an object or concept into the textual conditioning space and compose new natural sentences for text-to-image diffusion models. However, representing and editing specific visual attributes like material, style, layout, etc. remains a challenge, leading to a lack of disentanglement and editability. To address this, we propose a novel approach that leverages the step-by-step generation process of diffusion models, which generate images from low- to high-frequency information, providing a new perspective on representing, generating, and editing images. We develop Prompt Spectrum Space P*, an expanded textual conditioning space, and a new image representation method called ProSpect. ProSpect represents an image as a collection of inverted textual token embeddings encoded from per-stage prompts, where each prompt corresponds to a specific generation stage (i.e., a group of consecutive steps) of the diffusion model. Experimental results demonstrate that P* and ProSpect offer stronger disentanglement and controllability compared to existing methods. We apply ProSpect in various personalized attribute-aware image generation applications, such as image/text-guided material/style/layout transfer/editing, achieving previously unattainable results with a single image input without fine-tuning the diffusion models

Photosensing and Thermosensing by Phytochrome B Require Both Proximal and Distal Allosteric Features within the Dimeric Photoreceptor

Phytochromes (Phys) encompass a diverse collection of bilin-containing photoreceptors that help plants and microorganisms perceive light through photointerconversion between red light (Pr) and far-red light (Pfr)-absorbing states. In addition, Pfr reverts thermally back to Pr via a highly enthalpic process that enables temperature sensation in plants and possibly other organisms. Through domain analysis of the Arabidopsis PhyB isoform assembled recombinantly, coupled with measurements of solution size, photoconversion, and thermal reversion, we identified both proximal and distal features that influence all three metrics. Included are the downstream C-terminal histidine kinase-related domain known to promote dimerization and a conserved patch just upstream of an N-terminal Period/Arnt/Sim (PAS) domain, which upon removal dramatically accelerates thermal reversion. We also discovered that the nature of the bilin strongly influences Pfr stability. Whereas incorporation of the native bilin phytochromobilin into PhyB confers robust Pfr → Pr thermal reversion, that assembled with the cyanobacterial version phycocyanobilin, often used for optogenetics, has a dramatically stabilized Pfr state. Taken together, we conclude that Pfr acquisition and stability are impacted by a collection of opposing allosteric features that inhibit or promote photoconversion and reversion of Pfr back to Pr, thus allowing Phys to dynamically measure light, temperature, and possibly time

Online Focus Group Discussions to Engage Stigmatized Populations in Qualitative Health Research: Lessons Learned

Community participation in research involving stigmatized populations has been sub-optimal, and digital tools could potentially increase participation in qualitative research. This study aims to describe the implementation of an online chat-based FGD (Focus Group Discussion) with men who have sex with men (MSM) in China as part of formative research for the PIONEER project, determine the advantages and limitations associated with the approach, and assess the feasibility of deepening community participation in STI research. Participants were involved in four days of asynchronous FGDs on sexually transmitted diseases and answered questions about the online FGD method. Online FGDs allowed us to deepen participant engagement through bidirectional communication channels. Data from online FGDs directly informed recruitment strategies and community participation for a clinical trial. Overall, 63% (29/46) of men who had never participated in offline LGBTQ + activities joined online FGDs. Many participants (89%, 41/46) noted that online FGDs were more convenient, less socially awkward, and more anonymous than in-person qualitative research. We highlighted potential risks as well as mitigation strategies when using online FGDs. Online FGDs were feasible among this group of sexual minorities and may be particularly useful in many cities where stigma limits in-person research participation