A Tale of Two Villages: Debordering and Rebordering in the Bordered Community Scenic Area

Border is part of the entrenched history and reality of tourist mobility. This study takes the concept of border as the theorical basis to analyze how local borders are produced, developed and transformed in tourism communities. Taking China’s Hongcun Village, a bordered UNESCO World Cultural Heritage Site, and its neighboring community Jicun as the study cases, the authors conducted interviews and observation to explore how local borders are developed. The results show that local borders can be understood from five perspectives in Hongcun Scenic Area: administrative, physical, social-economic, functional and psychological. They are not fixed but interacting with each other and constantly changing. This paper contributes to the literature as it reveals that local borders are always driven by external forces and actors, strongly supported by the market economy. And it conceptualizes borders as processes including bordering, debordering and rebordering, which provides a dynamic perspective to understand tourism impacts

R2P: A Deep Learning Model from mmWave Radar to Point Cloud

Recent research has shown the effectiveness of mmWave radar sensing for object detection in low visibility environments, which makes it an ideal technique in autonomous navigation systems. In this paper, we introduce Radar to Point Cloud (R2P), a deep learning model that generates smooth, dense, and highly accurate point cloud representation of a 3D object with fine geometry details, based on rough and sparse point clouds with incorrect points obtained from mmWave radar. These input point clouds are converted from the 2D depth images that are generated from raw mmWave radar sensor data, characterized by inconsistency, and orientation and shape errors. R2P utilizes an architecture of two sequential deep learning encoder-decoder blocks to extract the essential features of those radar-based input point clouds of an object when observed from multiple viewpoints, and to ensure the internal consistency of a generated output point cloud and its accurate and detailed shape reconstruction of the original object. We implement R2P to replace Stage 2 of our recently proposed 3DRIMR (3D Reconstruction and Imaging via mmWave Radar) system. Our experiments demonstrate the significant performance improvement of R2P over the popular existing methods such as PointNet, PCN, and the original 3DRIMR design.Comment: arXiv admin note: text overlap with arXiv:2109.0918

3D Reconstruction of Multiple Objects by mmWave Radar on UAV

In this paper, we explore the feasibility of utilizing a mmWave radar sensor installed on a UAV to reconstruct the 3D shapes of multiple objects in a space. The UAV hovers at various locations in the space, and its onboard radar senor collects raw radar data via scanning the space with Synthetic Aperture Radar (SAR) operation. The radar data is sent to a deep neural network model, which outputs the point cloud reconstruction of the multiple objects in the space. We evaluate two different models. Model 1 is our recently proposed 3DRIMR/R2P model, and Model 2 is formed by adding a segmentation stage in the processing pipeline of Model 1. Our experiments have demonstrated that both models are promising in solving the multiple object reconstruction problem. We also show that Model 2, despite producing denser and smoother point clouds, can lead to higher reconstruction loss or even loss of objects. In addition, we find that both models are robust to the highly noisy radar data obtained by unstable SAR operation due to the instability or vibration of a small UAV hovering at its intended scanning point. Our exploratory study has shown a promising direction of applying mmWave radar sensing in 3D object reconstruction

VGDiffZero: Text-to-image Diffusion Models Can Be Zero-shot Visual Grounders

Large-scale text-to-image diffusion models have shown impressive capabilities across various generative tasks, enabled by strong vision-language alignment obtained through pre-training. However, most vision-language discriminative tasks require extensive fine-tuning on carefully-labeled datasets to acquire such alignment, with great cost in time and computing resources. In this work, we explore directly applying a pre-trained generative diffusion model to the challenging discriminative task of visual grounding without any fine-tuning and additional training dataset. Specifically, we propose VGDiffZero, a simple yet effective zero-shot visual grounding framework based on text-to-image diffusion models. We also design a comprehensive region-scoring method considering both global and local contexts of each isolated proposal. Extensive experiments on RefCOCO, RefCOCO+, and RefCOCOg show that VGDiffZero achieves strong performance on zero-shot visual grounding

Cell-Free Seminal mRNA and MicroRNA Exist in Different Forms

BACKGROUND: The great interest in cell-free mRNA, microRNA (miRNA) as molecular biomarkers for clinical applications, and as 'signaling' molecules for intercellular communication highlights the need to reveal their physical nature. Here this issue was explored in human cell-free seminal mRNA (cfs-mRNA) and miRNA (cfs-miRNA). METHODOLOGY/PRINCIPAL FINDINGS: Selected male reproductive organ-specific mRNAs, miRNAs, and piRNAs were quantified by quantitative real-time PCR in all experiments. While the stability of cfs-miRNA assessed by time-course analysis (up to 24 h at room temperature) was similar with cfs-mRNA, the reductive changes between cfs-miRNA and cfs-mRNA after filtration and Triton X-100 treatment on seminal plasma were very different, implying their different physical nature. Seminal microvesicles (SMVs) were then recovered and proportions of cfs-mRNA and cfs-miRNA within SMVs were quantified. The amounts of SMVs- sequestered cfs-mRNAs almost were the same as total cfs-mRNA, and were highly variable depending on the different sizes of SMVs. But most of cfs-miRNA was independent of SMVs and existed in the supernatant. The possible form of cfs-miRNA in the supernatant was further explored by filtration and protease K digestion. It passed through the 0.10-µm pore, but was degraded dramatically after intense protease K digestion. CONCLUSIONS/SIGNIFICANCE: The predominant cfs-mRNA is contained in SMVs, while most cfs-miRNA is bound with protein complexes. Our data explained the stability of extracellular RNAs in human semen, and shed light on their origins and potential functions in male reproduction, and strategy of developing them as biomarkers of male reproductive system