Making rural micro-regeneration strategies based on resident perceptions and preferences for traditional village conservation and development:The case of Huangshan village, China

Micro-regeneration is a gradual renewal strategy that uses small-scale interventions to improve the quality of the living environment and local community, as well as spur industrial development. It is the small-scale interventions that have continued to make micro-regeneration a viable economic rural renewal approach for traditional village conservation and development. As such, in this work we explore potential micro-regeneration strategies and promotions based on assessments of public perception and preferences in an “unlisted” traditional village in China (i.e., an area with limited investment for conservation compared to “listed”, renowned traditional villages). We aim to identify the most perceptible modes of village transformation and industrial development for rural micro-regeneration strategies in the Huangshan traditional village of China. We also tested how the social character of respondents significantly affected their preferences in this regard. The public participatory mapping results illustrated a spatially clustered pattern made up of small spaces and individual buildings demanding micro-regeneration interventions. The survey based on 150 residents living around these sites disclosed that a unified repair approach subsidized by government and traffic condition improvements are the most recognized modes of village transformation, and the tourism is the most perceived and preferred method for industrial development. Significant differences between public perceptions and preferences of both village transformation and industrial development were identified corresponding to gender and income demographics, while village transformation perceptions change is dependent on age. Therefore, our study demonstrates evidence-based recommendations for active and effective rural micro-regeneration practices

Domain Adaptation For Vehicle Detection In Traffic Surveillance Images From Daytime To Nighttime

Vehicle detection in traffic surveillance images is an important approach to obtain vehicle data and rich traffic flow parameters. Recently, deep learning based methods have been widely used in vehicle detection with high accuracy and efficiency. However, deep learning based methods require a large number of manually labeled ground truths (bounding box of each vehicle in each image) to train the Convolutional Neural Networks (CNN). In the modern urban surveillance cameras, there are already many manually labeled ground truths in daytime images for training CNN, while there are little or much less manually labeled ground truths in nighttime images. In this paper, we focus on the research to make maximum usage of labeled daytime images (Source Domain) to help the vehicle detection in unlabeled nighttime images (Target Domain). For this purpose, we propose a new method based on Faster R-CNN with Domain Adaptation (DA) to improve the vehicle detection at nighttime. With the assistance of DA, the domain distribution discrepancy of Source and Target Domains is reduced. We collected a new dataset of 2,200 traffic images (1,200 for daytime and 1,000 for nighttime) of 57,059 vehicles for training and testing CNN. In the experiment, only using the manually labeled ground truths of daytime data, Faster R- CNN obtained 82.84% as F-measure on the nighttime vehicle detection, while the proposed method (Faster R-CNN+DA) achieved 86.39% as F-measure on the nighttime vehicle detection

More comprehensive facial inversion for more effective expression recognition

Facial expression recognition (FER) plays a significant role in the ubiquitous application of computer vision. We revisit this problem with a new perspective on whether it can acquire useful representations that improve FER performance in the image generation process, and propose a novel generative method based on the image inversion mechanism for the FER task, termed Inversion FER (IFER). Particularly, we devise a novel Adversarial Style Inversion Transformer (ASIT) towards IFER to comprehensively extract features of generated facial images. In addition, ASIT is equipped with an image inversion discriminator that measures the cosine similarity of semantic features between source and generated images, constrained by a distribution alignment loss. Finally, we introduce a feature modulation module to fuse the structural code and latent codes from ASIT for the subsequent FER work. We extensively evaluate ASIT on facial datasets such as FFHQ and CelebA-HQ, showing that our approach achieves state-of-the-art facial inversion performance. IFER also achieves competitive results in facial expression recognition datasets such as RAF-DB, SFEW and AffectNet. The code and models are available at https://github.com/Talented-Q/IFER-master

MPPNet: Multi-Frame Feature Intertwining with Proxy Points for 3D Temporal Object Detection

Accurate and reliable 3D detection is vital for many applications including autonomous driving vehicles and service robots. In this paper, we present a flexible and high-performance 3D detection framework, named MPPNet, for 3D temporal object detection with point cloud sequences. We propose a novel three-hierarchy framework with proxy points for multi-frame feature encoding and interactions to achieve better detection. The three hierarchies conduct per-frame feature encoding, short-clip feature fusion, and whole-sequence feature aggregation, respectively. To enable processing long-sequence point clouds with reasonable computational resources, intra-group feature mixing and inter-group feature attention are proposed to form the second and third feature encoding hierarchies, which are recurrently applied for aggregating multi-frame trajectory features. The proxy points not only act as consistent object representations for each frame, but also serve as the courier to facilitate feature interaction between frames. The experiments on large Waymo Open dataset show that our approach outperforms state-of-the-art methods with large margins when applied to both short (e.g., 4-frame) and long (e.g., 16-frame) point cloud sequences. Code is available at https://github.com/open-mmlab/OpenPCDet.Comment: Accepted by ECCV 202

A Carbon Nanotube-based Hundred Watt-level Ka-band Backward Wave Oscillator

Carbon nanotube (CNT) cold-cathodes hold much promise in a variety of millimeter-wave and terahertz vacuum electronic radiation devices due to their inherent near instantaneous temporal turn-on and near-ideal ideal field electron emission performance. Here we report on the development of a CNT cold-cathode Ka -band backward-wave oscillator (BWO). Using a novel beam compression stage, theoretical studies, simulation results, and empirical findings collectively demonstrate that this device affords an unprecedentedly high output power of 230 W at a technologically important operating frequency of 33.65 GHz. The developed magnetic injection electron gun achieves a high emission current of 265.5 mA (emission current density of 188.3 mA/cm 2 ) and a high focused beam current density of 18.5 A/cm 2 , which our studies suggest, is essential to the BWOs high output power

A Map-matching Algorithm with Extraction of Multi-group Information for Low-frequency Data

The growing use of probe vehicles generates a huge number of GNSS data. Limited by the satellite positioning technology, further improving the accuracy of map-matching is challenging work, especially for low-frequency trajectories. When matching a trajectory, the ego vehicle's spatial-temporal information of the present trip is the most useful with the least amount of data. In addition, there are a large amount of other data, e.g., other vehicles' state and past prediction results, but it is hard to extract useful information for matching maps and inferring paths. Most map-matching studies only used the ego vehicle's data and ignored other vehicles' data. Based on it, this paper designs a new map-matching method to make full use of "Big data". We first sort all data into four groups according to their spatial and temporal distance from the present matching probe which allows us to sort for their usefulness. Then we design three different methods to extract valuable information (scores) from them: a score for speed and bearing, a score for historical usage, and a score for traffic state using the spectral graph Markov neutral network. Finally, we use a modified top-K shortest-path method to search the candidate paths within an ellipse region and then use the fused score to infer the path (projected location). We test the proposed method against baseline algorithms using a real-world dataset in China. The results show that all scoring methods can enhance map-matching accuracy. Furthermore, our method outperforms the others, especially when GNSS probing frequency is less than 0.01 Hz.Comment: 10 pages, 11 figures, 4 table