Guangzhou as livable city: Its origin, inheritance, and development

This article intends to explore the ideas and concepts that dominate the landmark versions of planning in a historical survey on the development of urban planning for the construction of Guangzhou. From the Late 17th C. to the Mid-19th C. Xiguan in Guangzhou witnessed the booming of the Thirteen-hong characterized by gardens and buildings in Western architectural styles. These characteristics constituted the architectural features and urban spatial patterns on both sides of the Pearl River, and caused the moving westward of the ancient city center to the Thirteen-hong Business District. After The Second Opium War Western merchants began their planning and construction of Shameen with Western planning techniques, which, together with the model of the Thirteen-hong, led to the urban modernization of Guangzhou urban planning. During the years from 1911 to 1948, the urban planning and construction in Guangzhou underwent a sequence of processes from simplicity to complexity, and from part to whole. There was also a process from the simple imitation of Western ideas and concepts of urban planning in Dashatou Island to the renovation of Guangzhou urban planning marked with road and park construction, This process includes the dismantle of the city walls for road construction in 1912, the prelude of modern urban planning of Guangzhou in 1914, the planning for network and city-round road and park construction in 1918, the idea of “Traffic First” in 1921, the regional studies and planning in 1923, the concept of functional division in 1932, the idea of implementing urban function division in 1920s and 1930s, and the transference from the initial techniques and measures to land management in 1937. After that there was the adoption of the "zonal cluster layout" along the Pearl River in 1984, the idea of the Planning of Urban Agglomeration of the Pearl River Delta in 1995, and the continuation of the “four land usage modes” in 2003. The idea and concept of urban planning for Guangzhou, thus derived localized from the practice of Western urban planning in the Thirteen-hong and Shameen, later underwent the municipal planning of Dashatou, the idyllic residential districts. The innovated regional green space in 2006, followed by the livable urban and rural planning in 2016, and up to the lately ecological city in 2018, all bear the marks of the early ideas and concepts realized in the Thirteen-hong, Shameen, and Dashatou. Therefore, it can be further concluded that the urban planning of Guangzhou, developed from the initial function of landscape beautification to the regulation of regional green environment of the Pearl River Delta, underwent finally a full process of imitation, learning, transformation, and innovation, resulting in an idea of green, open, and shared urban construction

JCS-Net : joint classification and super-resolution network for small-scale pedestrian detection in surveillance images

While Convolutional Neural Network (CNN)-based pedestrian detection methods have proven to be successful in various applications, detecting small-scale pedestrian from surveillance images is still challenging.The major reason is that the small-scale pedestrians lack much detailed information compared to the large-scale pedestrians. To solve this problem, we propose to utilize the relationship between the large-scale pedestrians and the corresponding small-scale pedestrians to help recover the detailed information of the small-scale pedestrians, thus improving the performance of detecting small-scale pedestrians. Specifically, a unified network (called JCS-Net) is proposed for small-scale pedestrian detection, which integrates the classification task and the super-resolution task in a unified framework. As a result, the super-resolution and classification are fully engaged and the super-resolution sub-network can recover some useful detailed information for the subsequent classification. Based on HOG+LUV and JCS-Net, multi-layer channel features (MCF) are constructed to train the detector. Experimental results on the Caltech pedestrian dataset and the KITTI benchmark demonstrate the effectiveness of the proposed method. To further enhance the detection, multi-scale MCF based on JCS-Net for pedestrian detection is also proposed, which achieves the state-of-the-art performance

N-Phenyl-N-(3-phenyl­prop-2-yn­yl)aniline

In the title compound, C21H17N, synthesized by a three-component coupling reaction in the presence of copper(I) iodide, the N-bound phenyl rings form a dihedral angle of 72.5 (1)° with each other. Thereare no remarkable inter­actions in the crystal structure

MCD64A1 Burnt Area Dataset Assessment using Sentinel-2 and Landsat-8 on Google Earth Engine: A Case Study in Rompin, Pahang in Malaysia

This research paper intends to explore the suitability of adopting the MCD64A1 product to detect burnt areas using Google Earth Engine (GEE) in Peninsular Malaysia. The primary aim of this study is to find out if the MCD64A1 is adequate to identify the small-scale fire in Peninsular Malaysia. To evaluate the MCD64A1, a fire that was instigated in Rompin, a district of Pahang on March 2021 has been chosen as the case study in this work. Although several other burnt area datasets had also been made available in GEE, only MCD64A1 is selected due to its temporal availability. In the absence of validation information associated with the fire from the Malaysian government, public news sources are utilized to retrieve details related to the fire in Rompin. Additionally, the MCD64A1 is also validated with the burnt area observed from the true color imagery produced from the surface reflectance of Sentinel-2 and Landsat-8. From the burnt area assessment, we scrutinize that the MCD64A1 product is practical to be exploited to discover the historical fire in Peninsular Malaysia. However, additional case studies involving other locations in Peninsular Malaysia are advocated to be carried out to substantiate the claims discussed in this work.Comment: 13th IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE 2023) - Accepted on 29 March 202

CURE: Flexible Categorical Data Representation by Hierarchical Coupling Learning

© 1989-2012 IEEE. The representation of categorical data with hierarchical value coupling relationships (i.e., various value-to-value cluster interactions) is very critical yet challenging for capturing complex data characteristics in learning tasks. This paper proposes a novel and flexible coupled unsupervised categorical data representation (CURE) framework, which not only captures the hierarchical couplings but is also flexible enough to be instantiated for contrastive learning tasks. CURE first learns the value clusters of different granularities based on multiple value coupling functions and then learns the value representation from the couplings between the obtained value clusters. With two complementary value coupling functions, CURE is instantiated into two models: coupled data embedding (CDE) for clustering and coupled outlier scoring of high-dimensional data (COSH) for outlier detection. These show that CURE is flexible for value clustering and coupling learning between value clusters for different learning tasks. CDE embeds categorical data into a new space in which features are independent and semantics are rich. COSH represents data w.r.t. an outlying vector to capture complex outlying behaviors of objects in high-dimensional data. Substantial experiments show that CDE significantly outperforms three popular unsupervised encoding methods and three state-of-the-art similarity measures, and COSH performs significantly better than five state-of-the-art outlier detection methods on high-dimensional data. CDE and COSH are scalable and stable, linear to data size and quadratic to the number of features, and are insensitive to their parameters

Low-Profile Ultra-Wideband Directional Dipole Antenna as a Feed for Reflectors in Radio Telescopes

In this paper, a small top plate is found useful to improve the impedance bandwidth of an ultra-wideband dipole antenna horizontally above a ground plane. A linearly-polarized prototype based on this new and simple design methodology can operate over nearly 3.5:1 bandwidth with return losses better than 10 dB, and with nearly stable radiation patterns, high BOR1 efficiency and aperture efficiency over the entire operating band

RoSAS: Deep Semi-Supervised Anomaly Detection with Contamination-Resilient Continuous Supervision

Semi-supervised anomaly detection methods leverage a few anomaly examples to yield drastically improved performance compared to unsupervised models. However, they still suffer from two limitations: 1) unlabeled anomalies (i.e., anomaly contamination) may mislead the learning process when all the unlabeled data are employed as inliers for model training; 2) only discrete supervision information (such as binary or ordinal data labels) is exploited, which leads to suboptimal learning of anomaly scores that essentially take on a continuous distribution. Therefore, this paper proposes a novel semi-supervised anomaly detection method, which devises \textit{contamination-resilient continuous supervisory signals}. Specifically, we propose a mass interpolation method to diffuse the abnormality of labeled anomalies, thereby creating new data samples labeled with continuous abnormal degrees. Meanwhile, the contaminated area can be covered by new data samples generated via combinations of data with correct labels. A feature learning-based objective is added to serve as an optimization constraint to regularize the network and further enhance the robustness w.r.t. anomaly contamination. Extensive experiments on 11 real-world datasets show that our approach significantly outperforms state-of-the-art competitors by 20%-30% in AUC-PR and obtains more robust and superior performance in settings with different anomaly contamination levels and varying numbers of labeled anomalies. The source code is available at https://github.com/xuhongzuo/rosas/.Comment: Accepted by Information Processing and Management (IP&M

Interface Effects on He Ion Irradiation in Nanostructured Materials

In advanced fission and fusion reactors, structural materials suffer from high dose irradiation by energetic particles and are subject to severe microstructure damage. He atoms, as a byproduct of the (n) transmutation reaction, could accumulate to form deleterious cavities, which accelerate radiation-induced embrittlement, swelling and surface deterioration, ultimately degrade the service lifetime of reactor materials. Extensive studies have been performed to explore the strategies that can mitigate He ion irradiation damage. Recently, nanostructured materials have received broad attention because they contain abundant interfaces that are efficient sinks for radiation-induced defects. In this review, we summarize and analyze the current understandings on interface effects on He ion irradiation in nanostructured materials. Some key challenges and research directions are highlighted for studying the interface effects on radiation damage in nanostructured materials