Design of Millimeter-wave Detector for Gyrotron Power Monitoring

The real-time power monitoring of gyrotron is one of the key issues in the operation of electron cyclotron resonance heating system. The detector can be used for real-time power monitoring. We analyzed the principle of diode detection and designed a D-band wideband detector based on Schottky diode in this paper. The detector includes a waveguide-to-microstrip transition, a matching circuit, a diode, and a low pass filter. A novel waveguide-to-microstrip transition was developed based on probe coupling. A wideband lossy matching circuit was developed based on tapered-line and series matching resistor. The simulation results show that when the input power is -30dBm at 140 GHz, the detection sensitivity is about 1600V/W.Comment: 12 pages, 19 figure

Dynamic Relationships among Composite Property Prices of Major Chinese Cities: Contemporaneous Causality through Vector Error Corrections and Directed Acyclic Graphs

The present study is the first one that investigates dynamic relations among composite real estate price indices of ten different cities in China during the years from 2005 to 2021. Utilizing the data recorded on a monthly basis, we apply VECM (vector error-correction modeling) and DAGs (directed acyclic graphs) in order to characterize contemporaneous causal relations among the ten real estate price indices. We use the PC algorithm to identify a pattern with non-directed edges and the LiNGAM algorithm to determine the causal ordering, based on which we calculate the results of innovation accounting. The LiNGAM algorithm adopted here effectively utilizes non-normality for facilitating the arrival of complete causal orderings. Our results show that price dynamics revealed through processes of price adjustments due to shocks to prices are rather sophisticated and such dynamics are, in general, dominated by price indices of Shanghai and Shenzhen, which are two top-tier cities among the four top-tier cities in China. This indicates that policy design on composite property prices should be focusing on price indices of Shanghai and Shenzhen

Spatial-Temporal Analysis of Residential Housing, Office Property, and Retail Property Price Index Correlations: Evidence from Ten Chinese Cities

Using correlation-based hierarchical analysis and synchronization analysis, this study focuses on monthly price indices for residential homes, office buildings, and retail properties in ten major Chinese cities for the years 2005 to 2021. Through these analyses, one can identify interactions and interdependence among the price indices, heterogeneous patterns in synchronizations of the price indices, and their evolving paths with time. Empirical findings suggest that the degree of real estate price comovements across all property types and cities is relatively low and stable from January 2017 to February 2020, followed by significant increases during the COVID-19 pandemic from March 2020 to January 2021 and significant decreases since February 2021 with the recovery of the economy. Several groups of property types and cities are determined in this study, each of which having its members reveal rather strong but volatile synchronizations of price indices. Rolling importance analysis does not suggest persistent increasing or decreasing trends for the real estate price associated with a specific property type and city. Policy studies on real estate price comovements may benefit from these findings here

Supported ITZ modification efficiencies via surface coating nanoparticles on aggregate and its influence on properties

In order to modify the porous interfacial transition zone (ITZ) microstructure of concrete more efficiently, a method of coating aggregate surfaces by using several nanoparticles was evaluated in this study. The compressive strength, chloride penetration of sound, and pre-loading samples were assessed in relation to the type of coating materials used (slag, nano-CaCO3, and nano-SiO2) and the designed coating thickness (5, 10, and 15 mu m). The ITZ microstructure was quantitatively determined via Backscattered electron (BSE) image analysis. Results showed that the overall performance of concrete is highly dependent on the coating materials and the designed coating thickness. Increasing the coating thickness of slag and nano-SiO2 could improve the chloride penetration resistance but decrease the compressive strength. Using nano-CaCO3 to coat the aggregate leads to a significant reduction in the properties of the so-prepared concrete. Though coating inert fine particles around aggregate could disturb the initial particle packing and modify the ITZ, it is not able to improve the overall concrete properties. Coating aggregate could determine the ITZ microstructure, especially within the region that is around 30 mu m away from aggregate surface

Deep Learning with S-shaped Rectified Linear Activation Units

Rectified linear activation units are important components for state-of-the-art deep convolutional networks. In this paper, we propose a novel S-shaped rectified linear activation unit (SReLU) to learn both convex and non-convex functions, imitating the multiple function forms given by the two fundamental laws, namely the Webner-Fechner law and the Stevens law, in psychophysics and neural sciences. Specifically, SReLU consists of three piecewise linear functions, which are formulated by four learnable parameters. The SReLU is learned jointly with the training of the whole deep network through back propagation. During the training phase, to initialize SReLU in different layers, we propose a "freezing" method to degenerate SReLU into a predefined leaky rectified linear unit in the initial several training epochs and then adaptively learn the good initial values. SReLU can be universally used in the existing deep networks with negligible additional parameters and computation cost. Experiments with two popular CNN architectures, Network in Network and GoogLeNet on scale-various benchmarks including CIFAR10, CIFAR100, MNIST and ImageNet demonstrate that SReLU achieves remarkable improvement compared to other activation functions.Comment: Accepted by AAAI-1

Exploring Domain Incremental Video Highlights Detection with the LiveFood Benchmark

Video highlights detection (VHD) is an active research field in computer vision, aiming to locate the most user-appealing clips given raw video inputs. However, most VHD methods are based on the closed world assumption, i.e., a fixed number of highlight categories is defined in advance and all training data are available beforehand. Consequently, existing methods have poor scalability with respect to increasing highlight domains and training data. To address above issues, we propose a novel video highlights detection method named Global Prototype Encoding (GPE) to learn incrementally for adapting to new domains via parameterized prototypes. To facilitate this new research direction, we collect a finely annotated dataset termed LiveFood, including over 5,100 live gourmet videos that consist of four domains: ingredients, cooking, presentation, and eating. To the best of our knowledge, this is the first work to explore video highlights detection in the incremental learning setting, opening up new land to apply VHD for practical scenarios where both the concerned highlight domains and training data increase over time. We demonstrate the effectiveness of GPE through extensive experiments. Notably, GPE surpasses popular domain incremental learning methods on LiveFood, achieving significant mAP improvements on all domains. Concerning the classic datasets, GPE also yields comparable performance as previous arts. The code is available at: https://github.com/ForeverPs/IncrementalVHD_GPE.Comment: AAAI 202