The Impact of Religion on Chinese Government, Society, and Civilians

This research project analyzes the importance of religion in Chinese society from ancient to contemporary times and how the role of religion has changed throughout history. The Cultural Revolution had a major impact on the perception and use of religion in Chinese society, and the effects still exist in the present day. In order to explore how religion functions in these specific time periods, this research examines various secondary sources, which include scholarly articles and interpretations. Moreover, primary sources, which include official documents of the government and news articles, show how the Chinese government and the citizens have diverse points of view regarding religion. This research shows that, despite the fact that the Chinese government and citizens have different views about the purpose and meaning of religion, people in China were and are still largely reliant on religion as a form of spiritual support

Image-Specific Information Suppression and Implicit Local Alignment for Text-based Person Search

Text-based person search (TBPS) is a challenging task that aims to search pedestrian images with the same identity from an image gallery given a query text. In recent years, TBPS has made remarkable progress and state-of-the-art methods achieve superior performance by learning local fine-grained correspondence between images and texts. However, most existing methods rely on explicitly generated local parts to model fine-grained correspondence between modalities, which is unreliable due to the lack of contextual information or the potential introduction of noise. Moreover, existing methods seldom consider the information inequality problem between modalities caused by image-specific information. To address these limitations, we propose an efficient joint Multi-level Alignment Network (MANet) for TBPS, which can learn aligned image/text feature representations between modalities at multiple levels, and realize fast and effective person search. Specifically, we first design an image-specific information suppression module, which suppresses image background and environmental factors by relation-guided localization and channel attention filtration respectively. This module effectively alleviates the information inequality problem and realizes the alignment of information volume between images and texts. Secondly, we propose an implicit local alignment module to adaptively aggregate all pixel/word features of image/text to a set of modality-shared semantic topic centers and implicitly learn the local fine-grained correspondence between modalities without additional supervision and cross-modal interactions. And a global alignment is introduced as a supplement to the local perspective. The cooperation of global and local alignment modules enables better semantic alignment between modalities. Extensive experiments on multiple databases demonstrate the effectiveness and superiority of our MANet

Design and Implementation of Reusable Components Using PowerBuilder

AbstractComponent technology is a key technology of software reuse. This paper investigates PowerBuilder based technology of software reuse, especially the technology of component design. To build a reusable component, reusable elements in the application system are firstly extracted. The reusable components are then used to form a reusable component library. When designing application system, suitable components are selected from the reusable library and then instantiated. Software system is implemented by composing the instanced components under a reusable framework. Practical results show that the use of reusable components can improve the efficiency of software development

Concurrence-Aware Long Short-Term Sub-Memories for Person-Person Action Recognition

Recently, Long Short-Term Memory (LSTM) has become a popular choice to model individual dynamics for single-person action recognition due to its ability of modeling the temporal information in various ranges of dynamic contexts. However, existing RNN models only focus on capturing the temporal dynamics of the person-person interactions by naively combining the activity dynamics of individuals or modeling them as a whole. This neglects the inter-related dynamics of how person-person interactions change over time. To this end, we propose a novel Concurrence-Aware Long Short-Term Sub-Memories (Co-LSTSM) to model the long-term inter-related dynamics between two interacting people on the bounding boxes covering people. Specifically, for each frame, two sub-memory units store individual motion information, while a concurrent LSTM unit selectively integrates and stores inter-related motion information between interacting people from these two sub-memory units via a new co-memory cell. Experimental results on the BIT and UT datasets show the superiority of Co-LSTSM compared with the state-of-the-art methods

Erasing, Transforming, and Noising Defense Network for Occluded Person Re-Identification

Occlusion perturbation presents a significant challenge in person re-identification (re-ID), and existing methods that rely on external visual cues require additional computational resources and only consider the issue of missing information caused by occlusion. In this paper, we propose a simple yet effective framework, termed Erasing, Transforming, and Noising Defense Network (ETNDNet), which treats occlusion as a noise disturbance and solves occluded person re-ID from the perspective of adversarial defense. In the proposed ETNDNet, we introduce three strategies: Firstly, we randomly erase the feature map to create an adversarial representation with incomplete information, enabling adversarial learning of identity loss to protect the re-ID system from the disturbance of missing information. Secondly, we introduce random transformations to simulate the position misalignment caused by occlusion, training the extractor and classifier adversarially to learn robust representations immune to misaligned information. Thirdly, we perturb the feature map with random values to address noisy information introduced by obstacles and non-target pedestrians, and employ adversarial gaming in the re-ID system to enhance its resistance to occlusion noise. Without bells and whistles, ETNDNet has three key highlights: (i) it does not require any external modules with parameters, (ii) it effectively handles various issues caused by occlusion from obstacles and non-target pedestrians, and (iii) it designs the first GAN-based adversarial defense paradigm for occluded person re-ID. Extensive experiments on five public datasets fully demonstrate the effectiveness, superiority, and practicality of the proposed ETNDNet. The code will be released at \url{https://github.com/nengdong96/ETNDNet}

Centralized Feature Pyramid for Object Detection

Visual feature pyramid has shown its superiority in both effectiveness and efficiency in a wide range of applications. However, the existing methods exorbitantly concentrate on the inter-layer feature interactions but ignore the intra-layer feature regulations, which are empirically proved beneficial. Although some methods try to learn a compact intra-layer feature representation with the help of the attention mechanism or the vision transformer, they ignore the neglected corner regions that are important for dense prediction tasks. To address this problem, in this paper, we propose a Centralized Feature Pyramid (CFP) for object detection, which is based on a globally explicit centralized feature regulation. Specifically, we first propose a spatial explicit visual center scheme, where a lightweight MLP is used to capture the globally long-range dependencies and a parallel learnable visual center mechanism is used to capture the local corner regions of the input images. Based on this, we then propose a globally centralized regulation for the commonly-used feature pyramid in a top-down fashion, where the explicit visual center information obtained from the deepest intra-layer feature is used to regulate frontal shallow features. Compared to the existing feature pyramids, CFP not only has the ability to capture the global long-range dependencies, but also efficiently obtain an all-round yet discriminative feature representation. Experimental results on the challenging MS-COCO validate that our proposed CFP can achieve the consistent performance gains on the state-of-the-art YOLOv5 and YOLOX object detection baselines.Comment: Code: https://github.com/QY1994-0919/CFPNe

Coupling Global Context and Local Contents for Weakly-Supervised Semantic Segmentation

Thanks to the advantages of the friendly annotations and the satisfactory performance, Weakly-Supervised Semantic Segmentation (WSSS) approaches have been extensively studied. Recently, the single-stage WSSS was awakened to alleviate problems of the expensive computational costs and the complicated training procedures in multi-stage WSSS. However, results of such an immature model suffer from problems of \emph{background incompleteness} and \emph{object incompleteness}. We empirically find that they are caused by the insufficiency of the global object context and the lack of the local regional contents, respectively. Under these observations, we propose a single-stage WSSS model with only the image-level class label supervisions, termed as \textbf{W}eakly-\textbf{S}upervised \textbf{F}eature \textbf{C}oupling \textbf{N}etwork (\textbf{WS-FCN}), which can capture the multi-scale context formed from the adjacent feature grids, and encode the fine-grained spatial information from the low-level features into the high-level ones. Specifically, a flexible context aggregation module is proposed to capture the global object context in different granular spaces. Besides, a semantically consistent feature fusion module is proposed in a bottom-up parameter-learnable fashion to aggregate the fine-grained local contents. Based on these two modules, \textbf{WS-FCN} lies in a self-supervised end-to-end training fashion. Extensive experimental results on the challenging PASCAL VOC 2012 and MS COCO 2014 demonstrate the effectiveness and efficiency of \textbf{WS-FCN}, which can achieve state-of-the-art results by $65.02\%$ and $64.22\%$ mIoU on PASCAL VOC 2012 \emph{val} set and \emph{test} set, $34.12\%$ mIoU on MS COCO 2014 \emph{val} set, respectively. The code and weight have been released at:~\href{https://github.com/ChunyanWang1/ws-fcn}{WS-FCN}.Comment: accepted by TNNL

Triplet Contrastive Learning for Unsupervised Vehicle Re-identification

Part feature learning is a critical technology for finegrained semantic understanding in vehicle re-identification. However, recent unsupervised re-identification works exhibit serious gradient collapse issues when directly modeling the part features and global features. To address this problem, in this paper, we propose a novel Triplet Contrastive Learning framework (TCL) which leverages cluster features to bridge the part features and global features. Specifically, TCL devises three memory banks to store the features according to their attributes and proposes a proxy contrastive loss (PCL) to make contrastive learning between adjacent memory banks, thus presenting the associations between the part and global features as a transition of the partcluster and cluster-global associations. Since the cluster memory bank deals with all the instance features, it can summarize them into a discriminative feature representation. To deeply exploit the instance information, TCL proposes two additional loss functions. For the inter-class instance, a hybrid contrastive loss (HCL) re-defines the sample correlations by approaching the positive cluster features and leaving the all negative instance features. For the intra-class instances, a weighted regularization cluster contrastive loss (WRCCL) refines the pseudo labels by penalizing the mislabeled images according to the instance similarity. Extensive experiments show that TCL outperforms many state-of-the-art unsupervised vehicle re-identification approaches. The code will be available at https://github.com/muzishen/TCL.Comment: Code: https://github.com/muzishen/TC

Modeling redistribution of α-HCH in Chinese soil induced by environment factors

This study explores long-term environmental fate of alpha-HCH in China from 1952 to 2007 using ChnGPERM (Chinese Gridded Pesticide Emission and Residue Model). The model captures well the temporal and spatial variations of alpha-HCH concentration in Chinese soils by comparing with a number of measured data across China in different periods. The results demonstrate alpha-HCH grasshopping effect in Eastern China and reveal several important features of the chemical in Northeast and Southeast China. It is found that Northeast China is a prominent sink region of alpha-HCH emitted from Chinese sources and alpha-HCH contamination in Southwest China is largely attributed to foreign sources. Southeast China is shown to be a major source contributing to alpha-HCH contamination in Northeast China, incurred by several environmental factors including temperature, soil organic carbon content, wind field and precipitation. (C) 2011 Elsevier Ltd. All rights reserved.This study explores long-term environmental fate of alpha-HCH in China from 1952 to 2007 using ChnGPERM (Chinese Gridded Pesticide Emission and Residue Model). The model captures well the temporal and spatial variations of alpha-HCH concentration in Chinese soils by comparing with a number of measured data across China in different periods. The results demonstrate alpha-HCH grasshopping effect in Eastern China and reveal several important features of the chemical in Northeast and Southeast China. It is found that Northeast China is a prominent sink region of alpha-HCH emitted from Chinese sources and alpha-HCH contamination in Southwest China is largely attributed to foreign sources. Southeast China is shown to be a major source contributing to alpha-HCH contamination in Northeast China, incurred by several environmental factors including temperature, soil organic carbon content, wind field and precipitation. (C) 2011 Elsevier Ltd. All rights reserved

Impact of Fish Farming on Phosphorus in Reservoir Sediments

Fish farming has seriously influenced the aquatic environment in Sancha reservoir in SW China since 1985 and has been strongly restricted since 2005. Thus, phosphorus speciation in a sediment core dated between 1945 and 2010 at cm-resolution and in surface sediments from Sancha reservoir may allow us track how fish farming impacts phosphorus dynamics in lake sediments. Fish farming shifts the major binding forms of phosphorus in sediments from organic to residual phosphorus, which mostly originated from fish feed. Sorption to metal oxides and association with organic matters are important mechanisms for phosphorus immobilisation with low fish farming activities, whereas calcium-bound phosphorous had an essential contribution to sediment phosphorus increases under intensive fish framing. Notwithstanding the shifting, the aforementioned phosphorus fractions are usually inert in the lake environment, therefore changing phosphorus mobility little. The use of fish feed and water-purification reagents, the most important additives for fish farming, introduce not only phosphorus but also large amounts of sand-sized minerals such as quartz into the lake, to which phosphorus weakly sorbs. The sand-sized minerals as additional sorbents increase the pool of easily mobilisable phosphorus in sediments, which will slow down the recovery of reservoir water due to its rapid re-mobilisation