We give a survey on the contribution of our research group to the cell theory of weighted Coxeter groups. We present some detailed account for the description of cells of the affine Weyl group $ \widetilde{C}_n $ in the quasi-split case and a brief account for that of the affine Weyl group $ \widetilde{B}_n $ in the quasi-split case and of the weighted universal Coxeter group in general case.

The trajectory optimization of cycling is hindered by the errors of positioning equipment, riding habits of non-motor vehicles, and other factors. It leads to quality problems, such as abnormal data and missing positioning information in the riding trajectory, impacting the application of trajectory-based riding-map inference and riding-path planning. To solve these problems, this paper creates a framework for improving the quality of cycling-trajectory data, based on the construction of a grid index, screening of abnormal trajectory points, elimination of wandering trajectory segments, elimination of illegal trajectory segments, calibration of drift trajectory segments, and recovery of missing trajectory. Comparative and ablation experiments are conducted by using a real non-motor-vehicle cycling-trajectory dataset. The experimental results verify that the proposed method improves the accuracy of cycling-map inference.

Shared-nothing distributed databases are designed for the high scalability and high availability request of Internet-based applications. There have been significant achievements in shared-nothing distributed databases, but for some shared-nothing databases with stateless computation layers, long conflict-detection paths challenge database performance under high-contention workloads. To solve this problem, we design two methods, pre-lock and local cache, together with a high-contention detection module that allow high-contention to be quickly detected and the corresponding high-contention-handling strategy applied. Experiments show that our design and optimization for high-contention transaction-processing architecture can improve the performance of distributed databases under high-contention workloads.

The diagnostic method based on dual-view fundus imaging is widely used in diabetic retinopathy (DR) screening. This method effectively solves the problems of image occlusion and limited field of view under single-view. This paper proposes a learning method of feature fusion between dual-view images based on the attention mechanism to improve the accuracy of DR classification by effectively integrating different view information. Due to the small proportion of lesions in fundus images, the self-attention mechanism was introduced to enhance the learning of local lesion features. Moreover, a cross-attention mechanism is proposed to effectively utilize information between dual-view images to improve the classification of dual-view fundus images. Experiments were performed on the internal DFiD dataset and public DeepDRiD dataset. The proposed method can effectively improve the accuracy of DR classification and can be used for large-scale DR screening to assist doctors in achieving an efficient diagnosis.

The popularity of intelligent devices such as smartphones and surveillance cameras has led to serious face privacy problems. Face de-identification is considered an effective tool for protecting face privacy by concealing identity information. However, most de-identification methods lack explicit control and controllable changes in identifying de-identified face images, resulting in de-identified images that are inapplicable to face authentication and retrieval and other identity-related tasks. Therefore, this study proposes an identity inter-relationship-consistent face de-identification task in which the identity inter-relationship between two arbitrary de-identified faces maintained the same as before de-identification. To this end, a task-driven identity inter-relationship consistent generative adversarial network is introduced to generate de-identified faces with a consistent identity inter-relationship. A rotation-based de-identifier was designed to modify the original identity features to be de-identified with identity inter-relationship consistency. In addition, identity control loss is introduced to guarantee a precise identity generation using a de-identified generator. Qualitative and quantitative results show that our method achieves improvements compared with exiting methods for de-identifying de-identified faces as well as for maintaining their identity inter-relationship consistent.

We propose a neural network training framework called momentum-updated representation with reconstruction constraint for 3D (three-dimensional) object recognition using 2D (two-dimensional) images without angle labels. First, self-supervised learning is employed to address the lack of angle labels. Second, we use momentum updating based on a dynamic queue to maintain the stability of the object representation. Furthermore, the reconstruction constraint is applied to the learning process with an auto-encoder module, which enables the representation to capture more semantic information of the objects. Finally, during training, a dynamic queue reduction strategy is proposed for handling the imbalanced data distribution. Experiments on two popular multi-view datasets, ModelNet and ShapeNet, demonstrate that the proposed method outperforms existing methods.

Review text contains comprehensive user and item information and it has a great influence on users’ purchase decision. When users interact with different target items, they may show complex interests. Therefore, accurately extracting review semantic features and modeling the contextual interaction between items and users is critical for learning user preferences and item attributes. Focusing on enhancing the personalization capture and dynamic interest modeling abilities of recommender systems, and considering the usefulness of different features, we propose a hierarchical description-aware personalized recommendation (DAPR) algorithm. At the word level of review text, we design a personalized information selection network to extract important word semantic features. At the review level, we design a neural network based on a cross-attention mechanism to dynamically learn the usefulness of reviews, concatenate review summaries as descriptions, and devise a co-attention network to capture rich context-aware features. The analysis of five Amazon datasets reveal that the proposed method can achieve comparable recommendation performance to the baseline models.

Mainstream sentence classification algorithms rely on a single word vector model to obtain the feature vector representation of text, which leads to insufficient text mapping ability. Therefore, a multi-kernel learning method is used to fuse multiple text representations based on different word vectors to improve the accuracy of sentence classification. In the process of fusing different kernel functions, traditional kernel function coefficient optimization methods often lead to long training time and difficulty in finding a local optimum. To address this problem, a new kernel function coefficient optimization method that continuously approximates the optimal kernel function coefficient value based on parameter space segmentation and breadth first search was developed. In this study, a support vector machine (SVM) was used as a classifier to perform classification experiments on seven text datasets, and the experimental results showed that the multi-kernel learning classification results were significantly better than those of single-kernel learning. Moreover, the proposed optimization method performed better than traditional methods with less training cost.

Chinese sentiment analysis is one of important researches in natural language processing, which aims to discover the sentimental tendencies in the Chinese text. In recent years, research on Chinese text sentiment analysis has made great progress in efficiencies, but few studies have explored the characteristics of the language and downstream task requirements. Therefore, in view of the particularity of Chinese text and the requirements of sentiment analysis, using the Chinese text sentiment analysis method that integrates multi-granularity semantic features, such as characters, words, radicals, and part-of-speech is proposed. This introduces radical features and emotional part-of-speech features based on character and word features. Additionally, this integration uses bidirectional the long short-term memory network (BLSTM), attention mechanism and recurrent convolutional neural network (RCNN). The softmax function is used to predict the sentimental tendencies by integrating multi-granularity semantic features. The comparative experiment results on the NLPECC (natural language processing and Chinese computing) dataset showed that the F₁ score of the proposed method was 84.80%, which improved the performance of the existing methods to some extent and completed the Chinese text sentiment analysis task.

In this study, Gly→Ala was introduced into three chains of the heterotrimeric model (abc); seven mutants were subsequently constructed, and the local structure and global motion changes were analyzed. DSC results showed that the T_m value of the single point mutation was reduced by about 15°C, while the double and triple point mutations did not form triple helical structures. MD simulation trajectories were analyzed by ladder models; the results showed that the value of the step parameter changes near the mutation point, indicating an unfolding of the triple helix structure. An elastic function was introduced to quantify the degree of collagen structure change. It was found that the hydrogen bond energy was highly correlated with the structural deformation fraction ( $ R^2=0.76 $ ), indicating that the mutation not only destroyed the hydrogen bond force, but also resulted in changes in the bending and motion states of the molecule. This study, combined with calculations and experiments, helped quantify the effects of glycine mutation on the overall structure and movement pattern of collagen. Hence, the study provides a theoretical basis for clarifying the pathogenic mechanism of glycine mutation.

This research study focuses on Tettigoniidea and Gryllidea insects distributed across Shanghai Pujiang Country Park, with data collected twice a month from April to December of Year 2020 and 2021. The results show that 8 species of Tettigonioidea, 16 species of Grylloidea, and 1 species of Gryllotalpidae live in Shanghai Pujiang Country Park. The adult phase and voltinism in their life cycles, moreover, were found to be stable. Most Tettigoniidea and Gryllidea tend to overwinter in soil as diapause eggs, and a proportion of them overwinter as nymphs. The research suggests, furthermore, that using the calling songs of Tettigoniidea and Gryllidea can be a simple and effective way to carry out studies about phenology and ecology of singing insect.

To investigate the influence of urban environmental differences on the leaf-unfolding phenology of trees, we extracted the leaf unfolding information of nine tree species using remote sensing data and analyzed their relationships with temperature, precipitation, and nighttime light in a pure forest in Shanghai. The results showed that : ① there were significant differences in the average onset of the leaf-unfolding phenology among species, from 95th to 104th day of the year; contrastingly, intra-species variation in leaf-unfolding date was greater, ranging from 69th to 138th day of the year. ② Different species exhibited different leaf phenology in response to environmental factors. Triadica sebifera was the most sensitive to precipitation changes, Taxodium distichum var. imbricatum was sensitive to precipitation changes and urbanization, and Koelreuteria bipinnata was sensitive to precipitation and climate changes. Other species were not sensitive to any environmental changes. ③ For species sensitive to environmental changes, the leaf-unfolding date was 45 days earlier when the average precipitation increase from 48 mm to 64 mm, and delayed by three days for every 1°C increase in average temperature before the growing season. The study showed that urban forest construction can be reasonably configured according to the response characteristics of a species to the environment, so that plants can better adapt to the environment and fulfill their roles in the ecosystem.

In this study, a benthic macroinvertebrates survey was conducted in the western Qingpu District. The Benthic Index of Biotic Integrity (B-IBI) was used to assess the regional water ecological health. The relationship between B-IBI and water quality indicators, including related indices, was analyzed, and the applicability of B-IBI in lake-swamp areas was explored. The results showed that the water ecological health of the study area was good overall, the proportion of healthy and sub-healthy sample sites was 67.7%, and B-IBI indexes of the lake-swamp areas are better than those of rivers. The B-IBI index can effectively indicate organic pollution and eutrophication in water bodies. In addition, a significant correlation among the biological indices was found, despite the differences in evaluation results. We conclude that the B-IBI index has good applicability for the water ecology assessment of lake-swamp areas.

The balance between supply and demand of ecosystem services is essential for sustainable development, while differentiated partitioning is important for optimal resource allocation. Based on the carnegie-ames-stanford-approach (CASA), the water balance equation, and the revised universal soil loss equation (RUSLE), this study depicts the supply and demand of carbon fixation and water and soil conservation services and their relationship in the Yangtze River Delta integration demonstration zone from 2000 to 2020. Self-organizing map (SOM) K-means two-order clustering technology is used to identify ecosystem service clusters dividing the ecological management zone. Management strategies are then proposed. The results show that: ① The supply of carbon fixation services continued to decrease but the demand increased. Additionally, the supply and demand of water and soil conservation services showed an increasing trend, and supply was less than demand. ② The supply-demand ratio of carbon fixation and soil conservation services showed an upward trend, whereas the supply-demand ratio of water conservation services showed a downward trend. Significant spatial differences were observed in the supply-demand ratio of ecosystem services. ③ Cluster analysis divided the demonstration area into different types of ecological regulation zones. The urban clusters in Wujiang and Shengze town districts are mainly to improve the capacity of water conservation services. The urban cluster in Qingpu District is to promote the water conservation and carbon fixation services capacity. The pilot start-up area and urban cluster in Jiashan City have a small gap between supply and demand, which focus on comprehensive protection. This study can provide decision-making support for resource allocation, ecological compensation, and coordinated development of regional integration in the demonstration area.

Herein, based on existing standards for the measurements of material degradation rates and the degradation abilities of microorganisms, four methods were designed to determine material degradation rates. These four methods included two standard methods (inoculums: composting, vermiculite+composting leachate) and two experimental methods (inoculums: vermiculite+Bacillus, vermiculite+thermophilic bacteria). For this, the raw paper and plastic film (polylactic acid, PLA) components of environmentally friendly tape, as well as the finished tapes, were used as test materials to compare the material degradation rates using the above methods. Throughout the 60-day test cycle, both the PLA films and raw paper presented high degradation rates according to the four methods. The degradation rate of finished tape products increased gradually under the composting and vermiculite+composting leachate treatment and marginally rapidly under the vermiculite+Bacillus treatment. Additionally, under the vermiculite + thermophilic bacteria treatment method, the finished tape materials displayed a markedly higher degradation rate than that produced by other methods (roughly 1.7 ~ 7.5 times). Thus, the addition of microorganisms, particularly thermophilic bacteria, enhances the testing efficiency of material biodegradation rates. Therefore, we suggest that the optimization of degradation cultures can improve the testing efficiency of material degradation parameters, allowing manufacturing enterprises to shorten the research and development cycles of biodegradable products.