MSG-BART: Multi-granularity Scene Graph-Enhanced Encoder-Decoder Language Model for Video-grounded Dialogue Generation

Generating dialogue grounded in videos requires a high level of understanding and reasoning about the visual scenes in the videos. However, existing large visual-language models are not effective due to their latent features and decoder-only structure, especially with respect to spatio-temporal relationship reasoning. In this paper, we propose a novel approach named MSG-BART, which enhances the integration of video information by incorporating a multi-granularity spatio-temporal scene graph into an encoder-decoder pre-trained language model. Specifically, we integrate the global and local scene graph into the encoder and decoder, respectively, to improve both overall perception and target reasoning capability. To further improve the information selection capability, we propose a multi-pointer network to facilitate selection between text and video. Extensive experiments are conducted on three video-grounded dialogue benchmarks, which show the significant superiority of the proposed MSG-BART compared to a range of state-of-the-art approaches.Comment: 5 pages,3 figure

Time-frequency optimization of RSEI: A case study of Yangtze River Basin

Remote Sensing Ecological Index (RSEI) is one of the most widely used ecological quality assessment indicators. Due to the noise caused by adverse atmoshperic conditions and other factors, the RSEI calculated from the original image usually has the phenomena of lack of information and unstable image quality. Therefore, based on Google Earth Engine (GEE) cloud platform, this study adopts three common data reconstruction algorithms firstly, namely: Savitory-Golay filter (SG), harmonic analysis of time series (HANTS), Whittaker Smoother (WS), which are used to reconstruct the original MODIS time series data in the Yangtze River Basin (YRB) from 2000 to 2020, in order to optimize the calculation process of RSEI. At the same time, three indicators (correlation coefficient (R), standard deviation (STD), root mean square error (RMSE)) are used for the accuracy evaluation. The results show that data reconstruction can fill gaps in RSEI, the reconstruction performance of WS and SG for four parameters is better than HANTS, and the four SG reconstructed sequences have the strongest correlation with the original sequences (R between 0.8 ∼ 1), while the WS reconstruction sequence has the lowest error value (both STD and RMSE are less than 1), both of them can correct the pixel value, which is conducive to maintaining the stability of RSEI in the temporal dimension; the RSEI produced by HANTS has the best accuracy, that is, R, STD, RMSE are respectively 0.898, 0.130, 0.104. As shown by the research, it is necessary to de-noise each parameter before synthesizing RSEI. This study can provide a theoretical basis for applying time-frequency algorithms to optimize the ecological monitoring performance of RSEI

Research on Cyanobacterial-Bloom Detection Based on Multispectral Imaging and Deep-Learning Method

Frequent outbreaks of cyanobacterial blooms have become one of the most challenging water ecosystem issues and a critical concern in environmental protection. To overcome the poor stability of traditional detection algorithms, this paper proposes a method for detecting cyanobacterial blooms based on a deep-learning algorithm. An improved vegetation-index method based on a multispectral image taken by an Unmanned Aerial Vehicle (UAV) was adopted to extract inconspicuous spectral features of cyanobacterial blooms. To enhance the recognition accuracy of cyanobacterial blooms in complex scenes with noise such as reflections and shadows, an improved transformer model based on a feature-enhancement module and pixel-correction fusion was employed. The algorithm proposed in this paper was implemented in several rivers in China, achieving a detection accuracy of cyanobacterial blooms of more than 85%. The estimate of the proportion of the algae bloom contamination area and the severity of pollution were basically accurate. This paper can lay a foundation for ecological and environmental departments for the effective prevention and control of cyanobacterial blooms

Application of Least-Squares Support Vector Machines for Quantitative Evaluation of Known Contaminant in Water Distribution System Using Online Water Quality Parameters

In water-quality, early warning systems and qualitative detection of contaminants are always challenging. There are a number of parameters that need to be measured which are not entirely linearly related to pollutant concentrations. Besides the complex correlations between variable water parameters that need to be analyzed also impairs the accuracy of quantitative detection. In aspects of these problems, the application of least-squares support vector machines (LS-SVM) is used to evaluate the water contamination and various conventional water quality sensors quantitatively. The various contaminations may cause different correlative responses of sensors, and also the degree of response is related to the concentration of the injected contaminant. Therefore to enhance the reliability and accuracy of water contamination detection a new method is proposed. In this method, a new relative response parameter is introduced to calculate the differences between water quality parameters and their baselines. A variety of regression models has been examined, as result of its high performance, the regression model based on genetic algorithm (GA) is combined with LS-SVM. In this paper, the practical application of the proposed method is considered, controlled experiments are designed, and data is collected from the experimental setup. The measured data is applied to analyze the water contamination concentration. The evaluation of results validated that the LS-SVM model can adapt to the local nonlinear variations between water quality parameters and contamination concentration with the excellent generalization ability and accuracy. The validity of the proposed approach in concentration evaluation for potassium ferricyanide is proven to be more than 0.5 mg/L in water distribution systems

An Online Contaminant Classification Method Based on MF-DCCA Using Conventional Water Quality Indicators

Emergent contamination warning systems are critical to ensure drinking water supply security. After detecting the existence of contaminants, identifying the types of contaminants is conducive to taking remediation measures. An online classification method for contaminants, which explored abnormal fluctuation information and the correlation between 12 water quality indicators adequately, is proposed to realize comprehensive and accurate discrimination of contaminants. Firstly, the paper utilized multi-fractal detrended fluctuation analysis (MF-DFA) to select indicators with abnormal fluctuation, used multi-fractal detrended cross-correlation analysis (MF-DCCA) to measure the cross-correlation between indicators. Subsequently, the algorithm fused the abnormal probability of each indicator and constructed the abnormal probability matrix to further judge the abnormal fluctuation of indicators using D–S evidence theory. Finally, the singularity index of the cross-correlation function and the selected indicators were used to classification by cosine distance. Experiments of five chemical contaminants at three concentration levels were implemented, and analysis results show the method can weaken disturbance of water quality background noise and other interfering factors. It effectively improved the classification accuracy at low concentrations compared with another three methods, including methods using triple standard deviation threshold and single indicator fluctuation analysis-only methods without fluctuation analysis. This can be applied to water quality emergency monitoring systems to reduce contaminant misclassification

DataSheet_1_GhBRX.1, GhBRX.2, and GhBRX4.3 improve resistance to salt and cold stress in upland cotton.docx

IntroductionAbiotic stress during growth readily reduces cotton crop yield. The different survival tactics of plants include the activation of numerous stress response genes, such as BREVIS RADIX (BRX).MethodsIn this study, the BRX gene family of upland cotton was identified and analyzed by bioinformatics method, three salt-tolerant and cold-resistant GhBRX genes were screened. The expression of GhBRX.1, GhBRX.2 and GhBRXL4.3 in upland cotton was silenced by virus-induced gene silencing (VIGS) technique. The physiological and biochemical indexes of plants and the expression of related stress-response genes were detected before and after gene silencing. The effects of GhBRX.1, GhBRX.2 and GhBRXL4.3 on salt and cold resistance of upland cotton were further verified.Results and discussionWe discovered 12, 6, and 6 BRX genes in Gossypium hirsutum, Gossypium raimondii and Gossypium arboreum, respectively. Chromosomal localization indicated that the retention and loss of GhBRX genes on homologous chromosomes did not have a clear preference for the subgenomes. Collinearity analysis suggested that segmental duplications were the main force for BRX gene amplification. The upland cotton genes GhBRX.1, GhBRX.2 and GhBRXL4.3 are highly expressed in roots, and GhBRXL4.3 is also strongly expressed in the pistil. Transcriptome data and qRT‒PCR validation showed that abiotic stress strongly induced GhBRX.1, GhBRX.2 and GhBRXL4.3. Under salt stress and low-temperature stress conditions, the activities of superoxide dismutase (SOD), peroxidase (POD) and catalase (CAT) and the content of soluble sugar and chlorophyll decreased in GhBRX.1-, GhBRX.2- and GhBRXL4.3-silenced cotton plants compared with those in the control (TRV: 00). Moreover, GhBRX.1-, GhBRX.2- and GhBRXL4.3-silenced cotton plants exhibited greater malondialdehyde (MDA) levels than did the control plants. Moreover, the expression of stress marker genes (GhSOS1, GhSOS2, GhNHX1, GhCIPK6, GhBIN2, GhSnRK2.6, GhHDT4D, GhCBF1 and GhPP2C) decreased significantly in the three target genes of silenced plants following exposure to stress. These results imply that the GhBRX.1, GhBRX.2 and GhBRXL4.3 genes may be regulators of salt stress and low-temperature stress responses in upland cotton.</p