The accuracy of historical situation values is required for traditional network security situation prediction(NSSP).There are discrepancies in the correlation and weighting of the various network security elements.To ...The accuracy of historical situation values is required for traditional network security situation prediction(NSSP).There are discrepancies in the correlation and weighting of the various network security elements.To solve these problems,a combined prediction model based on the temporal convolution attention network(TCAN)and bi-directional gate recurrent unit(BiGRU)network is proposed,which is optimized by singular spectrum analysis(SSA)and improved quantum particle swarmoptimization algorithm(IQPSO).This model first decomposes and reconstructs network security situation data into a series of subsequences by SSA to remove the noise from the data.Furthermore,a prediction model of TCAN-BiGRU is established respectively for each subsequence.TCAN uses the TCN to extract features from the network security situation data and the improved channel attention mechanism(CAM)to extract important feature information from TCN.BiGRU learns the before-after status of situation data to extract more feature information from sequences for prediction.Besides,IQPSO is proposed to optimize the hyperparameters of BiGRU.Finally,the prediction results of the subsequence are superimposed to obtain the final predicted value.On the one hand,IQPSO compares with other optimization algorithms in the experiment,whose performance can find the optimum value of the benchmark function many times,showing that IQPSO performs better.On the other hand,the established prediction model compares with the traditional prediction methods through the simulation experiment,whose coefficient of determination is up to 0.999 on both sets,indicating that the combined prediction model established has higher prediction accuracy.展开更多
Partial label learning is a weakly supervised learning framework in which each instance is associated with multiple candidate labels,among which only one is the ground-truth label.This paper proposes a unified formula...Partial label learning is a weakly supervised learning framework in which each instance is associated with multiple candidate labels,among which only one is the ground-truth label.This paper proposes a unified formulation that employs proper label constraints for training models while simultaneously performing pseudo-labeling.Unlike existing partial label learning approaches that only leverage similarities in the feature space without utilizing label constraints,our pseudo-labeling process leverages similarities and differences in the feature space using the same candidate label constraints and then disambiguates noise labels.Extensive experiments on artificial and real-world partial label datasets show that our approach significantly outperforms state-of-the-art counterparts on classification prediction.展开更多
Gastrointestinal tumors are common malignant tumors in the digestive tract, of which gastric cancer is the third leading cause of cancer death and colorectal cancer is the fourth most deadly cancer. Nowadays, surgical...Gastrointestinal tumors are common malignant tumors in the digestive tract, of which gastric cancer is the third leading cause of cancer death and colorectal cancer is the fourth most deadly cancer. Nowadays, surgical resection remains to be one of the main measures for treating digestive tract tumors, and gastrointestinal anastomosis remains to be a key step in gastrointestinal surgery. Mg and its alloys have great potentials to be used for gastrointestinal anastomosis as anastomotic nail materials due to their biodegradability, good mechanical properties and biocompatibility. In this study, Mg–2Zn–0.5Nd(ZN20) alloy fine wires showed great potential as surgical staples. When we performed in vitro corrosion experiments, drainage fluid was collected from different parts of the patient’s abdominal cavity after surgery for the first time to replace the traditional simulation fluid and to more realistically simulate the microenvironment required by the anastomotic nail. ZN20 alloy has an ultimate tensile strength of 256 MPa, an elongation rate of 12.56%, a tensile force in anastomosis of 16.8 N, and a rupture pressure after anastomosis at 17 kPa, which means that a sufficient mechanical support was provided after anastomosis.The statistical analysis and histopathological analysis of biochemical tests indicate that ZN20 alloy has no adverse effect on the normal metabolism of liver, kidney and body, but has superior biocompatibility and biosafety. This work confirms that ZN20 possesses a great application potential in the clinical field as a new type of gastrointestinal anastomotic nail material.展开更多
基金This work is supported by the National Science Foundation of China(61806219,61703426,and 61876189)by National Science Foundation of Shaanxi Provence(2021JM-226)by the Young Talent fund of the University,and the Association for Science and Technology in Shaanxi,China(20190108,20220106)by and the Innovation Capability Support Plan of Shaanxi,China(2020KJXX-065).
文摘The accuracy of historical situation values is required for traditional network security situation prediction(NSSP).There are discrepancies in the correlation and weighting of the various network security elements.To solve these problems,a combined prediction model based on the temporal convolution attention network(TCAN)and bi-directional gate recurrent unit(BiGRU)network is proposed,which is optimized by singular spectrum analysis(SSA)and improved quantum particle swarmoptimization algorithm(IQPSO).This model first decomposes and reconstructs network security situation data into a series of subsequences by SSA to remove the noise from the data.Furthermore,a prediction model of TCAN-BiGRU is established respectively for each subsequence.TCAN uses the TCN to extract features from the network security situation data and the improved channel attention mechanism(CAM)to extract important feature information from TCN.BiGRU learns the before-after status of situation data to extract more feature information from sequences for prediction.Besides,IQPSO is proposed to optimize the hyperparameters of BiGRU.Finally,the prediction results of the subsequence are superimposed to obtain the final predicted value.On the one hand,IQPSO compares with other optimization algorithms in the experiment,whose performance can find the optimum value of the benchmark function many times,showing that IQPSO performs better.On the other hand,the established prediction model compares with the traditional prediction methods through the simulation experiment,whose coefficient of determination is up to 0.999 on both sets,indicating that the combined prediction model established has higher prediction accuracy.
基金supported by the National Key Research&Develop Plan of China under Grant Nos.2017YFB1400700 and 2018YFB1004401the National Natural Science Foundation of China under Grant Nos.61732006,61702522,61772536,61772537,62076245,and 62072460Beijing Natural Science Foundation under Grant No.4212022。
文摘Partial label learning is a weakly supervised learning framework in which each instance is associated with multiple candidate labels,among which only one is the ground-truth label.This paper proposes a unified formulation that employs proper label constraints for training models while simultaneously performing pseudo-labeling.Unlike existing partial label learning approaches that only leverage similarities in the feature space without utilizing label constraints,our pseudo-labeling process leverages similarities and differences in the feature space using the same candidate label constraints and then disambiguates noise labels.Extensive experiments on artificial and real-world partial label datasets show that our approach significantly outperforms state-of-the-art counterparts on classification prediction.
文摘Gastrointestinal tumors are common malignant tumors in the digestive tract, of which gastric cancer is the third leading cause of cancer death and colorectal cancer is the fourth most deadly cancer. Nowadays, surgical resection remains to be one of the main measures for treating digestive tract tumors, and gastrointestinal anastomosis remains to be a key step in gastrointestinal surgery. Mg and its alloys have great potentials to be used for gastrointestinal anastomosis as anastomotic nail materials due to their biodegradability, good mechanical properties and biocompatibility. In this study, Mg–2Zn–0.5Nd(ZN20) alloy fine wires showed great potential as surgical staples. When we performed in vitro corrosion experiments, drainage fluid was collected from different parts of the patient’s abdominal cavity after surgery for the first time to replace the traditional simulation fluid and to more realistically simulate the microenvironment required by the anastomotic nail. ZN20 alloy has an ultimate tensile strength of 256 MPa, an elongation rate of 12.56%, a tensile force in anastomosis of 16.8 N, and a rupture pressure after anastomosis at 17 kPa, which means that a sufficient mechanical support was provided after anastomosis.The statistical analysis and histopathological analysis of biochemical tests indicate that ZN20 alloy has no adverse effect on the normal metabolism of liver, kidney and body, but has superior biocompatibility and biosafety. This work confirms that ZN20 possesses a great application potential in the clinical field as a new type of gastrointestinal anastomotic nail material.
