Studies on the selective retinal degeneration induced by sodium iodate （NaIO3） date back to 1941; Sorsby （1941） described the effect of intravenously injected NaIO3 solution on the rabbit retina. Since then, NaIO3...Studies on the selective retinal degeneration induced by sodium iodate （NaIO3） date back to 1941; Sorsby （1941） described the effect of intravenously injected NaIO3 solution on the rabbit retina. Since then, NaIO3-induced retinal degeneration has been described in different mammalian species including sheep, rabbit, rat and mouse with varying doses and routes of administration. At the present time,展开更多
On the basis of a detailed discussion of the development of total ionizing dose （TID） effect model, a new commercial-model-independent TID modeling approach for partially depleted silicon-on-insulator metal-oxide- s...On the basis of a detailed discussion of the development of total ionizing dose （TID） effect model, a new commercial-model-independent TID modeling approach for partially depleted silicon-on-insulator metal-oxide- semiconductor field effect transistors is developed. An exponential approximation is proposed to simplify the trap charge calculation. Irradiation experiments with 60Co gamma rays for IO and core devices are performed to validate the simulation results. An excellent agreement of measurement with the simulation results is observed.展开更多
In the upcoming large-scale Internet of Things(Io T),it is increasingly challenging to defend against malicious traffic,due to the heterogeneity of Io T devices and the diversity of Io T communication protocols.In thi...In the upcoming large-scale Internet of Things(Io T),it is increasingly challenging to defend against malicious traffic,due to the heterogeneity of Io T devices and the diversity of Io T communication protocols.In this paper,we propose a semi-supervised learning-based approach to detect malicious traffic at the access side.It overcomes the resource-bottleneck problem of traditional malicious traffic defenders which are deployed at the victim side,and also is free of labeled traffic data in model training.Specifically,we design a coarse-grained behavior model of Io T devices by self-supervised learning with unlabeled traffic data.Then,we fine-tune this model to improve its accuracy in malicious traffic detection by adopting a transfer learning method using a small amount of labeled data.Experimental results show that our method can achieve the accuracy of 99.52%and the F1-score of 99.52%with only 1%of the labeled training data based on the CICDDoS2019 dataset.Moreover,our method outperforms the stateof-the-art supervised learning-based methods in terms of accuracy,precision,recall and F1-score with 1%of the training data.展开更多
Earthquake precursor data have been used as an important basis for earthquake prediction.In this study,a recurrent neural network(RNN)architecture with long short-term memory(LSTM)units is utilized to develop a predic...Earthquake precursor data have been used as an important basis for earthquake prediction.In this study,a recurrent neural network(RNN)architecture with long short-term memory(LSTM)units is utilized to develop a predictive model for normal data.Furthermore,the prediction errors from the predictive models are used to indicate normal or abnormal behavior.An additional advantage of using the LSTM networks is that the earthquake precursor data can be directly fed into the network without any elaborate preprocessing as required by other approaches.Furthermore,no prior information on abnormal data is needed by these networks as they are trained only using normal data.Experiments using three groups of real data were conducted to compare the anomaly detection results of the proposed method with those of manual recognition.The comparison results indicated that the proposed LSTM network achieves promising results and is viable for detecting anomalies in earthquake precursor data.展开更多
By establishing a hybrid-units input-output model,this paper calculates total energy intensity coefficients and total pollution intensity coefficients of four energy sectors and 23 non-energy sectors.Meanwhile,using e...By establishing a hybrid-units input-output model,this paper calculates total energy intensity coefficients and total pollution intensity coefficients of four energy sectors and 23 non-energy sectors.Meanwhile,using export data for 2002-2006,it estimates China’s export-related energy consumption,CO<sub>2</sub> emissions and atmospheric pollution.The results reveal that the more China exports goods and services to other parts of the world,the more energy consumption, CO<sub>2</sub> emissions and atmospheric pollutants are transferred to China from outside its borders.Ihe tremendous damage of export-related pollution to China’s economy and the environment deserves serious attention.展开更多
基金supported in part by grants EY01545 and by core grant EY03040the Arnold and Mabel Beckman Foundationan unrestricted grant to the Department of Ophthalmology from Research to Prevent Blindness Inc.,New York,NY
文摘Studies on the selective retinal degeneration induced by sodium iodate （NaIO3） date back to 1941; Sorsby （1941） described the effect of intravenously injected NaIO3 solution on the rabbit retina. Since then, NaIO3-induced retinal degeneration has been described in different mammalian species including sheep, rabbit, rat and mouse with varying doses and routes of administration. At the present time,
基金Supported by the National Natural Science Foundation of China under Grant Nos 61404151 and 61574153
文摘On the basis of a detailed discussion of the development of total ionizing dose （TID） effect model, a new commercial-model-independent TID modeling approach for partially depleted silicon-on-insulator metal-oxide- semiconductor field effect transistors is developed. An exponential approximation is proposed to simplify the trap charge calculation. Irradiation experiments with 60Co gamma rays for IO and core devices are performed to validate the simulation results. An excellent agreement of measurement with the simulation results is observed.
基金supported in part by the National Key R&D Program of China under Grant 2018YFA0701601part by the National Natural Science Foundation of China(Grant No.U22A2002,61941104,62201605)part by Tsinghua University-China Mobile Communications Group Co.,Ltd.Joint Institute。
文摘In the upcoming large-scale Internet of Things(Io T),it is increasingly challenging to defend against malicious traffic,due to the heterogeneity of Io T devices and the diversity of Io T communication protocols.In this paper,we propose a semi-supervised learning-based approach to detect malicious traffic at the access side.It overcomes the resource-bottleneck problem of traditional malicious traffic defenders which are deployed at the victim side,and also is free of labeled traffic data in model training.Specifically,we design a coarse-grained behavior model of Io T devices by self-supervised learning with unlabeled traffic data.Then,we fine-tune this model to improve its accuracy in malicious traffic detection by adopting a transfer learning method using a small amount of labeled data.Experimental results show that our method can achieve the accuracy of 99.52%and the F1-score of 99.52%with only 1%of the labeled training data based on the CICDDoS2019 dataset.Moreover,our method outperforms the stateof-the-art supervised learning-based methods in terms of accuracy,precision,recall and F1-score with 1%of the training data.
基金supported by the Science for Earthquake Resilience of China(No.XH18027)Research and Development of Comprehensive Geophysical Field Observing Instrument in China's Mainland(No.Y201703)Research Fund Project of Shandong Earthquake Agency(Nos.JJ1505Y and JJ1602)
文摘Earthquake precursor data have been used as an important basis for earthquake prediction.In this study,a recurrent neural network(RNN)architecture with long short-term memory(LSTM)units is utilized to develop a predictive model for normal data.Furthermore,the prediction errors from the predictive models are used to indicate normal or abnormal behavior.An additional advantage of using the LSTM networks is that the earthquake precursor data can be directly fed into the network without any elaborate preprocessing as required by other approaches.Furthermore,no prior information on abnormal data is needed by these networks as they are trained only using normal data.Experiments using three groups of real data were conducted to compare the anomaly detection results of the proposed method with those of manual recognition.The comparison results indicated that the proposed LSTM network achieves promising results and is viable for detecting anomalies in earthquake precursor data.
文摘By establishing a hybrid-units input-output model,this paper calculates total energy intensity coefficients and total pollution intensity coefficients of four energy sectors and 23 non-energy sectors.Meanwhile,using export data for 2002-2006,it estimates China’s export-related energy consumption,CO<sub>2</sub> emissions and atmospheric pollution.The results reveal that the more China exports goods and services to other parts of the world,the more energy consumption, CO<sub>2</sub> emissions and atmospheric pollutants are transferred to China from outside its borders.Ihe tremendous damage of export-related pollution to China’s economy and the environment deserves serious attention.
