Active worms can cause widespread damages at so high a speed that effectively precludes human-directed reaction, and patches for the worms are always available after the damages have been caused, which has elevated th...Active worms can cause widespread damages at so high a speed that effectively precludes human-directed reaction, and patches for the worms are always available after the damages have been caused, which has elevated them self to a first-class security threat to Metropolitan Area Networks (MAN). Multi-agent system for Worm Detection and Containment in MAN (MWDCM) is presented to provide a first-class automatic reaction mechanism that automatically applies containment strategies to block the propagation of the worms and to protect MAN against worm scan that wastes a lot of network bandwidth and crashes the routers. Its user agent is used to detect the known worms. Worm detection agent and worm detection correlation agent use two-stage based decision method to detect unknown worms. They adaptively study the accessing in the whole network and dynamically change the working parameters to detect the unknown worms. MWDCM confines worm infection within a macro-cell or a micro-cell of the metropolitan area networks, the rest of the accesses and hosts continue functioning without disruption. MWDCM integrates Worm Detection System (WDS) and network management system. Reaction measures can be taken by using Simple Network Management Protocol (SNMP) interface to control broadband access server as soon as the WDS detect the active worm. MWDCM is very effective in blocking random scanning worms. Simulation results indicate that high worm infection rate of epidemics can be avoided to a degree by MWDCM blocking the propagation of the worms.展开更多
We investigate the production of ultracold ground state x^1∑7＋（u = 0） RbCs molecules in the lowest vibrational level via short-range photoassociation followed by spontaneous emission. The starting point is the las...We investigate the production of ultracold ground state x^1∑7＋（u = 0） RbCs molecules in the lowest vibrational level via short-range photoassociation followed by spontaneous emission. The starting point is the laser cooled 85Rb and laa cs atoms in a dual species, forced dark magneto-optical trap. The special intermediate level （5）O＋ （u = 10） correlated to the （2）311 electric state is achieved by the photoassociation process. The formed ground state X1∑＋ （u = 0） molecule is resonantly excited to the 2111 intermediate state by a 651 nm pulse laser and is ionized by a 532nm pulse laser and then detected by the time-of-flight mass spectrum. Saturation of the photoionization spectroscopy at large ionization laser energy is observed and the ionization efficiency is obtained from the fitting. The production of ultracold ground state 85Rblaacs molecules is facilitative for the further research about the manipulation of ultracold molecules in the rovibrational ground state.展开更多
This paper describes the results of a project on the inspection of visually inaccessible areas of nuclear containment liners and shells via the advanced Magnetostrictive sensor (MsS) Guided Wave (GW) nondestructive in...This paper describes the results of a project on the inspection of visually inaccessible areas of nuclear containment liners and shells via the advanced Magnetostrictive sensor (MsS) Guided Wave (GW) nondestructive inspection technique. Full scale mockups that simulated shell and liner regions of interest in the containment of both a Pressurized Water Reactor (PWR) and Boiling Water Reactor (BWR) were constructed. Inspections were performed on the mock-ups in three stages to discern the signal attenuation caused by flaws and caused by concrete in the structures. The effect of concrete being in close proximity to the liner and shell was determined, and the capability to detect and size flaws via this GW technique was evaluated.展开更多
We developed an automatic seismic wave and phase detection software based on PhaseNet,an efficient and highly generalized deep learning neural network for P-and S-wave phase picking.The software organically combines m...We developed an automatic seismic wave and phase detection software based on PhaseNet,an efficient and highly generalized deep learning neural network for P-and S-wave phase picking.The software organically combines multiple modules including application terminal interface,docker container,data visualization,SSH protocol data transmission and other auxiliary modules.Characterized by a series of technologically powerful functions,the software is highly convenient for all users.To obtain the P-and S-wave picks,one only needs to prepare threecomponent seismic data as input and customize some parameters in the interface.In particular,the software can automatically identify complex waveforms(i.e.continuous or truncated waves)and support multiple types of input data such as SAC,MSEED,NumPy array,etc.A test on the dataset of the Wenchuan aftershocks shows the generalization ability and detection accuracy of the software.The software is expected to increase the efficiency and subjectivity in the manual processing of large amounts of seismic data,thereby providing convenience to regional network monitoring staffs and researchers in the study of Earth's interior.展开更多
Automatic recognition and interpretation of engineering drawing plays an important role in computer aided engineering. Detecting the positional relation between entities is an important topic in this research field. I...Automatic recognition and interpretation of engineering drawing plays an important role in computer aided engineering. Detecting the positional relation between entities is an important topic in this research field. In this paper the concepts of adjacent relativity and container window of drawing entities were proposed. By means of container window, the adjacent irrelative entities can be detected quickly and effectively, which speeds up the process of adjacent relativity detection. Meanwhile, the algorithm of adjacent relativity detection was discussed.展开更多
Detection and observation of reactive intermediates is an essential step in investigation of reaction pathways.However,most reactive intermediates are unstable and present at low concentrations;their short lifetimes m...Detection and observation of reactive intermediates is an essential step in investigation of reaction pathways.However,most reactive intermediates are unstable and present at low concentrations;their short lifetimes make them difficult to detect and characterize.Supramolecular containers offer opportunities for the stabilization and characterization of those labile species,through isolation from the media and protection inside the cavity of the host.In this review,we summarize the examples of labile reaction intermediates that are stabilized and characterized with the help of supramolecular containers.The container compounds include carcerands,deep cavitands and amide naphthotubes.We focus on unstable guest species-cyclobutadiene,benzocyclopropenone,o-benzyne,1,2,4,6-cycloheptatetraene,anti-Bredt's olefin,fluorophenoxycarbene,O-acylisoamide,and hemiaminalthat act as intermediates in certain organic reactions.展开更多
Containerized microservices have been widely deployed in the industry.Meanwhile,security issues also arise.Many security enhancement mechanisms for containerized microservices require predefined rules and policies.How...Containerized microservices have been widely deployed in the industry.Meanwhile,security issues also arise.Many security enhancement mechanisms for containerized microservices require predefined rules and policies.However,it is challenging when it comes to thousands of microservices and a massive amount of real-time unstructured data.Hence,automatic policy generation becomes indispensable.In this paper,we focus on the automatic solution for the security problem:irregular traffic detection for RPCs.We propose Informer,a two-phase machine learning framework to track the traffic of each RPC and automatically report anomalous points.We first identify RPC chain patterns using density-based clustering techniques and build a graph for each critical pattern.Next,we solve the irregular RPC traffic detection problem as a prediction problem for attributed graphs with time series by leveraging spatial-temporal graph convolution networks.Since the framework builds multiple models and makes individual predictions for each RPC chain pattern,it can be efficiently updated upon legitimate changes in any graphs.In evaluations,we applied Informer to a dataset containing more than 7 billion lines of raw RPC logs sampled from a large Kubernetes system for two weeks.We provide two case studies of detected real-world threats.As a result,our framework found fine-grained RPC chain patterns and accurately captured the anomalies in a dynamic and complicated microservice production scenario,which demonstrates the effectiveness of Informer.Furthermore,we extensively evaluated the risk of adversarial attacks for our prediction model under different reality constraints and showed that the model is robust to such attacks in most real-world scenarios.展开更多
基金Partially supported by the Teaching and Research Award for Outstanding Young Teachers in High Education Institutions of MOE, China (No.200065).
文摘Active worms can cause widespread damages at so high a speed that effectively precludes human-directed reaction, and patches for the worms are always available after the damages have been caused, which has elevated them self to a first-class security threat to Metropolitan Area Networks (MAN). Multi-agent system for Worm Detection and Containment in MAN (MWDCM) is presented to provide a first-class automatic reaction mechanism that automatically applies containment strategies to block the propagation of the worms and to protect MAN against worm scan that wastes a lot of network bandwidth and crashes the routers. Its user agent is used to detect the known worms. Worm detection agent and worm detection correlation agent use two-stage based decision method to detect unknown worms. They adaptively study the accessing in the whole network and dynamically change the working parameters to detect the unknown worms. MWDCM confines worm infection within a macro-cell or a micro-cell of the metropolitan area networks, the rest of the accesses and hosts continue functioning without disruption. MWDCM integrates Worm Detection System (WDS) and network management system. Reaction measures can be taken by using Simple Network Management Protocol (SNMP) interface to control broadband access server as soon as the WDS detect the active worm. MWDCM is very effective in blocking random scanning worms. Simulation results indicate that high worm infection rate of epidemics can be avoided to a degree by MWDCM blocking the propagation of the worms.
基金Supported by the National Basic Research Program of China under Grant No 2012CB921603the National Natural Science Foundation of China under Grant Nos 61275209,11304189,61378015 and 11434007+1 种基金the National Natural Science Foundation for Excellent Research Team under Grant No 61121064the Program for Changjiang Scholars and Innovative Research Team in University under Grant No IRT13076
文摘We investigate the production of ultracold ground state x^1∑7＋（u = 0） RbCs molecules in the lowest vibrational level via short-range photoassociation followed by spontaneous emission. The starting point is the laser cooled 85Rb and laa cs atoms in a dual species, forced dark magneto-optical trap. The special intermediate level （5）O＋ （u = 10） correlated to the （2）311 electric state is achieved by the photoassociation process. The formed ground state X1∑＋ （u = 0） molecule is resonantly excited to the 2111 intermediate state by a 651 nm pulse laser and is ionized by a 532nm pulse laser and then detected by the time-of-flight mass spectrum. Saturation of the photoionization spectroscopy at large ionization laser energy is observed and the ionization efficiency is obtained from the fitting. The production of ultracold ground state 85Rblaacs molecules is facilitative for the further research about the manipulation of ultracold molecules in the rovibrational ground state.
文摘This paper describes the results of a project on the inspection of visually inaccessible areas of nuclear containment liners and shells via the advanced Magnetostrictive sensor (MsS) Guided Wave (GW) nondestructive inspection technique. Full scale mockups that simulated shell and liner regions of interest in the containment of both a Pressurized Water Reactor (PWR) and Boiling Water Reactor (BWR) were constructed. Inspections were performed on the mock-ups in three stages to discern the signal attenuation caused by flaws and caused by concrete in the structures. The effect of concrete being in close proximity to the liner and shell was determined, and the capability to detect and size flaws via this GW technique was evaluated.
基金This study is jointly sponsored by the Basic Scientific Research Fee of Institute of Geophysics,China Earthquake Administration(DQJB19A0114)the National Natural Science Foundation of China(41804047).
文摘We developed an automatic seismic wave and phase detection software based on PhaseNet,an efficient and highly generalized deep learning neural network for P-and S-wave phase picking.The software organically combines multiple modules including application terminal interface,docker container,data visualization,SSH protocol data transmission and other auxiliary modules.Characterized by a series of technologically powerful functions,the software is highly convenient for all users.To obtain the P-and S-wave picks,one only needs to prepare threecomponent seismic data as input and customize some parameters in the interface.In particular,the software can automatically identify complex waveforms(i.e.continuous or truncated waves)and support multiple types of input data such as SAC,MSEED,NumPy array,etc.A test on the dataset of the Wenchuan aftershocks shows the generalization ability and detection accuracy of the software.The software is expected to increase the efficiency and subjectivity in the manual processing of large amounts of seismic data,thereby providing convenience to regional network monitoring staffs and researchers in the study of Earth's interior.
文摘Automatic recognition and interpretation of engineering drawing plays an important role in computer aided engineering. Detecting the positional relation between entities is an important topic in this research field. In this paper the concepts of adjacent relativity and container window of drawing entities were proposed. By means of container window, the adjacent irrelative entities can be detected quickly and effectively, which speeds up the process of adjacent relativity detection. Meanwhile, the algorithm of adjacent relativity detection was discussed.
基金supported by the National Natural Science Foundation of China(Nos.22071144 and 22101169)Shanghai Scientific and Technological Committee(No.22010500300)。
文摘Detection and observation of reactive intermediates is an essential step in investigation of reaction pathways.However,most reactive intermediates are unstable and present at low concentrations;their short lifetimes make them difficult to detect and characterize.Supramolecular containers offer opportunities for the stabilization and characterization of those labile species,through isolation from the media and protection inside the cavity of the host.In this review,we summarize the examples of labile reaction intermediates that are stabilized and characterized with the help of supramolecular containers.The container compounds include carcerands,deep cavitands and amide naphthotubes.We focus on unstable guest species-cyclobutadiene,benzocyclopropenone,o-benzyne,1,2,4,6-cycloheptatetraene,anti-Bredt's olefin,fluorophenoxycarbene,O-acylisoamide,and hemiaminalthat act as intermediates in certain organic reactions.
基金supported by the National Science Foundation of the United States under Grant Nos.1801751 and 1956364.
文摘Containerized microservices have been widely deployed in the industry.Meanwhile,security issues also arise.Many security enhancement mechanisms for containerized microservices require predefined rules and policies.However,it is challenging when it comes to thousands of microservices and a massive amount of real-time unstructured data.Hence,automatic policy generation becomes indispensable.In this paper,we focus on the automatic solution for the security problem:irregular traffic detection for RPCs.We propose Informer,a two-phase machine learning framework to track the traffic of each RPC and automatically report anomalous points.We first identify RPC chain patterns using density-based clustering techniques and build a graph for each critical pattern.Next,we solve the irregular RPC traffic detection problem as a prediction problem for attributed graphs with time series by leveraging spatial-temporal graph convolution networks.Since the framework builds multiple models and makes individual predictions for each RPC chain pattern,it can be efficiently updated upon legitimate changes in any graphs.In evaluations,we applied Informer to a dataset containing more than 7 billion lines of raw RPC logs sampled from a large Kubernetes system for two weeks.We provide two case studies of detected real-world threats.As a result,our framework found fine-grained RPC chain patterns and accurately captured the anomalies in a dynamic and complicated microservice production scenario,which demonstrates the effectiveness of Informer.Furthermore,we extensively evaluated the risk of adversarial attacks for our prediction model under different reality constraints and showed that the model is robust to such attacks in most real-world scenarios.
文摘将氨显色反应方法应用在液化天然气(Liquefied Natural Gas,LNG)围护系统泄漏检测中,并探讨目标检测模型YOLOv7在氨显色图像泄漏点检测中的效果。针对LNG薄膜型围护系统泄漏检测需求,分析氨显色反应的原理,并根据该原理构建泄漏检测所需的抽注气系统、显色喷剂及图像采集终端;针对氨显色泄漏图像,将蓝色泄漏点进行标注并对数据进行MixUp数据增强;在泄漏点识别上,采用目标检测网络YOLOv7进行训练,在该数据集上平均准确度(mean Average Precision,mAP)达97.71%,可满足LNG围护系统自动化辅助检测要求。
