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Spectroscopy of X-Ray-Boosted Photoionization with an Intense Laser Pulse

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Chinese Physics Letters 2013年第8期30卷 50-53页

作者： ge yu-Cheng School of Physics and State Key Laboratory of Nuclear Physics and Technology Peking UniversityBeijing 100871

The present study shows that x-ray photoionization in the presence of an intense laser pulse has interesting energetic and statistical properties due to field modulation and interference between *** spectral cut-off energies,integral,and double integral reflect the strength,time,and interference of the laser field *** methods are proposed for precise intense-laser-pulse measurement in *** methods have the advantages of accuracy,simplicity,speed,and large dynamic ranges.

关键词： laser pulse intense

DRPS:efficient disk-resident parameter servers for distributed machine learning

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Frontiers of Computer Science 2022年第4期16卷 79-90页

作者： Zhen Song yu Gu Zhigang Wang ge yu School of Computer Science and Engineering Northeastern UniversityShenyang110819China College of Information Science and Engineering Ocean University of ChinaQingdao266100China

Parameter server(PS)as the state-of-the-art distributed framework for large-scale iterative machine learning tasks has been extensively ***,existing PS-based systems often depend on memory *** memory constraints,machine learning(ML)developers cannot train large-scale ML models in their rather small local ***,renting large-scale cloud servers is always economically infeasible for research teams and small *** this paper,we propose a disk-resident parameter server system named DRPS,which reduces the hardware requirement of large-scale machine learning tasks by storing high dimensional models on *** further improve the performance of DRPS,we build an efficient index structure for parameters to reduce the disk I/O *** on this index structure,we propose a novel multi-objective partitioning algorithm for the ***,a flexible workerselection parallel model of computation(WSP)is proposed to strike a right balance between the problem of inconsistent parameter versions(staleness)and that of inconsistent execution progresses(straggler).Extensive experiments on many typical machine learning applications with real and synthetic datasets validate the effectiveness of DRPS.

关键词： parameter servers machine learning disk resident parallel model

An evaluation and query algorithm for the influence of spatial location based on RkNN

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Frontiers of Computer Science 2021年第2期15卷 81-89页

作者： Jingke XU Yidan ZHAO ge yu School of Computer Science and Engineering Northeastern UniversityShenyang110819China School of Information and Control Engineering Shenyang Jianzhu UniversityShenyang110168China Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang110168China

This paper is devoted to the investigation of the evaluation and query algorithm problem for the influence of spatial location based on RkNN(reverse k nearest neighbor).On the one hand,an object can make contribution to multiple ***,for the existing measures for evaluating the influence of spatial location,an object only makes contribution to one location,and its influence is usually measured by the number of spatial objects in the *** this case,a new measure for evaluating the influence of spatial location based on the RkNN is *** the weight of the contribution is determined by the distance between the object and the location,the influence weight definition is given,which meets the actual *** the other hand,a query algorithm for the influence of spatial location is introduced based on the proposed ***,an algorithm named INCH(INtersection’s Convex Hull)is applied to get candidate regions,where all objects are ***,kNN and Range-k are used to refine ***,according to the proposed measure,the weights of objects in RkNN results are computed,and the influence of the location is *** experimental results on the real data show that the optimized algorithms outperform the basic algorithm on *** addition,in order to provide the best customer service in the location problem and make the best use of all infrastructures,a location algorithm with the query is presented based on *** influence of each facility is calculated in the location program and the equilibrium coefficient is used to evaluate the reasonability of the location in the *** smaller the equilibrium coefficient is,the more reasonability the program *** actual application shows that the location based on influence makes the location algorithm more reasonable and available.

关键词： spatial data reverse k nearest neighbor influence of spatial location location algorithm

Mining coterie patterns from Instagram photo trajectories for recommending popular travel routes

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Frontiers of Computer Science 2017年第6期11卷 1007-1022页

作者： Yaxin yu yuhai ZHAO ge yu Guoren WANG Department of Computer Science School of Computer Science & Engineering Northeastern University Shenyang 110819 China

Abstract Instagram is a popular photo-sharing social ap- plication. It is widely used by tourists to record their journey information such as location, time and interest. Consequently, a huge volume of get-tagged photos with spatio-temporal in- formation are generated along tourist＇s travel trajectories. Such Instagram photo trajectories consist of travel paths, travel density distributions, and traveller behaviors, prefer- ences, and mobility patterns. Mining Instagram photo trajec- tories is thus very useful for many mobile and location-based social applications, including tour guide and recommender systems. However, we have not found any work that extracts interesting group-like travel trajectories from Instagram pho- tos asynchronously taken by different tourists. Motivated by this, we propose a novel concept： coterie, which reveals representative travel trajectory patterns hidden in Instagram photos taken by users at shared locations and paths. Our work includes the discovery of （1） coteries, （2） closed co- teries, and （3） the recommendation of popular travel routes based on closed coteries. For this, we first build a statistically reliable trajectory database from Instagram get-tagged pho- tos. These trajectories are then clustered by the DBSCAN method to find tourist density. Next, we transform each raw spatio-temporal trajectory into a sequence of clusters. All dis- criminative closed coteries are further identified by a Cluster- Growth algorithm. Finally, distance-aware and conformity- aware recommendation strategies are applied on closed co- teries to recommend popular tour routes. Visualized demosand extensive experimental results demonstrate the effective- ness and efficiency of our methods.

关键词： tourists coterie closed coterie geotagged pho-tos Instagram trajectories recommendation popular travelroutes

Electronic Properties of Armchair Graphene Nanoribbons with Oxygenterminated Edges:A Density Functional Study

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Chinese Journal of Structural Chemistry 2015年第5期34卷 641-649页

作者： ge Hong-yu WANG Guo Department of Chemistry Capital Normal University

Armchair graphene nanoribbons with different proportions of edge oxygen atoms are analyzed in this study using the crystal orbital method,which is based on density functional *** buckled edges are present,all the nanoribbons are energetically *** the adjacent edge oxygen atoms,the isolated edge oxygen atoms cause semiconductor-metal transitions by introducing edge *** graphene nanoribbons with all oxygen atoms on the edges,band gap and carrier mobility vary with ribbon ***,this behavior is different from that of hydrogen-passivated graphene nanoribbons because of different effective widths,which are pictorially presented with crystal *** carrier mobilities are as 18%~65% magnitude as those of hydrogen-passivated nanoribbons and are of the order of 10^3 cm^2·V^-1·s^-1.

关键词： graphene nanoribbon semiconductor-metal transition crystal orbital carrier mobility density functional theory

Drought changes and the mechanism analysis for the North American Prairie

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Journal of Arid Land 2013年第1期5卷 1-14页

作者： ge yu DaveSAUCHYN YongFei LI State Key Laboratory of Lake Science and Environment Nanjing Institute of Geography and Limnology Chinese Academy of Sciences Nanjing 210008 China Prairie Adaptation and Research Collaborative University of Regina Regina $4S 7J7 Canada

The worst droughts in the central part of the North American Prairie in the past several hundred years have been reconstructed from tree-ring chronologies, suggesting that some drought years have exceeded the se- verity shown by the gauge record. A general circulation model of the geophysical Fluid Dynamics Laboratory （GFDL） has simulated climate changes for the area during the past 250 years driven by c^imatic forces, providing scenarios of extreme climate that can further diagnose the mechanisms. This study refined the drought signals from the tree ring data and GFDL modeling at inter-annual and decadal time scales and analyzed the potential mecha- nisms driving the droughts. Results showed that drought years with summer precipitation Ilower than the 10th per- centiles occurred during 1777-1789, 1847-1861 and 1886-1879 AD in the area. Both tree rings and model re- vealed that the frequency of droughts has been relatively consistent in a similar timing andl frequency with climate change. Monte Carlo analysis have detected that the tree ring chronologies have recorded drought years with probabilities of 9.3%-12.8%, and the model has simulated the droughts with probabilities 5..7%-17.8%. Under CO2 and aerosol forcing, the GFDL modeled the drought recurrences of 13 years and 25 years, which are very syn- chronous changes with tree rings and consistent with gauge records. The 20-a and 10-a time scale reoccurrences of droughts are very consistent with solar radiation cycles, and similar to the length of cycles in oceanic records, suggesting that terrestrial precipitation modeling is properly driven from sun-land-sea dynamics. Detected severity, variability and return periods of droughts from the present study make potential improvements in drought predictions and constructing scenarios for climate impacts and adaptation strategies.

关键词： drought tree ring chronology climate modeling probability central Prairie

A probabilistic generative model for tracking multi-knowledge concept mastery probability

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Frontiers of Computer Science 2024年第3期18卷 155-170页

作者： Hengyu LIU Tiancheng ZHANG Fan LI Minghe yu ge yu School of Computer Science and Engineering Northeastern UniversityShenyang 110169China Software College Northeastern UniversityShenyang 110169China

Knowledge tracing aims to track students’knowledge status over time to predict students’future performance *** a real environment,teachers expect knowledge tracing models to provide the interpretable result of knowledge *** chain-based knowledge tracing(MCKT)models,such as Bayesian Knowledge Tracing,can track knowledge concept mastery probability over ***,as the number of tracked knowledge concepts increases,the time complexity of MCKT predicting student performance increases exponentially(also called explaining away problem).When the number of tracked knowledge concepts is large,we cannot utilize MCKT to track knowledge concept mastery probability over *** addition,the existing MCKT models only consider the relationship between students’knowledge status and problems when modeling students’responses but ignore the relationship between knowledge concepts in the same *** address these challenges,we propose an inTerpretable pRobAbilistiC generative moDel(TRACED),which can track students’numerous knowledge concepts mastery probabilities over *** solve explain away problem,we design long and short-term memory(LSTM)-based networks to approximate the posterior distribution,predict students’future performance,and propose a heuristic algorithm to train LSTMs and probabilistic graphical model *** better model students’exercise responses,we proposed a logarithmic linear model with three interactive strategies,which models students’exercise responses by considering the relationship among students’knowledge status,knowledge concept,and *** conduct experiments with four real-world datasets in three knowledge-driven *** experimental results show that TRACED outperforms existing knowledge tracing methods in predicting students’future performance and can learn the relationship among students,knowledge concepts,and problems from students’exercise *** also conduct several case *** case studies show that

关键词： probabilistic graphical model deep learning knowledge tracing learner modeling

An integrated pipeline model for biomedical entity alignment

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Frontiers of Computer Science 2021年第3期15卷 81-95页

作者： yu HU Tiezheng NIE Derong SHEN yue KOU ge yu School of Computer Science and Engineering Northeastern UniversityShenyang 110819China

Biomedical entity alignment,composed of two subtasks:entity identification and entity-concept mapping,is of great research value in biomedical text mining while these techniques are widely used for name entity standardization,information retrieval,knowledge acquisition and ontology *** works made many efforts on feature engineering to employ feature-based models for entity identification and ***,the models depended on subjective feature selection may suffer error propagation and are not able to uti-lize the hidden *** rapid development in health-related research,researchers need an effective method to explore the large amount of available biomedical ***,we propose a two-stage entity alignment process,biomedical entity exploring model,to identify biomedical entities and align them to the knowledge base *** model aims to automatically obtain semantic information for extracting biomedical entities and mining semantic relations through the standard biomedical knowledge *** experiments show that the proposed method achieves better performance on entity *** proposed model dramatically improves the FI scores of the task by about 4.5%in entity identification and 2.5%in entity-concept mapping.

关键词： entity alignment biomedical text mining neural network model

Differentially private high-dimensional data publication via grouping and truncating techniques

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Frontiers of Computer Science 2019年第2期13卷 382-395页

作者： Ning WANG yu GU Jia XU Fangfang LI ge yu School of Computer Science and Engineering Northeastern University Shenyang 110819 China School of Computer Electronics and Information Guangxi University Guangxi 530004 China

The count of one column for high-dimensional datasets, i.e., the number of records containing this column, has been widely used in nuinerous applications such as analyzing popular spots based on check-in location information and mining valuable items from shopping records. However, this poses a privacy threat when directly publishing this information. Differential privacy (DP), as a notable paradigm for strong privacy guarantees, is thereby adopted to publish all column counts. Prior studies have verified that truncating records or grouping columns can effectively improve the accuracy of published results. To leverage the advantages of the two techniques, we combine these studies to further boost the accuracy of published results. However, the traditional penalty function, which measures the error imported by a given pair of parameters including truncating length and group size, is so sensitive that the derived parameters deviate from the optimal parameters significantly. To output preferable parameters, we first design a smart penalty function that is less sensitive than the traditional function. Moreover, a two-phase selection method is proposed to compute these parameters efficiently, together with the improvement in accuracy. Extensive experiments on a broad spectrum of real-world datasets validate the effectiveness of our proposals.

关键词： differential privacy high-dimensional data truncation optimization grouping penalty function