基于珠串分子动力学的漫游反应H+MgH的反应速率研究

本文使用珠串分子动力学计算了多通道漫游反应H+MgH→Mg+H_(2)的热速率系数,明确考虑了两个反应通道:紧致和漫游通道.首次将珠串分子动力学方法应用于多通道反应.借助新开发的方法,即用于准备初始结构的优化插值工具和自适应的力常数选择方法,成功获得了热速率系数.本文结果与其他理论方法,如变分过渡态理论和量子动力学结果一致.珠串分子动力学速率结果显示出速率对温度的负相关性,这与变分过渡态理论的结果相似,但与基态量子动力学的结果不同.

Rate Coecients and Kinetic Isotope E ects of Cl+XCl→XCl+Cl(X=H,D,Mu)Reactions from Ring Polymer Molecular Dynamics

The ring-polymer molecular dynamics(RPMD)was used to calculate the thermal rate coefficients and kinetic isotope effects of the heavy-light-heavy abstract reaction Cl+XCl→XCl+Cl(X=H,D,Mu).For the Cl+HCl reaction,the excellent agreement between the RPMD and experimental values provides a strong proof for the accuracy of the RPMD theory.And the RPMD results are also consistent with results from other theoretical methods including improved-canonical-variational-theory and quantum dynamics.The most novel finding is that there is a double peak in Cl+MuCl reaction near the transition state,leaving a free energy well.It comes from the mode softening of the reaction system at the peak of the potential energy surface.Such an explicit free energy well suggests strongly there is an observable resonance.And for the Cl+DCl reaction,the RPMD rate coefficient again gives very accurate results compared with experimental values.The only exception is at the temperature of 312.5 K,results from RPMD and all other theoretical methods are close to each other but slightly lower than the experimental value,which indicates experimental or potential energy surface deficiency.

ATLRec:An Attentional Adversarial Transfer Learning Network for Cross-Domain Recommendation

Entity linking is a new technique in recommender systems to link users'interaction behaviors in different domains,for the purpose of improving the performance of the recommendation task.Linking-based cross-domain recom-mendation aims to alleviate the data sparse problem by utilizing the domain-sharable knowledge from auxiliary domains.However,existing methods fail to prevent domain-specific features to be transferred,resulting in suboptimal results.In this paper,we aim to address this issue by proposing an adversarial transfer learning based model ATLRec,which effec-tively captures domain-sharable features for cross-domain recommendation.In ATLRec,we leverage adversarial learning to generate representations of user-item interactions in both the source and the target domains,such that the discrimina-tor cannot identify which domain they belong to,for the purpose of obtaining domain-sharable features.Meanwhile each domain learns its domain-specific features by a private feature extractor.The recommendation of each domain considers both domain-specific and domain-sharable features.We further adopt an attention mechanism to learn item latent factors of both domains by utilizing the shared users with interaction history,so that the representations of all items can be learned sufficiently in a shared space,even when few or even no items are shared by different domains.By this method,we can represent all items from the source and the target domains in a shared space,for the purpose of better linking items in different domains and capturing cross-domain item-item relatedness to facilitate the learning of domain-sharable knowledge.The proposed model is evaluated on various real-world datasets and demonstrated to outperform several state-of-the-art single-domain and cross-domain recommendation methods in terms of recommendation accuracy.

Scalable and Adaptive Joins for Tra jectory Data in Distributed Stream System

As a fundamental operation in LBS(location-based services),the trajectory similarity of moving objects has been extensively studied in recent years.However,due to the increasing volume of moving object trajectories and the demand of interactive query performance,the trajectory similarity queries are now required to be processed on massive datasets in a real-time manner.Existing work has proposed distributed or parallel solutions to enable large-scale trajectory similarity processing.However,those techniques cannot be directly adapted to the real-time scenario as it is likely to generate poor balancing performance when workload variance occurs on the incoming trajectory stream.In this paper,we propose a new workload partitioning framework,ART(Adaptive Framework for Real-Time Trajectory Similarity),which introduces practical algorithms to support dynamic workload assignment for RTTS(real-time trajectory similarity).Our proposal includes a processing model tailored for the RTTS scenario,a load balancing framework to maximize throughput,and an adaptive data partition manner designed to cut off unnecessary network cost.Based on this,our model can handle the large-scale trajectory similarity in an on-line scenario,which achieves scalability,effectiveness,and efficiency by a single shot.Empirical studies on synthetic data and real-world stream applications validate the usefulness of our proposal and prove the huge advantage of our approach over state-of-the-art solutions in the literature.

User Account Linkage Across Multiple Platforms with Location Data

Linking user accounts belonging to the same user across different platforms with location data has received significant attention,due to the popularization of GPS-enabled devices and the wide range of applications benefiting from user account linkage(e.g.,cross-platform user profiling and recommendation).Different from most existing studies which only focus on user account linkage across two platforms,we propose a novel model ULMP(i.e.,user account linkage across multiple platforms),with the goal of effectively and efficiently linking user accounts across multiple platforms with location data.Despite of the practical significance brought by successful user linkage across multiple platforms,this task is very challenging compared with the ones across two platforms.The major challenge lies in the fact that the number of user combinations shows an explosive growth with the increase of the number of platforms.To tackle the problem,a novel method GTkNN is first proposed to prune the search space by efficiently retrieving top-k candidate user accounts indexed with well-designed spatial and temporal index structures.Then,in the pruned space,a match score based on kernel density estimation combining both spatial and temporal information is designed to retrieve the linked user accounts.The extensive experiments conducted on four real-world datasets demonstrate the superiority of the proposed model ULMP in terms of both effectiveness and efficiency compared with the state-of-art methods.