检索结果-内蒙古大学图书馆

Disorder-enhanced layer Hall effect in a magnetic sandwich heterostructure

Physical Review B 2024年第11期109卷 115301-115301页

作者： Xiao-Xia Yi Chun-Bo Hua Rui Chen Bin Zhou Department of Physics Hubei University Wuhan 430062 China School of Electronic and Information Engineering Hubei University of Science and Technology Xianning 437100 China Key Laboratory of Intelligent Sensing System and Security of Ministry of Education Hubei University Wuhan 430062 China

The layer Hall effect is a type of Hall effect where electrons on the top and bottom layers deflect in opposite directions. In this paper, we investigate the disorder effects on the layer Hall effect in a magnetic sandwich heterostructure that supports the axion insulator and normal insulator phases. We demonstrate that the layer Hall effect in the axion insulator phase is robust against weak disorder. Most interestingly, we find that the layer Hall effect in the normal insulator phase is significantly enhanced with increasing disorder strength. Theoretically, the disorder-enhanced layer Hall effect at weak disorder is explained by using the Born approximation. Experimentally, we suggest the disorder-enhanced layer Hall effect could be detected by applying a perpendicular electric field.

关键词： Topological Hall effect Dilute magnetic topological insulators Topological insulators

来源：评论

学校读者我要写书评

暂无评论

V³H: View Variation and View Heredity for Incomplete Multiview Clustering

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2020年第3期1卷 233-247页

作者： Fang, Xiang Hu, Yuchong Zhou, Pan Wu, Dapeng Oliver The School of Computer Science and Technology Key Laboratory of Information Storage System Ministry of Education of China Huazhong University of Science and Technology Wuhan 430074 China The Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China The Department of Electrical and Computer Engineering University of Florida Gainesville 32611 FL United States

Real data often appear in the form of multiple incomplete views. Incomplete multiview clustering is an effective method to integrate these incomplete views. Previous methods only learn the consistent information between different views and ignore the unique information of each view, which limits their clustering performance and generalizations. To overcome this limitation, we propose a novel View Variation and View Heredity approach (V³H). Inspired by the variation and the heredity in genetics, V³H first decomposes each subspace into a variation matrix for the corresponding view and a heredity matrix for all the views to represent the unique information and the consistent information respectively. Then, by aligning different views based on their cluster indicator matrices, V³H integrates the unique information from different views to improve the clustering performance. Finally, with the help of the adjustable low-rank representation based on the heredity matrix, V³H recovers the underlying true data structure to reduce the influence of the large incompleteness. More importantly, V³H presents possibly the first work to introduce genetics to clustering algorithms for learning simultaneously the consistent information and the unique information from incomplete multiview data. Extensive experimental results on fifteen benchmark datasets validate its superiority over other state-of-the-arts. Impact Statement—Incomplete multiview clustering is a popular technology to cluster incomplete datasets from multiple sources. The technology is becoming more significant due to the absence of the expensive requirement of labeling these datasets. However, previous algorithms cannot fully learn the information of each view. Inspired by variation and heredity in genetics, our proposed algorithm V³H fully learns the information of each view. Compared with the state-of-the-art algorithms, V³H improves clustering

关键词： Incomplete multiview clustering view heredity view variation

来源：评论

学校读者我要写书评

暂无评论

Self-power generation scheduling model under uncertainty in Energy Intensive Enterprises

Self-power generation scheduling model under uncertainty in ...

引用

Chinese Control Conference (CCC)

作者： Liu Kun Feng Gao Zhaojie Wang Haifeng Zhang Xiaohong Guan Qiaozhu Zhai Jiang Wu State Key Laboratory for Manufacturing System Engineering Xi'an Jiaotong University Xian China State Key Laboratory for Manufacturing System Engineering Xi'an Jiaotong University 710049 China Ministry of Education Key Lab For Intelligent Networks and Networks Security Xi'an 710049 China

Scheduling of the self-generation plants is very helpful to both the energy saving and emission reduction for Energy Intensive Enterprises. Uncertainty exists, so how to reduce the cost considering the uncertainty is an important problem to be studied. In this paper, the objective function is to minimize the overall cost of electricity and gas, and two different models are presented: the deterministic model and the stochastic model. Uncertainties of electricity load and gas supply are captured and analyzed by the scenario tree method. Numerical experiments are performed under a system with four units. Results show that during off-peak periods, most of the enterprises load is afforded by the grid; and that during peak periods, most of load is afforded by the self-generation plants. In addition, the results also indicate that the cost is highly dependent on forecast errors. The more accurate prediction is, the less cost the enterprises need to pay.

关键词： Uncertainty Electronic mail Monte Carlo methods Optimal scheduling Load modeling Numerical models Coal

来源：评论

学校读者我要写书评

暂无评论

Load identification based on Factorial Hidden Markov Model and online performance analysis

Load identification based on Factorial Hidden Markov Model a...

引用

IEEE International Conference on Automation Science and Engineering (CASE)

作者： Siyun Chen Feng Gao Ting Liu State Key Laboratory for Manufacturing System Engineering Xi'an Jiaotong University Xi'an China Ting Liu is with Ministry of Education Key Lab for Intelligent Networks and Networks Security Xi'an Jiaotong University China

Load identification is important for the tasks such as load forecasting, demand response and energy management in smart buildings. The accuracy of the traditional methods depends on the dimension of load signatures, the sampling frequency and the stability of load profile. In this paper, a Factorial Hidden Markov Model (FHMM)-based method is proposed to analyze the aggregate load profile and identify the individual device. We extend the Viterbi algorithm to solve the FHMM directly, and this process is more efficient than the solution of the equivalent HMM by using the conventional Viterbi algorithm. The proposed method is insensitive to the stability and accuracy of power data, so it is suitable for the devices in buildings, even for the continuously variable loads. Two experiments with real power data are evaluated to illustrate the proposed method. Meanwhile, we focus on the online performance of the Viterbi algorithm. It is found that the states decoded by Viterbi are unreliable when the observed data are inside a confusing zone. Through analyzing the mechanism of the Viterbi algorithm, the judgment conditions the boundary of the confusing zone are given. We hope this work brings insight to the research on load identification and HMM.

关键词： Hidden Markov models Load modeling Viterbi algorithm Aggregates Performance evaluation Markov processes Steady-state

来源：评论

学校读者我要写书评

暂无评论

RKSeg+: make full use of Runge–Kutta methods in medical image segmentation

引用

Multimedia systems 2024年第2期30卷 65-65页

作者： Zhu, Mai Fu, Chong Wang, Xingwei School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education Shenyang110819 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China

The dynamical system perspective has been used to build efficient image classification networks and semantic segmentation networks. Furthermore, the Runge–Kutta (RK) methods are powerful tools for building networks from the dynamical systems perspective. Hence, the Runge–Kutta segmentation network (RKSeg) for medical image segmentation was born. Skip connections and multiple scaling are often used in common models but lack mathematical explanations. RKSeg interprets and uses skip connections based on the RK methods. Therefore, RKSeg greatly improves segmentation efficiency. However, it does not explain and use multiple scales from a dynamical system perspective but only inherits the multi-scale scheme of existing models. We compensate for this shortcoming by interpreting and using multiple scales based on the RK methods. In addition, the network structure also limits the excellent image classification networks as the backbones of RKSegs. Therefore, we modify the network structure to support more image classification networks as backbones. As a result, we propose a novel network structure RKSeg+. Our proposed RKSeg+ achieves better segmentation results with fewer parameters than RKSeg. Furthermore, RKSeg+, well configured with few parameters, outperforms state-of-the-art models on six of the ten organ datasets in the Medical Segmentation Decathlon. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Deep Feature Surgery: Towards Accurate and Efficient Multi-Exit Networks

arXiv

引用

arXiv 2024年

作者： Gong, Cheng Chen, Yao Luo, Qiuyang Lu, Ye Li, Tao Zhang, Yuzhi Sun, Yufei Zhang, Le College of Software Nankai University China College of Computer Science Nankai University China National University of Singapore Singapore School of Information and Communication Engineering University of Electronic Science and Technology of China China HAIHE Lab of ITAI China Tianjin Key Laboratory of Network and Data Security Technology China Key Laboratory of Data and Intelligent System Security Ministry of Education

Multi-exit network is a promising architecture for efficient model inference by sharing backbone networks and weights among multiple exits. However, the gradient conflict of the shared weights results in sub-optimal accuracy. This paper introduces Deep Feature Surgery (DFS), which consists of feature partitioning and feature referencing approaches to resolve gradient conflict issues during the training of multi-exit networks. The feature partitioning separates shared features along the depth axis among all exits to alleviate gradient conflict while simultaneously promoting joint optimization for each exit. Subsequently, feature referencing enhances multi-scale features for distinct exits across varying depths to improve the model accuracy. Furthermore, DFS reduces the training operations with the reduced complexity of backpropagation. Experimental results on Cifar100 and ImageNet datasets exhibit that DFS provides up to a 50.00% reduction in training time and attains up to a 6.94% enhancement in accuracy when contrasted with baseline methods across diverse models and tasks. Budgeted batch classification evaluation on MSDNet demonstrates that DFS uses about 2× fewer average FLOPs per image to achieve the same classification accuracy as baseline methods on Cifar100. The code is available at https: //***/GongCheng1919/dfs. Copyright © 2024, The Authors. All rights reserved.

关键词： Budget control

来源：评论

学校读者我要写书评

暂无评论

Distributed optimization strategy for multi-region power scheduling based on alternating direction method of multipliers

Distributed optimization strategy for multi-region power sch...

引用

World Congress on intelligent Control and Automation (WCICA)

作者： Chenlin Sun Jiang Wu Feng Gao Xiaohong Guan State Key Laboratory for Manufacturing System Engineering of Xi'an Jiaotong University Xi'an China Ministry of Education Key Lab for Intelligent Networks and Networks Security of Xi'an Jiaotong University Xi'an China Key Laboratory for Manufacturing System Engineering of Xi'an Jiaotong University Xi'an China

ISBN: (纸本)9781467384155

The large-scale and frequent cross-regional electricity allocations bring about the critical need to coordinate multi-region power scheduling. It is recognized that each sub-systems' taking charge of own scheduling makes the ideal centralized optimization method infeasible. In this paper, a distributed optimization strategy based on alternating direction method of multipliers (ADMM) is put forward to solve this problem. The strategy can achieve optimal solution of global power system as the ideal centralized optimization method does, by adding steps of information exchange and iterations of decision variables to subsystems' independent scheduling. The distributed bid cost minimization model with a price-wise constant bidding curve is built to describe the multi-region scheduling problem as precisely as possible. To suit the distributed model, the standard ADMM is improved, by expanding the size of sub-systems, adding inequality constraints, and considering the complex coupling relationship between sub-systems. This coordinating strategy uses the framework of augmented Lagrangian to decompose the scheduling problem into a distributed scheduling model for regional power grid, achieving global optimal solution by updating decision variables of each sub-system and Lagrange multipliers with mapping of sub-system information. Simulations on IEEE 30 system are conducted to show the computational performance and solution quality of the proposed method.

关键词： Transmission line matrix methods Mathematical model Power systems Optimization Computational modeling Minimization Standards

来源：评论

学校读者我要写书评

暂无评论

LAN: Learning Adaptive Neighbors for Real-Time Insider Threat Detection

arXiv

引用

arXiv 2024年

作者： Cai, Xiangrui Wang, Yang Xu, Sihan Li, Hao Zhang, Ying Liu, Zheli Yuan, Xiaojie the Key Laboratory of Data and Intelligent System Security Ministry of Education China the College of Cyber Science Nankai University Tianjin300350 China College of Computer Science Nankai University Tianjin300350 China Science and Technology on Communication Networks Laboratory Shijiazhuang050081 China

Enterprises and organizations are faced with potential threats from insider employees that may lead to serious consequences. Previous studies on insider threat detection (ITD) mainly focus on detecting abnormal users or abnormal time periods (e.g., a week or a day). However, a user may have hundreds of thousands of activities in the log, and even within a day there may exist thousands of activities for a user, requiring a high investigation budget to verify abnormal users or activities given the detection results. On the other hand, existing works are mainly post-hoc methods rather than real-time detection, which can not report insider threats in time before they cause loss. In this paper, we conduct the first study towards real-time ITD at activity level, and present a fine-grained and efficient framework LAN. Specifically, LAN simultaneously learns the temporal dependencies within an activity sequence and the relationships between activities across sequences with graph structure learning. Moreover, to mitigate the data imbalance problem in ITD, we propose a novel hybrid prediction loss, which integrates self-supervision signals from normal activities and supervision signals from abnormal activities into a unified loss for anomaly detection. We evaluate the performance of LAN on two widely used datasets, i.e., CERT r4.2 and CERT r5.2. Extensive and comparative experiments demonstrate the superiority of LAN, outperforming 9 state-of-the-art baselines by at least 9.92% and 6.35% in AUC for real-time ITD on CERT r4.2 and r5.2, respectively. Moreover, LAN can be also applied to post-hoc ITD, surpassing 8 competitive baselines by at least 7.70% and 4.03% in AUC on two datasets. Finally, the ablation study, parameter analysis, and compatibility analysis evaluate the impact of each module and hyper-parameter in LAN. The source code can be obtained from https://***/Li1Neo/LAN. Copyright © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

A rich ranking model based on the matthew effect optimization 7th

A rich ranking model based on the matthew effect optimizatio...

引用

7th International Conference on Computational data and Social Networks, CSoNet 2018

作者： Li, Jinzhong Liu, Guanjun Department of Computer Science and Technology College of Electronic and Information Engineering Jinggangshan University Ji’an343009 China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu610054 China Department of Computer Science and Technology College of Electronic and Information Engineering Tongji University Shanghai201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China

ISBN: (纸本)9783030046477

Most existing approaches of learning to rank treat the effectiveness of each query equally which results in a relatively lower ratio of queries with high effectiveness (i.e. rich queries) in the produced ranking model. Such ranking models need to be further optimized to increase the number of rich queries. In this paper, queries with different effectiveness are distinguished, and the queries with higher effectiveness are given higher weights. We modify the gradient in the LambdaMART algorithm based on a new perspective of Matthew effect to highlight the optimization of the rich queries and to produce the rich ranking model, and we present a consistency theorem for the modified optimization objective. Based on the effectiveness evaluation criteria for information retrieval, we introduce the Gini coefficient, mean-variance and quantity statistics to measure the performances of the ranking models. Experimental results show that the ranking models produced by the gradient-modified LambdaMART algorithm based on Matthew effect exhibit a stronger Matthew effect compared to the original LambdaMART algorithm. © Springer Nature Switzerland AG 2018.

关键词： Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Learning Counterfactual Explanation of Graph Neural Networks Via Generative Flow Network

SSRN

引用

SSRN 2023年

作者： He, Kangjia Liu, Li Zhang, Youmin Wang, Ye Liu, Qun Wang, Guoyin School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Key Laboratory of Computational Intelligence Chongqing China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing China

Counterfactual subgraphs explain Graph Neural Networks (GNNs) by answering the question: "How would the prediction change if a certain subgraph were absent in the input instance?" The differentiable proxy adjacency matrix is prevalent in current counterfactual subgraph discovery studies due to its ability to avoid exhaustive edge searching. However, a prediction gap exists when feeding the proxy matrix with continuous values and the thresholded discrete adjacency matrix to GNNs, compromising the optimization of the subgraph generator. Furthermore, the end-to-end learning schema adopted in the subgraph generator limits the diversity of counterfactual subgraphs. To this end, we propose CF-GFNExplainer, a flow-based approach for learning counterfactual subgraphs. CF-GFNExplainer employs a policy network with a discrete edge removal schema to construct counterfactual subgraph generation trajectories. Additionally, we introduce a reward and loss function designed to guide CF-GFNExplainer's optimization. The discrete adjacency matrix generated in each trajectory eliminates the prediction gap, enhancing the validity of the learned subgraphs. Furthermore, the multi-trajectories sampling strategy adopted in CF-GFNExplainer results in diverse counterfactual subgraphs. Extensive experiments conducted on synthetic and real-world datasets demonstrate the effectiveness of the proposed method in terms of validity and diversity. © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：