检索结果-内蒙古大学图书馆

Pyramid scheme in stock market:A kind of financial market simulation

Chinese Physics B 2021年第9期30卷 624-632页

作者： Yong Shi Bo Li Guang-Le Du School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Research Center on Fictitious Economy and Data Science Chinese Academy of SciencesBeijing 100190China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China Wenzhou Institute University of Chinese Academy of SciencesWenzhou 325001China School of Physical Sciences University of Chinese Academy of SciencesBeijing 100049China

Artificial stock market simulation based on agent is an important means to study financial *** on the assumption that the investors are composed of a main fund,small trend and contrarian investors characterized by four parameters,we simulate and research a kind of financial phenomenon with the characteristics of pyramid *** simulation results and theoretical analysis reveal the relationships between the rate of return of the main fund and the proportion of the trend investors in all small investors,the small investors'parameters of taking profit and stopping loss,the order size of the main fund and the strategies adopted by the main *** work is helpful to explain the financial phenomenon with the characteristics of pyramid schemes in financial markets,design trading rules for regulators and develop trading strategies for investors.

关键词： pyramid scheme agent-based model artificial stock market herd behavior

来源：评论

学校读者我要写书评

暂无评论

Real-time Energy Dispatching Strategy for Microgrid Based on Improved SD3 Algorithm

Journal of Network Intelligence

引用

Journal of Network Intelligence 2023年第4期8卷 1338-1357页

作者： Hang, Tian-Xiao He, Wen-Wu Pei, Bo-Yu Lin, Mei-Li Li, Pei-Qiang School of Computer Science and Mathematics Fujian University of Technology Xuefu South Road Fujian Province Fuzhou City350118 China School of Computer Science and Mathematics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Xuefu South Road Fujian Province Fuzhou City350118 China School of Electronic Electrical Engineering and Physics Fujian University of Technology Xuefu South Road Fujian Province Fuzhou City350118 China

Deep reinforcement learning algorithms has attracted attention in the field of energy management in recent years, which can effectively interact with the environment and learn relevant policies to achieve specific goals. To eliminate Q-value estimation bias, in this paper we propose a real-time energy dispatching strategy for microgrid based on SD3 algorithm, and introduce prioritized experience replay and Huber loss, to develop an improved algorithm, called SD3 with loss-adjusted prioritized experience replay (SD3-LAP). A grid-connected microgrid that includes photovoltaic system, wind turbine, micro-turbine, energy storage system and electrical load is built to simulate the environment and validate the proposed methods. The problem of real-time energy dispatching for microgrid is recast as a Markov Decision Process, which aims to minimize the operating costs under the condition that the constraints of power balance and the upper and lower limits of SOC of the energy storage system are satisfied. Experimental results show that compared with baseline models, the energy dispatching strategies provide by SD3 algorithm and SD3-LAP algorithm are more effective and robust, and the mechanism of LAP is a general purposed plug-in that can effectively improve the performance of related reinforcement learning algorithm. The daily average operating cost of SD3-LAP algorithm and SD3 algorithm are respectively 37.9% and 22.89% lower than TD3 algorithm, while the daily average operating cost of of TD3 algorithm is 21.9% lower than DQN. In addition, compared with the original algorithms, namely SD3, TD3 and DQN, the LAP mechanism reduces respectively the daily average operating cost by 19.5%, 11.7%, and 20.1%. © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Fast One-Pass Approximation for Matrix-Based Rényi's Entropy

SSRN

引用

SSRN 2024年

作者： Gong, Tieliang Dong, Yuxin Gao, Zeyu Chen, Hong Zhang, Weizhan Shaanxi Xi’an710049 China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Shaanxi Xi’an710049 China Department of Oncology University of Cambridge CambridgeCB2 1TN United Kingdom Department of Mathematics and Statistics Huazhong Agriculture University Hubei Wuhan430070 China

This paper is concerned with improving the computational efficiency of the matrix-based Rényi's α-order entropy, which enables directly being accessed from the given data without knowing the probability density function of the underlying data. However, leveraging either trace estimation or the eigenvalue-based methods will cost O(n3) in time complexity, where n is the number of observed samples. The intensive computational demands impede its utility in modern large-scale machine learning tasks. Recently, the Hutch++ based entropy approximation was developed, by converting the original entropy estimation problem into stochastic trace estimation. It reduces the time complexity to O(n2s) (s © 2024, The Authors. All rights reserved.

关键词： Distillation

来源：评论

学校读者我要写书评

暂无评论

Hierarchical features-based targeted aspect extraction from online reviews

引用

Intelligent data Analysis 2021年第1期25卷 205-223页

作者： He, Jin Li, Lei Wang, Yan Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei Anhui China School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China Department of Computing Macquarie University Sydney Australia MiningLamp Academy of Sciences Mininglamp Techonologies Beijing China

With the prevalence of online review websites, large-scale data promote the necessity of focused analysis. This task aims to capture the information that is highly relevant to a specific aspect. However, the broad scope of the aspects of the various products makes this task overarching but challenging. A commonly used solution is to modify the topic models with additional information to capture the features for a specific aspect (referred to as a targeted aspect). However, the existing topic models, either perform the full analysis to capture features as many as possible or estimate the similarity to capture features as coherent as possible, overlook the fine-grained semantic relations between the features, resulting in the captured features coarse and confusing. In this paper, we propose a novel Hierarchical Features-based Topic Model (HFTM) to extract targeted aspects from online reviews, then to capture the aspect-specific features. Specifically, our model can not only capture the direct features posing target-to-feature semantics but also capture the latent features posing feature-to-feature semantics. The experiments conducted on real-world datasets demonstrate that HFTMl outperforms the state-of-the-art baselines in terms of both aspect extraction and document classification. © 2021 - IOS Press. All rights reserved.

关键词： Aspect extraction Focused analysis Online reviews Text mining Topic modeling

来源：评论

学校读者我要写书评

暂无评论

Sara: Controllable Makeup Transfer with Semantic-Guided Alignment and Region-Adaptive Normalization

SSRN

引用

SSRN 2024年

作者： Zhong, Xiaojing Huang, Xinyi Wu, Zhonghua Lin, Guosheng Wu, Qingyao School of Software Engineering South China University of Technology China School of Computer Science and Engineering Nanyang Technological University Singapore Key Laboratory of Big Data and Intelligent Robot Ministry of Education China Peng Cheng Laboratory China SenseTime Research Hong Kong

Makeup transfer is a process of transferring the makeup style from a reference image to the source image, while preserving the source image's identity. In addition to achieving fine-grained control over the makeup transfer, incorporating semantic alignment into the transformation is crucial, as the poses of the reference and source images are often inconsistent. We propose a novel Semantic-guided Alignment and Region-Adaptive normalization framework (SARA) to effectively transfer makeup styles under misaligned poses, offering flexible control to meet the demands of real-world applications, such as partial transfer, intensity adjustment, and makeup removal. Specifically, SARA comprises three modules: Firstly, we propose a semantic-guided alignment module to explicitly construct dense correspondence between the reference image and the target semantic map, employing unbalanced optimal transport matching to handle semantic region mismatches. Secondly, a region-adaptive normalization module is responsible for dynamically combining the warped style features with shape-independent style codes obtained by region-wise average pooling, mitigating feature loss during the alignment. Lastly, a makeup fusion module progressively fuses the identity features with the makeup styles to render the final output image. Furthermore, we combine optimal transport with histogram matching to generate the pseudo ground truth, which is used to facilitate the transfer in terms of both spatial alignment and color distribution. Experimental results show that our proposed SARA outperforms existing methods on two public datasets. © 2024, The Authors. All rights reserved.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Parallel Dual-Branch Fusion Network for Epileptic Seizure Prediction

引用

Computers in Biology and Medicine 2024年 176卷 108565-108565页

作者： Ma, Hongcheng Wu, Yajing Tang, Yongqiang Chen, Rui Xu, Tao Zhang, Wensheng School of Information and Communication Engineering Hainan University Haikou China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and Technology Taiyuan China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China

Epilepsy is a prevalent chronic disorder of the central nervous system. The timely and accurate seizure prediction using the scalp Electroencephalography (EEG) signal can make patients adopt reasonable preventive measures before seizures occur and thus reduce harm to patients. In recent years, deep learning-based methods have made significant progress in solving the problem of epileptic seizure prediction. However, most current methods mainly focus on modeling short- or long-term dependence in EEG, while neglecting to consider both. In this study, we propose a Parallel Dual-Branch Fusion Network (PDBFusNet) which aims to combine the complementary advantages of Convolutional Neural Network (CNN) and Transformer. Specifically, the features of the EEG signal are first extracted using Mel Frequency Cepstral Coefficients (MFCC). Then, the extracted features are delivered into the parallel dual-branches to simultaneously capture the short- and long-term dependencies of EEG signal. Further, regarding the Transformer branch, a novel feature fusion module is developed to enhance the ability of utilizing time, frequency, and channel information. To evaluate our proposal, we perform sufficient experiments on the public epileptic EEG dataset CHB-MIT, where the accuracy, sensitivity, specificity and precision are 95.76%, 95.81%, 95.71% and 95.71%, respectively. PDBFusNet shows superior performance compared to state-of-the-art competitors, which confirms the effectiveness of our proposal. © 2024 Elsevier Ltd

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

DBSSL: A Scheme to Detect Backdoor Attacks in Self-Supervised Learning Models

引用

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Huang, Yuxian Yang, Geng Yuan, Dong Yu, Shui Nanjing University of Posts and Telecommunication College of Computer Science and Software China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing China University of Sydney School of Electrical and Information Engineering Australia University of Technology Sydney School of Computer Science Australia

Recently, self-supervised learning has garnered significant attention for its ability to extract high-quality features from unlabeled data. However, existing research indicates that backdoor attacks can pose significant threats to self-supervised learning. Additionally, due to the substantial training overhead, traditional backdoor detection methods in supervised learning, such as meta-learning, are not well-suited for self-supervised learning. Current backdoor detection methods for self-supervised learning can only defend against backdoor attacks triggered by patch. To address this challenge, we proposes DBSSL, a scheme that can efficiently detect backdoor attacks in self-supervised learning models. In DBSSL, we connect backdoor attacks with adversarial attacks and use adversarial perturbations for detection. Specifically, we first conduct targeted adversarial attacks on the samples, classifying them into different classes. Then, we employ the Z-Score method to analyze these adversarial perturbations. If outliers are present, we can infer that the self-supervised learning model has been backdoored. Unlike previous works, our scheme is better suited for downstream users with constrained computation resources and exhibits excellent detection capabilities for backdoor attacks caused by patch -type or global triggers. Abundant theoretical analysis has demonstrated the feasibility of our scheme. Moreover, extensive experiments show that our method performs well in detecting prevalent backdoor attacks in self-supervised learning, achieving detection accuracy exceeding 95%. © 2004-2012 IEEE.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Autonomous discrimination method of refracted stars based on projection error

Autonomous discrimination method of refracted stars based on...

引用

2021 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Systems

作者： Ma, Yan Jiang, Jie The School of Instrumentation and Optoelectronic Engineering Beihang University No. 37 Xueyuan Road Beijing100191 China Precision Opto-mechatronics Technology Key Laboratory of Education Ministry Beijing Advanced Innovation Center for Big Data-Based Precision Medicine China

ISBN: (纸本)9781510655690

Correctly distinguishing between refracted stars and nonrefracted stars is a prerequisite for a single field of view celestial navigation system. An autonomous discrimination method of refracted stars based on projection error is proposed that distinguishes between refracted and nonrefracted stars and estimates the attitude simultaneously depending only on knowledge of the star point centroid coordinates. Firstly, a rough attitude is calculated according to the body vectors and inertia vectors of all stars in the image. Next, for each of the stars, the reprojection vector is calculated and compared with its body vector. Stars with large residual errors are classified as refracted stars. Then the attitude is recomputed without these refracted stars until all the residual errors are small. The classification precision is 98% and the attitude estimation error can be reduced by 68% compared with the attitude estimated before refracted star discrimination. © 2022 SPIE.

关键词： Stars

来源：评论

学校读者我要写书评

暂无评论

ThyroidNet:A Deep Learning Network for Localization and Classification of Thyroid Nodules

引用

Computer Modeling in engineering & Sciences 2024年第4期139卷 361-382页

作者： Lu Chen Huaqiang Chen Zhikai Pan Sheng Xu Guangsheng Lai Shuwen Chen Shuihua Wang Xiaodong Gu Yudong Zhang Ultrasonic Department Zhongda Hospital Affiliated to Southeast UniversityNanjing210009China School of Physics and Information Engineering Jiangsu Second Normal UniversityNanjing211200China School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia State Key Laboratory of MillimeterWaves Southeast UniversityNanjing210096China Jiangsu Province Engineering Research Center of Basic Education Big Data Application Jiangsu Second Normal UniversityNanjing211200China School of Software Engineering Quanzhou Normal UniversityQuanzhou362000China Department of Biological Sciences Xi’an Jiaotong-Liverpool UniversitySuzhou215123China

Aim:This study aims to establish an artificial intelligence model,ThyroidNet,to diagnose thyroid nodules using deep learning techniques ***:A novel method,ThyroidNet,is introduced and evaluated based on deep learning for the localization and classification of thyroid ***,we propose the multitask TransUnet,which combines the TransUnet encoder and decoder with multitask ***,we propose the DualLoss function,tailored to the thyroid nodule localization and classification *** balances the learning of the localization and classification tasks to help improve the model’s generalization ***,we introduce strategies for augmenting the ***,we submit a novel deep learning model,ThyroidNet,to accurately detect thyroid ***:ThyroidNet was evaluated on private datasets and was comparable to other existing methods,including U-Net and *** results show that ThyroidNet outperformed these methods in localizing and classifying thyroid *** achieved improved accuracy of 3.9%and 1.5%,***:ThyroidNet significantly improves the clinical diagnosis of thyroid nodules and supports medical image analysis *** research directions include optimization of the model structure,expansion of the dataset size,reduction of computational complexity and memory requirements,and exploration of additional applications of ThyroidNet in medical image analysis.

关键词： ThyroidNet deep learning TransUnet multitask learning medical image analysis

来源：评论

学校读者我要写书评

暂无评论

key-Point Based Distribution Probability Optimization for Object Detection in Remote Sensing Images 7

Key-Point Based Distribution Probability Optimization for Ob...

引用

7th International Conference on Computer and Communications, ICCC 2021

作者： Zhu, Xiaoke Wang, Yufei Chen, Xiaopan Li, Danyang Zhang, Jitong Cai, Yulong Henan University School of Computer and Information Engineering Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

ISBN: (纸本)9781665409506

Recently, object detection in remote sensing images has been an important topic in computer vision. As a mainstream approach to detectors, single-stage detectors usually compute the important points of the bounding box to get the location of the target, and optimize them by Focal Loss. One of the development trends for one-stage detectors is to combine with Localization Quality Estimation (LQE), which can provide accurate ranking scores, and thus bring benefits to the Non-Maximum Suppression as well as the following detection results. In this paper, we propose a one-stage detector, named key-point based Distribution Probability Optimization (KDPO), for remote sensing object detection. Specially, we first classify and locate the object through five key points. Then, we merge the quality estimation into the class prediction vector to form a joint representation of localization quality and classification. Next, we utilize a vector to represent arbitrary distribution of box locations. The improved representations avoid the inconsistency risk and accurately describe the flexible distribution of the data. Meanwhile, we use the improved Focal Loss for the succeed optimization. The proposed method is tested on two popular aerial image datasets, including HRSC2016 and DOTA. In all experiments, our approach achieves promising performance comparing to the state-of-the-art detectors. © 2021 IEEE.

关键词： Object detection

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：