检索结果-内蒙古大学图书馆

Cooperator-driven and defector-driven punishments: How do they influence cooperation?

Physical Review E 2019年第5期100卷 052304-052304页

作者： Pengbi Cui (崔鹏碧) Zhi-Xi Wu Tao Zhou Xiaojie Chen School of Astronautics Northwestern Polytechnical University Xi'an 710072 People's Republic of China National Key Laboratory of Aerospace Flight Dynamics Xi'an 710072 People's Republic of China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu 610054 China Institute of Computational Physics and Complex Systems Lanzhou University Lanzhou Gansu 730000 China and Key Laboratory for Magnetism and Magnetic Materials of the Ministry of Education Lanzhou University Lanzhou Gansu 730000 China Institute of Fundamental and Frontier Science University of Electronic Science and Technology of China Chengdu 610073 China Big Data Research Center University of Electronic Science and Technology of China Chengdu 611731 China School of Mathematical Sciences University of Electronic Science and Technology of China Chengdu 611731 China

Economic studies have shown that there are two types of regulation schemes which can be considered as a vital part of today's global economy: self-regulation enforced by self-regulation organizations to govern industry practices and government regulation which is considered as another scheme to sustain corporate adherence. An outstanding problem of particular interest is to understand quantitatively the role of these regulation schemes in evolutionary dynamics. Typically, punishment usually occurs for enforcement of regulations. Taking into account both types of punishments to influence the regulations, we develop a game model where six evolutionary situations with corresponding combinations of strategies are considered. Furthermore, a semianalytical method is developed to allow us to give accurate estimations of the boundaries between the phases of full defection and nondefection. We find that, associated with the evolutionary dynamics, for an infinite well-mixed population, the mix of both punishments performs better than one punishment alone in promoting public cooperation, but for a networked population the cooperator-driven punishment turns out to be a better choice. We also reveal the monotonic facilitating effects of the synergy effect, punishment fine, and feedback sensitivity on the public cooperation for an infinite well-mixed population. Conversely, for a networked population an optimal intermediate range of feedback sensitivity is needed to best promote punishers' populations. Overall, a networked structure is overall more favorable for punishers and further for public cooperation, because of both network reciprocity and mutualism between punishers and cooperators who do not punish defectors. We provide physical understandings of the observed phenomena, through a detailed statistical analysis of frequencies of different strategies and spatial pattern formations in different evolution situations. These results provide valuable insights into how to select

关键词： Collective behavior in networks Competition Fitness Population dynamics Collective dynamics Evolutionary games Lattices Mean field theory Monte Carlo methods

来源：评论

学校读者我要写书评

暂无评论

Response of Soil Fertility to Soil Erosion on a Regional Scale: A Case Study of Northeast China

SSRN

引用

SSRN 2023年

作者： Xiong, Junfeng Wu, Hanyi Yi, Shaorong Lin, Chen Ma, Ronghua International Research Center of Big Data for Sustainable Development Goals Beijing100094 China Key Laboratory of Watershed Geographic Sciences Nanjing Institute of Geography and Limnology Chinese Academy of Sciences Nanjing210008 China State Key Laboratory of Remote Sensing Science Jointly Sponsored by Beijing Normal University Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences Beijing100875 China Key Laboratory of Environmental Change and Natural Disaster Ministry of Education Beijing Normal University Beijing100875 China School of Water Conservancy and Ecological Engineering Nanchang Institute of Technology Nanchang330099 China University of Chinese Academy of Sciences Nanjing211135 China

Climate change and human activities have increased the risk of soil erosion, resulting in widespread soil degradation in many regions worldwide. In this study, our purpose was to estimate the spatial and temporal changes in the soil fertility index of Northeast China from the 1990s to the 2010s from a regional perspective. The soil erosion modulus, calculated using the Revised Universal Soil Loss Equation, was combined with soil nutrient data for four nutrients in two time stages, and the response of soil fertility to soil erosion was analysed under nine land-use patterns. The results indicated that soil fertility in Northeast China had decreased by 4.46% from the 1990s to the 2010s, with the fertility of cultivated land showing the smallest decline at 1.69%, followed by grassland at 4.81%, and forest land at 6.21%, in that order. Compared with unchanged land use, soil fertility for changed land use decreased in a more obvious manner. The IFI was negatively correlated with the soil erosion modulus, especially when the average soil erosion modulus exceeded 2 t/(ha/yr). Furthermore, IFI was mostly negative, indicating obvious soil degradation. The contribution rate of nutrient loss in the 1990s averaged over 70%, which was significantly higher than that in the 2010s, indicating that the soil and water conservation measures implemented by most city governments after 2010 had been effective. In most cases, the contribution rates of the 1990s_TK and the 1990s_SOC were significantly higher than those of the other indicators, with the highest contribution rate of SOC loss being observed for cultivated land and the highest contribution rate of TK loss being observed for forest- and grass-lands. Forestland is conducive to maintaining soil fertility, and when other types of land are converted into forestland the contribution rate of nutrient loss becomes lower. © 2023, The Authors. All rights reserved.

关键词： Land use

来源：评论

学校读者我要写书评

暂无评论

Incorporating Surprisingly Popular Algorithm and Euclidean Distance-based Adaptive Topology into PSO

arXiv

引用

arXiv 2021年

作者： Wu, Xuan Han, Jizong Wang, Di Gao, Pengyue Cui, Quanlong Chen, Liang Liang, Yanchun Huang, Han Lee, Heow Pueh Miao, Chunyan Zhou, You Wu, Chunguo Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun130012 China Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly Nanyang Technological University 639798 Singapore State Key Laboratory of Superhard Materials International Center for Computational Method and Software College of Physics Jilin University Changchun130012 China Commercial Quality and Efficiency Department Baidu Inc Beijing100085 China Department of Computer Science Shantou University Shantou515063 China School of Computer Science Zhuhai College of Science and Technology Zhuhai519041 China School of Software Engineering The South China University of Technology Guangzhou510641 China Department of Mechanical Engineering National University of Singapore 117575 Singapore School of Computer Science and Engineering Nanyang Technological University 639798 Singapore College of Software Jilin University Changchun130012 China

While many Particle Swarm Optimization (PSO) algorithms only use fitness to assess the performance of particles, in this work, we adopt Surprisingly Popular Algorithm (SPA) as a complementary metric in addition to fitness. Consequently, particles that are not widely known also have the opportunity to be selected as the learning exemplars. In addition, we propose a Euclidean distance-based adaptive topology to cooperate with SPA, where each particle only connects to k number of particles with the shortest Euclidean distance during each iteration. We also introduce the adaptive topology into heterogeneous populations to better solve large-scale problems. Specifically, the exploration sub-population better preserves the diversity of the population while the exploitation sub-population achieves fast convergence. Therefore, large-scale problems can be solved in a collaborative manner to elevate the overall performance. To evaluate the performance of our method, we conduct extensive experiments on various optimization problems, including three benchmark suites and two real-world optimization problems. The results demonstrate that our Euclidean distance-based adaptive topology outperforms the other widely adopted topologies and further suggest that our method performs significantly better than state-of-the-art PSO variants on small, medium, and large-scale problems. Copyright © 2021, The Authors. All rights reserved.

关键词： Small-world networks

来源：评论

学校读者我要写书评

暂无评论

Large language models illuminate a progressive pathway to artificial intelligent healthcare assistant

Medicine Plus

引用

Medicine Plus 2024年第2期1卷 102-124页

作者： Mingze Yuan Peng Bao Jiajia Yuan Yunhao Shen Zifan Chen Yi Xie Jie Zhao Quanzheng Li Yang Chen Li Zhang Lin Shen Bin Dong Center for Data Science Peking UniversityBeijing 100871China Department of Gastrointestinal Oncology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education)Peking University Cancer Hospital and Institute Beijing 100142China National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijing 100871China Beijing International Center for Mathematical Research Peking UniversityBeijing 100871China Center for Machine Learning Research Peking University Beijing 100871China National Biomedical Imaging Center Peking UniversityBeijing 100871China Peking University Changsha Institute for Computing and Digital Economy Changsha 410205China Massachusetts General Hospital Boston MA 02114-2696USA Harvard Medical School BostonMA 02115USA

With the rapid development of artificial intelligence,large language models(LLMs)have shown promising capabilities in mimicking human-level language comprehen-sion and *** has sparked significant interest in applying LLMs to enhance various aspects of healthcare,ranging from medical education to clinical decision ***,medicine involves multifaceted data modalities and nuanced reasoning skills,presenting challenges for integrating *** review introduces the fundamental applications of general-purpose and specialized LLMs,demon-strating their utilities in knowledge retrieval,research support,clinical workflow automation,and diagnostic *** the inherent multimodality of medicine,the review emphasizes the multimodal LLMs and discusses their ability to process diverse data types like medical imaging and electronic health records to augment diagnostic *** address LLMs'limitations regarding personalization and complex clinical reasoning,the review further explores the emerging develop-ment of LLM-powered autonomous agents for ***,it summarizes the evaluation methodologies for assessing LLMs'reliability and safety in medical *** have transformative potential in medicine;however,there is a pivotal need for continuous optimizations and ethical oversight before these models can be effectively integrated into clinical practice.

关键词： Large language models Artificial intelligence Medicine Healthcare assistant Prompt engineering In-context learning

来源：评论

学校读者我要写书评

暂无评论

An efficient and effective automatic image annotation using positive and negative example images

引用

ICIC Express Letters 2011年第8 B期5卷 2927-2932页

作者： Song, Haiyu Li, Xiongfei Wang, Pengjie Bao, Cuizhu Dong, Yuying Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Jilin University No. 2699 Qianjin Avenue Changchun 130012 China Institute of Computer Graphics and Image Processing Dalian Nationalities University No. 18 Liaohe Road Eco. and Tech. Dev. Zone Dalian 116600 China College of Computer Science and Engineering Dalian Nationalities University No. 18 Liaohe Road Eco. and Tech. Dev. Zone Dalian 116600 China College of Life Science Dalian Nationalities University No. 18 Liaohe Road Eco. and Tech. Dev. Zone Dalian 116600 China

Automatic image annotation is an active topic and difficult task in computer vision domain, which has attracted more and more researchers' attention. Many approaches have been proposed to automatically annotate images. To improve the annotation quality, most of them have profound principlessophisticated modelsand high time complexities. How to efficiently annotate image is becoming one key issue in the field of computer vision. We propose a very efficient and effective approach called keyword vector classification approach, whose model is very simple. Experiments on Corel5K dataset show that the annotation performance of the our proposed approach is almost four times better than translation model and twice better than CMRM (in terms of precision, recall and F1) while the time cost of ours surprisingly decreases. In addition, to our knowledge, our proposed approach outperforms most of existing ones in terms of N+. © 2011 ISSN 1881-803X.

关键词： Image retrieval

来源：评论

学校读者我要写书评

暂无评论

The Discretization for a Special Class of Ideal Projectors

引用

International Scholarly Research Notices 2012年第1期2012卷

作者： Zhe Li Shugong Zhang Tian Dong Department of Applied Mathematics School of Science Changchun University of Science and Technology Changchun 130022 *** Key Laboratory of Symbolic Computation and Knowledge Engineering (Ministry of Education) School of Mathematics Jilin University Changchun 130012 ***

We focus on a special class of ideal projectors, subspaces, which possesses two classes of D -invariant polynomial subspaces. The first is a classical type, while the second is a new class. With matrix computation, we...

关键词：

来源：评论

学校读者我要写书评

暂无评论

Science as Exploration in a knowledge Landscape: Tracing Hotspots or Seeking Opportunity?

arXiv

引用

arXiv 2023年

作者： Liu, Feifan Zhang, Shuang Xia, Haoxiang Institute of Systems Engineering Dalian University of Technology No.2 Linggong Road Liaoning Dalian116024 China Center for Big Data and Intelligent Decision-Making Dalian University of Technology Linggong Road Liaoning Dalian116024 China Institute for Advanced Intelligence Dalian University of Technology Linggong Road Liaoning Dalian116024 China Key Laboratory of Social Computing and Cognitive Intelligence Ministry of Education of China Linggong Road Liaoning Dalian116024 China

The selection of research topics by scientists can be viewed as an exploration process conducted by individuals with cognitive limitations traversing a complex cognitive landscape influenced by both individual and social factors. While existing theoretical investigations have provided valuable insights, the intricate and multifaceted nature of modern science hinders the implementation of empirical experiments. This study leverages advancements in deep learning techniques to investigate the patterns and dynamic mechanisms of topic-transition among scientists. By constructing the knowledge space across 6 large-scale disciplines, we depict the trajectories of scientists' topic transitions within this space, measuring the flow and distance of research regions across different sub-spaces. Our findings reveal a predominantly conservative pattern of topic transition at the individual level, with scientists primarily exploring local knowledge spaces. Furthermore, simulation modeling analysis identifies research intensity, driven by the concentration of scientists within a specific region, as the key facilitator of topic transition. Conversely, the knowledge distance between fields serves as a significant barrier to exploration. Notably, despite potential opportunities for breakthrough discoveries at the intersection of subfields, empirical evidence suggests that these opportunities do not exert a strong pull on scientists, leading them to favor familiar research areas. Our study provides valuable insights into the exploration dynamics of scientific knowledge production, highlighting the influence of individual cognition, social factors, and the intrinsic structure of the knowledge landscape itself. These findings offer a framework for understanding and potentially shaping the course of scientific progress. © 2023, CC BY-NC-ND.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

GenFace: A Large-Scale Fine-Grained Face Forgery Benchmark and Cross Appearance-Edge Learning

arXiv

引用

arXiv 2024年

作者： Zhang, Yaning Yu, Zitong Wang, Tianyi Huang, Xiaobin Shen, Linlin Gao, Zan Ren, Jianfeng Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Nanyang Technological University 50 Nanyang Ave Block N 4 639798 Singapore Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518129 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University China Jinan250014 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China School of Computer Science University of Nottingham Ningbo China

The rapid advancement of photorealistic generators has reached a critical juncture where the discrepancy between authentic and manipulated images is increasingly indistinguishable. Thus, benchmarking and advancing techniques detecting digital manipulation become an urgent issue. Although there have been a number of publicly available face forgery datasets, the forgery faces are mostly generated using GAN-based synthesis technology, which does not involve the most recent technologies like diffusion. The diversity and quality of images generated by diffusion models have been significantly improved and thus a much more challenging face forgery dataset shall be used to evaluate SOTA forgery detection literature. In this paper, we propose a large-scale, diverse, and fine-grained high-fidelity dataset, namely GenFace, to facilitate the advancement of deepfake detection, which contains a large number of forgery faces generated by advanced generators such as the diffusion-based model and more detailed labels about the manipulation approaches and adopted generators. In addition to evaluating SOTA approaches on our benchmark, we design an innovative Cross Appearance-Edge Learning (CAEL) detector to capture multi-grained appearance and edge global representations, and detect discriminative and general forgery traces. Moreover, we devise an Appearance-Edge Cross-Attention (AECA) module to explore the various integrations across two domains. Extensive experiment results and visualizations show that our detection model outperforms the state of the arts on different settings like cross-generator, cross-forgery, and cross-dataset evaluations. Code and datasets will be available at https://***/Jenine-321/GenFace. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Test-Time Domain Adaptation by Learning Domain-Aware Batch Normalization

arXiv

引用

arXiv 2023年

作者： Wu, Yanan Chi, Zhixiang Wang, Yang Plataniotis, Konstantinos N. Feng, Songhe Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education Beijing Jiaotong University Beijing100044 China School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China The Edward S Rogers Sr. ECE Department University of Toronto TorontoM5S3G8 Canada Department of Computer Science and Software Engineering Concordia University MontrealH3G2J1 Canada

Test-time domain adaptation aims to adapt the model trained on source domains to unseen target domains using a few unlabeled images. Emerging research has shown that the label and domain information is separately embedded in the weight matrix and batch normalization (BN) layer. Previous works normally update the whole network naively without explicitly decoupling the knowledge between label and domain. As a result, it leads to knowledge interference and defective distribution adaptation. In this work, we propose to reduce such learning interference and elevate the domain knowledge learning by only manipulating the BN layer. However, the normalization step in BN is intrinsically unstable when the statistics are re-estimated from a few samples. We find that ambiguities can be greatly reduced when only updating the two affine parameters in BN while keeping the source domain statistics. To further enhance the domain knowledge extraction from unlabeled data, we construct an auxiliary branch with label-independent self-supervised learning (SSL) to provide supervision. Moreover, we propose a bi-level optimization based on meta-learning to enforce the alignment of two learning objectives of auxiliary and main branches. The goal is to use the auxiliary branch to adapt the domain and benefit main task for subsequent inference. Our method keeps the same computational cost at inference as the auxiliary branch can be thoroughly discarded after adaptation. Extensive experiments show that our method outperforms the prior works on five WILDS real-world domain shift datasets. Our method can also be integrated with methods with label-dependent optimization to further push the performance boundary. Our code is available at https://***/ynanwu/MABN. © 2023, CC BY.

关键词： Time domain analysis

来源：评论

学校读者我要写书评

暂无评论

A layered parallel algorithm for data clustering via fuzzy c-means technique

Research Square

引用

Research Square 2021年

作者： Mu, Yashuang Wei, Wei Guo, Hongyue Sun, Lijun Key Laboratory of Grain Information Processing and Control Henan University of Technology Ministry of Education Zhengzhou450001 China School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou450001 China College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China School of Maritime Economics and Management Dalian Maritime University Dalian116026 China Collaborative Innovation Center for Transport Studies Dalian Maritime University Dalian116026 China

In this study, a layered parallel algorithm via fuzzy c-means (FCM) technique, called LP-FCM, is proposed in the framework of Map-Reduce for data clustering problems. The LP-FCM mainly contains three layers. The first layer follows a parallel data partitioning method which is developed to randomly divide the original dataset into several subdatasets. The second layer uses a parallel cluster centers searching method based on Map-Reduce, where the classic FCM algorithm is applied to search the cluster centers for each subdataset in Map phases, and all the centers are gathered to the Reduce phase for the confirmation of the final cluster centers through FCM technique again. The third layer implements a parallel data clustering method based on the final cluster centers. After comparing with some famous classic random initialization sequential clustering algorithms which include K-means, K-medoids, FCM and MinMaxK-means on 20 benchmark datasets, the feasibility in terms of clustering accuracy is evaluated. Furthermore, the clustering time and the parallel performance are also tested on some generated large-scale datasets for the parallelism. © 2021, CC BY.

关键词： K-means clustering

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：