检索结果-内蒙古大学图书馆

arXiv 2022年

作者： Stray, Jonathan Halevy, Alon Assar, Parisa Hadfield-Menell, Dylan Boutilier, Craig Ashar, Amar Beattie, Lex Ekstrand, Michael Leibowicz, Claire Sehat, Connie Moon Johansen, Sara Kerlin, Lianne Vickrey, David Singh, Spandana Vrijenhoek, Sanne Zhang, Amy Andrus, McKane Helberger, Natali Proutskova, Polina Mitra, Tanushree Vasan, Nina Center for Human-Compatible Artificial Intelligence UC Berkeley United States Meta AI United States Department of Electrical Engineering and Computer Science MIT United States Google Research United States Spotify Inc. Computer Science Department Boise State University United States Partnership on AI Hacks/Hackers Brainstorm The Stanford Lab for Mental Health Innovation Department of Psychiatry and Behavioral Sciences Stanford University United States BBC United Kingdom Meta United States New America's Open Technology Institute United States Faculty of Law Information Law University of Amsterdam Netherlands Allen School of Computer Science & Engineering University of Washington United States Institute for Information Law University of Amsterdam Netherlands Information and Computer Science University of Washington United States

Recommender systems are the algorithms which select, filter, and personalize content across many of the world's largest platforms and apps. As such, their positive and negative effects on individuals and on societies have been extensively theorized and studied. Our overarching question is how to ensure that recommender systems enact the values of the individuals and societies that they serve. Addressing this question in a principled fashion requires technical knowledge of recommender design and operation, and also critically depends on insights from diverse fields including social science, ethics, economics, psychology, policy and law. This paper is a multidisciplinary effort to synthesize theory and practice from different perspectives, with the goal of providing a shared language, articulating current design approaches, and identifying open problems. It is not a comprehensive survey of this large space, but a set of highlights identified by our diverse author cohort. We collect a set of values that seem most relevant to recommender systems operating across different domains, then examine them from the perspectives of current industry practice, measurement, product design, and policy approaches. Important open problems include multi-stakeholder processes for defining values and resolving trade-offs, better values-driven measurements, recommender controls that people use, non-behavioral algorithmic feedback, optimization for long-term outcomes, causal inference of recommender effects, academic-industry research collaborations, and interdisciplinary policy-making. Copyright © 2022, The Authors. All rights reserved.

关键词： Recommender systems

来源：评论

学校读者我要写书评

暂无评论

Non-Variational ADAPT algorithm for quantum simulations

arXiv

引用

arXiv 2024年

作者： Tang, Ho Lun Chen, Yanzhu Biswas, Prakriti Magann, Alicia B. Arenz, Christian Economou, Sophia E. Department of Physics Virginia Tech BlacksburgVA24061 United States Virginia Tech Center for Quantum Information Science and Engineering BlacksburgVA24061 United States Department of Physics Florida State University TallahasseeFL32306 United States School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States Quantum Algorithms and Applications Collaboratory Sandia National Laboratories AlbuquerqueNM87185 United States

We explore a non-variational quantum state preparation approach combined with the ADAPT operator selection strategy in the application of preparing the ground state of a desired target Hamiltonian. In this algorithm, energy gradient measurements determine both the operators and the gate parameters in the quantum circuit construction. We compare this non-variational algorithm with ADAPT-VQE and with feedback-based quantum algorithms in terms of the rate of energy reduction, the circuit depth, and the measurement cost in molecular simulation. We find that despite using deeper circuits, this new algorithm reaches chemical accuracy at a similar measurement cost to ADAPT-VQE. Since it does not rely on a classical optimization subroutine, it may provide robustness against circuit parameter errors due to imperfect control or gate synthesis. © 2024, CC BY.

关键词： Hamiltonians

来源：评论

学校读者我要写书评

暂无评论

A Time-Series Higher-Order network Hierarchical Evolution Model for Dynamic Interaction of Nodes

SSRN

引用

SSRN 2022年

作者： Yang, Xiaoteng Wu, Zhenqiang Zhang, WenBo Javaid, Shumaila School of Computer Science Shaanxi Normal University Xi’an710119 China Key Laboratory of Modern Teaching Technology Ministry of Education Xi’an710062 China Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai201804 China Frontiers Science Center for Intelligent Autonomous Systems Shanghai201210 China

The evolution of hierarchies in complex systems is an exciting phenomenon since hierarchies exist in many social and biological networks. So far, many approaches have been proposed, however, they are either based on the locality of the nodes or the cluster relationships between networks. These approaches often use traditional time-series network modeling methods that focus only on local static relationships between different nodes but ignore the dynamic interactions between real network nodes. Local static and dynamic global relationships should be considered to understand better the generation and maintenance of hierarchical phenomena in networks. To address this problem, we propose a long-term, flexible hierarchical evolution model for explaining the hierarchical phenomena arising in networks. Our model can integrate both local static and dynamic global information in the network and use time and higher-order inter-node interactions to integrate static and dynamic relationships. New interactions in the network are formed based on preference information between nodes, while old links fade out over time. In addition, we derive critical points for the hierarchical evolution of the system over long periods. Importantly, our model allows the comparison of network rank formation across different data as a way to perform rank evolution studies. © 2022, The Authors. All rights reserved.

关键词： Time series

来源：评论

学校读者我要写书评

暂无评论

Chances and Challenges of Chatgpt and Similar Models for Education in M&S 23

Chances and Challenges of Chatgpt and Similar Models for Edu...

引用

Proceedings of the Winter Simulation Conference

作者： Andreas Tolk Philip Barry Margaret L. Loper Ghaith Rabadi William T. Scherer Levent Yilmaz The MITRE Corporation Modeling and Analysis Innovation Center Charlottesville USA L3Harris Corporation Mission Analysis Group Herndon USA Georgia Tech Research Institute Georgia Institute of Technology Atlanta USA University of Central Florida School of Modeling Simulation and Training Orlando USA University of Virgina Department of Systems and Information Engineering Charlottesville USA Auburn University Samuel Ginn College of Engineering Computer Science and Software Engineering Auburn USA

ISBN: (纸本)9798350369663

This position paper summarizes the inputs of a group of experts from academia and industry presenting their view on chances and challenges of using ChatGPT within Modeling and Simulation education. The experts also address the need to evaluate continuous education as well as education of faculty members to address scholastic challenges and opportunities while meeting the expectation of industry. Generally, the use of ChatGPT is encouraged, but it needs to be embedded into an updated curriculum with more emphasis on validity constraints, systems thinking, and ethics.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Adaptive RBF-Neural-network Control with Force Observer for Teleoperation Robotic System 1st

Adaptive RBF-Neural-Network Control with Force Observer for...

引用

1st International Conference on Cognitive Computation and Systems, ICCCS 2022

作者： An, Zhaohui Min, Gaochen Jiang, Xiangzuo Yu, Xinbo He, Wei Silvestre, Carlos School of Intelligence Science and Technology Institute of Artificial Intelligence University of Science and Technology Beijing Beijing100083 China Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing Beijing100083 China Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education University of Science and Technology Beijing Beijing100083 China Donald Bren School of Information and Computer Sciences University of California IrvineCA92697 United States Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau China LARSyS Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal

ISBN: (纸本)9789819927883

In this paper, an adaptive radial basis function neural network (RBFNN) control strategy is proposed for bilateral teleoperation robotic system with uncertainty and time delay. Meantime, the force observer method is used to estimate the interaction force between the slave robot and environment. The model of teleoperation robotic system is analyzed, and then the uncertainty of bilateral teleoperation robotic system is estimated by using RBFNN. Using Lyapunov stability theorem, the stability of teleoperation robotic system under our proposed control is proved. Finally, the effectiveness of the proposed control algorithm is verified by Matlab Simulink, and position tracking and force estimation performance of the teleoperation robotic system are guaranteed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Time delay

来源：评论

学校读者我要写书评

暂无评论

A Multi-Modal Behavior Quantitative Analysis Model for Autism Early Screening

A Multi-Modal Behavior Quantitative Analysis Model for Autis...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Jiayi Lei E Zhang Yingying She Xin Wang Yuhan Liao Bin Hu Hang Wu Minqiang Yang Jiajia Tian Yong Wang School of Informatics Xiamen University China Department of Occupational Therapy Education University of Kansas Medical Center USA School of Film National Institute for Data Science in Health and Medicine Xiamen University China School of Computer Science and Technology Beijing Institute of Technology China Together Education Institute China School of Information Science and Engineering Lanzhou University China Pediatric Department Fujian Medical University Union Hospital China

Human-computer Interaction (HCI) and Machine Learning (ML) technologies have potential for the behavioral screening of autistic children but how to design a tool and analyse behavior reliably is challenging. Based on psychophysiological computation, this paper proposes an interactive behavior perception analytical model for autism screening. We presented the multi-scenario reactive behavior paradigms that designed based on the atypical characteristics of autistic children. We recorded the eye movement data and facial data of 91 participants, and performed multi-modal feature extraction, used machine learning to train classification model. We conducted comparative experiments, and the experimental results verified the advantages of multi-scenario paradigms and multi-modal feature groups, which indicates that our analysis methods and screening models are effective and reliable and have real research significance.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A novel transfer learning-based model for diagnosing malaria from parasitized and uninfected red blood cell images

引用

Decision Analytics Journal 2023年 9卷

作者： Qadri, Azam Mehmood Raza, Ali Eid, Fatma Abualigah, Laith Institute of Computer Science Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan 64200 Pakistan Technology Management Stony Brook University 11794 NY United States Department of Applied Artificial Intelligence College of Computing and Informatics Sungkyunkwan University Seoul 03063 South Korea Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos 13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan MEU Research Unit Middle East University Amman 11831 Jordan Applied Science Research Center Applied science private university Amman 11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang 11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya 27500 Malaysia

Malaria represents a potentially fatal communicable illness triggered by the Plasmodium parasite. This disease is transmitted to humans through the bites of Anopheles mosquitoes that carry the infection. This disease has significant and devastating consequences on the health systems of fragile countries, particularly in sub-Saharan Africa. Malaria affects red blood cells by invading and replicating within them, destroying them, and releasing toxic byproducts into the bloodstream. The parasite's ability to stick and modify the surface of red blood cells can cause them to become sticky, obstructing blood flow in vital organs such as the brain and spleen. Therefore, efficient approaches for the early detection of malaria are critical to saving patients’ lives. The main aim of this study is to develop an efficient model for early malaria diagnosis. We used malaria images based on parasitized and uninfected red blood cells for the study experiments. We applied neural network-based approaches such as Neural Search Architecture network (NASNet) and compared its performance with machine learning techniques. Moreover, we proposed a novel NNR (NASNet Random forest) method for feature engineering. The proposed NNR approach first extracts spatial features from input malaria images, then class prediction probability features are extracted from these spatial features. The feature set obtained from the data extraction trains machine learning models. Our comprehensive experiments show that the support vector machine outperformed state-of-the-art models, achieving a high-performance score of 99% and having an inference time near 0.025 s. We validated the performance using k-fold cross-validation and optimized the hyperparameters through tuning. Our proposed research has improved the early diagnosis of malaria and can assist medical specialists in reducing the mortality rate. © 2023 The Author(s)

关键词： Deep learning Machine learning Malaria diagnosis Neural search architecture network Red blood cells Transfer learning

来源：评论

学校读者我要写书评

暂无评论

Efficient automatic segmentation for multi-level pulmonary arteries: The PARSE challenge

arXiv

引用

arXiv 2023年

作者： Luo, Gongning Wang, Kuanquan Liu, Jun Li, Shuo Liang, Xinjie Li, Xiangyu Gan, Shaowei Wang, Wei Dong, Suyu Wang, Wenyi Yu, Pengxin Liu, Enyou Wei, Hongrong Wang, Na Guo, Jia Li, Huiqi Zhang, Zhao Zhao, Ziwei Gao, Na An, Nan Pakzad, Ashkan Rangelov, Bojidar Dou, Jiaqi Tian, Song Liu, Zeyu Wang, Yi Sivalingam, Ampatishan Punithakumar, Kumaradevan Qiu, Zhaowen Gao, Xin School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Computer and Data Science Department of Biomedical Engineering Case Western Reserve University Cleveland44106 United States Institute of Information and Computer Engineering NorthEast Forestry University Harbin150040 China Infervision Medical Technology Co. Ltd. Beijing China Sensetime Shanghai China Beijing Institute of Technology Beijing China Peking University Beijing China Northeastern University Shenyang China Shanghai Jiao Tong University Shanghai China London United Kingdom Center for Biomedical Imaging Research Tsinghua University Beijing China Philips Healthcare China Chongqing University Chongqing China University of Alberta Edmonton Canada Computer Electrical and Mathematical Sciences & Engineering Division King Abdullah University of Science and Technology 4700 KAUST Thuwal23955 Saudi Arabia

Efficient automatic segmentation of multi-level (i.e. main and branch) pulmonary arteries (PA) in CTPA images plays a significant role in clinical applications. However, most existing methods concentrate only on main PA or branch PA segmentation separately and ignore segmentation efficiency. Besides, there is no public large-scale dataset focused on PA segmentation, which makes it highly challenging to compare the different methods. To benchmark multi-level PA segmentation algorithms, we organized the first Pulmonary ARtery SEgmentation (PARSE) challenge. On the one hand, we focus on both the main PA and the branch PA segmentation. On the other hand, for better clinical application, we assign the same score weight to segmentation efficiency (mainly running time and GPU memory consumption during inference) while ensuring PA segmentation accuracy. We present a summary of the top algorithms and offer some suggestions for efficient and accurate multi-level PA automatic segmentation. We provide the PARSE challenge as open-access for the community to benchmark future algorithm developments at https://***/Parse2022/. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

来源：评论

学校读者我要写书评

暂无评论

Generalized diffusion process with nonlocal interactions: Continuous time random walk model and stochastic resetting

arXiv

引用

arXiv 2025年

作者： Trajanovski, Pece Petreska, Irina Górska, Katarzyna Kocarev, Ljupco Sandev, Trifce Research Center for Computer Science and Information Technologies Macedonian Academy of Sciences and Arts Bul. Krste Misirkov 2 Skopje1000 Macedonia Institute of Physics Faculty of Natural Sciences and Mathematics Ss. Cyril and Methodius University Arhimedova 3 Skopje1000 Macedonia Institute of Nuclear Physics Polish Academy of Sciences ul. Radzikowskiego 152 KrakówPL-31342 Poland Faculty of Computer Science and Engineering Ss. Cyril and Methodius University PO Box 393 Skopje1000 Macedonia Department of Physics Korea University Seoul02841 Korea Republic of

A space fractional diffusion-like equation is introduced, which embodies the nonlocality in time, represented by the memory kernel and the non-locality in space. A specific example of the nonlocal term is considered in combination with three different forms of the memory kernel. To analyse the probability density function, we utilize the subordination approach. Subsequently, the corresponding continuous time random walk model is presented. Furthermore, we investigate the effects of the stochastic resetting on the dynamics of the process and we showed that in the long time limit the system approaches a nonequilibrium stationary state. © 2025, CC BY.

关键词： Probability density function

来源：评论

学校读者我要写书评

暂无评论

CATD: Unified Representation Learning for EEG-to-fMRI Cross-Modal Generation

arXiv

引用

arXiv 2024年

作者： Yao, Weiheng Lyu, Zhihan Mahmud, Mufti Zhong, Ning Lei, Baiying Wang, Shuqiang Southern University of Science and Technology Shenzhen518055 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Department of Game Design Faculty of Arts Uppsala University Sweden Department of Information and Computer Science SDAIA-KFUPM Joint Research Center for AI Interdisciplinary Research Center for Biosystems and Machines King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia The Faculty of Engineering Maebashi Institute of Technology Gunma Japan Chongqing University of Posts and Telecommunications Chongqing China School of Biomedical Engineering Shenzhen University Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging Health Science Center Guangdong Shenzhen518000 China

Multi-modal neuroimaging analysis is crucial for a comprehensive understanding of brain function and pathology, as it allows for the integration of different imaging techniques, thus overcoming the limitations of individual modalities. However, the high costs and limited availability of certain modalities pose significant challenges. To address these issues, this paper proposes the Condition-Aligned Temporal Diffusion (CATD) framework for end-to-end cross-modal synthesis of neuroimaging, enabling the generation of functional magnetic resonance imaging (fMRI)-detected Blood Oxygen Level Dependent (BOLD) signals from more accessible Electroencephalography (EEG) signals. By constructing Conditionally Aligned Block (CAB), heterogeneous neuroimages are aligned into a latent space, achieving a unified representation that provides the foundation for cross-modal transformation in neuroimaging. The combination with the constructed Dynamic Time-Frequency Segmentation (DTFS) module also enables the use of EEG signals to improve the temporal resolution of BOLD signals, thus augmenting the capture of the dynamic details of the brain. Experimental validation demonstrates that the framework improves the accuracy of brain activity state prediction by 9.13% (reaching 69.8%), enhances the diagnostic accuracy of brain disorders by 4.10% (reaching 99.55%), effectively identifies abnormal brain regions, enhancing the temporal resolution of BOLD signals. The proposed framework establishes a new paradigm for cross-modal synthesis of neuroimaging by unifying heterogeneous neuroimaging data into a latent representation space, showing promise in medical applications such as improving Parkinson’s disease prediction and identifying abnormal brain regions. Copyright © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

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：