检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Lei, Wenhui Xu, Wei Li, Kang Zhang, Xiaofan Zhang, Shaoting School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Shanghai AI Lab Shanghai China School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Hefei China West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu China

Recent advancements in foundation models have shown significant potential in medical image analysis. However, there is still a gap in models specifically designed for medical image localization. To address this, we introduce MedLAM, a 3D medical foundation localization model that accurately identifies any anatomical part within the body using only a few template scans. MedLAM employs two self-supervision tasks: unified anatomical mapping (UAM) and multi-scale similarity (MSS) across a comprehensive dataset of 14,012 CT scans. Furthermore, we developed MedLSAM by integrating MedLAM with the Segment Anything Model (SAM). This innovative framework requires extreme point annotations across three directions on several templates to enable MedLAM to locate the target anatomical structure in the image, with SAM performing the segmentation. It significantly reduces the amount of manual annotation required by SAM in 3D medical imaging scenarios. We conducted extensive experiments on two 3D datasets covering 38 distinct organs. Our findings are twofold: 1) MedLAM can directly localize anatomical structures using just a few template scans, achieving performance comparable to fully supervised models;2) MedLSAM closely matches the performance of SAM and its specialized medical adaptations with manual prompts, while minimizing the need for extensive point annotations across the entire dataset. Moreover, MedLAM has the potential to be seamlessly integrated with future 3D SAM models, paving the way for enhanced segmentation performance. Our code is public at https://***/openmedlab/MedLSAM. © 2023, CC BY.

关键词： Image segmentation

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CEC: Crowdsourcing-based Evolutionary Computation for Distributed Optimization

arXiv

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arXiv 2023年

作者： Wei, Feng-Feng Chen, Wei-Neng Guo, Xiao-Qi Zhao, Bowen Jeon, Sang-Woon Zhang, Jun The School of Computer Science and Engineering South China University of Technology Guangzhou510006 China The State Key Laboratory of Subtropical Building Science With The Guangdong-Hong Kong Joint Innovative Platform of Big Data and Computational Intelligence South China University of Technology Guangzhou510006 China The Pazhou Lab Guangzhou510006 China Guangzhou Institute of Technology Xidian University Guangzhou510006 China The Department of Electronics Engineering and Communication Engineering Hanyang University Ansan15588 Korea Republic of

Crowdsourcing is an emerging computing paradigm that takes advantage of the intelligence of a crowd to solve complex problems effectively. Besides collecting and processing data, it is also a great demand for the crowd to conduct optimization. Inspired by this, this paper intends to introduce crowdsourcing into evolutionary computation (EC) to propose a crowdsourcing-based evolutionary computation (CEC) paradigm for distributed optimization. EC is helpful for optimization tasks of crowdsourcing and in turn, crowdsourcing can break the spatial limitation of EC for large-scale distributed optimization. Therefore, this paper firstly introduces the paradigm of crowdsourcing-based distributed optimization. Then, CEC is elaborated. CEC performs optimization based on a server and a group of workers, in which the server dispatches a large task to workers. Workers search for promising solutions through EC optimizers and cooperate with connected neighbors. To eliminate uncertainties brought by the heterogeneity of worker behaviors and devices, the server adopts the competitive ranking and uncertainty detection strategy to guide the cooperation of workers. To illustrate the satisfactory performance of CEC, a crowdsourcing-based swarm optimizer is implemented as an example for extensive experiments. Comparison results on benchmark functions and a distributed clustering optimization problem demonstrate the potential applications of CEC. Copyright © 2023, The Authors. All rights reserved.

关键词： Crowdsourcing

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Identifying discreditable firms in a large-scale ownership network

arXiv

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arXiv 2022年

作者： Zhou, Tao Lee, Yan-Li Li, Qian Chen, Duanbing Xie, Wenbo Wu, Tong Zeng, Tu CompleX Lab University of Electronic Science and Technology of China Chengdu711731 China School of Computer and Software Engineering Xihua University Chengdu610039 China Business Big Data Inc. Chengdu610095 China Union Big Data Inc. Chengdu610299 China School of Computer Science Southwest Petroleum University Chengdu610500 China

Violations of laws and regulations about food safety, production safety, quality standard and environmental protection, or negative consequences from loan, guarantee and pledge contracts, may result in operating and credit risks of firms. The above illegal or trust-breaking activities are collectively called discreditable activities, and firms with discreditable activities are named as discreditable firms. Identification of discreditable firms is of great significance for investment attraction, bank lending, equity investment, supplier selection, job seeking, and so on. In this paper, we collect registration records of about 1.13 × 108 Chinese firms and construct an ownership network with about 6 million nodes, where each node is a firm who has invested at least one firm or has been invested by at least one firm. Analysis of publicly available records of discreditable activities show strong network effect, namely the probability of a firm to be discreditable is remarkably higher than the average probability given the fact that one of its investors or investees is discreditable. In comparison, for the risk of being a discreditable firm, an investee has higher impact than an investor in average. The impact of a firm on surrounding firms decays along with the increasing topological distance, analogous to the well-known "three degrees of separation" phenomenon. The uncovered correlation of discreditable activities can be considered as a representative example of network effect, in addition to the propagation of diseases, opinions and human behaviors. Lastly, we show that the utilization of the network effect largely improves the accuracy of the algorithm to identify discreditable firms. Copyright © 2022, The Authors. All rights reserved.

关键词： Crime

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Beamforming Design for RIS-Aided AF Relay Networks

arXiv

引用

arXiv 2023年

作者： Wang, Xuehui Shu, Feng Chen, Riqing Zhang, Peng Zhang, Qi Xia, Guiyang Shi, Weiping Wang, Jiangzhou School of Information and Communication Engineering Hainan University Haikou570228 China School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China Digital Fujian Institute of Big Data for Agriculture Fujian Agriculture and Forestry University Fuzhou350002 China Institute of Intelligent Agriculture Anhui Agricultural University Hefei230036 China School of Engineering University of Kent CanterburyCT2 7NT United Kingdom

Since reconfigurable intelligent surface (RIS) is considered to be a passive reflector for rate performance enhancement, a RIS-aided amplify-and-forward (AF) relay network is presented. By jointly optimizing the beamforming matrix at AF relay and the phase shifts matrices at RIS, two schemes are put forward to address a maximizing signal-to-noise ratio (SNR) problem. Firstly, aiming at achieving a high rate, a high-performance alternating optimization (AO) method based on Charnes-Cooper transformation and semidefinite programming (CCT-SDP) is proposed, where the optimization problem is decomposed to three subproblems solved by CCT-SDP and rank-one solutions can be recovered by Gaussian randomization. While the optimization variables in CCT-SDP method are matrices, which leads to extremely high complexity. In order to reduce the complexity, a low-complexity AO scheme based on Dinkelbachs transformation and successive convex approximation (DT-SCA) is put forward, where matrices variables are transformed to vector variables and three decoupled subproblems are solved by DT-SCA. Simulation results verify that compared to two benchmarks (i.e. a RIS-assisted AF relay network with random phase and a AF relay network without RIS), the proposed CCT-SDP and DT-SCA schemes can harvest better rate performance. Furthermore, it is revealed that the rate of the low-complexity DT-SCA method is close to that of CCT-SDP method. Copyright © 2023, The Authors. All rights reserved.

关键词： Beamforming

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Base Conversion Model Based on DNA Strand Displacement 14th

Base Conversion Model Based on DNA Strand Displacement

引用

14th International Conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2019

作者： Chen, Zhao Yin, Zhixiang Cui, Jianzhong Tang, Zhen Zhang, Qiang School of Mathematics and Big Data Anhui University of Science and Technology HuainanAnhui232001 China School of Mathematics Physics and Statistics Shanghai University of Engineering Science Shanghai201620 China School of Electronic and Information Engineering Anhui University of Science and Technology HuainanAnhui232001 China Department of Computer Huainan Union University HuainanAnhui232038 China School of Computer Science Dalian University of Technology DalianLiaoning116024 China

ISBN: (纸本)9789811534140

DNA computing has the advantages of high parallelism and large storage. In this paper, DNA strand displacement techniques are applied to binary to decimal system. In the field of DNA calculation, binary numerical calculation is relatively mature, but it is difficult to implement decimal calculation. So it is very necessary to study the conversion of binary system to decimal system. In this paper, DNA strand displacement are used to construct a logic model that converts binary system into decimal system. The output strand is obtained through DNA strand displacement reaction in the logical device, and the decimal result is judged by the number of hairpin structures formed. The model has good operability, flexibility. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Numbering systems

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Application and development of elastic fibers focusing on composition and structure 6

Application and development of elastic fibers focusing on co...

引用

2021 6th International Seminar on Advances in Materials Science and engineering, ISAMSE 2021

作者： Zhang, Jin Sun, Qinyue Wang, Yumin Dong, Xiongwei Xiong, Yi Zeng, Wei Department of Pharmacy Zhongnan Hospital Wuhan University Wuhan430071 China Science and Technology Institute Key Laboratory of Textile Fiber and Products Ministry of Education Laboratory for Electron Microscopy Wuhan Textile University Hubei Wuhan430073 China National Engineering Research Center for Agro-Ecological Big Data Analysis and Application School of Electronics and Information Engineering Anhui University Anhui Hefei230601 China School of Physics and Technology MOE Key Laboratory of Artificial Micro- and Nano-Structures Wuhan University Wuhan430072 China

Elastic fabric is comfortable to wear, and has been a consumer textile due to its good shape retention and wrinkle resistance after washing. This paper briefly introduces the types and performance characteristics of several common elastic fibers, and analyzes the relationship between the structure and elasticity of different elastic fibers. Finally, the development trend of elastic fibers is proposed. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Elasticity

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Probe Machine Based Computing Model for Solving Satisfiability Problem 14th

Probe Machine Based Computing Model for Solving Satisfiabili...

引用

14th International Conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2019

作者： Cui, Jianzhong Yin, Zhixiang Yang, Jing Geng, Xianya Zhang, Qiang School of Electronic and Information Engineering Anhui University of Science and Technology HuainanAnhui232001 China School of Mathematics Physics and Statistics Shanghai University of Engineering Science Shanghai201620 China School of Mathematics and Big Data Anhui University of Science and Technology HuainanAnhui232001 China Department of Computer Huainan Union University HuainanAnhui232038 China School of Computer Science Dalian University of Technology DalianLiaoning116024 China

ISBN: (纸本)9789811534140

Probe Machine (PM) is a recently reported theoretical model with massive parallelism. Particularly, Turing Machine (TM) had been proven to be the special case of PM. The paper proposed a PM based computing model for satisfiability problem. PM is capable of searching pairs of data fiber that are complementary to pre-devised probes, leading to the connection of pairs of data in parallel. We encoded the assignment to variables in the given 3-SAT formula into data fibers and devised probes between pairs of data fiber, the unique satisfying assignment of a hard 3-SAT formula could be generated within just one step of probe operation. More generally, for an arbitrary 3-SAT formula with variables and clauses, we presented a method for deciding the satisfiability using the concept of potential probe. Complexity analysis shows the encoding complexity and time complexity of the proposed model are o(n) and o(1), respectively. The distinguishing characteristics of the proposed model lie in two aspects. On one hand, solution to NP-complete problem was generated in just one step of probe operation rather than found in vast solution space. On the other hand, the proposed model is highly parallel. Most important of all, the parallelism increases with problem size. This marks a giant step in computational theory. With the parallel search capability inherited in PM, the size of NP-complete search problems is expected to be increased further. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Probes

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Efficient Test-Time Model Adaptation without Forgetting

arXiv

引用

arXiv 2022年

作者： Niu, Shuaicheng Wu, Jiaxiang Zhang, Yifan Chen, Yaofo Zheng, Shijian Zhao, Peilin Tan, Mingkui School of Software Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot Ministry of Education China Tencent AI Lab China National University of Singapore Singapore Pazhou Laboratory China

Test-time adaptation (TTA) seeks to tackle potential distribution shifts between training and testing data by adapting a given model w.r.t. any testing sample. This task is particularly important for deep models when the test environment changes frequently. Although some recent attempts have been made to handle this task, we still face two practical challenges: 1) existing methods have to perform backward computation for each test sample, resulting in unbearable prediction cost to many applications;2) while existing TTA solutions can significantly improve the test performance on out-of-distribution data, they often suffer from severe performance degradation on in-distribution data after TTA (known as catastrophic forgetting). In this paper, we point out that not all the test samples contribute equally to model adaptation, and high-entropy ones may lead to noisy gradients that could disrupt the model. Motivated by this, we propose an active sample selection criterion to identify reliable and non-redundant samples, on which the model is updated to minimize the entropy loss for test-time adaptation. Furthermore, to alleviate the forgetting issue, we introduce a Fisher regularizer to constrain important model parameters from drastic changes, where the Fisher importance is estimated from test samples with generated pseudo labels. Extensive experiments on CIFAR-10-C, ImageNet-C, and ImageNet-R verify the effectiveness of our proposed method. Code is available at https://***/mr-eggplant/EATA. Copyright © 2022, The Authors. All rights reserved.

关键词： Entropy

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LadRa-Net: Locally-aware dynamic re-read attention net for sentence semantic matching

arXiv

引用

arXiv 2021年

作者： Zhang, Kun Lv, Guangyi Wu, Le Chen, Enhong Liu, Qi Wang, Meng Key Laboratory of Knowledge Engineering with Big Data School of Computer and Information Hefei University of Technology Anhui Hefei230029 China AI Lab at Lenovo Research Beijing100094 China University of Science and Technology of China Hefei230026 China

Sentence semantic matching requires an agent to determine the semantic relation between two sentences, which is widely used in various natural language tasks, such as Natural Language Inference (NLI), Paraphrase Identification (PI), and so on. Much recent progress has been made in this area, especially attention-based methods and pre-trained language model based methods. However, most of these methods focus on all the important parts in sentences in a static way and only emphasize how important the words are to the query, inhibiting the ability of attention mechanism. In order to overcome this problem and boost the performance of attention mechanism, we propose a novel dynamic re-read attention, which can pay close attention to one small region of sentences at each step and re-read the important parts for better sentence representations. Based on this attention variation, we develop a novel Dynamic Re-read Network (DRr-Net) for sentence semantic matching. Moreover, selecting one small region in dynamic re-read attention seems insufficient for sentence semantics, and employing pre-trained language models as input encoders will introduce incomplete and fragile representation problems. To this end, we extend DRr-Net to Locally-Aware Dynamic Re-read Attention Net (LadRa-Net), in which local structure of sentences is employed to alleviate the shortcoming of Byte-Pair Encoding (BPE) in pre-trained language models and boost the performance of dynamic reread attention. Extensive experiments on two popular sentence semantic matching tasks demonstrate that DRr-Net can significantly improve the performance of sentence semantic matching. Meanwhile, LadRa-Net is able to achieve better performance by considering the local structures of sentences. In addition, it is exceedingly interesting that some discoveries in our experiments are consistent with some findings of psychological research. © 2021, CC BY.

关键词： Computational linguistics

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Optimization of human body attitude detection based on mask RCNN 8

Optimization of human body attitude detection based on mask ...

引用

8th International Conference on Orange technology, ICOT 2020

作者： Yin, Zhenhua Wang, Xiaogang Li, Lingli Anhui Institute of Information Engineering Computer and Software Engineering Department Wuhu China Changzhou College of Information Technology Software and Big Data Department Changzhou China Beijing Huasheng Jingshi Information Technology Co. Ltd Computer and Software Engineering Department Beijing China

ISBN: (纸本)9781665418522

Mask RCNN has the advantage of higher accuracy than other human body attitude detection models, and has been widely used for multi-person human body attitude detection. However, Mask RCNN has a low detection speed. Therefore, we propose a MobileNet-based Mask RCNN optimization model to accelerate the inference calculation speed of Mask RCNN. At the same time, in order to further improve the detection accuracy of Mask RCNN, we propose a method by using pixel segmentation results to assist the detection of key points in the human body, which improves the speed of reasoning and reduces the rate of false detection to some extent. © 2020 IEEE.

关键词： Pixels

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