检索结果-内蒙古大学图书馆

Topology-Aware Wavelet Mamba for Airway Structure Segmentation in Postoperative Recurrent Nasopharyngeal Carcinoma CT Scans

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Huang, Haishan Liang, Pengchen Lin, Naier Wang, Luxi Pu, Bin Chen, Jianguo Chang, Qing Shen, Xia Ran, Guo School of Software Engineering Sun Yat-sen University Guangdong Province Zhuhai519000 China School of Microelectronics Shanghai University Shanghai201800 China Electronic and Computer Engineering The Hong Kong University of Science and Technology 999077 Hong Kong Department Shanghai Key Laboratory of Gastric Neoplasms Department of Surgery Shanghai Institute of Digestive Surgery Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai200025 China Department of Radiology Eye & ENT Hospital Fudan University Shanghai200031 China Department of Anesthesiology Eye & ENT Hospital Fudan University Shanghai200031 China

Nasopharyngeal carcinoma (NPC) patients often undergo radiotherapy and chemotherapy, which can lead to postoperative complications such as limited mouth opening and joint stiffness, particularly in recurrent cases that require re-surgery. These complications can affect airway function, making accurate postoperative airway risk assessment essential for managing patient care. Accurate segmentation of airway-related structures in postoperative CT scans is crucial for assessing these risks. This study introduces TopoWMamba (Topology-aware Wavelet Mamba), a novel segmentation model specifically designed to address the challenges of postoperative airway risk evaluation in recurrent NPC patients. TopoWMamba combines wavelet-based multi-scale feature extraction, state-space sequence modeling, and topology-aware modules to segment airway-related structures in CT scans robustly. By leveraging the Wavelet-based Mamba Block (WMB) for hierarchical frequency decomposition and the Snake Conv VSS (SCVSS) module to preserve anatomical continuity, TopoWMamba effectively captures both fine-grained boundaries and global structural context, crucial for accurate segmentation in complex postoperative scenarios. Through extensive testing on the NPCSegCT dataset, TopoWMamba achieves an average Dice score of 88.02%, outperforming existing models such as UNet, Attention UNet, and SwinUNet. Additionally, TopoWMamba is tested on the SegRap 2023 Challenge dataset, where it shows a significant improvement in trachea segmentation with a Dice score of 95.26%. The proposed model provides a strong foundation for automated segmentation, enabling more accurate postoperative airway risk evaluation. Copyright © 2025, The Authors. All rights reserved.

关键词： Chemotherapy

A proof-of-majority consensus protocol for blockchain-enabled collaboration infrastructure of 5G network slice brokers 2

学校读者我要写书评

暂无评论

A proof-of-majority consensus protocol for blockchain-enable...

2nd ACM International Symposium on Blockchain and Secure Critical Infrastructure, BSCI 2020, Co-located with AsiaCCS 2020

作者： Lin, Wenmin Xu, Xiaolong Qi, Lianyong Zhang, Xuyun Dou, Wanchun Khosravi, Mohammad R. Institute of VR and Intelligent Systems Alibaba Business School Hangzhou Normal University Hangzhou China Nanjing University of Information and Technology Nanjing China Qufu Normal University Rizhao China Department of Computing Macquarie University Sydney Australia State Key Laboratory of Novel Software Department of Computer Science and Technology Nanjing University Nanjing China Department of Electrical and Electronic Engineering Shiraz University of Technology Shiraz Department of Computer Engineering Persian Gulf University Bushehr Iran

ISBN: (纸本)9781450376105

With network slicing technology, 5G changes the classical mobile network business model from a network-operator-oriented business to a more open system with multiple stakeholders. In this context, a network slice broker plays the role of agency between network resource providers and vertical service providers, to lease and provide on-demand network slice services. In practice, a single network slice broker normally owns limited types of network slices. And a collaboration infrastructure could be built up to enable network slice sharing among multiple network slice brokers, so as to improve user experience, and enhance the resource utilization rate. However, a major challenge with such an infrastructure is how to support fair and secure slice trading process among multiple un-trusted network slice providers. Blockchain technology, on the other side, brings in high opportunities to implement a fair and secure trading system without a trusted third-party. In view of this observation, this paper proposes a design of blockchain-enabled collaboration infrastructure for network slice brokers, where a group of registered brokers can trade their network slices in a fair and secure manner. To enhance the accountability of slice trading records, transactions are verified and sorted by each broker node, which are further packed into blocks and appended to a shared ledger maintained by each member in the collaboration network. More specifically, we design a Proof-of-Majority (PoM) consensus protocol to sort slice trading transactions before packing transaction blocks of the shared ledger. Furthermore, an optimization for the basic PoM protocol is discussed, with dependency resolving strategy to improve system performance. Experiments are conducted to verify the feasibility of our proposal. © 2020 ACM.

关键词： 5G mobile communication systems

AMPCliff: quantitative definition and benchmarking of activity cliffs in antimicrobial peptides

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Li, Kewei Wu, Yuqian Li, Yinheng Guo, Yutong Wang, Yan Liang, Yiyang Fan, Yusi Huang, Lan Zhang, Ruochi Zhou, Fengfeng College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun130012 China School of Software Jilin University Jilin Changchun130012 China School of Life Sciences Jilin University Jilin Changchun130012 China Department of Computer Science Columbia University 116th and Broadway New York CityNY10027 United States School of Artificial Intelligence Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun130012 China School of Biology and Engineering Guizhou Medical University Guizhou Guiyang550025 China School of Computer Engineering Changchun University of Engineering Jilin Changchun130103 China Changchun Wenli High School Jilin Changchun130062 China

Since the mechanism of action of drug molecules in the human body is difficult to reproduce in the in vitro environment, it becomes difficult to reveal the causes of the activity cliff phenomenon of drug molecules. We found out the AC of small molecules has been extensively investigated but limited knowledge is accumulated about the AC phenomenon in peptides with canonical amino acids. Understanding the mechanism of AC in canonical amino acids might help understand the one in drug molecules. This study introduces a quantitative definition and benchmarking framework AMPCliff for the AC phenomenon in antimicrobial peptides (AMPs) composed by canonical amino acids. A comprehensive analysis of the existing AMP dataset reveals a significant prevalence of AC within AMPs. AMPCliff quantifies the activities of AMPs by the MIC, and defines 0.9 as the minimum threshold for the normalized BLOSUM62 similarity score between a pair of aligned peptides with at least two-fold MIC changes. This study establishes a benchmark dataset of paired AMPs in Staphylococcus aureus from the publicly available AMP dataset GRAMPA, and conducts a rigorous procedure to evaluate various AMP AC prediction models, including nine machine learning, four deep learning algorithms, four masked language models, and four generative language models. Our analysis reveals that these models are capable of detecting AMP AC events and the pre-trained protein language model ESM2 demonstrates superior performance across the evaluations. The predictive performance of AMP activity cliffs remains to be further improved, considering that ESM2 with 33 layers only achieves the Spearman correlation coefficient 0.4669 for the regression task of the MIC values on the benchmark dataset. Source code and additional resources are available at https://***/supp/or https://***/Kewei2023/AMPCliff-generation. © 2024, CC BY-NC-SA.

关键词： Deep learning

A Security Framework to Protect Edge Supported software Defined Internet of Things Infrastructure 15th

学校读者我要写书评

暂无评论

A Security Framework to Protect Edge Supported Software Defi...

15th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2019

作者： Rafique, Wajid Khan, Maqbool Sarwar, Nadeem Dou, Wanchun State Key Laboratory for Novel Software Technology Nanjing University Nanjing China The Department of Computer Science and Technology Nanjing University Nanjing China Department of Computer Science Bahria University Lahore Pakistan

ISBN: (纸本)9783030301453

Managing the huge IoT infrastructure poses a vital challenge to the network community. software Defined Networking (SDN), due to its characteristics of centralized network management has been considered as an optimal choice to manage IoT. Edge computing brings cloud recourses near the IoT to localize the cloud demands. Consequently, SDN, IoT, and edge computing can be combined into a framework to create a resourceful SDIoT-Edge architecture to efficiently orchestrate cloud services and utilize resource-limited IoT devices in a flexible way. Besides a wide adoption of IoT, the vulnerabilities present in this less secure infrastructure can be exploited by the adversaries to attack the OpenFlow channel using Distributed Denial of Service (DDoS) attacks. DDoS on OpenFlow channel have the ability to disrupt the whole network hence, providing security for the OpenFlow channel is a key challenge in SDIoT-Edge. We propose a security framework called SDIoT-Edge Security (SIESec) against the security vulnerabilities present in this architecture. SIESec prototype employs machine learning-based classification strategy, blacklist integration, and contextual network flow filtering to efficiently defend against the DDoS attacks. We perform extensive simulations using Floodlight controller and Mininet network emulator. Our results proclaim that SIESec provides extensive security against OpenFlow channel DDoS attacks and pose a very less overhead on the network. © 2019, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Edge computing

Development of an Artificial Intelligence Model for Individualized FSH Daily Dosage Guidance During Ovulation Stimulation

学校读者我要写书评

暂无评论

SSRN

SSRN 2023年

作者： Duan, Liyan Xia, Yu Mei, Jie Zhang, Hui Zhu, YingChun Huang, ChenYnag Yan, Guijun Kong, Na Li, Wujun Sun, Haixiang Center for Reproductive Medicine and Obstetrics and Gynecology Drum Tower Clinic Medical College Nanjing Medical University Nanjing210008 China Center for Molecular Reproductive Medicine Nanjing University Nanjing210008 China National Institute of Healthcare Data Science Nanjing University Jiangsu Nanjing210008 China National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China Center for Medical Big Data Nanjing Drum Tower Hospital Affifiliated Drum Tower Hospital Medical School of Nanjing University Jiangsu Nanjing210008 China State Key Laboratory of Reproductive Medicine China International Joint Research Center on Environment and Human Health Center for Global Health School of Public Health Nanjing Medical University Nanjing211166 China Department of Reproductive Medicine Center Nanjing Drum Tower Hospital State Key Laboratory of Pharmaceutical Biotechnology Nanjing University Nanjing210023 China

Background: We aim to develop a real-time, interpretable artificial intelligence (AI) system, using an algorithm for estimating follicle stimulating hormone (FSH) daily dose in individualized controlled ovarian stimulation (COS).Methods: Between January 2018 and December 2020, a total of 13,788 cycles of GnRHa long-protocol ovulation stimulation cycles were included from the Reproductive Medicine Centre of Nanjing Drum Tower Hospital. We used a method of cross-temporal and cross-feature joint encoding to learn the time series information X=X1, X2,..., XN of the N days to get its hidden layer representation H. Then, we applied K-nearest neighbors (KNN) with the hidden layer representation H to obtain the ***: Our proposed CTFE model obtained Accuracy and Weighted-F1 metrics of 0.737 and 0.733, to predict the daily FSH dose respectively, which are the highest among other popular models for processing time series data. On days 1 and 5, Lasso regression predicted the FSH daily dosage with an accuracy of 0.696 and 0.522, respectively, whereas our technique predicted it with 0.834 and 0.819. The introduction of a sliding window mechanism after 13 days helps to maintain good performance, although accuracy drops due to insufficient ***: Our study demonstrated that the CTFE model can be used to calculate the daily FSH dose with high accuracy. This model may be easily applied in daily clinical practice by ***: This work was supported by the National Natural science Foundation of China (NO.82030040), Scientific research project of Jiangsu Province Association of Maternal and Child Health (No. FYX202203).Declaration of Interest: HX S, WJ L,GJ Y, N K, J M, LY D and Y X are listed as inventors of patents relating to this work. The authors affirm that they have no known financial or interpersonal conflicts that would have seemed to have an impact on the research presented in this *** Approval: The study had ethical approval fro

关键词： Hospitals

H(curl2)-conforming quadrilateral spectral element method for quad-curl problems

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Wang, Lixiu Shan, Weikun Li, Huiyuan Zhang, Zhimin Beijing Computational Science Research Center Beijing100193 China School of Science Henan University of Technology Zhengzhou Henan450001 China State Key Laboratory of Computer Science Laboratory of Parallel Computing Institute of Software Chinese Academy of Sciences Beijing100190 China Department of Mathematics Wayne State University MI48202 United States

In this paper, we propose an H(curl2)-conforming quadrilateral spectral element method to solve quad- curl problems. Starting with generalized Jacobi polynomials, we first introduce quasi-orthogonal poly- nomial systems for vector fields over rectangles. H(curl2)-conforming elements over arbitrary convex quadrilaterals are then constructed explicitly in a hierarchical pattern using the contravariant transform together with the bilinear mapping from the reference square onto each quadrilateral. It is astonishing that both the simplest rectangular and quadrilateral spectral elements possess only 8 degrees of freedom on each physical element. In the sequel, we propose our H(curl2)-conforming quadrilateral spectral element approximation based on the mixed weak formulation to solve the quad-curl equation and its eigenvalue problem. Numerical results show the effectiveness and efficiency of our method. Copyright © 2021, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

An artificially intelligent magnetic resonance spectroscopy quantification method: Comparison between QNet and LCModel on the cloud computing platform CloudBrain-MRS

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Lin, Meijin Guo, Lin Chen, Dicheng Chen, Jianshu Tu, Zhangren Huang, Xu Wang, Jianhua Qi, Ji Long, Yuan Huang, Zhiguo Guo, Di Qu, Xiaobo Han, Haiwei College of Ocean & Earth Sciences Xiamen University Xiamen361102 China Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen361102 China National Integrated Circuit Industry Education Integration Innovation Platform Xiamen University Xiamen361100 China Department of Electronic Science Xiamen University Xiamen361102 China Department of Radiology the First Affiliated Hospital of Xiamen University School of Medicine Xiamen University Xiamen361100 China Software Technology Company Limited Suzhou215163 China School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China

Objctives: This work aimed to statistically compare the metabolite quantification of human brain magnetic resonance spectroscopy (MRS) between the deep learning method QNet and the classical method LCModel through an easy-to-use intelligent cloud computing platform CloudBrain-MRS. Materials and Methods: In this retrospective study, two 3 T MRI scanners Philips Ingenia and Achieva collected 61 and 46 in vivo 1H magnetic resonance (MR) spectra of healthy participants, respectively, from the brain region of pregenual anterior cingulate cortex from September to October 2021. The analyses of Bland-Altman, Pearson correlation and reasonability were performed to assess the degree of agreement, linear correlation and reasonability between the two quantification methods. Results: Fifteen healthy volunteers (12 females and 3 males, age range: 21-35 years, mean age ± standard deviation = 27.4 ± 3.9 years) were recruited. The analyses of Bland-Altman, Pearson correlation and reasonability showed high to good consistency and very strong to moderate correlation between the two methods for quantification of total N-acetylaspartate (tNAA), total choline (tCho), and inositol (Ins) (relative half interval of limits of agreement = 3.04%, 9.3%, and 18.5%, respectively;Pearson correlation coefficient r = 0.775, 0.927, and 0.469, respectively). In addition, quantification results of QNet are more likely to be closer to the previous reported average values than those of LCModel. Conclusion: There were high or good degrees of consistency between the quantification results of QNet and LCModel for tNAA, tCho, and Ins, and QNet generally has more reasonable quantification than LCModel. © 2025, CC BY.

关键词： Metabolites

Pruning random resistive memory for optimizing analogue AI

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Li, Yi Wang, Songqi Zhao, Yaping Wang, Shaocong Zhang, Woyu He, Yangu Lin, Ning Cui, Binbin Chen, Xi Zhang, Shiming Jiang, Hao Lin, Peng Zhang, Xumeng Qi, Xiaojuan Wang, Zhongrui Xu, Xiaoxin Shang, Dashan Liu, Qi Cheng, Kwang-Ting Liu, Ming Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Laboratory of Microelectronic Devices & Integrated Technology Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China ACCESS – AI Chip Center for Emerging Smart Systems InnoHK Centers Hong Kong Science Park Hong Kong State Key Lab of Fabrication Technologies for Integrated Circuits Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China Frontier Institute of Chip and System Fudan University Shanghai200433 China College of Computer Science and Technology Zhejiang University Zhejiang310027 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong University of Chinese Academy of Sciences Beijing100049 China Institute of Mind The University of Hong Kong Hong Kong

The rapid advancement of artificial intelligence (AI) has been marked by the large language models exhibiting human-like intelligence. However, these models also present unprecedented challenges to energy consumption and environmental sustainability, which are exacerbated by the increasing number of users and the development of even larger models. One promising solution is to revisit analogue computing, a technique that predates digital computing and exploits emerging analogue electronic devices, such as resistive memory, which features in-memory computing, high scalability, and nonvolatility that addresses the von Neumann bottleneck, slowdown of Moore’s law, and volatile DRAM of conventional digital hardware. However, analogue computing still faces the same challenges as before: programming nonidealities and expensive programming due to the underlying devices physics. Therefore, leveraging the efficiency advantage while mitigating the programming disadvantage of analogue computing with resistive memory is a major open problem in AI hardware and electronics communities. Here, we report a universal solution, software-hardware co-design using structural plasticity-inspired edge pruning to optimize the topology of a randomly weighted analogue resistive memory neural network. software-wise, the topology of a randomly weighted neural network is optimized by pruning connections rather than precisely tuning resistive memory weights. Hardware-wise, we reveal the physical origin of the programming stochasticity using transmission electron microscopy, which is leveraged for large-scale and low-cost implementation of an overparameterized random neural network containing high-performance sub-networks. We implemented the co-design on a 40nm 256K resistive memory macro, observing 17.3% and 19.9% accuracy improvements in image and audio classification on FashionMNIST and Spoken digits datasets, as well as 9.8% (2%) improvement in PR (ROC) in image segmentation on DRIVE datasets, r

关键词： Energy utilization

Semi-automated Construction of Air Pollution Domain Ontology 1

学校读者我要写书评

暂无评论

9th International Conference on Frontier Computing, FC 2019

作者： Liu, Bo Zhang, Jiahui Li, Jianqiang Qu, Guangzhi Li, Yong Lang, Jianlei School of Software Engineering Faculty of Information Technology Beijing University of Technology Beijing100124 China Computer Science and Engineering Department Oakland University RochesterMI48309 United States Key Laboratory of Beijing on Regional Air Pollution Control College of Environmental and Energy Engineering Beijing University of Technology Beijing100124 China

ISBN: (数字)9789811532504

ISBN: (纸本)9789811532498

Air pollution has become a substantial environmental issue in China, which has seriously affected human health. To clarify the relationship among air pollutants, pollution sources, influencing factors, evaluation indicators and harms, it is essential to build a domain ontology for air pollution. Domain ontologies have been gradually accepted as a method to indicate the relationship between terms. However, there is not a complete ontology in air pollution domain, and building domain ontologies manually is time-consuming and inconvenient. In this paper, a semi-automatic approach is presented to build an ontology in air pollution domain. This paper proposes a method of entity relationship joint extraction based on attention mechanism, which is combined with core concept mining method to extract knowledge, and then concepts, relationships, and relevant instances are organized in hierarchy. Finally, the ontology model is constructed semi-automatically and semantic inference is also carried out. The research shows that this knowledge extraction method can avoid the error accumulation caused by entity recognition and relationship extraction and deepen the inner connection between them, and a large amount of effective knowledge in the field of air pollution can be extracted. In addition, the ontology constructed based on this method can also visually analyze the relationship between various classes and concepts in the field of air pollution, deduce the pollutant propagation path, and provide practical experience for the semi-automatic ontology construction in other fields and data support for further research. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Ontology