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Tentorial ectopic schwannoma: A case report and literature review

Radiology Case Reports 2023年第3期18卷 1058-1064页

作者： Yan, Hui Zhang, Hui College of Medical Imaging Shanxi Medical University Shanxi Province Taiyuan 030001 China Department of Radiology First Hospital of Shanxi Medical University No. 85 South Jiefang Road Yingze District Shanxi Province Taiyuan 030001 China Intelligent Imaging Big Data and Functional Nano-imaging Engineering Research Center of Shanxi Province First Hospital of Shanxi Medical University Shanxi Province Taiyuan 030001 China Shanxi Key Laboratory of Intelligent Imaging and Nanomedicine First Hospital of Shanxi Medical University Shanxi Province Taiyuan 030001 China

Schwannomas arising within the central nervous system unrelated to a major cranial nerve rarely originate from the dura, particularly the tentorium. Hereby, the reported case is a 39-year-old female with blurred vision in her right eye for 1 month who was diagnosed with tentorial ectopic schwannoma. The tumor was almost totally resected through a posterior median incision approach without any neurological deficits. The clinical presentations and radiographic findings of such cases are discussed in the article. © 2022

关键词： Magnetic resonance imaging Meningioma Schwannoma Tentorium

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Logical Relation Modeling and Mining in Hyperbolic Space for Recommendation

Logical Relation Modeling and Mining in Hyperbolic Space for...

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International Conference on data engineering

作者： Yanchao Tan Hang Lv Zihao Zhou Wenzhong Guo Bo Xiong Weiming Liu Chaochao Chen Shiping Wang Carl Yang College of Computer and Data Science Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Institute for Artificial Intelligence University of Stuttgart Stuttgart Germany College of Computer Science Zhejiang University Hangzhou China Department of Computer Science Emory University Atlanta United States

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

The sparse interactions between users and items have aggravated the difficulty of their representations in recommender systems. Existing methods leverage tags to alleviate the sparsity problem but ignore prevalent logical relations among items and tags (e.g., membership, hierarchy, and exclusion), which can be leveraged to enhance the accuracy of modeling user preferences and conducting recommendations. To this end, we propose to extract logical relations among item tags from existing tag taxonomies and exploit the individual strengths of the Poincaré and the Lorentz models in hyperbolic space for logical relation modeling towards enhanced recommendations. Moreover, we find that the logical relations directly extracted from existing tag taxonomies can be inaccurate and coarse. Therefore, we further devise innovative consistency-based and granularity- based weighting mechanisms based on user behavior patterns for data-driven logical relation mining that can be jointly optimized along with recommendations in an end-to-end fashion. Extensive experiments on four real-world benchmark datasets show drastic performance gains brought by our proposed framework, which constantly achieves an average of 8.25% improvement over state-of-the-art competitors regarding both Recall and NDCG metrics. Insightful case studies further demonstrate that our automatically refined logical relations are highly accurate and interpretable.

关键词： Measurement Accuracy Taxonomy Semantics Predictive models Performance gain Linguistics

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Decoupled Prototype Learning for Reliable Test-Time Adaptation

arXiv

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arXiv 2024年

作者： Wang, Guowei Ding, Changxing Tan, Wentao Tan, Mingkui the School of Electronic and Information Engineering South China University of Technology 381 Wushan Road Tianhe District Guangzhou510000 China the Pazhou Lab Guangzhou510330 China the School of Future Technology South China University of Technology 381 Wushan Road Tianhe District Guangzhou510000 China the School of Software Engineering the Key Laboratory of Big Data and Intelligent Robot Ministry of Education South China University of Technology Guangzhou510006 China

Test-time adaptation (TTA) is a task that continually adapts a pre-trained source model to the target domain during inference. One popular approach involves fine-tuning model with cross-entropy loss according to estimated pseudo-labels. However, its performance is significantly affected by noisy pseudo-labels. This study reveals that minimizing the classification error of each sample causes the cross-entropy loss’s vulnerability to label noise. To address this issue, we propose a novel Decoupled Prototype Learning (DPL) method that features prototype-centric loss computation. First, we decouple the optimization of class prototypes. For each class prototype, we reduce its distance with positive samples and enlarge its distance with negative samples in a contrastive manner. This strategy prevents the model from overfitting to noisy pseudo-labels. Second, we propose a memory-based strategy to enhance DPL’s robustness for the small batch sizes often encountered in TTA. We update each class’s pseudo-feature from a memory in a momentum manner and insert an additional DPL loss. Finally, we introduce a consistency regularization-based approach to leverage samples with unconfident pseudo-labels. This approach transfers feature styles of samples with unconfident pseudo-labels to those with confident pseudo-labels. Thus, more reliable samples for TTA are created. The experimental results demonstrate that our methods achieve state-of-the-art performance on domain generalization benchmarks, and reliably improve the performance of self-training-based methods on image corruption benchmarks. The code will be released. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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SUBDIFFERENTIALLY POLYNOMIALLY BOUNDED FUNCTIONS AND GAUSSIAN SMOOTHING-BASED ZEROTH-ORDER OPTIMIZATION ∗

arXiv

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arXiv 2024年

作者： Lei, Ming Pong, Ting Kei Sun, Shuqin Yue, Man-Chung College of Applied Mathematics Chengdu University of Information Technology Chengdu China Department of Applied Mathematics The Hong Kong Polytechnic University Hong Kong Key Laboratory of Optimization Theory and Applications China West Normal University of Sichuan Province School of Mathematics Education China West Normal University Nanchong China Musketeers Foundation Institute of Data Science Department of Data and Systems Engineering The University of Hong Kong Hong Kong

We study the class of subdifferentially polynomially bounded (SPB) functions, which is a rich class of locally Lipschitz functions that encompasses all Lipschitz functions, all gradient- or Hessian-Lipschitz functions, and even some non-smooth locally Lipschitz functions. We show that SPB functions are compatible with Gaussian smoothing (GS), in the sense that the GS of any SPB function is well-defined and satisfies a descent lemma akin to gradient-Lipschitz functions, with the Lipschitz constant replaced by a polynomial function. Leveraging this descent lemma, we propose GS-based zeroth-order optimization algorithms with an adaptive stepsize strategy for minimizing SPB functions, and analyze their convergence rates with respect to both relative and absolute stationarity measures. Finally, we also establish the iteration complexity for achieving a (δ, ϵ)-approximate stationary point, based on a novel quantification of Goldstein stationarity via the GS gradient that could be of independent interest. Copyright © 2024, The Authors. All rights reserved.

关键词： Gaussian distribution

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Visualizing nonlinear resonance in nanomechanical systems via single-electron tunneling

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Nano Research 2021年第4期14卷 1156-1161页

作者： Xinhe Wang Lin Cong Dong Zhu Zi Yuan Xiaoyang Lin Weisheng Zhao Zaiqiao Bai Wenjie Liang Ximing Sun Guang-Wei Deng Kaili Jiang Fert Beijing Research Institute School of Microelectronics&Beijing Advanced Innovation Centre for Big Data and Brain Computing(BDBC)Beihang UniversityBeijing 100191China State Key Laboratory of Low-Dimensional Quantum Physics Department of Physics and Tsinghua-Foxconn Nanotechnology Research CenterTsinghua UniversityBeijing 100084China Key Laboratory of Quantum Information CASUniversity of Science and Technology of ChinaHefei 230026China Department of Physics Beijing Normal UniversityBeijing 100875China Institute of Physics Chinese Academy of SciencesBeijing 100080China Institute of Nuclear and New Energy Technology Collaborative Innovation Center of Advanced Nuclear Energy TechnologyKey Laboratory of Advanced Reactor EngineeringTsinghua UniversityBeijing 100084China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu 610054China

Numerous reports have elucidated the importance of mechanical resonators comprising quantum-dot-embedded carbon nanotubes(CNTs)for studying the effects of single-electron ***,there is a need to investigate the single-electron transport that drives a large amplitude into a nonlinear ***,a CNT hybrid device has been investigated,which comprises a gate-defined quantum dot that is embedded into a mechanical resonator under strong actuation *** Coulomb peak positions synchronously oscillate with the mechanical vibrations,enabling a single-electron Chopper*1 ***,the vibration amplitude of the CNT versus its frequency can be directly visualized via detecting the time-averaged single-electron tunneling *** understand this phenomenon,a general formula is derived for this time-averaged single-electron tunneling current,which agrees well with the experimental *** using this visualization method,a variety of nonlinear motions of a CNT mechanical oscillator have been directly recorded,such as Duffing nonlinearity,parametric resonance,and double-,fractional-,mixed-frequency *** approach opens up burgeoning opportunities for investigating and understanding the nonlinear motion of a nanomechanical system and its interactions with electron transport in quantum regimes.

关键词： carbon nanotube mechanical resonator quantum dot nonlinear coupling

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Qualitative Spatial Reasoning with Oriented Point Relation in 3D Space

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Chinese Journal of Electronics 2019年第2期28卷 325-330页

作者： WANG Shengsheng DONG Ruyi SONG Wenzhuo WANG Chuangfeng College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computing and Knowledge Engineering of Ministry of Education Jilin Vocational College of Industry and Technology

Among qualitative direction relation models, Oriented point relation algebra（OPRAm） is a remarkable model for robot navigation with uncertain direction information. It has great advantages in providing powerful expressions with very limited information compared with other point-based spatial relation *** original OPRAm is defined in 2D space, and its model and reasoning algorithm are found not applicable in 3D space. We proposed a novel direction relation model named OPRA3Dmto extend the original OPRAm to 3D space,and presented a new reasoning algorithm on Oriented point relation algebra in three dimension（OPRA3Dm）. A further study was carried out for composition reasoning on OPRA3Dm. The proposed reasoning algorithm will deduce new information which cannot be directly detected by hardware. The experiment showed the algorithm had some practical significance, it can be applied to the Unmanned aerial vehicle（UAV） navigation and similar scenarios.

关键词： Qualitative spatial reasoning Oriented point relation algebra in three dimension(OPRA3Dm) Composition reasoning Unmanned aerial vehicle navigation

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Highly Sensitive Acetic Anhydride Sensor Based on Cd-Doped Biphase In2o3 Microspheres

SSRN

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SSRN 2023年

作者： Meng, Fanli Zhang, Hua Guo, Yinghao Zhang, Haoting College of Information Science and Engineering Northeastern University Shenyang110819 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang110819 China Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Northeastern University Shenyang110819 China

New drugs emerge in an endless stream, the global drug supply is increasing, and the number of drug users is steadily rising, so the detection of acetic anhydride-a raw material for drug preparation is of great significance for controlling the spread of drugs. The crystal phase of semiconductor metal oxides has an important impact on oxygen vacancy, microscopic morphology, specific surface area, and high-energy surface exposure, which in turn affects the catalytic and gas-sensing effects of materials. Ions doped can sometimes achieve the purpose of changing the crystal phase. In this paper, Cd-doped biphase (cubic phase and hexagonal phase) In2O3 nanospheres sensitive to acetic anhydride were synthesized via one easy hydrothermal process. The fabricated sensor has high sensitivity (372) to 100 ppm acetic anhydride in the dry environment of 220 °C, excellent selectivity and good stability. X-ray diffraction (XRD), energy dispersive spectroscopy (EDS) analysis, and scanning electron microscopy (SEM) were used to observe the crystal phase, elemental information and nanoscale appearance of the prepared compounds, and the gas-sensitive mechanism was briefly analyzed according to the characterization information. The enhancing acetic anhydride sensing properties are owing to the change of internal electronic channels and the crystal structure of In2O3 material caused by Cd-doped. © 2023, The Authors. All rights reserved.

关键词： Scanning electron microscopy

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Can Near-Real-Time Satellite Precipitation Estimations Accurately Reproduce the Record-Breaking Meiyu Event in 2020

SSRN

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SSRN 2023年

作者： Liu, Wen Lü, Haishen Zhu, Yonghua Wang, Xiaoyi Gao, Pei Su, Jianbin State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering National Cooperative Innovation Center for Water Safety and Hydro-science College of Hydrology and Water Resources Hohai University Nanjing210098 China National Tibetan Plateau Data Center Key Laboratory of Tibetan Environmental Changes and Land Surface Processes Institute of Tibetan Plateau Research Chinese Academy of Sciences Beijing100101 China

Due to the continuous heavy rainfall, the Meiyu season is frequently accompanied by a multitude of flood-related disasters. Hence, an accurate and timely monitoring of Meiyu precipitation is crucial for effectively preventing and mitigating these calamities. Given the high potential utility of the near-real-time (NRT) products from Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), namely IMERG-E and IMERG-L products, in acquiring precipitation data, it is important to quantify their errors and uncertainties during the Meiyu season. In this study, using the gauge precipitation observations as benchmark, the performance of IMERG NRT products is assessed and compared during the record-breaking Meiyu event in 2020. Meanwhile, the Motion Vector Kalman product from Global Satellite Mapping of Precipitation (GSMaP-MVK) is also utilized as parallel control. Results indicate that the three satellite products can typically capture the spatial-temporal evolution of Meiyu precipitation while IMERG-L has the best statistical performance. Although the peak time of IMERG-L is about two hours earlier than the observations, it can generally reproduce the diurnal cycle of observed precipitation amount with slight underestimation of peak value. In contrast, IMERG-E and GSMaP-MVK perform much worse. In terms of precipitation frequency and intensity, the two IMERG products both show poor performance in reproducing their diurnal variation. Besides, the IMERG products both outperform GSMaP-MVK on almost all precipitation intensity groups, except for light rain. Meanwhile, potentially affected by varying weather circulations, the bias of IMERG products in the afternoon is significantly higher than in the morning for all precipitation intensities. More importantly, the performance fluctuation of IMERG in reproducing observed diurnal cycle of rainstorm events is closely related to their varied data sources. These findings can serve as a valuable reference fo

关键词： Uncertainty analysis

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Event Argument Extraction with Two-Dimensional Enhanced Fusion Mechanism

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Computer engineering and Applications 2025年第10期61卷 111-119页

作者： Wang, Luxiang Chen, Yanping Huang, Hui Huang, Ruizhang Qin, Yongbin Engineering Research Center of Ministry of Education for Text Computing and Cognitive Intelligence School of Computer Science and Technology Guizhou University Guiyang550025 China State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China

Addressing the lack of interaction between trigger and arguments, and the lack of interaction within channels in existing studies of event argument extraction, this paper proposes a two-dimensional enhanced fusion mechanism for event argument extraction (W2-ARG) that incorporates a 2D enhanced fusion method. It inserts identifiers on both sides of a trigger word in a sentence, which encodes information about the event type. In the learning process, it is effective to enhance the interaction between trigger and argument, and encode the trigger separately to highlight its information in the sentence. The event argument extraction is implemented as a two-dimensional representation of label prediction. It has the advantage to capture semantic interactions of words at different distances through dilated convolution. Subsequently, a channel attention module is applied to enhance the interactions within the channel to strengthen the information transfer within the channel. Finally, the Laplace operator is utilized to highlight the positional features of event arguments in the semantic space to improve the performance. The proposed model is experimented on the ACE05-EN and ERE-EN datasets. Experimental results show clear performance improvement of the proposed model compared with other event argument extraction methods. © 2025 Journal of Computer engineering and Applications Beijing Co., Ltd.;Science Press. All rights reserved.

关键词： BERT channel attention event argument extraction Laplace operator sentence planarization representation

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Abnormal traffic detection system in SDN based on deep learning hybrid models

arXiv

引用

arXiv 2023年

作者： Wang, Kun Fu, Yu Duan, Xueyuan Liu, Taotao Xu, Jianqiao Department of Information Security Naval University of Engineering Hubei Wuhan430033 China School of Mathematics and Information Engineering Xinyang Vocational and Technical College Henan Xinyang464000 China College of Computer and Information Technology Xinyang Normal University Henan Xinyang464000 China Henan Key Laboratory of Analysis and Applications of Education Big Data Xinyang Normal University Xinyang464000 China

Software defined network (SDN) provides technical support for network construction in smart cities, However, the openness of SDN is also prone to more network attacks. Traditional abnormal traffic detection methods have complex algorithms and find it difficult to detect abnormalities in the network promptly, which cannot meet the demand for abnormal detection in the SDN environment. Therefore, we propose an abnormal traffic detection system based on deep learning hybrid model. The system adopts a hierarchical detection technique, which first achieves rough detection of abnormal traffic based on port information. Then it uses wavelet transform and deep learning techniques for fine detection of all traffic data flowing through suspicious switches. The experimental results show that the proposed detection method based on port information can quickly complete the approximate localization of the source of abnormal traffic. the accuracy, precision, and recall of the fine detection are significantly improved compared with the traditional method of abnormal traffic detection in SDN. © 2023, CC BY-NC-SA.

关键词： Wavelet transforms

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