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Photocatalyzed Reverse Polarity Oxidative Povarov Reaction of Glycine Derivatives with Maleimides

Chinese Journal of Chemistry 2021年第12期39卷 3238-3244页

作者： Yongxin Zhang Wei Jiang Xiazhen Bao Yifeng Qiu Yong Yuan Caixia Yang Congde Huo Gansu International Scientific and Technological Cooperation Base of Water-Retention Chemical Functional Materials Key Laboratory of Eco-Environment-Related Polymer MaterialsMinistry of EducationCollege of Chemistry and Chemical EngineeringNorthwest Normal UniversityLanzhouGansu 730070China

An oxidative tandem(4+2)-cyclization/aromatization of N-aryl glycine derivatives with electron-deficient alkenes was first achieved using Ru(bpy)_(3)Cl_(2) as a photocatalyst and TBHP as an oxidant in combination with visible-light irradiation by blue ***-electron oxidation and the followingα-deprotonation of glycine derivatives affordα-amino alkyl radicals,which were then trapped by electrophilic *** an atom-economic and efficient method,a variety of pyrrolo[3,4-c]quinoline-1,3-diones have been obtained in good yields under mild reaction conditions.

关键词： Phptocatalysis Povarov reaction Cyclization Radical reactions Oxidation

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A novel image-based approach for interactive characterization of rock fracture spacing in a tunnel face

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Journal of Rock Mechanics and Geotechnical engineering 2022年第4期14卷 1077-1088页

作者： Jiayao Chen Yifeng Chen Anthony G.Cohn Hongwei Huang Jianhong Man Lijun Wei Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education Department of Geotechnical EngineeringTongji UniversityShanghai200092China School of Computing University of LeedsLeedsLS29JTUK Department of Computer Science and Technology Tongji UniversityShanghai211985China School of Civil Engineering Shandong UniversityJinan250061China Luzhong Institute of Safety Environmental Protection Engineering and Materialsand School of Mechanical and Electrical EngineeringQingdao University of Science and TechnologyQingdao255000China

This paper presents a novel integrated method for interactive characterization of fracture spacing in rock tunnel *** main procedure includes four steps:(1)Automatic extraction of fracture traces,(2)digitization of trace maps,(3)disconnection and grouping of traces,and(4)interactive measurement of fracture set spacing,total spacing,and surface rock quality designation(S-RQD)*** evaluate the performance of the proposed method,sample images were obtained by employing a photogrammetrybased scheme in tunnel *** were then conducted to determine the optimal parameter values(*** threshold,angle threshold,and number of fracture trace grouping)for characterizing rock fracture *** applying the identified optimal parameters involved in the model,the proposed method could lead to excellent qualitative results to a new tunnel *** perform a quantitative analysis,three methods(***,straightening,and the proposed method)were employed in the same study and comparisons were *** proposed method agrees well with the field measurement in terms of the maximum and average values of measured spacing ***,the proposed method has reasonably good accuracy and interactive advantage for estimating the ultimate fracture spacing and *** can be a possible extension of existing methods for fracture spacing characterization for two-dimensional(2D)rock tunnel faces.

关键词： Fracture spacing Rock tunnel Image processing Fracture set grouping

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A Fine-Grained Anomaly Detection Method Fusing Isolation Forest and Knowledge Graph Reasoning 19th

A Fine-Grained Anomaly Detection Method Fusing Isolation For...

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19th International Conference on Web Information Systems and Applications, WISA 2022

作者： Xu, Jie Zhou, Jiantao College of Computer Science Engineering Research Center of Ecological Big Data Ministry of Education National and Local Joint Engineering Research Center of Mongolian Intelligent Information Processing Technology Inner Mongolia Cloud Computing and Service Software Engineering Laboratory Inner Mongolia Social Computing and Data Processing Key Laboratory Inner Mongolia Discipline Inspection and Supervision Big Data Key Laboratory Inner Mongolia Big Data Analysis Technology Engineering Laboratory Inner Mongolia University Hohhot China

ISBN: (纸本)9783031203084

Anomaly detection aims to find outliers data that do not conform to expected behaviors in a specific scenario, which is indispensable and critical in current safety environments related studies. However, when performing outlier detection on a large-scale multidimensional dataset, most of the traditional methods make no distinctions between classes of outliers and lack the reasoning ability and the explainability of the analysis results, which leads to the low accuracy of global outlier detection and the inability to effectively identify local outliers. In this paper, we propose a fine-grained anomaly detection method which well utilizes isolation forest and knowledge graph reasoning tactics. First, a better extracting data feature method and a more reasonable weighted strategy are used to find global outliers based on the traditional isolation forest algorithm, and then we construct a custom rule base according to the in-depth research and analysis of global abnormal data, and at last the ontology knowledge is reasoned based on such rule base to achieve the detection of local outliers. Through analysis of extensive experiment results, our method could effectively discover more abnormal data without big loss of time costs, has strong generalization, and correspondingly improves the performance for abnormal detection. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Anomaly detection

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Application of functional near-infrared spectroscopy(fNIRS) for studying the influence of capsaicin stimulation on brain perception

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Food Science and Human Wellness 2025年

作者： Ran Tao Hang Liang Huiting Qiao Xin Liu Na Xu Ling Wang Xinglian Xu Lingqi Li Yujia Yang Peng Wang State Key Laboratory of Meat Quality Control and Cultured Meat Development， Ministry of Education China， Jiangsu Collaborative Innovation Center of Meat Production and Processing， Quality and Safety Control， College of Food Science and Technology， Nanjing Agricultural University Danyang SuHuichuang Brain Scientific Research Center School of Biological Science and Medical Engineering， Beihang University College of Computer Science， Sichuan Normal University

Spicy taste have been utilized in the food industry as appetizers， stomachics， and beverages and in the health sector to alleviate high cholesterol and hypertension. However， an understanding of neural mechanisms and cognitive processes associated with spicy diets remains limited due to the lack of instruments that directly monitoring the brain’s reaction to spicy sensations. This study aimed to examine the neural responses of participants to oral capsaicin solutions through functional near-infrared spectroscopy and reveal distinct neural activation patterns associated with spiciness induction and relief phases. Results showed that the perception of spiciness is a dynamic process， and significant differences in sensory intensity and emotion were observed between the phases. The prefrontal regions of the brain were activated during spicy flavor perception and presented clear lateralization effects. Further spatial localization results indicated that the orbitofrontal， dorsolateral prefrontal， and ventral lateral prefrontal cortices were the main causes of difference in activation among the phases. In addition， correlational analyses indicated that spicy flavor perception activated cortical areas related to emotion and reward processing. Overall， this research enhances our understanding of the neural mechanisms underlying addiction to spicy food and may be useful in the development of food on the basis of consumers’ preferences and behavioral responses.

关键词： Taste Capsaicin fNIRS Prefrontal cortex Oral irritation

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CHDNet: Enhanced Arbitrary Style Transfer via Condition Harmony DiffusionNet

CHDNet: Enhanced Arbitrary Style Transfer via Condition Harm...

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International Joint Conference on Neural Networks (IJCNN)

作者： Wenkai He Jianhui Zhao Ying Fang Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China School of Computer Science Wuhan University Wuhan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Arbitrary Style Transfer (AST) renders an image by adopting the style of any chosen artwork while preserving its content structure. Despite the widespread popularity of feedforward AST methods, they tend to merely optimize the statistical characteristics of images, leading to unnatural outputs and displeasing low-quality distortions. In contrast, diffusion models effectively address this issue by reconstructing the overall image. Unlike typical image generation tasks controlled by a single condition, style transfer demands the simultaneous consideration of multiple conditions. We propose Condition Harmony DiffusionNet (CHDNet), which distinguishes between style and content conditions, integrating them into harmonious conditions to collaboratively guide the generation process. We innovatively introduce a content aware attention, designed to extract semantic features of the content image across multiple dimensions, distinctly setting it apart from the style condition. Furthermore, we have improved the skip connections in the diffusion model, which introduces a slight increase in model complexity but results in a substantial improvement in the representation of fine details. Further refinements in the image sampling process empower us with great control over the stylization effect in the generated results. Our method successfully employs diffusion models via harmonious conditions to solve AST, achieving outstanding effects. Experiments demonstrate that our method achieves stateof-the-art arbitrary style transfer.

关键词： Measurement Image synthesis Computational modeling Semantics Neural networks Process control Image sampling

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CROSS-SCENE PERSON TRAJECTORY ANOMALY DETECTION BASED ON RE-IDENTIFICATION

CROSS-SCENE PERSON TRAJECTORY ANOMALY DETECTION BASED ON RE-...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Li, Yuanxun Wu, Ancong Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (纸本)9781665438643

In this work, we consider the cross-scene person trajectory anomaly detection problem, which detects the anomalous trajectories across multiple nonoverlapping scenes. This problem is highly significant for public security, but it is still underexplored. Since the trajectory is not continuous across nonoverlapping camera views, we take use of person re-identification (re-ID) to associate the same pedestrian in different scenes while mitigating its inaccuracy by a directional probabilistic graph. To better distinguishing normal samples from anomalies, We formulate a maximized margin graph autoencoder (MMGAE) model, and the reconstruction error of the MMGAE is regarded as an anomaly indicator for the sample. To verify the effectiveness of our approach, we collected and labeled a new dataset. we also explore the impact of the re-ID performance on the anomaly detection problem and the effect of an inaccurately constructed graph on the MMGAE. © 2021 IEEE

关键词： Trajectories

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Robust Cross-modal Medical Image Translation via Diffusion Model and Knowledge Distillation

Robust Cross-modal Medical Image Translation via Diffusion M...

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International Joint Conference on Neural Networks (IJCNN)

作者： Yuehan Xia Saifeng Feng Jianhui Zhao Zhiyong Yuan Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China School of Computer Science Wuhan University Wuhan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Medical image translation holds significant value, but its difficulty is amplified due to variations in noise patterns and the requisite anatomical invariance of image content. Various deep learning approaches, such as mainstream Generative Adversarial Networks (GANs), have been developed to learn multimodal mappings for obtaining translated images. However, the results produced by generators remain far from perfect for medical images, given the challenging requirements of style variations in noise patterns and anatomical invariance. In this paper, a medical image translation framework is proposed based on a diffusion model and knowledge distillation. To enhance the robustness of adversarial training and the accuracy of generated images, unlike traditional GANs, this framework incorporates an adaptive forward diffusion module for data augmentation following the generator. Additionally, the discriminator is designed as a timestep-dependent discriminator. Both real and generated images undergo the same forward diffusion process, and the discriminator learns to discriminate between real and generated images at each time step. Finally, an additional refinement network is composed of structurally similar but differently inputted teacher and student modules. Unlike existing knowledge distillation approaches, our teacher module is designed as a registration network with more inputs to better learn noise distribution and further refine translation results during training. Subsequently, knowledge is thoroughly distilled into the student module to ensure the generation of superior translation results. Extensive experiments on two public medical image datasets, along with comparisons with SOTA methods, demonstrate that the model produces higher quality and more robust images.

关键词： Knowledge engineering Training Noise Diffusion processes Diffusion models Generative adversarial networks Brain modeling

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MFN: Explainable DNA triple helixes Stabilized Design based on mCGR and flow network

MFN: Explainable DNA triple helixes Stabilized Design based ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiaoru Wen Lijun Sun Lei Xie Yanfen Zheng Ben Cao Bin Wang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

DNA triple helix structure, as a highly specific gene targeting tool, enable gene regulation by precisely identifying and binding to target DNA sequences. However, the limits of design quality and efficiency affect their wide application in gene therapy. Therefore, in this paper, we propose an antiparallel DNA triple helixes design method-MFN based on matrix chaotic game representation (mCGR) and flow network. Leveraging the structural characteristics of DNA sequences, this method employs the mCGR algorithm to construct an initial matrix, generating a set of DNA sequences that conform to foundational constraints. Then, these sequences are mapped as stream network nodes to screen crosstalk structures by path search, and the whole process is observable and interpretable. Experimental results show that MFN significantly improves the design efficiency of triple helix structure and reduces crosstalk phenomenon. Wet experiments further verify the effectiveness of the method. In summary, MFN achieves an efficient and high-quality design of DNA triple helixes and provides a new idea for targeted gene therapy.

关键词： Scalability Design methodology DNA Crosstalk Genomics Games Reliability engineering Regulation Gene therapy Bioinformatics

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Single-View Scene Point Cloud Human Grasp Generation

arXiv

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arXiv 2024年

作者： Wang, Yan-Kang Xing, Chengyi Wei, Yi-Lin Wu, Xiao-Ming Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Stanford University StanfordCA United States Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

In this work, we explore a novel task of generating human grasps based on single-view scene point clouds, which more accurately mirrors the typical real-world situation of observing objects from a single viewpoint. Due to the incompleteness of object point clouds and the presence of numerous scene points, the generated hand is prone to penetrating into the invisible parts of the object and the model is easily affected by scene points. Thus, we introduce S2HGrasp, a framework composed of two key modules: the Global Perception module that globally perceives partial object point clouds, and the DiffuGrasp module designed to generate high-quality human grasps based on complex inputs that include scene points. Additionally, we introduce S2HGD dataset, which comprises approximately 99,000 single-object single-view scene point clouds of 1,668 unique objects, each annotated with one human grasp. Our extensive experiments demonstrate that S2HGrasp can not only generate natural human grasps regardless of scene points, but also effectively prevent penetration between the hand and invisible parts of the object. Moreover, our model showcases strong generalization capability when applied to unseen objects. Our code and dataset are available at https://***/iSEE-laboratory/S2HGrasp. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Distributed Event-Triggered Impulsive Consensus Control of Nonlinear Multi-Agent Systems Under Malicious Attacks 48

Distributed Event-Triggered Impulsive Consensus Control of N...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Zhu, Jiaying He, Wangli Ge, Xiaohua East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China Swinburne University of Technology School of Science Computing and Engineering Technologies MelbourneVIC3122 Australia

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

This paper addresses a secure event-triggered consensus control problem for a class of nonlinear leader-following multi-agent systems (MASs) under denial of service (DoS) attacks and deception attacks. The novelty of this study lies in the development of a secure event-triggered impulsive control (ETIC) method that can effectively deal with the effects of random DoS and deception attacks, while also achieving the desired resource-efficient consensus control performance. More specifically, in order to save the previous communication resources, an event-triggered impulsive consensus control protocol is proposed to reduce data exchanges among the agents. Our further aim is to simultaneously guarantee the mean-square exponential consensus performance of the controlled MAS and achieve attack resilience as well as resource efficiency. Sufficient conditions on the consensus performance analysis are derived, and the lower bound of impulsive sequences is further specified to exclude the Zeno behavior. Finally, a numerical example involving a group of Chua's circuits is given to illustrate the effectiveness of the derived theoretical results. © 2022 IEEE.

关键词： Multi agent systems

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