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IsoVISoR: Towards 3D Mesoscale Brain Mapping of Large Mammals at Isotropic Sub-micron Resolution

Neuroscience Bulletin 2025年第2期41卷 344-348页

作者： Chao-Yu Yang Yan Shen Xiaoyang Qi Lufeng Ding Yanyang Xiao Qingyuan Zhu Hao Wang Cheng Xu Pak-Ming Lau Pengcheng Zhou Fang Xu Guo-Qiang Bi Division of Life Sciences and Medicine CAS Key Laboratory of Brain Function and DiseaseUniversity of Science and Technology of ChinaHefei230026China Interdisciplinary Center for Brain Information Shenzhen-Hong Kong Institute of Brain ScienceShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055China Center for Integrative Imaging Hefei National Research Center for Physical Sciences at the MicroscaleUniversity of Science and Technology of ChinaHefei230027China National Engineering Laboratory for Brain-Inspired Intelligence Technology and Application MoE Key Laboratory of Brain-Inspired Intelligent Perception and CognitionSchool of Information Science and TechnologyUniversity of Science and Technology of ChinaHefei230026China Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing Institute of Artificial IntelligenceHefei Comprehensive National Science CenterHefei230088China Faculty of Life and Health Sciences Shenzhen University of Advanced TechnologyShenzhen518107China CAS Key Laboratory of Brain Connectome and Manipulation The Brain Cognition and Brain Disease InstituteShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055China

Dear Editor,The mammalian brain exhibits cross-scale complexity in neuronal morphology and connectivity,the study of which demands high-resolution morphological reconstruction of individual neurons across the entire brain[1-4].Current commonly used approaches for such mesoscale brain mapping include two main types of three-dimensional fluorescence microscopy:the block-face methods,and the lightsheet-based methods[5,6].In general,the high imaging speed and light efficiency of light-sheet microscopy make it a suitable tool for high-throughput volumetric imaging,especially when combined with tissue-clearing ***,large brain samples pose major challenges to this approach.

关键词： brain mapping three dimensional fluorescence microscopy mesoscale brain mapping lightsheet based methods block face methods high throughput volumetric imaging tissue clearing techniques

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Offloading Algorithm for Edge Computing Tasks Based on Energy Optimization 8

Offloading Algorithm for Edge Computing Tasks Based on Energ...

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8th IEEE International Conference on Smart Cloud, SmartCloud 2023

作者： Gao, Xing Gu, Haonan Liu, Fang Hu, Wei Wuhan City College Wuhan China College of Computer Science and Technology Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China

ISBN: (纸本)9798350313505

Current research in computation offloading encompasses various aspects, including task partitioning and allocation, resource management, communication, and data management. This paper focuses on developing new algorithms and techniques to enhance the efficiency and effectiveness of the offloading process while addressing challenges arising from dynamic and heterogeneous mobile environments. Researchers are also examining the trade-off between offloading benefits like improved performance and energy efficiency, and the associated overhead costs such as communication and data management. This paper proposes an optimization strategy for computational offloading to enhance the Grey Wolf algorithm. The solution aims to quickly optimize system energy consumption while considering the arithmetic power constraint of the mobile edge computing server and time delay constraints. Simulation experiments demonstrate that the algorithm in this paper significantly outperforms the traditional Grey Wolf and PSO algorithms in terms of both performance and computational speed. ©2023 IEEE.

关键词： Information management

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Leveraging catastrophic forgetting to develop safe diffusion models against malicious finetuning 24

Leveraging catastrophic forgetting to develop safe diffusion...

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Proceedings of the 38th International Conference on Neural Information processing Systems

作者： Jiadong Pan Hongcheng Gao Zongyu Wu Taihang Hu Li Su Qingming Huang Liang Li Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS and University of Chinese Academy of Sciences University of Chinese Academy of Sciences The Pennsylvania State University Nankai University Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS

ISBN: (纸本)9798331314385

Diffusion models (DMs) have demonstrated remarkable proficiency in producing images based on textual prompts. Numerous methods have been proposed to ensure these models generate safe images. Early methods attempt to incorporate safety filters into models to mitigate the risk of generating harmful images but such external filters do not inherently detoxify the model and can be easily bypassed. Hence, model unlearning and data cleaning are the most essential methods for maintaining the safety of models, given their impact on model parameters. However, malicious fine-tuning can still make models prone to generating harmful or undesirable images even with these methods. Inspired by the phenomenon of catastrophic forgetting, we propose a training policy using contrastive learning to increase the latent space distance between clean and harmful data distribution, thereby protecting models from being fine-tuned to generate harmful images due to forgetting. The experimental results demonstrate that our methods not only maintain clean image generation capabilities before malicious fine-tuning but also effectively prevent DMs from producing harmful images after malicious fine-tuning. Our method can also be combined with other safety methods to maintain their safety against malicious fine-tuning further.

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A Hierarchical Helical Carbon Nanotube Fiber Artificial Ligament

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Advanced Fiber Materials 2023年第5期5卷 1549-1551页

作者： Chunxue Li Ruilong Jia Yingkui Yang Guangfu Liao College of Ecological Environment and Urban Construction Fujian University of TechnologyFuzhou 350118China The Key Research Laboratory of Benefiting Qi for Acting Blood Circulation Method to Treat Multiple Sclerosis of State Administration of Traditional Chinese Medicine/Research Center of Neurobiology Shanxi University of Chinese MedicineJinzhong 030619People’s Republic of China State Key Laboratory of New Textile Materials and Advanced Processing Technologies Wuhan Textile UniversityWuhan 430200China College of Material Engineering Fujian Agriculture and Forestry UniversityFuzhou 350002China

For the optimal functional recovery of force-transmitting connective tissues,obtaining grafts that are mechanically robust and integrating them with host bone effectively to tolerate high loads during violent joint motions is both crucial and *** research proposes that a hierarchical helical carbon nanotube fiber,which has the considerably high mechanical strength,and can integrate with the host bone and restore movement in animals,is a very promising artificial *** above research marks a significant development in artificial ligament via the innovative utilization of hierarchical helical carbon nanotube fiber.

关键词： Hierarchical helical carbon nanotube fiber Artificial ligament Mechanical strength Large animal models

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Scheduling Algorithm for Low Energy Consumable Parallel Task Application Based on DVFS 1

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7th International Conference on Smart Computing and Communication, SmartCom 2022

作者： Liu, Xun Zhao, Hui College of Computer Science and Technology Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China Henan University Kaifeng475000 China

ISBN: (数字)9783031281242

ISBN: (纸本)9783031281235

With the continuous improvement of various high-performance computing systems, various data centers had also been fully expanded. Energy consumption and actual performance measurement were very important indicators, which were also key issues in how to judge parallel calls for some tasks in high-performance computer systems. Modern processors were basically equipped with software control functions such as DVFS (Dynamic Voltage Frequency Scaling), in the actual system operation to ensure that the system could ensure the reasonable operation of the system while reducing energy consumption indicators. This paper considered how the designed scheduling algorithm first divides tasks reasonably to ensure that the maximum completion time and energy consumption of the processor were sufficiently reduced when the directed acyclic graph was executed. Then considered making reasonable adjustments to the processor frequency using DVFS technology to adapt to the task while ensuring the critical path of the task. At the end of the article, make sure that the experimental verification algorithm could ensure that the task was completed and could reduce the energy consumption during task execution as much as possible. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Energy utilization

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Label Semantic Dynamic Guidance Network for Remote Sensing Image Scene Classification

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Chai, Borui Zhao, Tianming Yang, Runou Zhang, Nan Tian, Tian Tian, Jinwen Huazhong University of Science and Technology National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Wuhan430074 China

The remote sensing image scene classification continues to face significant challenges due to high intraclass diversity and interclass similarity. Existing methods mainly use semantic associations between images to establish deep semantic associations between classes, ignoring the rich high-level semantic knowledge contained in the label text. This high-level information are especially valuable for distinguishing between confusing categories, as it enables the model to capture both similarity and distinctive features effectively. In this article, we introduce a novel approach that incorporates label semantic information and proposes a plug-and-play framework to guide classification model learning of intraclass and interclass relationships. Specifically, our framework includes a dynamic soft label module (DSLM), which uses textual semantics to facilitate classification model learning of interclass relationships via soft labels at the target level. In addition, we design a coarse-to-fine contrastive module (CFCM) to integrate textual semantics into contrastive learning, guiding the model in capturing intraclass and interclass relationships at the feature level. Our framework is compatible with both convolutional neural network (CNN)-based and vision transformer (ViT)-based classification architectures and is employed solely during training to minimize computational overhead. Experimental results on four datasets validate the effectiveness of our approach. © 2025 IEEE.

关键词： Contrastive Learning

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Development of a Universal RNA Dual-Terminal Labeling Method for Sensing RNA-Ligand Interactions

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CCS Chemistry 2023年第1期5卷 221-233页

作者： Longhuai Cheng Dejun Ma Jie Zhang Xueying Kang Yang Wu Yi Zhao Long Yi Zhen Xi State Key Laboratory of Elemento-Organic Chemistry Department of Chemical BiologyCollege of ChemistryNational Pesticide Engineering Research CenterCollaborative Innovation Center of Chemical Science and EngineeringNankai UniversityTianjin 300071 State Key Laboratory of Organic-Inorganic Composites and Beijing Key Laboratory of Bioprocess Beijing University of Chemical Technology(BUCT)Beijing 100029 Key Laboratory of Intelligent Information Processing Research Center for Ubiquitous Computing SystemsInstitute of Computing TechnologyChinese Academy of SciencesBeijing 100190

Dual labeling of an RNA can provide Förster resonance energy transfer(FRET)sensors for studying RNA folding,miRNA maturation,and RNA-protein ***,we report the development of a highly efficient strategy for direct dual-terminal labeling of any RNA of *** explored new Michael cycloaddition for facile labeling of 5′-terminal RNA with improved *** chemical tetrazinylation of RNA at the 3′-terminus was achieved with the highly efficient and catalysis-free tetrazine-cycloalkyne *** single-terminal labeling methods were combined for dual-terminal labeling of an RNA including short hairpin RNA,pre-miRNA,riboswitch,and noncoding ***,these dual-labeled RNA-based FRET sensors were used to monitor RNA-ligand interactions in vitro and in live *** is anticipated that these universal RNA labeling strategies will be useful to study RNA structures and functions.

关键词： dual labeling Förster resonance energy transfer sensor Michael cycloaddition tetrazinecycloalkyne ligation RNA-ligand interaction

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Named Entity Recognition of Electronic Medical Records based on BERT-BiLSTM-Biaffine Model

Named Entity Recognition of Electronic Medical Records based...

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2023 Asia-Pacific Conference on Image processing, Electronics and Computers, IPEC 2023

作者： Wang, Peng Gu, Jinguang Institute of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Prov. Key Laboratory of Intelligent Information Processing and Real Time Industrial Systems Wuhan University of Science and Technology Wuhan China

In the case of the specific task of identifying named entities within electronic medical record, it is hard to determine the boundary of nested entities, and existing NER systems have insufficient decoding performance. Based on the pre training model BERT, this paper introduces a novel network structure called Biaffine Layer using a bidirectional LSTM layer. The network uses a dual affine attention mechanism for semantic information learning, which can better interact with the semantic information of entity heads and entity tails, thereby achieving better recognition results for entities. Due to the sparsity of named entity datasets and the uneven distribution of entity categories, traditional binary cross entropy loss functions require multiple rounds of training to decode entities. In this paper, we have modified the binary cross entropy loss function to make the proposed model faster decode the entities that need to be identified. The model performs well, according to the experimental findings. The approach suggested in this paper offers a fresh approach to the NER issue raised by electronic medical records, and it is anticipated to considerably boost the effectiveness and caliber of clinical medical research. © Published under licence by IOP Publishing Ltd.

关键词： Decoding

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Implicit Learning of Rotational Decoupling Detection Heads Applied to Remote Sensing Object Detection

Implicit Learning of Rotational Decoupling Detection Heads A...

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2023 Asia-Pacific Conference on Image processing, Electronics and Computers, IPEC 2023

作者： Song, Chengkun Ding, Sheng Chen, Yingying School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Kagoshima University Kagoshima Japan

ISBN: (纸本)9781665457033

Remote sensing image features complicated background, big image size, small object pixels, and variable object direction due to the arbitrary shooting angle. Conventional object detector has poor detection effect on remote sensing image, which is prone to missed detection and false detection. In view of this, an improved YOLOv5 object detection algorithm was proposed for remote sensing image. First, the C3 module was improved. The output feature information of each convolutional module was transmitted to the deep network, so as to preserve more fine-grained feature information and enhance the small object detection capability. The improved downsampling mechanism integrates convolution downsampling with maximum pooling downsampling mechanism in downs amp ling to separate foreground and background information while retaining feature information. A lightweight rotatory decoupling detection head with implicit learning was proposed to address the conflict between regression and classification, thus improving the network detection capability for objects in any direction. In the meantime, an implicit learning matrix was added to guarantee the lightweight detection head and improve the network detection accuracy for multiple tasks. According to experimental results, under the same model size, compared with the original YOLOv5 detection method, the detection accuracy and detection speed of the proposed method were improved by 8% and 15 frames, or 14.7%, respectively on DOTA-v1.5 data set, effectively improving the detection accuracy and speed in remote sensing image detection. The detection accuracy was increased by 3.5% on coco data set in natural scenario. The method herein can still effectively improve the detection accuracy in natural scenario, proving effectiveness of the modified YOLOv5 method. © 2023 IEEE.

关键词： Object detection

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Specific Emitter Identification based on CNN and Transformer 5

Specific Emitter Identification based on CNN and Transformer

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5th International Academic Exchange Conference on Science and technology Innovation, IAECST 2023

作者： Wang, Bin Gao, Ning Wang, Feng The 38th Research Institute of China Electronics Technology Group Corporation Key Laboratory of Aperture Array and Space Application Key Laboratory of Intelligent Information Processing Anhui Hefei230088 China Hefei University of Technology School of Computer and Information Hefei230601 China

ISBN: (纸本)9798350357738

Specific emitter identification(SEI) plays an increasingly crucial and potential role in both military and civilian scenarios. It refers to a process to discriminate individual emitters from each other by analyzing extracted characteristics from given radio signals. In this paper, we present a novel radiation source identification method named CNNFormer based on a hybrid architecture of Convolutional Neural Network and Transformer, harnessing the strengths of both architectures to enhance feature extraction and sequence modeling. We demonstrate the effectiveness of our approach on simulation datasets, achieving state-of-the-art accuracy in emitter identification. We showcase outstanding performance of our method on simulation datasets, achieving 98.29% three-class identification accuracy the emitter dataset with three devices and 97.60% ten-class identification accuracy on an RF fingerprint dataset with ten XSPR transmitters. Furthermore, our model allows for insight into the key features contributing to identification. This work contributes to enhancing the security and reliability of wireless communication systems. © 2023 IEEE.

关键词： Convolutional neural networks

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