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Unsupervised content-preserving transformation for optical microscopy

Light(Science & Applications) 2021年第3期10卷 390-400页

作者： Xinyang Li Guoxun Zhang Hui Qiao Feng Bao Yue Deng Jiamin Wu Yangfan He Jingping Yun Xing Lin Hao Xie Haoqian Wang Qionghai Dai Department of Automation Tsinghua UniversityBeijing 100084China Tsinghua Shenzhen International Graduate School Tsinghua UniversityShenzhen 518055China Institute for Brain and Cognitive Sciences Tsinghua UniversityBeijing 100084China School of Astronautics Beihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing 100191China Department of Pathology Sun Yat-sen University Cancer CenterGuangzhou 510060China State Key Laboratory of Oncology in South China Guangzhou 510060China Collaborative Innovation Center for Cancer Medicine Guangzhou 510060China Beijing Innovation Center for Future Chips Tsinghua UniversityBeijing 100084China

The development of deep learning and open access to a substantial collection of imaging data together provide a potential solution for computational image transformation,which is gradually changing the landscape of optical imaging and biomedical ***,current implementations of deep learning usually operate in a supervised manner,and their reliance on laborious and error-prone data annotation procedures remains a barrier to more general ***,we propose an unsupervised image transformation to facilitate the utilization of deep learning for optical microscopy,even in some cases in which supervised models cannot be *** the introduction of a saliency constraint,the unsupervised model,named Unsupervised content-preserving Transformation for Optical Microscopy(UTOM);can learn the mapping between two image domains without requiring paired training data while avoiding distortions of the image *** shows promising performance in a wide range of biomedical image transformation tasks,including in silico histological staining,fluorescence image restoration,and virtual fluorescence *** evaluations reveal that UTOM achieves stable and high-fidelity image transformations across different imaging conditions and *** anticipate that our framework will encourage a paradigm shift in training neural networks and enable more applications of artificial intelligence in biomedical imaging.

关键词： transformation preserving image

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Identifying spatiotemporal traffic patterns in large-scale urban road networks using a modified nonnegative matrix factorization algorithm

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Journal of Traffic and Transportation Engineering(English Edition) 2020年第4期7卷 529-539页

作者： Xiaolei Ma Yi Li Peng Chen School of Transportation Science and Engineering Beihang UniversityBeijing 100191China Beiing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeiing 100191China

The identification and analysis of spatiotemporal traffic patterns in road networks constitute a crucial process for sophisticated traffic management and *** methods based on mathematical equations and statistical models can hardly be applicable to large-scale urban road networks,where traffic states exhibit high degrees of dynamics and ***,advances in data collection and processing have provided new opportunities to effectively understand spatiotemporal traffic patterns in large-scale road networks using data-driven ***,limited efforts have been exerted to explore the essential structure of the networks when conducting a spatiotemporal analysis of traffic *** this end,this study proposes a modified nonnegative matrix factorization algorithm that processes high-dimensional traffic data and provides an improved representation of the global traffic *** matrix factorization,cluster analysis is conducted based on the obtained low-dimensional representative matrices,which contain different traffic patterns and serve as the basis for exploring the temporal dynamics and spatial structure of network *** applicability and effectiveness of the proposed approach are examined in a road network of beijing,*** show that the methods exhibit considerable potential for identifying and interpreting the spatiotemporal traffic patterns over the entire network and provide a systematic and efficient approach for analyzing the network-level traffic state.

关键词： Large-scale network Traffic state Spatiotemporal patterns Nonnegative matrix factorization

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Therapeutic hypothermia for acute ischemic stroke: from preclinical studies to clinical trials

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Science China Life Sciences 2025年

作者： Xi Chen Jin Xu Jiachen He Jiaqi Guo Shuaili Xu Xuefan Yao Yuanyuan Liu Xiaohan Xu Huimin Wei Ming Li Chuanjie Wu Longfei Wu Marc Fisher Xunming Ji Di Wu Department of Neurology and China-America Institute of Neuroscience Xuanwu Hospital Beijing Institute of Brain Disorders Capital Medical University Department of Education Xuanwu Hospital Capital Medical University Department of Neurobiology Heilongjiang Provincial Key Laboratory of Neurobiology Harbin Medical University Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Biological Science and Medical Engineering Beihang University Department of Neurology Beth Israel Deaconess Medical Center Harvard Medical School

Therapeutic hypothermia(TH) is acknowledged as a promising neuroprotective strategy in clinical settings. However, its application in managing acute ischemic stroke(AIS) remains unclear due to variable clinical outcomes in bench and bedsides. A comprehensive review of original studies concerning hypothermia in ischemic stroke was conducted, sourcing data from PubMed, Web of Science, Embase, and Ovid Medicine databases covering the period from January 1, 1990 to October 31, 2023. Our search strategy yielded 1,218 articles from PubMed, 1,094 from Web of Science, 3,083 from Embase, and 2,841 from Ovid Medicine. After removing duplicates, review articles, meta-analyses, and in vitro studies focusing on hypoxic-ischemic encephalopathy or global cerebral ischemia, a total of 304 articles out of 5,669 papers were ultimately selected for in-depth analysis. Overall, we have found that there are significant differences in depth, duration, and delay between bench and bedside studies. We want to introduce the concepts of “actual brain temperature”, “hypothermia initiation time”, and “effective hypothermic duration” as crucial for the optimization of hypothermic therapy in AIS. We recommend critical parameters for the clinical translation of hypothermia, including a target temperature range of 34–35°C, a duration of 2–4 h, immediate initiation post-insult, and natural rewarming processes. We also advocate for selective brain cooling when reperfusion therapy is achieved. We find great differences in administrating TH for AIS between bench and bedsides. More efforts are still needed to enhance the likelihood of successful clinical translation and deepen the understanding of hypothermia's role in AIS treatment.

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Does graph distillation see like vision dataset counterpart? 23

Does graph distillation see like vision dataset counterpart?

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Beining Yang Kai Wang Qingyun Sun Cheng Ji Xingcheng Fu Hao Tang Yang You Jianxin Li School of Computer Science and Engineering Beihang University and Advanced Innovation Center for Big Data and Brain Computing Beihang University National University of Singapore Carnegie Mellon University

Training on large-scale graphs has achieved remarkable results in graph representation learning, but its cost and storage have attracted increasing concerns. Existing graph condensation methods primarily focus on optimizing the feature matrices of condensed graphs while overlooking the impact of the structure information from the original graphs. To investigate the impact of the structure information, we conduct analysis from the spectral domain and empirically identify substantial Laplacian Energy Distribution (LED) shifts in previous works. Such shifts lead to poor performance in cross-architecture generalization and specific tasks, including anomaly detection and link prediction. In this paper, we propose a novel Structure-broadcasting Graph dataset Distillation (SGDD) scheme for broadcasting the original structure information to the generation of the synthetic one, which explicitly prevents overlooking the original structure information. Theoretically, the synthetic graphs by SGDD are expected to have smaller LED shifts than previous works, leading to superior performance in both cross-architecture settings and specific tasks. We validate the proposed SGDD across 9 datasets and achieve state-of-the-art results on all of them: for example, on the YelpChi dataset, our approach maintains 98.6% test accuracy of training on the original graph dataset with 1,000 times saving on the scale of the graph. Moreover, we empirically evaluate there exist 17.6% ~ 31.4% reductions in LED shift crossing 9 datasets. Extensive experiments and analysis verify the effectiveness and necessity of the proposed designs. The code is available in the https://***/RingBDStack/SGDD.

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Chitosan-based thermosensitive hydrogel with long-term release of murine nerve growth factor for neurotrophic keratopathy

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Neural Regeneration Research 2024年第3期19卷 680-686页

作者： Jie Wu Yulei Huang Hanrui Yu Kaixiu Li Shifeng Zhang Guoqing Qiao Xiao Liu Hongmei Duan Yifei Huang Kwok-Fai So Zhaoyang Yang Xiaoguang Li Liqiang Wang Department of Ophthalmology the Third Medical CenterChinese PLA General HospitalBeijingChina The PLA Medical College Department of NephrologyChinese PLA Institute of NephrologyState Key Laboratory of Kidney DiseasesNational Clinical Research Center for Kidney DiseasesBeijingChina Department of Ophthalmology Hainan Hospital of Chinese PLA General HospitalSanyaHainan ProvinceChina Medical School of Chinese PLA BeijingChina Department of Neurobiology School of Basic Medical SciencesCapital Medical UniversityBeijingChina Guangdong-Hongkong-Macao Institute of CNS Regeneration Ministry of Education CNS Regeneration Collaborative Joint LaboratoryJinan UniversityGuangzhouGuangdong ProvinceChina Bioland Laboratory(Guangzhou Regenerative Medicine and Health Guangdong Laboratory) GuangzhouGuangdong ProvinceChina Department of Ophthalmology and State Key Laboratory of Brain and Cognitive Sciences The University of Hong KongHong Kong Special Administrative RegionChina Center for Brain Science and Brain-Inspired Intelligence Guangdong-Hong Kong-Macao Greater Bay AreaGuangzhouGuangdong ProvinceChina Co-innovation Center of Neuroregeneration Nantong UniversityNantongJiangsu ProvinceChina School of Engineering Medicine Beijing Key Laboratory for Biomaterials and Neural RegenerationBeihang UniversityBeijingChina Beijing International Cooperation Bases for Science and Technology on Biomaterials and Neural Regeneration Beijing Advanced Innovation Center for Big Data-Based Precision MedicineBeihang UniversityBeijingChina

Neurotrophic keratopathy is a persistent defect of the corneal epithelium,with or without stromal ulceration,due to corneal nerve deficiency caused by a variety of *** treatment options for neurotrophic keratopathy are *** this study,an ophthalmic solution was constructed from a chitosan-based thermosensitive hydrogel with long-term release of murine nerve growth factor(CTH-mNGF).Its effectiveness was evaluated in corneal denervation(CD)mice and patients with neurotrophic *** the preclinical setting,CTH-mNGF was assessed in a murine corneal denervation ***-mNGF was transparent,thermosensitive,and ensured sustained release of mNGF for over 20 hours on the ocular surface,maintaining the local mNGF concentration around 1300 pg/mL in *** denervation mice treated with CTH-mNGF for 10 days showed a significant increase in corneal nerve area and total corneal nerve length compared with non-treated and CTH treated mice.A subsequent clinical trial of CTH-mNGF was conducted in patients with stage 2 or 3 neurotrophic *** received topical CTH-mNGF twice daily for 8 *** sodium images,Schirmer’s test,intraocular pressure,Cochet-Bonnet corneal perception test,and best corrected visual acuity were *** total,six patients(total of seven eyes)diagnosed with neurotrophic keratopathy were *** 8 weeks of CTH-mNGF treatment,all participants showed a decreased area of corneal epithelial defect,as stained by ***,six out of seven eyes had fluorescence staining scores<***,best corrected visual acuity,intraocular pressure,Schirmer’s test and Cochet-Bonnet corneal perception test results showed no significant *** increase in corneal nerve density was observed by in vivo confocal microscopy after 8 weeks of CTH-mNGF treatment in three out of seven *** study demonstrates that CTH-mNGF is transparent,thermosensitive,and has sustained-release *** effecti

关键词： chitosan corneal reinnervation murine nerve growth factor neurotrophic keratopathy thermosensitive hydrogel

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Poincaré Differential Privacy for Hierarchy-Aware Graph Embedding

arXiv

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arXiv 2023年

作者： Wei, Yuecen Yuan, Haonan Fu, Xingcheng Sun, Qingyun Peng, Hao Li, Xianxian Hu, Chunming Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China School of Software Beihang University Beijing China Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University China

Hierarchy is an important and commonly observed topological property in real-world graphs that indicate the relationships between supervisors and subordinates or the organizational behavior of human groups. As hierarchy is introduced as a new inductive bias into the Graph Neural Networks (GNNs) in various tasks, it implies latent topological relations for attackers to improve their inference attack performance, leading to serious privacy leakage issues. In addition, existing privacy-preserving frameworks suffer from reduced protection ability in hierarchical propagation due to the deficiency of adaptive upper-bound estimation of the hierarchical perturbation boundary. It is of great urgency to effectively leverage the hierarchical property of data while satisfying privacy guarantees. To solve the problem, we propose the Poincaré Differential Privacy framework, named PoinDP, to protect the hierarchy-aware graph embedding based on hyperbolic geometry. Specifically, PoinDP first learns the hierarchy weights for each entity based on the Poincaré model in hyperbolic space. Then, the Personalized Hierarchy-aware Sensitivity is designed to measure the sensitivity of the hierarchical structure and adaptively allocate the privacy protection strength. Besides, the Hyperbolic Gaussian Mechanism (HGM) is proposed to extend the Gaussian mechanism in Euclidean space to hyperbolic space to realize random perturbations that satisfy differential privacy under the hyperbolic space metric. Extensive experiment results on five real-world datasets demonstrate the proposed PoinDP's advantages of effective privacy protection while maintaining good performance on the node classification task. Copyright © 2023, The Authors. All rights reserved.

关键词： Backpropagation

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Graph algorithms: parallelization and scalability

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Science China(Information Sciences) 2020年第10期63卷 234-254页

作者： Wenfei FAN Kun HE Qian LI Yue WANG School of Informatics University of Edinburgh Shenzhen Institute of Computing Sciences Shenzhen University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Guangdong Province Key Laboratory of Popular High Performance Computers Shenzhen University

For computations on large-scale graphs, one often resorts to parallel algorithms. However, parallel algorithms are difficult to write, debug and analyze. Worse still, it is difficult to make algorithms parallelly scalable, such that the more machines are used, the faster the algorithms run. Indeed, it is not yet known whether any PTIME computational problems admit parallelly scalable algorithms on shared-nothing *** it possible to parallelize sequential graph algorithms and guarantee convergence at the correct results as long as the sequential algorithms are correct? Moreover, does a PTIME parallelly scalable problem exist on shared-nothing systems? This position paper answers both questions in the affirmative.

关键词： parallelization parallel scalability PTIME problems graph algorithms shared-nothing systems

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Autonomous On-ramp Merge Strategy Using Deep Reinforcement Learning in Uncertain Highway Environment

Autonomous On-ramp Merge Strategy Using Deep Reinforcement L...

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2022 IEEE International Conference on Unmanned Systems, ICUS 2022

作者： Wu, Sifan Tian, Daxin Zhou, Jianshan Duan, Xuting Sheng, Zhengguo Zhao, Dezong Beijing Key Laboratory For Cooperative Vehicle Infrastructure Systems&Safety Cooperative Control Beijing China Beijing Advanced Innovation Center For Big Data and Brain Computing Beijing China Department of Engineering and Design Richmond United Kingdom University of Sussex Richmond United Kingdom James Watt School of Engineering Glasgow United Kingdom University of Glasgow Glasgow United Kingdom

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

On-ramp merge is a complex traffic scenario in autonomous driving. Because of the uncertainty of the driving environment, most rule-based models cannot solve such a problem. In this study, we design a Deep Reinforcement Learning (DRL) method to solve the issue of ramp merges in uncertain scenarios and modify the structure of the Twin Delayed Deep Deterministic policy gradient algorithm (TD3), using Long Short-Term Memory (LSTM) to select an action based on temporal information. The proposed method is applied in the on-ramp merge and verified in the Simulation of Urban Mobility (SUMO). Results show that the proposed method performs significantly better generalization in uncertain traffic scenarios. © 2022 IEEE.

关键词： Long short-term memory

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Real-time partitioned scheduling: Exploiting the inter-resource affinity for task allocation on multiprocessors

Real-time partitioned scheduling: Exploiting the inter-resou...

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作者： Akram, Naveed Li, Jianxin Bai, Yan Zhang, Yangyang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China School of Engineering and Technology University of Washington Tacoma WA United States

Real-time edge computing is forging its place in cloud computing rapidly, and requirements for high-performance edge devices are becoming increasingly complex. Multiprocessor edge devices are an attractive choice to meet these higher performance requirements. However, multiprocessor devices encounter inherent challenges when handling on-chip shared resources. The concurrent access to these resources by the tasks requesting more than one shared resource and running on multiple processors may face huge blocking times, which can lead to missed hard real-time deadlines and cause a catastrophic system failure. To reduce the task blocking time, we propose a task allocation algorithm that takes advantage of inter-resource affinity and allocates all the tasks accessing multiple shared resources having inter-resource affinity on the same processor of the multiprocessor. The proposed algorithm reduces global resources and remote blocking, which subsequently increase schedulability of task sets and reduce the processor utilization. In our experiments, we compare the proposed task allocation algorithm SRTA with existing well-known task allocation strategies SPA, ROP, and blocking-agnostic FFD. The experimental results reveal that the SRTA, on the average, can allocate 1.80, 2.34, and 3.14 times more task sets as compared to ROP, SPA, and FFD, respectively and reduces the number of globally shared resources significantly. © 2019 John Wiley & Sons, Ltd.

关键词： Scheduling

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Patching Weak Convolutional Neural Network Models through Modularization and Composition

arXiv

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arXiv 2022年

作者： Qi, Binhang Sun, Hailong Gao, Xiang Zhang, Hongyu SKLSDE Lab Beihang University China The University of Newcastle Australia Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

Despite great success in many applications, deep neural networks are not always robust in practice. For instance, a convolutional neuron network (CNN) model for classification tasks often performs unsatisfactorily in classifying some particular classes of objects. In this work, we are concerned with patching the weak part of a CNN model instead of improving it through the costly retraining of the entire model. Inspired by the fundamental concepts of modularization and composition in software engineering, we propose a compressed modularization approach, CNNSplitter, which decomposes a strong CNN model for N-class classification into N smaller CNN modules. Each module is a sub-model containing a part of the convolution kernels of the strong model. To patch a weak CNN model that performs unsatisfactorily on a target class (TC), we compose the weak CNN model with the corresponding module obtained from a strong CNN model. The ability of the weak CNN model to recognize the TC can thus be improved through patching. Moreover, the ability to recognize non-TCs is also improved, as the samples misclassified as TC could be classified as non-TCs correctly. Experimental results with two representative CNNs on three widely-used datasets show that the averaged improvement on the TC in terms of precision and recall are 12.54% and 2.14%, respectively. Moreover, patching improves the accuracy of non-TCs by 1.18%. The results demonstrate that CNNSplitter can patch a weak CNN model through modularization and composition, thus providing a new solution for developing robust CNN models. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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