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arXiv 2022年

作者： Ji, Jiahao Wang, Jingyuan Jiang, Zhe Jiang, Jiawei Zhang, Hu State Key Laboratory of Software Development Environment School of Computer Science & Engineering Beihang University Beijing China Peng Cheng Laboratory Shenzhen China Department of Computer & Information Science & Engineering The University of Florida United States MOE Engineering Research Center of Advanced Computer Application Technology School of Computer Science & Engineering Beihang University Beijing China

High-performance traffic flow prediction model designing, a core technology of Intelligent Transportation system, is a long-standing but still challenging task for industrial and academic communities. The lack of integration between physical principles and data-driven models is an important reason for limiting the development of this field. In the literature, physics-based methods can usually provide a clear interpretation of the dynamic process of traffic flow systems but are with limited accuracy, while data-driven methods, especially deep learning with black-box structures, can achieve improved performance but can not be fully trusted due to lack of a reasonable physical basis. To bridge the gap between purely data-driven and physics-driven approaches, we propose a physics-guided deep learning model named Spatio- Temporal Differential Equation Network (STDEN), which casts the physical mechanism of traffic flow dynamics into a deep neural network framework. Specifically, we assume the traffic flow on road networks is driven by a latent potential energy field (like water flows are driven by the gravity field), and model the spatio-temporal dynamic process of the potential energy field as a differential equation network. STDEN absorbs both the performance advantage of data-driven models and the interpretability of physics-based models, so is named a physics-guided prediction model. Experiments on three real-world traffic datasets in Beijing show that our model outperforms state-of-the-art baselines by a significant margin. A case study further verifies that STDEN can capture the mechanism of urban traffic and generate accurate predictions with physical meaning. The proposed framework of differential equation network modeling may also cast light on other similar applications. Copyright © 2022, The Authors. All rights reserved.

关键词： Differential equations

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Neuromodulated Meta-Learning

arXiv

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arXiv 2024年

作者： Wang, Jingyao Guo, Huijie Qiang, Wenwen Li, Jiangmeng Zheng, Changwen Xiong, Hui Hua, Gang University of Chinese Academy of Sciences Beijing China National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Thrust of Artificial Intelligence Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Wormpex AI Research LLC BellevueWA98004 United States

Humans excel at adapting perceptions and actions to diverse environments, enabling efficient interaction with the external world. This adaptive capability relies on the biological nervous system (BNS), which activates different brain regions for distinct tasks. Meta-learning similarly trains machines to handle multiple tasks but relies on a fixed network structure, not as flexible as BNS. To investigate the role of flexible network structure (FNS) in meta-learning, we conduct extensive empirical and theoretical analyses, finding that model performance is tied to structure, with no universally optimal pattern across tasks. This reveals the crucial role of FNS in meta-learning, ensuring meta-learning to generate the optimal structure for each task, thereby maximizing the performance and learning efficiency of meta-learning. Motivated by this insight, we propose to define, measure, and model FNS in meta-learning. First, we define that an effective FNS should possess frugality, plasticity, and sensitivity. Then, to quantify FNS in practice, we present three measurements for these properties, collectively forming the structure constraint with theoretical supports. Building on this, we finally propose Neuromodulated Meta-Learning (NeuronML) to model FNS in meta-learning. It utilizes bi-level optimization to update both weights and structure with the structure constraint. Extensive theoretical and empirical evaluations demonstrate the effectiveness of NeuronML on various tasks. Code is publicly available at https://***/WangJingyao07/NeuronML. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Transition Propagation Graph Neural Networks for Temporal Networks

arXiv

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arXiv 2023年

作者： Zheng, Tongya Feng, Zunlei Zhang, Tianli Hao, Yunzhi Song, Mingli Wang, Xingen Wang, Xinyu Zhao, Ji Chen, Chun The College of Computer Science Zhejiang University Hangzhou China The College of Software Technology Zhejiang University Hangzhou China ZJU-Bangsun Joint Research Center Zhejiang University China Shanghai Institute for Advanced Study of Zhejiang University China Shanghai Pudong Development Bank Co. Ltd. Shanghai Branch Shanghai China The College of Computer Science Zhejiang University Peng Cheng Laboratory China

researchers of temporal networks (e.g., social networks and transaction networks) have been interested in mining dynamic patterns of nodes from their diverse interactions. Inspired by recently powerful graph mining methods like skip-gram models and Graph Neural Networks (GNNs), existing approaches focus on generating temporal node embeddings sequentially with nodes' sequential interactions. However, the sequential modeling of previous approaches cannot handle the transition structure between nodes' neighbors with limited memorization capacity. Detailedly, an effective method for the transition structures is required to both model nodes' personalized patterns adaptively and capture node dynamics accordingly. In this paper, we propose a method, namely TransItion Propagation Graph Neural Networks (TIP-GNN), to tackle the challenges of encoding nodes' transition structures. The proposed TIP-GNN focuses on the bilevel graph structure in temporal networks: besides the explicit interaction graph, a node's sequential interactions can also be constructed as a transition graph. Based on the bilevel graph, TIP-GNN further encodes transition structures by multi-step transition propagation and distills information from neighborhoods by a bilevel graph convolution. Experimental results over various temporal networks reveal the efficiency of our TIP-GNN, with at most 7.2% improvements of accuracy on temporal link prediction. Extensive ablation studies further verify the effectiveness and limitations of the transition propagation module. Our code is available at https://***/doujiang-zheng/TIP-GNN. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Deep learning in digital pathology image analysis:a survey

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Frontiers of Medicine 2020年第4期14卷 470-487页

作者： Shujian Deng Xin Zhang Wen Yan Eric I-Chao Chang Yubo Fan Maode Lai Yan Xu School of Biological Science and Medical Engineering Beihang UniversityBeijing 100191China Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education and State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Biomedical Engineering Beihang UniversityBeijing 100191China Microsoft Research Asia Beijing 100080China Department of Pathology School of MedicineZhejiang UniversityHangzhou 310007China

deep learning(DL)has achieved state-of-the-art performance in many digital pathology analysis *** methods usually require hand-crafted domain-specific features,and DL methods can learn representations without manually designed *** terms of feature extraction,DL approaches are less labor intensive compared with conventional machine learning *** this paper,we comprehensively summarize recent DL-based image analysis studies in histopathology,including different tasks(e.g.,classification,semantic segmentation,detection,and instance segmentation)and various applications(e.g.,stain normalization,cell/gland/region structure analysis).DL methods can provide consistent and accurate *** is a promising tool to assist pathologists in clinical diagnosis.

关键词： pathology deep learning segmentation detection classification

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Association of PM_(2.5)Reduction with Improved Kidney Function:A Nationwide Quasiexperiment among Chinese Adults

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Health Data Science 2022年第1期2022卷 115-123页

作者： Yiqun Han Tao Xue Frank J.Kelly Yixuan Zheng Yao Yao Jiajianghui Li Jiwei Li Chun Fan Pengfei Li Tong Zhu Environmental Research Group MRC Centre for Environment and HealthImperial College LondonLondonUK BIC-ESAT and SKL-ESPC College of Environmental Sciences and EngineeringCenter for Environment and HealthPeking UniversityBeijing 100871China Institute of Reproductive and Child Health/Ministry of Health Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics School of Public HealthPeking UniversityBeijingChina Center of Air Quality Simulation and System Analysis Chinese Academy of Environmental Planning100012 BeijingChina China Center for Health Development Studies Peking UniversityBeijingChina College of Computer Science and Technology Zhejiang UniversityHangzhouChina Computer Center Peking University and Peng Cheng LaboratoryChina Advanced Institute of Information Technology Peking UniversityChina

*** evidence from human studies has revealed the adverse impact of ambient fine particles(PM_(2.5))on health outcomes related to metabolic disorders and distant *** exposure to ambient PM_(2.5)leads to kidney impairment remains *** rapid air quality improvement driven by the clean air actions in China since 2013 provides an opportunity for a quasiexperiment to investigate the beneficial effect of PM_(2.5)reduction on kidney *** on two repeated nationwide surveys of the same population of 5115 adults in 2011 and 2015,we conducted a difference-indifference *** in long-term exposure to ambient PM_(2.5)were associated with changes in kidney function biomarkers,including estimated glomerular filtration rate by serum creatinine(GFRscr)or cystatin C(GFRcys),blood urea nitrogen(BUN),and uric acid(UA).*** a 10μg/m^(3)reduction in PM_(2.5),a significant improvement was observed for multiple kidney functional biomarkers,including GFRscr,BUN and UA,with a change of 0.42(95%confidence interval[CI]:0.06,0.78)mL/min/1.73m^(2),-0.38(-0.64,-0.12)mg/dL,and-0.06(-0.12,-0.00)mg/dL,respectively.A lower socioeconomic status,indicated by rural residence or low educational level,enhanced the adverse effect of PM_(2.5)on kidney *** results support a significant nephrotoxicity of PM_(2.5)based on multiple serum biomarkers and indicate a beneficial effect of improved air quality on kidney function.

关键词： Kidney organs ambient

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山地与极地冰川表面生态系统DNA病毒的多样性与功能

引用

Science Bulletin 2023年第20期68卷 2418-2433,M0006页

作者：刘勇勤焦念志钟旭臧琳张锐肖湘施一张志好陶晔白丽萍高变利杨芸兰黄星煜计慕侃刘军志刘鹏飞姚檀栋 Center for Pan-third Pole Environment Lanzhou UniversityLanzhou 730000China State Key Laboratory of Tibetan Plateau Earth System Resources and Environment(TPESRE)Institute of Tibetan Plateau ResearchChinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100101China State Key Laboratory of Marine Environmental Science College of Ocean and Earth SciencesXiamen UniversityXiamen 361005China Department of Earth Oceanand Atmospheric SciencesThe University of British ColumbiaVancouver V6T 1Z4Canada Institute for Advanced Study Shenzhen UniversityShenzhen 518000China State Key Laboratory of Microbial Metabolism School of Life Sciences and BiotechnologyShanghai Jiao Tong UniversityShanghai 200240China CAS Key Laboratory of Pathogenic Microbiology and Immunology Institute of MicrobiologyChinese Academy of SciencesBeijing 100101China State Key Laboratory of Lake Science and Environment Nanjing Institute of Geography and LimnologyChinese Academy of SciencesNanjing 210008China Key Laboratory of Development and Application of Rural Renewable Energy Biogas Institute of Ministry of Agriculture and Rural AffairsChengdu 610041China

冰川覆盖了地球表面的10%,是典型的极端环境.病毒在冰川表面生态系统中丰富且活跃,在控制微生物群落中发挥关键作用.然而,我们对冰川表面生态系统病毒及其对下游生态系统的潜在影响了解非常有限.本研究构建了冰川表面生态系统DNA病毒基... 详细信息

冰川覆盖了地球表面的10%,是典型的极端环境.病毒在冰川表面生态系统中丰富且活跃,在控制微生物群落中发挥关键作用.然而,我们对冰川表面生态系统病毒及其对下游生态系统的潜在影响了解非常有限.本研究构建了冰川表面生态系统DNA病毒基因组数据集(SgVG),将已有冰川DNA病毒基因组数量扩展了15倍.数据集包含了来自全球38个山地和极地冰川,涵盖雪、冰、融水和冰尘的10,840个DNA病毒物种.冰川表面DNA病毒特异性高,但对公众健康的潜在风险极低.冰川表面病毒群落结构主要受生境影响,且冰尘病毒最为活跃.裂解性病毒在所有生境中普遍存在,而雪和冰中溶源性病毒的相对比例较高.此外,冰川表面病毒可侵染冰川中83%的门或纲的原核微生物,并且能够编码具有促进宿主代谢和协助宿主适应寒冷环境等功能的多种辅助代谢基因.本研究系统地描述了山地和极地冰川表面生态系统DNA病毒的多样性和功能,为评估冰川DNA病毒的生态功能、安全风险及构建区域生态模型提供了重要科学依据.

关键词：生态系统地球表面生态模型极端环境寒冷环境溶源性原核微生物相对比例

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A New Low-power Pixel Circuit for OLEDoS Microdisplay 17

A New Low-power Pixel Circuit for OLEDoS Microdisplay

引用

17th China International Forum on Solid State Lighting and 2020 International Forum on Wide Bandgap Semiconductors China, SSLChina: IFWS 2020

作者： Hao, Deng Aiying, Guo Feng, Ran Shanghai University Microelectronic Research Development Center 99 Shangda Road Baoshan District Shanghai200444 China Shanghai University Key Laboratory of Advanced Display and System Applications 99 Shangda Road Baoshan District Shanghai200072 China

ISBN: (纸本)9780738111889

In order to solve the problem of high bandwidth data volume and low power consumption of OLEDoS microdisplay panels, this paper proposes a new type of digital low-power pixel circuit. The driving circuit includes a plurality of pixel driving units, which can store multi-pixel bit information at one time, thereby reducing the amount of data read operation. Compared with the traditional digital pixel circuit, the proposed circuit can skip the grayscale data writing process when the picture is still or the rate of change is low, thus reducing the data flow and power consumption. © 2020 IEEE.

关键词： Electric power utilization

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Quantum reversible circuits for multiplicative inverse

引用

EPJ Quantum Technology 2022年第1期9卷 1-17页

作者： Luo, Qing-bin Yang, Guo-wu Li, Xiao-yu Li, Qiang Department of Computer Science and Technology School of Information Engineering Hubei Minzu University Enshi China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Big data research Center & School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu University of Information Technology Chengdu China

The synthesis of quantum circuits for multiplicative inverse over $\operatorname{GF}(2^{8})$ are discussed in this paper. We first convert the multiplicative inverse operation in $\operatorname{GF}(2^{8})$ to arithmetic operations in the composite field $\operatorname{GF}((2^{4})^{2})$ , and then discuss the expressions of the square calculation, the inversion calculation and the multiplication calculation separately in the finite field $\operatorname{GF}(2^{4})$ , where the expressions of multiplication calculation in $\operatorname{GF}(2^{4})$ are given directly in $\operatorname{GF}(2^{4})$ and given through being transformed into the composite field $\operatorname{GF}((2^{2})^{2})$ . Then the quantum circuits of these calculations are realized one by one. Finally, two quantum circuits for multiplicative inverse over $\operatorname{GF}(2^{8})$ are synthesized. They both use 21 qubits, the first quantum circuit uses 55 Toffoli gates and 107 CNOT gates and the second one uses 37 Toffoli gates and 209 CNOT gates. As an example of the application of multiplication inverse, we apply these quantum circuits to the implementations of the S-box quantum circuit of the AES cryptographic algorithm. Two quantum circuits for implementing the S-box of the AES cryptographic algorithm are presented. The first quantum circuit uses 21 qubits, 55 Toffoli gates, 131 CNOT gates and 4 NOT gates and the second one uses 21 qubits, 37 Toffoli gates, 233 CNOT gates and 4 NOT gates. Through the evaluation of quantum cost, the two quantum circuits of the S-box of AES cryptographic algorithm use less quantum resources than the existing schemes.

关键词：

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Zero-shot Nuclei Detection via Visual-Language Pre-trained Models

arXiv

引用

arXiv 2023年

作者： Wu, Yongjian Zhou, Yang Saiyin, Jiya Wei, Bingzheng Lai, Maode Shou, Jianzhong Fan, Yubo Xu, Yan School of Biological Science and Medical Engineering State Key Laboratory of Software Development Environment Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education Beijing Advanced Innovation Center for Biomedical Engineering Beihang University Beijing100191 China Xiaomi Corporation Beijing100085 China Department of Pathology School of Medicine Zhejiang University Zhejiang China Provincial Key Laboratory of Disease Proteomics and Alibaba Zhejiang University Joint Research Center of Future Digital Healthcare Hangzhou310053 China Chinese Academy of Medical Sciences Peking Union Medical College Beijing100021 China

Large-scale visual-language pre-trained models (VLPM) have proven their excellent performance in downstream object detection for natural scenes. However, zero-shot nuclei detection on H&E images via VLPMs remains underexplored. The large gap between medical images and the web-originated text-image pairs used for pre-training makes it a challenging task. In this paper, we attempt to explore the potential of the object-level VLPM, Grounded Language-Image Pre-training (GLIP) model, for zero-shot nuclei detection. Concretely, an automatic prompts design pipeline is devised based on the association binding trait of VLPM and the image-to-text VLPM BLIP, avoiding empirical manual prompts engineering. We further establish a self-training framework, using the automatically designed prompts to generate the preliminary results as pseudo labels from GLIP and refine the predicted boxes in an iterative manner. Our method achieves a remarkable performance for label-free nuclei detection, surpassing other comparison methods. Foremost, our work demonstrates that the VLPM pre-trained on natural image-text pairs exhibits astonishing potential for downstream tasks in the medical field as well. Code will be released at ***/VLPMNuD. Copyright © 2023, The Authors. All rights reserved.

关键词： Zero-shot learning

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Transformer based multiple instance learning for weakly supervised histopathology image segmentation

arXiv

引用

arXiv 2022年

作者： Qian, Ziniu Li, Kailu Lai, Maode Chang, Eric I-Chao Wei, Bingzheng Fan, Yubo Xu, Yan School of Biological Science and Medical Engineering State Key Laboratory of Software Development Environment Key Laboratory of Biomechanics Mechanobiology of Ministry of Education Beijing Advanced Innovation Centre for Biomedical Engineering Beihang University Beijing100191 China China Pharmaceutical University Nanjing210009 China Zhejiang University Hangzhou310058 China Microsoft Research Beijing100080 China Xiaomi Corporation Beijing100085 China

Hispathological image segmentation algorithms play a critical role in computer aided diagnosis technology. The development of weakly supervised segmentation algorithm alleviates the problem of medical image annotation that it is time-consuming and labor-intensive. As a subset of weakly supervised learning, Multiple Instance Learning (MIL) has been proven to be effective in segmentation. However, there is a lack of related information between instances in MIL, which limits the further improvement of segmentation performance. In this paper, we propose a novel weakly supervised method for pixel-level segmentation in histopathology images, which introduces Transformer into the MIL framework to capture global or long-range dependencies. The multi-head self-attention in the Transformer establishes the relationship between instances, which solves the shortcoming that instances are independent of each other in MIL. In addition, deep supervision is introduced to overcome the limitation of annotations in weakly supervised methods and make the better utilization of hierarchical information. The state-of-the-art results on the colon cancer dataset demonstrate the superiority of the proposed method compared with other weakly supervised methods. It is worth believing that there is a potential of our approach for various applications in medical images. Copyright © 2022, The Authors. All rights reserved.

关键词： Image segmentation

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