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28th International Conference on Computational Linguistics, COLING 2020

作者： Xu, Chunpu Li, Yu Li, Chengming Ao, Xiang Yang, Min Tian, Jinwen Shenzhen Key Laboratory for High Performance Data Mining Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Huazhong University of Science and Technology China Shanghai Jiao Tong University China Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China

ISBN: (纸本)9781952148279

Image paragraph captioning (IPC) aims to generate a fine-grained paragraph to describe the visual content of an image. Significant progress has been made by deep neural networks, in which the attention mechanism plays an essential role. However, conventional attention mechanisms tend to ignore the past alignment information, which often results in problems of repetitive captioning and incomplete captioning. In this paper, we propose an Interactive key-value Memory-augmented Attention model for image Paragraph captioning (IMAP) to keep track of the attention history (salient objects coverage information) along with the update-chain of the decoder state and therefore avoid generating repetitive or incomplete image descriptions. In addition, we employ an adaptive attention mechanism to realize adaptive alignment from image regions to caption words, where an image region can be mapped to an arbitrary number of caption words while a caption word can also attend to an arbitrary number of image regions. Extensive experiments on a benchmark dataset (i.e., Stanford) demonstrate the effectiveness of our IMAP model. © 2020 COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. All rights reserved.

关键词： Deep neural networks

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Transformation driven visual reasoning

arXiv

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arXiv 2020年

作者： Hong, Xin Lan, Yanyan Pang, Liang Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Institute for AI Industry Research Tsinghua University Beijing China

This paper defines a new visual reasoning paradigm by introducing an important factor, i.e. transformation. The motivation comes from the fact that most existing visual reasoning tasks, such as CLEVR in VQA, are solely defined to test how well the machine understands the concepts and relations within static settings, like one image. We argue that this kind of state driven visual reasoning approach has limitations in reflecting whether the machine has the ability to infer the dynamics between different states, which has been shown as important as state-level reasoning for human cognition in Piaget’s theory. To tackle this problem, we propose a novel transformation driven visual reasoning task. Given both the initial and final states, the target is to infer the corresponding single-step or multi-step transformation, represented as a triplet (object, attribute, value) or a sequence of triplets, respectively. Following this definition, a new dataset namely TRANCE is constructed on the basis of CLEVR, including three levels of settings, i.e. Basic (single-step transformation), Event (multi-step transformation), and View (multi-step transformation with variant views). Experimental results show that the state-of-the-art visual reasoning models perform well on Basic, but are still far from human-level intelligence on Event and View. We believe the proposed new paradigm will boost the development of machine visual reasoning. More advanced methods and real data need to be investigated in this direction. The resource of TVR is available at https://***/TVR. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

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Towards Lightweight and Efficient Distributed Intrusion Detection Framework

Towards Lightweight and Efficient Distributed Intrusion Dete...

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2021 IEEE Global Communications Conference (GLOBECOM)

作者： Shuai Yuan Hongwei Li Rui Zhang Meng Hao Yiran Li Rongxing Lu School of Computer Science and Engineering University of Electronic Science and Technology of China China Chinese Academy of Sciences Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chongqing China Faculty of Computer Science University of New Brunswick Canada

ISBN: (纸本)9781728181059

Federated learning (FL), as a promising distributed learning paradigm, has put many efforts into distributed intrusion detection systems (IDS), for defending against various malicious attacks, such as SQL injection and DDoS attacks. Compared with traditional IDS based on centralized deep learning (DL), FL-based solutions require not to share users' raw data while yielding better detection performance. However, state-of-the-art FL-based methods still suffer from two key limitations: 1) insufficient detection performance on non-independent and identically distributed (non-IID) data, and 2) high communication and computational overheads due to the utilization of large-scale neural network models. In this paper, we propose a lightweight collaborative intrusion detection framework, called CoLGBM, the first of its kind in the regime of decentralized IDS, where decision tree and light gradient boosting machine (LGBM) are combined for constructing the detection scheme. The main insight is that through combining user-trained decision trees (each user's decision tree is derived from its own data with unique distribution), our framework can perform effectively on non-IID data while working efficiently for handling enormous samples. Compared with the current FL-based methods, our CoLGBM achieves higher accuracy and lower overhead on both IID and non-IID data. Extensive experiment results demonstrate our scheme with high-level performance.

关键词： Deep learning Distance learning Neural networks Intrusion detection Distributed databases SQL injection Boosting

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An interactive viral genome evolution network analysis system enabling rapid large-scale molecular tracing of SARS-CoV-2

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science Bulletin 2022年第7期67卷 665-669页

作者： Yunchao Ling Ruifang Cao Jiaqiang Qian Jiefu Li Haokui Zhou Liyun Yuan Zhen Wang Liangxiao Ma Guangyong Zheng Guoping Zhao Zefeng Wang Guoqing Zhang Yixue Li CAS Key Laboratory of Computational Biology Bio-Med Big Data CenterShanghai Institute of Nutrition and HealthUniversity of Chinese Academy of SciencesChinese Academy of SciencesShanghai 200031China Institute of Synthetic Biology Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Hangzhou Institute for Advanced Study University of Chinese Academy of SciencesHangzhou 310024China Collaborative Innovation Center for Genetics and Development Fudan UniversityShanghai 200433China CAS Center for Excellence in Molecular Cell Science Chinese Academy of SciencesShanghai 200031China School of Life Sciences and Biotechnology Shanghai Jiao Tong UniversityShanghai 200240China Guangzhou Laboratory Guangzhou 510005China

The comprehensive analyses of the SARS-CoV-2 genomes could provide a global picture of how the virus was transmitted among different populations,which may help predict the oncoming trends of the *** main approach for ... 详细信息

关键词：快速分析方法 thoroughly among

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Multi-label Contrastive Focal Loss for Pedestrian Attribute Recognition

Multi-label Contrastive Focal Loss for Pedestrian Attribute ...

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International Conference on Pattern Recognition

作者： Xiaoqiang Zheng Zhenxia Yu Lin Chen Fan Zhu Shilong Wang Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China School of computer science Chengdu University Of Information Technology Chengdu China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China

Pedestrian Attribute Recognition (PAR) has received extensive attention during the past few years. With the advances of deep convolutional neural networks (CNNs), the performance of PAR has been significantly improved. Existing methods tend to acquire attribute-specific features by designing various complex network structures with additional modules. Such additional modules, however, dramatically increase the number of network parameters. Meanwhile, the problems of class imbalance and hard attribute retrieving remain underestimated in PAR. In this paper, we explore the optimization mechanism of the training processing to account for these problems and propose a new loss function called Multi-label Contrastive Focal Loss (MCFL). This proposed MCFL emphasizes the hard and minority attributes by using a separated re-weighting mechanism for different positive and negative classes to alleviate the impact of the imbalance. MCFL is also able to enlarge the gaps between the intra-class of multi-label attributes, to force CNNs to extract more subtle discriminative features. We evaluate the proposed MCFL on three large public pedestrian datasets, including RAP, PA-100K, and PETA. The experimental results indicate that the proposed MCFL with the ResNet-50 backbone is able to outperform other state-of-the-art approaches in term of mean accuracy.

关键词： Training Visualization Force Complex networks Feature extraction Pattern recognition Convolutional neural networks

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Efficient Federated Learning with Spike Neural networks for Traffic Sign Recognition

arXiv

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arXiv 2022年

作者： Xie, Kan Zhang, Zhe Li, Bo Kang, Jiawen Niyato, Dusit Xie, Shengli Wu, Yi The School of Automation Guangdong University of Technology Guangzhou510006 China The Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China The Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China The 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China The Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China The School of Data Science and Technology Heilongjiang University Harbin150080 China The Institute for Cryptology & Network Security Heilongjiang University Harbin150080 China School of Computer Science and Engineering Nanyang Technological University Singapore

With the gradual popularization of self-driving, it is becoming increasingly important for vehicles to smartly make the right driving decisions and autonomously obey traffic rules by correctly recognizing traffic signs. However, for machine learning-based traffic sign recognition on the Internet of Vehicles (IoV), a large amount of traffic sign data from distributed vehicles is needed to be gathered in a centralized server for model training, which brings serious privacy leakage risk because of traffic sign data containing lots of location privacy information. To address this issue, we first exploit privacy-preserving federated learning to perform collaborative training for accurate recognition models without sharing raw traffic sign data. Nevertheless, due to the limited computing and energy resources of most devices, it is hard for vehicles to continuously undertake complex artificial intelligence tasks. Therefore, we introduce powerful Spike Neural networks (SNNs) into traffic sign recognition for energy-efficient and fast model training, which is the next generation of neural networks and is practical and well-fitted to IoV scenarios. Furthermore, we design a novel encoding scheme for SNNs based on neuron receptive fields to extract information from the pixel and spatial dimensions of traffic signs to achieve high-accuracy training. Numerical results indicate that the proposed federated SNN outperforms traditional federated convolutional neural networks in terms of accuracy, noise immunity, and energy efficiency as well. Copyright © 2022, The Authors. All rights reserved.

关键词： Traffic signs

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XAI-PSSGAN: Perception-Enhanced Spectrum Shift Generative Adversarial network with Explainable AI System for NIR-II Fluorescence Molecular Imaging

XAI-PSSGAN: Perception-Enhanced Spectrum Shift Generative Ad...

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IEEE International Symposium on Biomedical Imaging

作者： Lidan Fu Binchun Lu Lingbing Li Xiaojing Shi Jie Tian Zhenhua Hu CAS Key Laboratory of Molecular Imaging Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Department of Precision Instrument Tsinghua University Beijing China Interventional Radiology Department Chinese PLA General Hospital Beijing China Beijing Advanced Innovation Center for Big Data-based Precision Medicine School of Engineering Medicine Beihang University Beijing China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and Technology Xidian University Xi'an China National Key Laboratory of Kidney Diseases Beijing China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Fluorescence imaging in the second near-infrared window (NIR-II) facilitates the real-time optical contrast for in vivo biomedical imaging. However, the detection noise is an inevitable byproduct of the real-time imaging process. Additionally, fluorescent probes currently approved for clinical use remain limited within the wavelength of NIR-IIa (1000–1300 nm). NIR-IIb (1500–1700 nm) imaging has the potential to deliver higher resolution than NIR-IIa, but relies on toxic fluorescent probes not approved for human use. To support clinical high-precision imaging, we propose a NIR-II fluorescence enhanced imaging method based on perception-enhanced spectrum shift generative adversarial network with explainable AI systems (xAI-PSSGAN). The proposed method tackles the problem of limited unpaired data training, and generates NIR-IIb resembling images with enhanced resolution and reduced noise. Experimental results demonstrate that proposed method effectively improves the imaging quality and surpasses competing alternatives, which is of great significance to promote the high-quality NIR-II imaging in clinical practice.

关键词： Training Image resolution Explainable AI Statistical analysis Noise Imaging Fluorescence Generative adversarial networks Real-time systems Probes

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Quantum Lovász local lemma: Shearer’s bound is tight 2019

Quantum Lovász local lemma: Shearer’s bound is tight

引用

51st Annual ACM SIGACT Symposium on Theory of computing, STOC 2019

作者： He, Kun Li, Qian Sun, Xiaoming Zhang, Jiapeng CAS Key Lab of Network Data Science and Technology Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China Shenzhen Institute of Computing Sciences Shenzhen University China University of California San DiegoCA United States

ISBN: (纸本)9781450367059

Lovász Local Lemma (LLL) is a very powerful tool in combinatorics and probability theory to show the possibility of avoiding all "bad" events under some "weakly dependent" condition. Over the last decades, the algorithmic aspect of LLL has also attracted lots of attention in theoretical computer science. A tight criterion under which the abstract version LLL (ALLL) holds was given by Shearer. It turns out that Shearer’s bound is generally not tight for variable version LLL (VLLL). Recently, Ambainis et al. introduced a quantum version LLL (QLLL), which was then shown to be powerful for the quantum satisfiability problem. In this paper, we prove that Shearer’s bound is tight for QLLL, i.e., the relative dimension of the smallest satisfying subspace is completely characterized by the independent set polynomial, affirming a conjecture proposed by Sattath et al. Our result also shows the tightness of Gilyén and Sattath’s algorithm, and implies that the lattice gas partition function fully characterizes quantum satisfiability for almost all Hamiltonians with large enough qudits. Commuting LLL (CLLL), LLL for commuting local Hamiltonians which are widely studied in the literature, is also investigated here. We prove that the tight regions of CLLL and QLLL are different in general. This result might imply that it is possible to design an algorithm for CLLL which is still efficient beyond Shearer’s bound. In applications of LLLs, the symmetric cases are most common, i.e., the events are with the same probability and the Hamiltonians are with the same relative dimension. We give the first lower bound on the gap between the symmetric VLLL and Shearer’s bound. Our result can be viewed as a quantitative study on the separation between quantum and classical constraint satisfaction problems. Additionally, we obtain similar results for the symmetric CLLL. As an application, we give lower bounds on the critical thresholds of VLLL and CLLL for several of the most common lattices. ©

关键词： Hamiltonians

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A Novel Configurable High-precision and Low-cost Circuit Design of Sigmoid and Tanh Activation Function

A Novel Configurable High-precision and Low-cost Circuit Des...

引用

IEEE International Conference on Integrated Circuits, Technologies and Applications (ICTA)

作者： Feng Liu Bowen Zhang Gang Chen Guoliang Gong Huaxiang Lu Wenchang Li Institute of Semiconductors Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Semiconductor Neural Network Intelligent Perception and Computing Technology Beijing Key Lab Beijing China Center for Excellence in Brain Science and Intelligence Technology CAS Shanghai China

ISBN: (纸本)9781665417488

This paper proposes a configurable Sigmoid and Tanh activation function circuit design by second-order approximation and deviation compensation. The proposed circuit is synthesized in TSMC 180nm technology within a 7218 um 2 area at 500MHz, it is also deployed on Xilinx ZC706 FPGA by 158 LUTs and 46 FFs at 333MHz. It achieves almost 4.68× better precision and 20.2% less area than previous works. The experimental results show that our method has almost no accuracy loss on LSTM Chinese speech recognition tasks and the YOLOv3 image recognition post-processing task.

关键词： Integrated circuit technology Image recognition Costs Conferences Speech recognition Hardware Circuit synthesis

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Relations of rotation and chromospheric activity to stellar age for FGK dwarfs from Kepler and LAMOST

arXiv

引用

arXiv 2024年

作者： Ye, Lifei Bi, Shaolan Zhang, Jinghua Sun, Tiancheng Long, Liu Ge, Zhishuai Li, Tanda Zhang, Xianfei Chen, Xunzhou Li, Yaguang Zhou, Jianzhao Xiang, Maosheng Institute for Frontiers in Astronomy and Astrophysics Beijing Normal University Beijing102206 China Department of Astronomy Beijing Normal University Beijing100875 China South-Western Institute for Astronomy Research Yunnan University Chenggong District Kunming650500 China Beijing Planetarium Beijing Academy of Science and Technology Beijing100044 China Research Center for Intelligent Computing Platforms Zhejiang Laboratory Hangzhou311100 China Institute for Astronomy University of Hawai‘i 2680 Woodlawn Drive HonoluluHI96822 United States CAS Key Laboratory of Optical Astronomy National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China

The empirical relations between rotation period, chromospheric activity, and age can be used to estimate stellar age. To calibrate these relations, we present a catalog, including the masses and ages of 52,321 FGK dwarfs, 47,489 chromospheric activity index logRHK+ , 6,077 rotation period Prot and variability amplitude Sph, based on data from LAMOST DR7, Kepler and Gaia DR3. We find a pronounced correlation among Prot, age, and [Fe/H] throughout the main-sequence phase for F dwarfs. However, the decrease of logRHK+ over time is not significant except for those with [Fe/H] rot and logRHK+ are reliable age probes in the ranges ∼ 2-11 Gyr and ∼ 2-13 Gyr, respectively. K dwarfs exhibit a prominent decrease in logRHK+ within the age range of ∼ 3-13 Gyr when the relation of Prot − τ is invalid. These relations are very important for promptly estimating the age of a vast number of stars, thus serving as a powerful tool in advancing the fields of exoplanet properties, stellar evolution, and Galactic-archaeology. Copyright © 2024, The Authors. All rights reserved.

关键词： Rotation

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