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Memory Analysis for Memristors and Memristive Recurrent Neural Networks

IEEE/CAA Journal of Automatica Sinica 2020年第1期7卷 96-105页

作者： Gang Bao Yide Zhang Zhigang Zeng Key Laboratory of Cascaded Hydropower Stations Operation and Control Electrical Engineering and New EnergyChina Three Gorges UniversityYichang 443002and Hubei Key Laboratory of Applied Mathematics(Hubei University)Wuhan 430074China Department of Medical Engineering California Institute of TechnologyPasadenaCalifornia 91125 USA School of Artificial Intelligence and Automation Huazhong University of Science and Technologyand also with the Key Laboratory of Image Processing and Intelligent Control of Education Ministry of ChinaWuhan 430074China

Traditional recurrent neural networks are composed of capacitors, inductors, resistors, and operational *** neural networks are constructed by replacing resistors with memristors. This paper focuses on the memory analysis,i.e. the initial value computation, of memristors. Firstly, we present the memory analysis for a single memristor based on memristors’ mathematical models with linear and nonlinear ***, we present the memory analysis for two memristors in series and parallel. Thirdly, we point out the difference between traditional neural networks and those that are memristive. Based on the current and voltage relationship of memristors, we use mathematical analysis and SPICE simulations to demonstrate the validity of our methods.

关键词： Dopant drift memory memristive neural networks memristor

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Real-time Multi-person Eyeblink Detection in the Wild for Untrimmed Video

arXiv

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arXiv 2023年

作者： Zeng, Wenzheng Xiao, Yang Wei, Sicheng Gan, Jinfang Zhang, Xintao Cao, Zhiguo Fang, Zhiwen Zhou, Joey Tianyi Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China School of Biomedical Engineering Southern Medical University Guangzhou510515 China Department of Rehabilitation Medicine Zhujiang Hospital Southern Medical University Guangzhou510280 China Singapore Singapore

Real-time eyeblink detection in the wild can widely serve for fatigue detection, face anti-spoofing, emotion analysis, etc. The existing research efforts generally focus on single-person cases towards trimmed video. However, multi-person scenario within untrimmed videos is also important for practical applications, which has not been well concerned yet. To address this, we shed light on this research field for the first time with essential contributions on dataset, theory, and practices. In particular, a large-scale dataset termed MPEblink that involves 686 untrimmed videos with 8748 eyeblink events is proposed under multi-person conditions. The samples are captured from unconstrained films to reveal "in the wild" characteristics. Meanwhile, a real-time multi-person eyeblink detection method is also proposed. Being different from the existing counterparts, our proposition runs in a one-stage spatio-temporal way with end-to-end learning capacity. Specifically, it simultaneously addresses the sub-tasks of face detection, face tracking, and human instance-level eyeblink detection. This paradigm holds 2 main advantages: (1) eyeblink features can be facilitated via the face's global context (e.g., head pose and illumination condition) with joint optimization and interaction, and (2) addressing these sub-tasks in parallel instead of sequential manner can save time remarkably to meet the real-time running requirement. Experiments on MPEblink verify the essential challenges of real-time multi-person eyeblink detection in the wild for untrimmed video. Our method also outperforms existing approaches by large margins and with a high inference speed. Copyright © 2023, The Authors. All rights reserved.

关键词： Face recognition

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A2J-Transformer: Anchor-to-Joint Transformer Network for 3D Interacting Hand Pose Estimation from a Single RGB image

A2J-Transformer: Anchor-to-Joint Transformer Network for 3D ...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Changlong Jiang Yang Xiao Cunlin Wu Mingyang Zhang Jinghong Zheng Zhiguo Cao Joey Tianyi Zhou Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Alibaba Group Centre for Frontier AI Research Agency for Science Technology and Research (A*STAR) Singapore Institute of High Performance Computing Agency for Science Technology and Research (A*STAR) Singapore

3D interacting hand pose estimation from a single RGB image is a challenging task, due to serious self-occlusion and inter-occlusion towards hands, confusing similar appearance patterns between 2 hands, ill-posed joint position mapping from 2D to 3D, etc.. To address these, we propose to extend A2J-the state-of-the-art depth-based 3D single hand pose estimation method-to RGB domain under interacting hand condition. Our key idea is to equip A2J with strong local-global aware ability to well capture interacting hands' local fine details and global articulated clues among joints jointly. To this end, A2J is evolved under Transformer's non-local encoding-decoding framework to build A2J- Transformer. It holds 3 main advantages over A2J. First, self-attention across local anchor points is built to make them global spatial context aware to better capture joints' articulation clues for resisting occlusion. Secondly, each anchor point is regarded as learnable query with adaptive feature learning for facilitating pattern fitting capacity, instead of having the same local representation with the others. Last but not least, anchor point locates in 3D space instead of 2D as in A2J, to leverage 3D pose prediction. Experiments on challenging InterHand 2.6M demonstrate that, A2J-Transformer can achieve state-of-the-art model-free performance (3.38mm MPJPE advancement in 2-hand case) and can also be applied to depth domain with strong generalization. The code is avaliable at https://***/ChanglongJiangGit/A2J-Transformer.

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Composing Photos Like a Photographer

Composing Photos Like a Photographer

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Hong, Chaoyi Du, Shuaiyuan Xian, Ke Lu, Hao Cao, Zhiguo Zhong, Weicai Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Minist Educ Key Lab Image Proc & Intelligent Control Wuhan Hubei Peoples R China Huawei CBG Consumer Cloud Serv Prod & Big Data Platform Dept Shenzhen Peoples R China

ISBN: (纸本)9781665445092

We show that explicit modeling of composition rules benefits image cropping. image cropping is considered a promising way to automate aesthetic composition in professional photography. Existing efforts, however;only model such professional knowledge implicitly, e.g., by ranking from comparative candidates. Inspired by the observation that natural composition traits always follow a specific rule, we propose to learn such rules in a discriminative manner, and more importantly, to incorporate learned composition clues explicitly in the model. To this end, we introduce the concept of the key composition map (KCM) to encode the composition rules. The KCM can reveal the common laws hidden behind different composition rules and can inform the cropping model of what is important in composition. With the KCM, we present a novel cropping-by-composition paradigm and instantiate a network to implement composition-aware image cropping. Extensive experiments on two benchmarks justify that our approach enables effective, interpretable, and fast image cropping.

关键词： Photography Computer vision Computational modeling Benchmark testing Pattern recognition

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Deep learning-enhanced fluorescence microscopy via degeneration decoupling

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OPTICS EXPRESS 2020年第10期28卷 14859-14873页

作者： Liu, Jiahao Huang, Xiaoshuai Chen, Liangyi Tan, Shan Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Minist Educ China Lab Image Proc & Intelligent Control Wuhan Peoples R China Peking Univ Inst Mol Med Beijing Key Lab Cardiometab Mol Med State Key Lab Membrane Biol Beijing 100871 Peoples R China

Deep learning-based reconstruction has emerged as an effective tool in fluorescence microscopy, with the potential to resolve diffraction-limited structures. However, most deep-learning reconstruction methods employed an end-to-end strategy, which ignored physical laws in the imaging process and made the preparation of training data highly challenging as well. In this study, we have proposed a novel deconvolution algorithm based on an imaging model, deep-learning priors and the alternating direction method of multipliers. This scheme decouples the reconstruction into two separate sub-problems, one for deblurring and one for restraining noise and artifacts. As a result of the decoupling, we are able to introduce deep-learning image priors and a variance stabilizing transform against targeted image degeneration due to the low photon budget. Deep-learning priors are learned from the general image dataset, in which biological images do not have to be involved, and are more powerful than hand-designed ones. Moreover, the use of the imaging model ensures high fidelity and generalization. Experiments on various kinds of measurement data show that the proposed algorithm outperforms existing state-of-the-art deconvolution algorithms in resolution enhancement and generalization. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

关键词： Diffraction limit Fluorescence microscopy High power lasers image processing image restoration Optical imaging

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Multiobjective Optimization on Hierarchical Refugee Evacuation and Resource Allocation for Disaster Management

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MATHEMATICAL PROBLEMS IN ENGINEERING 2020年第1期2020卷 1-18页

作者： Wang, Jian Shen, Danqing Yu, Mingzhu Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China Shenzhen Univ Coll Management Inst Big Data Intelligent Management & Decis Shenzhen 518060 Peoples R China

This paper studies a location-allocation problem to determine the selection of emergency shelters, medical centers, and distribution centers after the disaster. The evacuation of refugees and allocation of relief resources are also considered. A mixed-integer nonlinear multiobjective programming model is proposed to characterize the problem. The hierarchical demand of different refugees and the limitations of relief resources are considered in the model. We employ a combination of the simulated annealing (SA) algorithm and the particle swarm optimization (PSO) algorithm method to solve the complex model. To optimize the result of our proposed algorithm, we absorb the group search, crossover, and mutation operator of GA into SA. We conduct a case study in a district of Beijing in China to validate the proposed methodology. Some computational experiments are conducted to analyze the impact of different factors, such as the target weight setting, selection of candidate shelters, and quantity of relief resources.

关键词： Simulated annealing

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Watch the Speakers: A Hybrid Continuous Attribution Network for Emotion Recognition in Conversation With Emotion Disentanglement

Watch the Speakers: A Hybrid Continuous Attribution Network ...

引用

International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Shanglin Lei Xiaoping Wang Guanting Dong Jiang Li Yingjian Liu School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Institute of Artificial Intelligence Huazhong University of Science and Technology (HUST) Wuhan China

Emotion Recognition in Conversation (ERC) has attracted widespread attention in the natural language processing field due to its enormous potential for practical applications. Existing ERC methods face challenges in achieving generalization to diverse scenarios due to insufficient modeling of context, ambiguous capture of dialogue relationships and overfitting in speaker modeling. In this work, we present a Hybrid Continuous Attributive Network (HCAN) to address these issues in the perspective of emotional continuation and emotional attribution. Specifically, HCAN adopts a hybrid recurrent and attention-based module to model global emotion continuity. Then a novel Emotional Attribution Encoding (EAE) is proposed to model intra- and inter-emotional attribution for each utterance. Moreover, aiming to enhance the robustness of the model in speaker modeling and improve its performance in different scenarios, A comprehensive loss function emotional cognitive loss $\mathcal{L}_{EC}$ is proposed to alleviate emotional drift and overcome the overfitting of the model to speaker modeling. Our model achieves state-of-the-art performance on three datasets, demonstrating the superiority of our work. Another extensive comparative experiments and ablation studies on three benchmarks are conducted to provided evidence to support the efficacy of each module. Further exploration of generalization ability experiments shows the plug-and-play nature of the EAE module in our method.

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Parametric Design Optimization of a Universal Supernumerary Robotic Limb

Parametric Design Optimization of a Universal Supernumerary ...

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International Symposium on Micromechatronics and Human Science (MHS)

作者： Jun Huo Bo Yang Hongge Ru Jian Huang Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology (HUST) Wuhan China

ISBN: (纸本)9781665407748

It is a positive trend for hemiplegia with wearable robots in rehabilitation training. Recently, wearable Supernumerary Robotic Limb (SRL) is rising to a hot spot. The difficulty in modeling SRL for hemiplegia is how to make the SRL mechanism match the human upper and lower limbs' size and function simultaneously. In this paper, a simplified model of a four Degree of Freedoms (DOFs) universal SRL (USRL) is introduced. Based on workspace similarity, which is evaluated by Jaccard Index (JI), we establish a multi-object optimization (MOO) model. By applying the Particle Swarm Optimization (PSO) algorithm, we have found the optimal SRL mechanism parameters that meet the requirement of the human upper limb and the cane simultaneously. Meanwhile, a few impact factors are discussed briefly in the present proposal. The simulation results show that the USRL has a sound match with the human and could assist the hemiplegia subject as a supplemental limb.

关键词： Training Simulation Wearable robots Market research Manufacturing Indexes Proposals

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Accurate inference of gene regulatory interactions from spatial gene expression with deep contrastive learning

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BIOINFORMATICS 2022年第3期38卷 746-753页

作者： Zheng, Lujing Liu, Zhenhuan Yang, Yang Shen, Hong-Bin Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ SJTU Paris Elite Inst Technol SPEIT Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Key Lab Shanghai Educ Commiss Intelligent Interac Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Key Lab Syst Control & Informat Proc Minist Educ China Shanghai 200240 Peoples R China

Motivation: Reverse engineering of gene regulatory networks (GRNs) has long been an attractive research topic in system biology. Computational prediction of gene regulatory interactions has remained a challenging problem due to the complexity of gene expression and scarce information resources. The high-throughput spatial gene expression data, like in situ hybridization images that exhibit temporal and spatial expression patterns, has provided abundant and reliable information for the inference of GRNs. However, computational tools for analyzing the spatial gene expression data are highly underdeveloped. Results: In this study, we develop a new method for identifying gene regulatory interactions from gene expression images, called ConGRI. The method is featured by a contrastive learning scheme and deep Siamese convolutional neural network architecture, which automatically learns high-level feature embeddings for the expression images and then feeds the embeddings to an artificial neural network to determine whether or not the interaction exists. We apply the method to a Drosophila embryogenesis dataset and identify GRNs of eye development and mesoderm development. Experimental results show that ConGRI outperforms previous traditional and deep learning methods by a large margin, which achieves accuracies of 76.7% and 68.7% for the GRNs of early eye development and mesoderm development, respectively. It also reveals some master regulators for Drosophila eye development.

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A Born-Again Takagi-Sugeno-Kang Fuzzy Classifier with Decoupled Fuzzy Dark Knowledge Distillation

SSRN

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SSRN 2024年

作者： Zhang, Xiongtao Yin, Zezong Jiang, Yunliang Jiang, Yizhang Sun, Danfeng Liu, Yong School of Information Engineering Huzhou University Huzhou31300 China Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua321004 China School of Computer Science and Technology Zhejiang Normal University Jinhua321004 China School of Artificial Intelligence and Computer Science Jiangnan University Wuxi 214122 China Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Ministry of Education Wuhan430074 China School of Computer Science Hangzhou Dianzi University Hangzhou310018 China College of Control Science and Engineering Zhejiang University Hangzhou310027 China

In order to enpower the hight performance as well as interpretability of low-order TSK fuzzy classifier, a born-again TSK fuzzy classier embedded with decoupled fuzzy dark knowledge distillation called HTSK-LLM-DKD is proposed in this study. HTSK-LLM DKD achieves the following distinctive characteristics: 1) It takes high-order TSK as teacher model and low-order TSK fuzzy classifier as student model, and leverages the proposed Least Learning Machine based Decoupling Knowledge Distillation to distill the fuzzy dark knowledge from high-order TSK fuzzy classifier to low-order TSK fuzzy classifier, which resulting in low-order TSK fuzzy classifier endowed with enhanced performance surpassing or at least comparable to high-order TSK fuzzy classifier;specifically 2) The Negative Euclidean distance between the output of teacher model and each class is employed to obtain the teacher logits, and then it computes teacher/student soft labels by the softmax function with distilling temperature parameter;3) By reformulating the Kullback Leibler divergence, it decouples fuzzy dark knowledge into target class knowledge and non-target class knowledge, and transfers them to student model, which taking HTSK-LLM-DKD with outstanding performance and relatively fast running speed. Extensive experiment results on UCI benchmarking datasets and real-world dataset Cleveland Heart Disease demonstrate the effectiveness of the proposed HTSK-LLM-DKD. © 2024, The Authors. All rights reserved.

关键词： Students

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