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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Mingzhe Dai Xiaoshuang Shi Xiaofeng Zhu Tingrui Pan Kang Li School of Software Engineering University of Science and Technology of China Hefei China West China Biomedical Big Data Center West China Hospital of Sichuan University Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Sichuan Artificial Intelligence Research Institute Yibin China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Shanghai Artificial Intelligence Laboratory Shanghai China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Deep multiple instance learning (MIL) has attracted considerable attention in medical image analysis, since it only requires image-level labels for model training without using fine-grained (or patch) annotations. Unfortunately, MIL-based methods might lose some significant patch features. Although pseudo-label-based methods, which assign a pre-defined label to each patch, can explore more patch-level features, they might bring label noise and make the patch-level features lose diversity, thereby possibly restricting the model performance. To overcome this issue, we propose a novel gradient-based patch target generation (PTG) module to dynamically produce a feature vector for each patch as its target. Additionally, based on the PTG module, we propose a patch-target guided dual-branch deep MIL framework for 3D MRI data analysis, where both the two branches consist of a CNN model to extract patch-level features, an attention module to interpret the significance of patches, and a bag-level classifier, while the second branch also contains the PTG module to generate patch targets of patches. Moreover, the two branches are alternatively updated in our framework, resulting in a bi-level optimization problem, and thus we design a bi-level optimization algorithm to solve our proposed objective function. Extensive experiments demonstrate the superior classification and interpretation performance of the proposed framework over recent state-of-the-art methods. Codes are available at https://***/daimz1213/mil2024.

关键词： Training Analytical models Solid modeling Three-dimensional displays Annotations Magnetic resonance imaging Feature extraction Vectors Optimization Biomedical imaging

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Specialising neural network potentials for accurate properties and application to the mechanical response of titanium

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npj Computational Materials 2021年第1期7卷 1908-1918页

作者： Tongqi Wen Rui Wang Lingyu Zhu Linfeng Zhang Han Wang David J.Srolovitz Zhaoxuan Wu Department of Materials Science and Engineering City University of Hong KongKowloonHong KongSARChina Hong Kong Institute for Advanced Study City University of Hong KongKowloonHong KongSARChina Beijing Institute of Big Data Research BeijingChina Laboratory of Computational Physics Institute of Applied Physics and Computational MathematicsBeijingChina Department of Mechanical Engineering The University of Hong KongHong KongSARChina International Digital Economy Academy(IDEA) ShenzhenChina

Large scale atomistic simulations provide direct access to important materials phenomena not easily accessible to experiments or quantum mechanics-based calculation *** and efficient interatomic potentials are the key enabler,but their development remains a challenge for complex materials and/or complex *** learning potentials,such as the Deep Potential(DP)approach,provide robust means to produce general purpose interatomic ***,we provide a methodology for specialising machine learning potentials for high fidelity simulations of complex phenomena,where general potentials do not *** an example,we specialise a general purpose DP method to describe the mechanical response of two allotropes of titanium(in addition to other defect,thermodynamic and structural properties).The resulting DP correctly captures the structures,energies,elastic constants andγ-lines of Ti in both the HCP and BCC structures,as well as properties such as dislocation core structures,vacancy formation energies,phase transition temperatures,and thermal *** DP thus enables direct atomistic modelling of plastic and fracture behaviour of *** approach to specialising DP interatomic potential,DPspecX,for accurate reproduction of properties of interest“X”,is general and extensible to other systems and properties.

关键词： titanium enable neural

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Dual Contrastive Learning Model for Representation Learning and Background Debiasing in SAR Object Classification

Dual Contrastive Learning Model for Representation Learning ...

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SAR in big data Era: Models, Methods and Applications (bigSARdata)

作者： Jing Wang Sirui Tian Xiaolin Feng Bo Zhang Fan Wu Chao Wang School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing China International Research Center of Big Data for Sustainable Development Goals Beijing China Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

As a popular representation learning framework, contrastive learning has been widely used in SAR image interpretation for feature extraction. Recent studies have shown that some features obtained by the model are related to the background information due to the spurious correlation of the backgrounds during the SAR data collection. As a result, the generalization ability of the representation learning model will be greatly affected by the background information and cannot satisfy the requirements of practical applications. This paper constructed a multi-branch dual contrastive learning model that simultaneously compares the similarities between objects and backgrounds of various images. The motivation is to alleviate the impact of the spurious correlation between background information and the object category. The experimental results and interpretable Shapley value demonstrated that the proposed method significantly improved the generalization performance of the SAR object classification algorithm.

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Boosting the Electrochemical Performance of Li-and Mn-Rich Cathodes by a Three-in-One Strategy

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Nano-Micro Letters 2021年第12期13卷 311-321页

作者： Wei He Fangjun Ye Jie Lin Qian Wang Qingshui Xie Fei Pei Chenying Zhang Pengfei Liu Xiuwan Li Laisen Wang Baihua Qu Dong-Liang Peng State Key Lab of Physical Chemistry of Solid Surface Collaborative Innovation Center of Chemistry for Energy MaterialsCollege of Materials and Pen-Tung Sah Institute of Micro-Nano Science and TechnologyXiamen UniversityXiamen 361005People’s Republic of China College of Chemistry and Chemical Engineering Xiamen UniversityXiamen 361005People’s Republic of China Zhengzhou Key Laboratory of Big Data Analysis and Application Henan Academy of Big DataZhengzhou UniversityZhengzhou 450002People’s Republic of China Fujian Provincial Key Laboratory of Light Propagation and Transformation College of Information Science and EngineeringHuaqiao UniversityXiamen 361021People’s Republic of China Shenzhen Research Institute of Xiamen University Shenzhen 518000People’s Republic of China

There are plenty of issues need to be solved before the practi-cal application of Li-and Mn-rich cathodes,including the detrimental voltage decay and mediocre rate capability,*** doping can e ectively solve the above problems,but cause the loss of *** introduction of appropriate defects can compensate the capacity loss;however,it will lead to structural mismatch and stress ***,a three-in-one method that combines cation–polyanion co-doping,defect construction,and stress engineering is *** co-doped Na^(+)/SO_(4)^(2-)can stabilize the layer framework and enhance the capacity and voltage *** induced defects would activate more reac-tion sites and promote the electrochemical ***,the unique alternately distributed defect bands and crystal bands structure can alleviate the stress accumulation caused by changes of cell parameters upon ***,the modified sample retains a capacity of 273 mAh g^(-1)with a high-capacity retention of 94.1%after 100 cycles at 0.2 C,and 152 mAh g^(-1)after 1000 cycles at 2 C,the corresponding voltage attenuation is less than 0.907 mV per cycle.

关键词： Li-and Mn-rich cathodes Cation–polyanion co-doping Defect and stress engineering Good structure stability Electrochemical performance

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Towards Clinician-Preferred Segmentation: Leveraging Human-in-the-Loop for Test Time Adaptation in Medical Image Segmentation

arXiv

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arXiv 2024年

作者： Hu, Shishuai Liao, Zehui Liu, Zeyou Xia, Yong National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China Ningbo Institute of Northwestern Polytechnical University Ningbo315048 China Research and Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen518057 China

Deep learning-based medical image segmentation models often face performance degradation when deployed across various medical centers, largely due to the discrepancies in data distribution. Test Time Adaptation (TTA) methods, which adapt pre-trained models to test data, have been employed to mitigate such discrepancies. However, existing TTA methods primarily focus on manipulating Batch Normalization (BN) layers or employing prompt and adversarial learning, which may not effectively rectify the inconsistencies arising from divergent data distributions. In this paper, we propose a novel Human-in-the-loop TTA (HiTTA) framework that stands out in two significant ways. First, it capitalizes on the largely overlooked potential of clinician-corrected predictions, integrating these corrections into the TTA process to steer the model towards predictions that coincide more closely with clinical annotation preferences. Second, our framework conceives a divergence loss, designed specifically to diminish the prediction divergence instigated by domain disparities, through the careful calibration of BN parameters. Our HiTTA is distinguished by its dual-faceted capability to acclimatize to the distribution of test data whilst ensuring the model’s predictions align with clinical expectations, thereby enhancing its relevance in a medical context. Extensive experiments on a public dataset underscore the superiority of our HiTTA over existing TTA methods, emphasizing the advantages of integrating human feedback and our divergence loss in enhancing the model’s performance and adaptability across diverse medical centers. © 2024, CC BY-NC-ND.

关键词： Forecasting

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Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and Artificial Bee Colony 22

Optimizing Ethanol Production in Escherichia Coli Using a Hy...

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Proceedings of the 6th International Conference on Advances in Artificial Intelligence

作者： Mohamad Faiz Dzulkalnine Mohd Saberi Mohamad Yee Wen Choon Muhammad Akmal Remli Hany Alashwal Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University UAE and Big Data Analytics Center United Arab Emirates University UAE Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Malaysia and Department of Data Science Universiti Malaysia Kelantan Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University UAE and Big Data Analytics Center United Arab Emirates University UAE

ISBN: (纸本)9781450396943

Metabolic engineering for biomass production using microorganisms’ cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the Artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy.

关键词： Gene Knockouts Particle Swarm Optimization Artificial Bee Colony Ethanol Flux Balance Analysis

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Near-Field Multiuser Communications Aided by Movable Antennas

arXiv

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arXiv 2024年

作者： Ding, Jingze Zhu, Lipeng Zhou, Zijian Jiao, Bingli Zhang, Rui School of Electronics Peking University Beijing100871 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore School of Science and Engineering The Chinese University of Hong Kong Guangdong Shenzhen518172 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China

This letter investigates movable antenna (MA)-aided downlink (DL) multiuser communication systems under the near-field channel condition, where both the base station (BS) and the users are equipped with MAs to fully exploit the degrees of freedom (DoFs) in antenna position optimization. We develop a general channel model to accurately describe the channel characteristics in the near-field region and formulate an MA-position optimization problem to minimize the BS’s transmit power subject to users’ individual rate constraints. To solve this problem, we propose a two-loop dynamic neighborhood pruning particle swarm optimization (DNPPSO) algorithm that significantly reduces the computational complexity as compared to the standard particle swarm optimization (PSO) algorithm while achieving similar performance. Simulation results validate the effectiveness and advantages of the proposed scheme in power-saving for near-field multiuser communications. © 2024, CC BY.

关键词： Particle swarm optimization (PSO)

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scBERC: A Batch Effect-Removed Clustering method for single-cell omics

scBERC: A Batch Effect-Removed Clustering method for single-...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yitao Zhou Fan Yang Ying Wang Feng Zeng Department of Automation National Institute for Data Science in Health and Medicine Xiamen Key Laboratory of Big Data Intelligent Analysis & Decision Xiamen University Xiamen China State Key Laboratory of Cellular Stress Biology School of Life Sciences Research Unit of Cellular Stress of CAMS Cancer Research Center School of Medicine Xiamen University Xiamen China

Single-cell clustering is a pivotal technique in deciphering single-cell omics data, enabling us to gain insights into cell function and heterogeneity. However, the presence of batch effects in single-cell omics data poses significant challenges to the accuracy and reliability of single-cell clustering. To address these challenges, we develop a Batch Effect-Removed Clustering method(scBERC) that incorporates a novel single-cell augmentation strategy and contrastive learning. By augmenting cells with artificially induced variations, our approach captures the full range of cell states, accounting for both biological heterogeneity and batch-specific variations. This augmentation strategy enriches the training data while the contrastive learning strategy better extracts real biological features, allowing the deep subspace clustering algorithm to learn robust and generalized patterns, thereby mitigating the biases introduced by batch effects. Experimental results on twelve real single-cell datasets demonstrate that scBERC outperforms existing single-cell clustering methods, significantly improving clustering performance and facilitating downstream analysis.

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AVR: Attention based Salient Visual Relationship Detection 14

AVR: Attention based Salient Visual Relationship Detection

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14th International Conference on Digital Image Processing, ICDIP 2022

作者： Li, Hualiang Lv, Jianming Xiao, Qinzhe Liu, Yuzhong Wu, Siqi Huang, Xingjie Key Laboratory of Occupational Health and Safety of Guangdong Power Grid Co. Ltd. Electric Power Research Institute of Guangdong Power Grid Co. Ltd. Guangdong Guangzhou China Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education Guangdong Guangzhou China School of Computer Science and Engineering South China University of Technology Guangdong Guangzhou China

ISBN: (纸本)9781510657564

Visual relationship detection aims to locate objects in images and recognize the relationships between objects. Traditional methods treat all observed relationships in an image equally, which causes a relatively poor performance in the detection tasks on complex images with abundant visual objects and various relationships. To address this problem, we propose an attention based model, namely AVR, to achieve salient visual relationships based on both local and global context of the relationships. Specifically, AVR recognizes relationships and measures the attention on the relationships in the local context of an input image by fusing the visual features, semantic and spatial information of the relationships. AVR then applies the attention to assign important relationships with larger salient weights for effective information filtering. Furthermore, AVR is integrated with the priori knowledge in the global context of image datasets to improve the precision of relationship prediction, where the context is modeled as a heterogeneous graph to measure the priori probability of relationships based on the random walk algorithm. Comprehensive experiments are conducted to demonstrate the effectiveness of AVR in several real-world image datasets, and the results show that AVR outperforms state-of-the-art visual relationship detection methods significantly by up to 87.5% in terms of recall. © 2022 SPIE.

关键词： Information filtering

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Localization of Dummy data Injection Attacks in Power Systems Considering Incomplete Topological Information: A Spatio-Temporal Graph Wavelet Convolutional Neural Network Approach

arXiv

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arXiv 2024年

作者： Qu, Zhaoyang Dong, Yunchang Li, Yang Song, Siqi Jiang, Tao Li, Min Wang, Qiming Wang, Lei Bo, Xiaoyong Zang, Jiye Xu, Qi School of Electrical Engineering Northeast Electric Power University Jilin China Jilin Electric Power Research Institute of State Grid Changchun China Northeast Electric Power Design and Institute Co. Ltd. China Power Engineering Consulting Group China School of Computer Northeast Electric Power University Jilin China Jilin Engineering Technology Research Center of Intelligent Electric Power Big Data Processing Jilin China Department of Civil and Environmental Engineering The Hong Kong University of Science and Technology Hong Kong

The emergence of novel the dummy data injection attack (DDIA) poses a severe threat to the secure and stable operation of power systems. These attacks are particularly perilous due to the minimal Euclidean spatial separation between the injected malicious data and legitimate data, rendering their precise detection challenging using conventional distance-based methods. Furthermore, existing research predominantly focuses on various machine learning techniques, often analyzing the temporal data sequences post-attack or relying solely on Euclidean spatial characteristics. Unfortunately, this approach tends to overlook the inherent topological correlations within the non-Euclidean spatial attributes of power grid data, consequently leading to diminished accuracy in attack localization. To address this issue, this study takes a comprehensive approach. Initially, it examines the underlying principles of these new DDIAs on power systems. Here, an intricate mathematical model of the DDIA is designed, accounting for incomplete topological knowledge and alternating current (AC) state estimation from an attacker's perspective. This model aims to mitigate the vulnerabilities associated with direct current (DC) flow-based attack data generation methods that can be readily detected. Subsequently, by integrating a priori knowledge of grid topology and considering the temporal correlations within measurement data and the topology-dependent attributes of the power grid, this study introduces temporal and spatial attention matrices. These matrices adaptively capture the spatio-temporal correlations within the attacks. Leveraging gated stacked causal convolution and graph wavelet sparse convolution, the study jointly extracts spatio-temporal DDIA features. This methodology significantly enhances the dynamic correlation mining capability while improving computational efficiency in the neighborhood. Finally, the research proposes a DDIA localization method based on spatio-temporal graph

关键词： Convolution

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