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arXiv 2024年

作者： Wu, Sifan Chen, Haipeng Yin, Yifang Hu, Sihao Feng, Runyang Jiao, Yingying Yang, Ziqi Liu, Zhenguang College of Computer Science and Technology Jilin University Changchun China A*STAR Singapore Georgia Institute of Technology Atlanta United States School of Artificial Intelligence Jilin University Changchun China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China Institute of Blockchain and Data Security Hangzhou China

Human pose estimation in videos has long been a compelling yet challenging task within the realm of computer vision. Nevertheless, this task remains difficult because of the complex video scenes, such as video defocus and self-occlusion. Recent methods strive to integrate multi-frame visual features generated by a backbone network for pose estimation. However, they often ignore the useful joint information encoded in the initial heatmap, which is a byproduct of the backbone generation. Comparatively, methods that attempt to refine the initial heatmap fail to consider any spatiotemporal motion features. As a result, the performance of existing methods for pose estimation falls short due to the lack of ability to leverage both local joint (heatmap) information and global motion (feature) dynamics. To address this problem, we propose a novel joint-motion mutual learning framework for pose estimation, which effectively concentrates on both local joint dependency and global pixel-level motion dynamics. Specifically, we introduce a context-aware joint learner that adaptively leverages initial heatmaps and motion flow to retrieve robust local joint feature. Given that local joint feature and global motion flow are complementary, we further propose a progressive joint-motion mutual learning that synergistically exchanges information and interactively learns between joint feature and motion flow to improve the capability of the model. More importantly, to capture more diverse joint and motion cues, we theoretically analyze and propose an information orthogonality objective to avoid learning redundant information from multi-cues. Empirical experiments show our method outperforms prior arts on three challenging benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Motion estimation

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AMPCliff: quantitative definition and benchmarking of activity cliffs in antimicrobial peptides

arXiv

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arXiv 2024年

作者： Li, Kewei Wu, Yuqian Li, Yinheng Guo, Yutong Wang, Yan Liang, Yiyang Fan, Yusi Huang, Lan Zhang, Ruochi Zhou, Fengfeng College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun130012 China School of Software Jilin University Jilin Changchun130012 China School of Life Sciences Jilin University Jilin Changchun130012 China Department of Computer Science Columbia University 116th and Broadway New York CityNY10027 United States School of Artificial Intelligence Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun130012 China School of Biology and Engineering Guizhou Medical University Guizhou Guiyang550025 China School of Computer Engineering Changchun University of Engineering Jilin Changchun130103 China Changchun Wenli High School Jilin Changchun130062 China

Since the mechanism of action of drug molecules in the human body is difficult to reproduce in the in vitro environment, it becomes difficult to reveal the causes of the activity cliff phenomenon of drug molecules. We found out the AC of small molecules has been extensively investigated but limited knowledge is accumulated about the AC phenomenon in peptides with canonical amino acids. Understanding the mechanism of AC in canonical amino acids might help understand the one in drug molecules. This study introduces a quantitative definition and benchmarking framework AMPCliff for the AC phenomenon in antimicrobial peptides (AMPs) composed by canonical amino acids. A comprehensive analysis of the existing AMP dataset reveals a significant prevalence of AC within AMPs. AMPCliff quantifies the activities of AMPs by the MIC, and defines 0.9 as the minimum threshold for the normalized BLOSUM62 similarity score between a pair of aligned peptides with at least two-fold MIC changes. This study establishes a benchmark dataset of paired AMPs in Staphylococcus aureus from the publicly available AMP dataset GRAMPA, and conducts a rigorous procedure to evaluate various AMP AC prediction models, including nine machine learning, four deep learning algorithms, four masked language models, and four generative language models. Our analysis reveals that these models are capable of detecting AMP AC events and the pre-trained protein language model ESM2 demonstrates superior performance across the evaluations. The predictive performance of AMP activity cliffs remains to be further improved, considering that ESM2 with 33 layers only achieves the Spearman correlation coefficient 0.4669 for the regression task of the MIC values on the benchmark dataset. Source code and additional resources are available at https://***/supp/or https://***/Kewei2023/AMPCliff-generation. © 2024, CC BY-NC-SA.

关键词： Deep learning

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PBCounter: Weighted Model Counting on Pseudo-Boolean Formulas

arXiv

引用

arXiv 2023年

作者： Lai, Yong Xu, Zhenghang Yin, Minghao College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China School of Computer Science and Information Technology Northeast Normal University Changchun130017 China

In Weighted Model Counting (WMC), we assign weights to literals and compute the sum of the weights of the models of a given propositional formula where the weight of an assignment is the product of the weights of its literals. The current WMC solvers work on Conjunctive Normal Form (CNF) formulas. However, CNF is not a natural representation for human-being in many applications. Motivated by the stronger expressive power of pseudo-Boolean (PB) formulas than CNF, we propose to perform WMC on PB formulas. Based on a recent dynamic programming algorithm framework called ADDMC for WMC, we implement a weighted PB counting tool PBCounter. We compare PBCounter with the state-of-the-art weighted model counters SharpSAT-TD, ExactMC, D4, and ADDMC, where the latter tools work on CNF with encoding methods that convert PB constraints into a CNF formula. The experiments on three domains of benchmarks show that PBCounter is superior to the model counters on CNF formulas. Copyright © 2023, The Authors. All rights reserved.

关键词： Dynamic programming

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Complementary knowledge Compilation Using the Hyper Extension Rule

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Chinese Journal of Electronics 2019年第2期28卷 259-265页

作者： NIU Dangdang LIU Lei LYU Shuai XU Yue College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University

We introduce the concept of Complementary formula(COMF), which is a new and non-equivalent way for knowledge compilation(KC). Based on the Hyper extension rule(HER) which is an expansion of Extension rule(ER), we design a compilation algorithm which can formula compile each Conjunctive normal form(CNF)formula to complementary Fully complementary connected diagram(c-FCCD), named as C2C(CNF formula to cFCCD). Theoretically, c-FCCD is a kind of complementary formulae of the input formulae and can support all queries and partial transformations in KC map. Experimentally,C2C is competitive with the EPCCL compilers KCER,C2E, UKCHER, DKCHER and IKCHER.

关键词： knowledge compilation Extension rule Hyper extension rule Compilation language Each pair contains complementary literals(EPCCL) theory

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Multimodal Medical Image Feature Representation and Fusion for AD Early Diagnosis

Multimodal Medical Image Feature Representation and Fusion f...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jing Qu Ying Qu Xueliang Bai Lanxin Xu Han Wang Guixia Liu College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China College of Science North China University of Science and Technology Tangshan China School of Information Science and Technology Institute of Computational Biology Northeast Normal University Changchun China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Alzheimer's disease (AD) is progressive and gets worse with time. Early detection is an effective treatment for the disease, which can timely implement effective interventions. The multi-modal medical image contains more comprehensive and abundant information about the disease, which can reflect the different aspects of AD and reduce the potential bias in single-modal diagnosis. However, an effective and reliable solution is still lacking, which can integrate multimodal neuroimaging effectively for the precision diagnosis and treatment of AD. In this work, we proposed a feature representation and fusion model of MRI and PET scans based on a multiscale convolution network and cross-attention for AD early diagnosis, named camAD. Compared with the existing models, camAD performs better with fewer pre-processing steps. Using the Alzheimer’s disease neuroimaging initiative (ADNI) datasets, we demonstrated that integrating multi-modality data outperforms single-modality models in accuracy, specificity, sensitivity, AUC, and F1 scores. Our models exhibited significantly good accuracy and generality in four classification tasks, which provided a promising way to understand the underlying mechanisms of the disorder's progression.

关键词： Neuroimaging Accuracy Sensitivity Convolution Magnetic resonance imaging Feature extraction Data models Reliability Alzheimer's disease Medical diagnostic imaging

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Building a Private Cloud Based on Microservices for Computer Science laboratory in Universities 7

Building a Private Cloud Based on Microservices for Computer...

引用

7th International Conference on Information Science and Control engineering, ICISCE 2020

作者： Liu, Hao Wang, Zhe Huang, Lan Wang, Kangping Jilin University School of Computer Science and Technology Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin University Changchun China

ISBN: (纸本)9781728164069

Computer science labs in universities have different demands in cloud computing compared with typical IT companies. They differ greatly in software requirements and resource utilization. As an instance, we propose building a private cloud based on microservices with Docker and Kubernetes in our laboratory to match our labs' typical *** private cloud proposed in this paper is composed of multiple components such as authentication, service gateway, distributed storage, knowledge management, code management, etc. Furthermore, some typical research software systems are deployed as microservices in the private cloud. To satisfy complex software environment requirements, a multi version microservice is implemented. In our microservices platform, users can submit their own microservices and manage these microservices' life cycle through a web interface. In addition, the platform will automatically start and stop low frequency services to improve resource utilization rate. We use laboratory resources to conduct experiments and build a platform. With deploying the platform in our laboratory, it shows that the platform meets most of typical demands in research work and improves research efficiency. © 2020 IEEE.

关键词： Laboratories

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Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5731-5748页

作者： Sun, Geng Xiao, Jian Li, Jiahui Wang, Jiacheng Kang, Jiawen Niyato, Dusit Mao, Shiwen the College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China the College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore the School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore the School of Automation Guangdong University of Technology Guangzhou510641 China the Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

—Unmanned aerial vehicles (UAVs) have emerged as the potential aerial base stations (BSs) to improve terrestrial communications. However, the limited onboard energy and antenna power of a UAV restrict its communication range and transmission capability. To address these limitations, this work employs collaborative beamforming through a UAV-enabled virtual antenna array to improve transmission performance from the UAV to terrestrial mobile users, under interference from non-associated BSs and dynamic channel conditions. Specifically, we introduce a memory-based random walk model to more accurately depict the mobility patterns of terrestrial mobile users. Following this, we formulate a multi-objective optimization problem (MOP) focused on maximizing the transmission rate while minimizing the flight energy consumption of the UAV swarm. Given the NP-hard nature of the formulated MOP and the highly dynamic environment, we transform this problem into a multi-objective Markov decision process and propose an improved evolutionary multi-objective reinforcement learning algorithm. Specifically, this algorithm introduces an evolutionary learning approach to obtain the approximate Pareto set for the formulated MOP. Moreover, the algorithm incorporates a long short-term memory network and hyper-sphere-based task selection method to discern the movement patterns of terrestrial mobile users and improve the diversity of the obtained Pareto set. Simulation results demonstrate that the proposed method effectively generates a diverse range of non-dominated policies and outperforms existing methods. Additional simulations demonstrate the scalability and robustness of the proposed CB-based method under different system parameters and various unexpected circumstances. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Markov processes

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Multi-Objective Optimization for UAV Swarm-Assisted IoT with Virtual Antenna Arrays

arXiv

引用

arXiv 2023年

作者： Li, Jiahui Sun, Geng Duan, Lingjie Wu, Qingqing The College of Computer Science and Technology Jilin University Changchun130012 China Pillar of Engineering Systems and Design Singapore University of Technology and Design Singapore487372 Singapore Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China

Unmanned aerial vehicle (UAV) network is a promising technology for assisting Internet-of-Things (IoT), where a UAV can use its limited service coverage to harvest and disseminate data from IoT devices with low transmission abilities. The existing UAV-assisted data harvesting and dissemination schemes largely require UAVs to frequently fly between the IoTs and access points, resulting in extra energy and time costs. To reduce both energy and time costs, a key way is to enhance the transmission performance of IoT and UAVs. In this work, we introduce collaborative beamforming into IoTs and UAVs simultaneously to achieve energy and time-efficient data harvesting and dissemination from multiple IoT clusters to remote base stations (BSs). Except for reducing these costs, another non-ignorable threat lies in the existence of the potential eavesdroppers, whereas the handling of eavesdroppers often increases the energy and time costs, resulting in a conflict with the minimization of the costs. Moreover, the importance of these goals may vary relatively in different applications. Thus, we formulate a multi-objective optimization problem (MOP) to simultaneously minimize the mission completion time, signal strength towards the eavesdropper, and total energy cost of the UAVs. We prove that the formulated MOP is an NP-hard, mixed-variable optimization, and large-scale optimization problem. Thus, we propose a swarm intelligence-based algorithm to find a set of candidate solutions with different trade-offs which can meet various requirements in a low computational complexity. We also show that swarm intelligence methods need to enhance solution initialization, solution update, and algorithm parameter update phases when dealing with mixed-variable optimization and large-scale problems. Simulation results demonstrate the proposed algorithm outperforms state-of-the-art swarm intelligence algorithms and also show that the proposed method can reduce time and energy costs significantly

关键词： Multiobjective optimization

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ESAE: Evolutionary Strategy-Based Architecture Evolution 14th

ESAE: Evolutionary Strategy-Based Architecture Evolution

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14th International Conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2019

作者： Gu, Xue Meng, Ziyao Liang, Yanchun Xu, Dong Huang, Han Han, Xiaosong Wu, Chunguo Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun130012 China Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education School of Computer Zhuhai College of Jilin University Zhuhai519041 China Department of Electrical Engineering and Computer Science Informatics Institute and Christopher S. Bond Life Sciences Center University of Missouri ColumbiaMO65211 United States School of Software Engineering South China University of Technology GuangZhou510006 China

ISBN: (纸本)9789811534249

Although deep neural networks (DNNs) play important roles in many fields, the architecture design of DNNs can be challenging due to the difficulty of input data representation, the huge number of parameters and the complex layer relationships. To overcome the obstacles of architecture design, we developed a new method to generate the optimal structure of DNNs, named Evolutionary Strategy-based Architecture Evolution (ESAE), consisting of a bi-level representation and a probability distribution learning approach. The bi-level representation encodes architectures in the gene and parameter levels. The probability distribution learning approach ensures the efficient convergence of the architecture searching process. By using Fashion-MNIST and CIFAR-10, the effectiveness of the proposed ESAS is verified. The evolved DNNs, starting from a trivial initial architecture with one single convolutional layer, achieved the accuracies of 94.48% and 93.49% on Fashion-MNIST and CIFAR-10, respectively, and require remarkably less hardware costs in terms of GPUs and running time, compared with the existing state-of-the-art manual screwed architectures. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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AutoFE-Pointer: Auto-weighted feature extractor based on pointer network for DNA methylation prediction

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International Journal of Biological Macromolecules 2025年 311卷 143668页

作者： Feng, Xin Xin, Ruihao Wu, Jiezhang Zheng, Jiaxin Wang, Chenyan Yu, Cuinan School of Science Jilin Institute of Chemical Technology Jilin130000 China State Key Laboratory of Inorganic Synthesis and Preparative Chemistry College of Chemistry Jilin University Changchun130012 China College of Information and Control Engineering Jilin Institute of Chemical Technology Jilin130000 China College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Civil Aviation General Hospital Beijing100123 China School of Engineering Westlake University Zhejiang Hangzhou310030 China Hunan Bojii Life Technology Co. Ltd. Hunan Province Changsha410000 China

DNA methylation is a critical epigenetic modification that plays a central role in gene regulation, cellular differentiation, and the development of various diseases, including cancers. Aberrant methylation patterns have emerged as both biomarkers and mechanistic drivers in pathogenesis, underscoring the urgent need for precise and efficient predictive tools. Although some deep learning techniques have advanced methylation prediction, most existing models are trained independently on single-species datasets. This species-specific approach limits efficiency because each model can only handle one dataset at a time, often at the expense of predictive performance. Additionally, the state-of-the-art deep learning models tend to have enormous parameter counts and computational overhead, making them impractical for integration into local offline software applications. To overcome these challenges, we propose AutoFE-Pointer, a lightweight and novel framework that harnesses an improved softened pointer network to dynamically extract and weight features from diverse DNA sequences. AutoFE-Pointer is designed to simultaneously process 17 different benchmark datasets spanning multiple species, achieving superior performance compared to models that are trained individually on single-species data. In doing so, it not only offers state-of-the-art predictive accuracy and robust cross-species generalization but also significantly reduces computational demands, facilitating its deployment in local offline environments. This breakthrough represents a significant advancement in the field of epigenetic modeling and computational biology. © 2025

关键词： Enterprise software

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