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

Journal of Advanced Research 2025年

作者： Li, Kewei Wu, Yuqian Li, Yinheng Guo, Yutong Kong, Yanwen 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 Changchun 130012 China School of Software Jilin University Jilin Changchun 130012 China School of Life Sciences Jilin University Jilin Changchun 130012 China Department of Computer Science Columbia University 116th and Broadway New York City 10027 NY United States School of Artificial Intelligence Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun 130012 China School of Biology and Engineering Guizhou Medical University Guizhou Guiyang 550025 China School of Computer Engineering Changchun University of Engineering Jilin Changchun 130103 China Changchun Wenli High School Jilin Changchun 130062 China

Introduction: Activity cliff (AC) is a phenomenon that a pair of similar molecules differ by a small structural alternation but exhibit a large difference in their biochemical activities. This phenomenon affects various tasks ranging from virtual screening to lead optimization in drug development. The AC of small molecules has been extensively investigated but limited knowledge is accumulated about the AC phenomenon in pharmaceutical peptides with canonical amino acids. Objectives: This study introduces a quantitative definition and benchmarking framework AMPCliff for the AC phenomenon in antimicrobial peptides (AMPs) composed by canonical amino acids. Methods: 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. Results: A comprehensive analysis of the existing AMP dataset reveals a significant prevalence of AC within AMPs. AMPCliff quantifies the activities of AMPs by the metric minimum inhibitory concentration (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. 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 −log(MIC) values on the benchmark dataset. Conclusion: Our findings highlight limitations in current deep learning-based representation models. To more accurately capture the properties of antimicrobial peptides (AMPs), it is ess

关键词： Activity cliff (AC) AMPCliff Antimicrobial peptide (AMP) Benchmark Minimum inhibitory concentration (MIC)

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Recovering Accurate Labeling Information from Partially Valid Data for Effective Multi-Label Learning

arXiv

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arXiv 2020年

作者： Li, Ximing Wang, Yang College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education Jilin University China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology China School of Computer Sci & Information Engineering Hefei University of Technology China

Partial Multi-label Learning (PML) aims to induce the multi-label predictor from datasets with noisy supervision, where each training instance is associated with several candidate labels but only partially valid. To address the noisy issue, the existing PML methods basically recover the ground-truth labels by leveraging the ground-truth confidence of the candidate label, i.e., the likelihood of a candidate label being a ground-truth one. However, they neglect the information from non-candidate labels, which potentially contributes to the ground-truth label recovery. In this paper, we propose to recover the ground-truth labels, i.e., estimating the ground-truth confidences, from the label enrichment, composed of the relevance degrees of candidate labels and irrelevance degrees of non-candidate labels. Upon this observation, we further develop a novel two-stage PML method, namely Partial MultiLabel Learning with Label Enrichment-Recovery (PML3ER), where in the first stage, it estimates the label enrichment with unconstrained label propagation, then jointly learns the ground-truth confidence and multi-label predictor given the label enrichment. Experimental results validate that PML3ER outperforms the state-of-the-art PML methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Recovery

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Joint optimization QoS and security of wireless communication networks 7

Joint optimization QoS and security of wireless communicatio...

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7th International Conference on Information Technology: IoT and Smart City, ICIT 2019

作者： Su, Xiaochun Liu, Yanheng Wan, Jian College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation Jilin University Changchun130012 China Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China

ISBN: (纸本)9781450376631

Currently, security and quality of service (QoS) are critical for any network. The main purpose of security mechanisms is to provide identity authentication, protect information, and ensure that the received information is not tampered with;the aim of QoS is to provide better network communication services. Security and QoS are not independent of each other. The security mechanism affects QoS and vice versa. Both QoS and security pose resource management problems and are conflicting when resources are limited. QoS requires security mechanisms to ensure proper allocation and service cost, and an inappropriate security mechanism will reduce the performance of the communication quality. Currently, numerous real-time, big data, and diverse applications and services have emerged. Moreover, the number of potential threats is expected to increase dramatically. Therefore, combining QoS and security is important. However, as they are difficult to model owing to their internal conflict, QoS and security are normally separated or optimized at a particular point using game theory. From the perspective of resource allocation, this study uses optimization method and considers security and QoS resource optimization by dividing resources into two categories: space (bandwidth) and time (delay). In order to harmonize the QoS and security, in this paper, the two conflicting terms "communication" and "security" are considered as two individual aspect. Furthermore, we construct a model wherein the joint utility is maximized by controlling the packet length. We formulate the utility functions of these two aspects with respect to the effective throughput. The results show that the presented optimization method enables a mobile node to adaptively select specific packet length based on the optimum curve. With the obtained packet length values, the joint utility reach to the maximum so that satisfactory communication quality can be realized without compromising the security, thereby improvin

关键词： Resource allocation

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Copy-Move Forgery Detection Based on Interest Point and Local Search Algorithm

Copy-Move Forgery Detection Based on Interest Point and Loca...

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IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS)

作者： Fang Mei Jing Lyu Yingda Lyu Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

ISBN: (数字)9781728138633

ISBN: (纸本)9781728138640

Copy-move forgery detection mainly includes block-based and interest point-based methods, but both of them have shortcomings, such as weak resistance to affine transformations, weak ability to locate the forged region. To solve these problems, a novel algorithm is proposed in this paper, which involves two-stage search algorithm. The first stage takes image blocks as the search unit, and in the second stage, the forger region is searched at the pixel level. Experimental results demonstrate that the proposed method performants better compared to recent state-of-the-art methods.

关键词： Rail to rail outputs

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An Unmanned Aerial Vehicle Pose Estimation System Guided by Desired Shot

An Unmanned Aerial Vehicle Pose Estimation System Guided by ...

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IEEE International Conference on Automation, Electronics and Electrical engineering (AUTEEE)

作者： Xinglei Dou Qijian Ding Qiao Du Min Yang Yuzhou Huo Xin Wang College of Software Engineering Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineer of Ministry of Education Jilin University Changchun China

ISBN: (数字)9781728150307

ISBN: (纸本)9781728150314

Unmanned aerial vehicles (UAVs) are widely used in aerial photography nowadays for their strong maneuverability, good image quality and high cost performance, while they have limited battery capacity and difficulty in operation. It is difficult to reach the ideal pose and get the desired shot by manual manipulation in a short time. To solve these problems, an UAV pose estimation system guided by desired shot is constructed. Either a sequence of images or a video is taken as input. The user interacts with the system to express the desired shot. The 3D-reconstruction is done by Structure from Motion (SfM). Then the Perspective-n-Point (PnP) problem is solved to estimate the ideal pose of the UAV. The experimental results proves that the system is effective and valid.

关键词： Three-dimensional displays Feature extraction Unmanned aerial vehicles Cameras Pose estimation Software engineering Data mining

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Attention-based multi-level feature fusion for named entity recognition 29

Attention-based multi-level feature fusion for named entity ...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Yang, Zhiwei Chen, Hechang Zhang, Jiawei Ma, Jing Chang, Yi College of Computer Science and Technology Jilin University Changchun China School of Artificial Intelligence Jilin University Changchun China IFM Lab Department of Computer Science Florida State University TallahasseeFL United States Department of Computer Science Hong Kong Baptist University Hong Kong Hong Kong Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education China International Center of Future Science Jilin University Changchun China

ISBN: (纸本)9780999241165

Named entity recognition (NER) is a fundamental task in the natural language processing (NLP) area. Recently, representation learning methods (e.g., character embedding and word embedding) have achieved promising recognition results. However, existing models only consider partial features derived from words or characters while failing to integrate semantic and syntactic information (e.g., capitalization, inter-word relations, keywords, lexical phrases, etc.) from multi-level perspectives. Intuitively, multi-level features can be helpful when recognizing named entities from complex sentences. In this study, we propose a novel framework called attention-based multi-level feature fusion (AMFF), which is used to capture the multi-level features from different perspectives to improve NER. Our model consists of four components to respectively capture the local character-level, global character-level, local word-level, and global word-level features, which are then fed into a BiLSTM-CRF network for the final sequence labeling. Extensive experimental results on four benchmark datasets show that our proposed model outperforms a set of state-of-the-art baselines. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Embeddings

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Joint Resource Management for Energy-efficient UAV-Assisted SWIPT-MEC: A Deep Reinforcement Learning Approach

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IEEE Internet of Things Journal 2025年

作者： Chen, Yue Kang, Hui Li, Jiahui Sun, Geng Wang, Boxiong Wang, Jiacheng Liang, Cong Liang, Shuang Niyato, Dusit Jilin University College of Computer Science and Technology Changchun130012 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Nanyang Technological University College of Computing and Data Science 639798 Singapore China Mobile Communications Group Jilin Co. Ltd China Northeast Normal University School of Information Science and Technology Changchun130117 China

The integration of simultaneous wireless information and power transfer (SWIPT) technology in 6G Internet of Things (IoT) networks faces significant challenges in remote areas and disaster scenarios where ground infrastructure is unavailable. This paper proposes a novel unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system enhanced by directional antennas to provide both computational resources and energy support for ground IoT terminals. However, such systems require multiple trade-off policies to balance UAV energy consumption, terminal battery levels, and computational resource allocation under various constraints, including limited UAV battery capacity, non-linear energy harvesting characteristics, and dynamic task arrivals. To address these challenges comprehensively, we formulate a bi-objective optimization problem that simultaneously considers system energy efficiency and terminal battery sustainability. We then reformulate this non-convex problem with a hybrid solution space as a Markov decision process (MDP) and propose an improved soft actor-critic (SAC) algorithm with an action simplification mechanism to enhance its convergence and generalization capabilities. Simulation results have demonstrated that our proposed approach outperforms various baselines in different scenarios, achieving efficient energy management while maintaining high computational performance. Furthermore, our method shows strong generalization ability across different scenarios, particularly in complex environments, validating the effectiveness of our designed boundary penalty and charging reward mechanisms. © 2014 IEEE.

关键词： Resource allocation

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Hypergraph-driven soft semantics flexible learning for visible–infrared person re-identification

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engineering Applications of Artificial Intelligence 2025年 158卷

作者： Zhu, Jiacheng Ge, Hongwei Liu, Yuxuan Wu, Chunguo Fan, Jiulin School of Computer Science and Technology Dalian University of Technology No. 2 Linggong Road Dalian Liaoning 116024 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University No. 2699 Qianjin Street Changchun Jilin 130012 China Huadian Coal Industry Group Co Ltd. No. 2 Xuanwumennei Street Beijing 100031 China

Visible–infrared person re-identification (VI-ReID) aims to match the images of the person across different modalities. The main challenge in the engineering application of VI-ReID lies in the considerable modality gap between visible and infrared images. Existing methods primarily focus on learning low-order hard semantics to reduce the modality gap, such as specific body parts (e.g., arms, legs, and hands), which are modality-specific and sensitive to cross-modality variations, leading to difficult modality alignment. However, high-order soft semantics contain more modality-invariant features that can be flexibly learned by the model to better align the cross-modality features and reduce the modality gap. To better learn soft semantics, we propose a novel, hypergraph-driven soft semantics flexible learning network (HSFLNet), which extracts hierarchical semantics and flexibly explores the relationships among the extracted soft semantics by using hypergraph neural networks (HGNNs). Specifically, first, we propose a soft semantics mining (SSM) module to capture and flexibly fuse hierarchical features, which integrates low- and high-level semantics across channel and spatial dimensions to align modalities. Second, a hypergraph-driven soft semantics flexible learning (HSFL) module is designed, which employs HGNNs to explore the relationships among soft semantics. These semantics are adaptively learned through gating mechanisms to capture modality-invariant features, thereby reducing the modality gap. We evaluate the performance of HSFLNet on SYSU-MM01, RegDB, and low-light cross-modality (LLCM) datasets, achieving a Rank-1/mean average precision of 75.09%/72.11%, 95.82%/92.42%, and 65.42%/68.55%, respectively. Experimental results demonstrate that HSFLNet outperforms state-of-the-art VI-ReID methods. Our code is available at https://***/Jiacheng813/HSFLNet. © 2025 Elsevier Ltd

关键词： Cross-modality retrieval Hypergraph neural networks Soft semantics Visible–infrared person re-identification

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UAV Swarm-enabled Collaborative Secure Relay Communications with Time-domain Colluding Eavesdropper

arXiv

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arXiv 2023年

作者： Zhang, Chuang Sun, Geng Wu, Qingqing Li, Jiahui Liang, Shuang Niyato, Dusit Leung, Victor C.M. The College of Computer Science and Technology Jilin University Changchun130012 China The Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China Pillar of Engineering Systems and Design Singapore University of Technology and Design 487372 Singapore The School of Information Science and Technology Northeast Normal University Changchun130024 China The School of Computer Science and Engineering Nanyang Technological University 639798 Singapore The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

Unmanned aerial vehicles (UAVs) as aerial relays are practically appealing for assisting Internet of Things (IoT) network. In this work, we aim to utilize the UAV swarm to assist the secure communication between the micro base station (MBS) equipped with the planar array antenna (PAA) and the IoT terminal devices by collaborative beamforming (CB), so as to counteract the effects of collusive eavesdropping attacks in time-domain. Specifically, we formulate a UAV swarm-enabled secure relay multi-objective optimization problem (US2RMOP) for simultaneously maximizing the achievable sum rate of associated IoT terminal devices, minimizing the achievable sum rate of the eavesdropper and minimizing the energy consumption of UAV swarm, by jointly optimizing the excitation current weights of both MBS and UAV swarm, the selection of the UAV receiver, the position of UAVs and user association order of IoT terminal devices. Furthermore, the formulated US2RMOP is proved to be a non-convex, NP-hard and large-scale optimization problem. Therefore, we propose an improved multi-objective grasshopper algorithm (IMOGOA) with some specific designs to address the problem. Simulation results exhibit the effectiveness of the proposed UAV swarm-enabled collaborative secure relay strategy and demonstrate the superiority of IMOGOA. Copyright © 2023, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Joint Task Offloading and Resource Allocation in Aerial-Terrestrial UAV Networks with Edge and Fog Computing for Post-Disaster Rescue

arXiv

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arXiv 2023年

作者： Sun, Geng He, Long Sun, Zemin Wu, Qingqing Liang, Shuang Li, Jiahui Niyato, Dusit Leung, Victor C.M. The College of Computer Science and Technology Jilin University Changchun130012 China The 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 Shanghai China The School of Information Science and Technology Northeast Normal University Changchun130024 China Pillar of Engineering Systems and Design Singapore University of Technology and Design 487372 Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

Unmanned aerial vehicles (UAVs) play an increasingly important role in assisting fast-response post-disaster rescue due to their fast deployment, flexible mobility, and low cost. However, UAVs face the challenges of limited battery capacity and computing resources, which could shorten the expected flight endurance of UAVs and increase the rescue response delay during performing mission-critical tasks. To address this challenge, we first present a three-layer post-disaster rescue computing architecture by leveraging the aerial-terrestrial edge capabilities of mobile edge computing (MEC) and vehicle fog computing (VFC), which consists of a vehicle fog layer, a UAV client layer, and a UAV edge layer. Moreover, we formulate a joint task offloading and resource allocation optimization problem (JTRAOP) with the aim of maximizing the time-average system utility. Since the formulated JTRAOP is proved to be NP-hard, we propose an MEC-VFC-aided task offloading and resource allocation (MVTORA) approach, which consists of a game theoretic algorithm for task offloading decision, a convex optimization-based algorithm for MEC resource allocation, and an evolutionary computation-based hybrid algorithm for VFC resource allocation. Simulation results validate that the proposed approach can achieve superior system performance compared to the other benchmark schemes, especially under heavy system workloads. Copyright © 2023, The Authors. All rights reserved.

关键词： Fog computing

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