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Spring pair method of finding saddle points using the minimum energy path as a compass

Physical Review E 2024年第6期110卷 064123-064123页

作者： Gang Cui Kai Jiang Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education School of Mathematics and Computational Science Xiangtan University Xiangtan Hunan 411105 China

Finding index-1 saddle points is crucial for understanding phase transitions. In this work, we propose a simple yet efficient approach, the spring pair method (SPM), to accurately locate saddle points. Without requiring the Hessian information, the SPM evolves a single pair of spring-coupled particles on an energy surface. By designing complementary drifting and climbing dynamics based on gradient decomposition, the spring pair converges onto the minimum energy path (MEP) and spontaneously aligns its orientation with the MEP tangent, providing a reliable ascent direction for efficient convergence to saddle points. The SPM fundamentally differs from traditional surface walking methods that rely on the eigenvectors of the Hessian, which may deviate from the MEP tangent and potentially lead to convergence failure or undesired saddle points. The efficiency of the SPM for finding saddle points is verified by ample examples, including Lennard-Jones clusters, Morse clusters, water clusters, and the Landau energy functional involving quasicrystal phase transitions.

关键词： Lennard-Jones potential

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RBP-Former: Joint Prediction of RNA-protein Binding Sites on Full-length RNA Transcripts for Multiple RBPs

RBP-Former: Joint Prediction of RNA-protein Binding Sites on...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Li, Yichong Liu, Xiaojian Cheng, Fan Pan, Xiaoyong Yang, Yang Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai200240 China

ISBN: (纸本)9798350386226

RNA-binding proteins (RBPs) are essential for gene expression, and the complex RNA-protein interaction mechanisms require analysis of global RNA information. Therefore, accurate prediction of RBP binding sites on full-length RNA transcripts is crucial for understanding these mechanisms and their roles in diseases. While machine learning methods can predict RBP binding to RNA fragments, extending this to full-length transcripts presents challenges due to sequence length and data imbalance. In this paper, we introduce RBP-Former, a binding site joint prediction model designed specifically for full-length RNA transcripts that can be used for multiple RBPs. This model processes information at both coarse and fine-grained levels to fully exploit sequence data and its interactions with multiple RBPs. We develop multi-level imbalance learning strategies, achieving favorable results on imbalanced data. Our method outperforms existing methods in predicting binding sites on full-length RNA transcripts for multiple RBPs, demonstrating its effectiveness in handling imbalanced label and sample distributions. © 2024 IEEE.

关键词： Binding sites

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Amplified emission and lasing enabled by high-Q quasi-bound states in asymmetric dielectric metasurfaces via eigenfield perturbation

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Optics Express 2025年第10期33卷 21551-21565页

作者： Xu, Ke Fei, Yuhang Sun, Yingying Feng, Jian Deng, Xuesong Fang, Ming Huang, Zhixiang The Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Anhui Hefei230039 China The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230039 China

Symmetry-protected bound states in the continuum represent a distinct class of nonradiative modes in continuous media. By introducing asymmetric parameters into a symmetric metasurface, the inherent symmetry can be broken, transforming BICs—originally decoupled from external radiation channels—into high-Q quasi-BICs with controlled radiation capabilities. However, manufacturing constraints pose a significant challenge in fabricating nanostructures with ultra-small asymmetric parameters, thereby limiting the achievable Q-factor. In this work, we propose an asymmetric perforated metasurface structure that supports BICs. When the perforations are shifted along the x or y axis to a specific position, the linewidth vanishes, and simulations confirm the emergence of a BIC in the system, with a quality factor reaching up to 109. Furthermore, we demonstrate that selective eigenmode perturbation can effectively transform BICs into practical quasi-BICs. Notably, for the same structure, perturbing the weak-field region yields higher Q-factors, offering a flexible approach to optimizing performance. This approach enables precise Q-factor control and enhanced radiation properties. Furthermore, we design a laser cavity structure based on these quasi-BICs, achieving amplified emission and lasing with excellent agreement between simulations and theoretical predictions. These findings expand the BIC library and offer new methods for Q-factor manipulation, paving the way for high-performance optoelectronic devices such as nanolasers, biosensors, and nonlinear harmonic generators. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Q factor measurement

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Optimized Multi-kernel Dictionary Learning and Its Application in Complex Industrial Processes Monitoring 41

Optimized Multi-kernel Dictionary Learning and Its Applicati...

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第41届中国控制会议

作者： Kai Zhong Zhengping Ding Donghui Pan Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education Institutes of Physical Science and Information TechnologyAnhui University School of Mathematical Science Anhui University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

The data in the chemical process is often complex in structure,and the coupling and correlation between variables are ***,it is difficult for conventional monitoring models to learn the essential characteristics of process data,which greatly affects the performance of the *** at the above problems,this paper proposes an optimized multi-kernel dictionary learning method,which is suitable for the industrial processes with strong nonlinearity and complex data ***,the synthetic kernel fused by multiple kernel functions is used to map the process data to a new high-dimensional feature space to mine the multivariate distribution characteristics of the ***,the grid search method is used to optimize the model parameters in the kernel function for better model ***,the effectiveness and practicability of the proposed method in fault detection are verified on TE benchmark platform.

关键词： Dictionary learning Multi-kernel function Chemical process monitoring

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Improving Fake News Detection of Influential Domain via Domain- and Instance-Level Transfer 29

Improving Fake News Detection of Influential Domain via Doma...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Nan, Qiong Wang, Danding Zhu, Yongchun Sheng, Qiang Shi, Yuhui Cao, Juan Li, Jintao Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China

Both real and fake news in various domains, such as politics, health, and entertainment are spread via online social media every day, necessitating fake news detection for multiple domains. Among them, fake news in specific domains like politics and health has more serious potential negative impacts on the real world (e.g., the infodemic led by COVID-19 misinformation). Previous studies focus on multi-domain fake news detection, by equally mining and modeling the correlation between domains. However, these multi-domain methods suffer from a seesaw problem: the performance of some domains is often improved at the cost of hurting the performance of other domains, which could lead to an unsatisfying performance in specific domains. To address this issue, we propose a Domain- and Instance-level Transfer Framework for Fake News Detection (DITFEND), which could improve the performance of specific target domains. To transfer coarse-grained domain-level knowledge, we train a general model with data of all domains from the meta-learning perspective. To transfer fine-grained instance-level knowledge and adapt the general model to a target domain, we train a language model on the target domain to evaluate the transferability of each data instance in source domains and re-weigh each instance’s contribution. Offline experiments on two datasets demonstrate the effectiveness of DITFEND. Online experiments show that DITFEND brings additional improvements over the base models in a real-world scenario. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Fake detection

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Stress recognition based on graph structure representation of facial StO2

Stress recognition based on graph structure representation o...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wang, Jiaxiu Liu, Xinyu Jia, Yingjuan Chen, Tong Huang, Jingru Zhang, Zheyuan Southwest University College of Electronic and Information Engineering Chongqing400715 China Southwest University Institute of Legal Psychology and Intelligent Computing Chongqing400715 China Southwest University Chongqing Key Laboratory of Non-Linear Circuits and Intelligent Information Processing Chongqing400715 China

ISBN: (纸本)9798350337488

Stress is an integral state that affects physical and mental health of individuals. Tissue Oxygen Saturation (StO2) is an emerging physiological signal and can reflect different stress states. Previous studies have simply extracted features of StO2 from specific regions of the face without exploring potential associations between them. In this paper, we analyze the facial Region of Interest (ROI) based on StO2, so as to explore the deep relationship between ROIs. Building upon this, we construct, for the first time, a StO2-based facial graph structure for stress recognition by using ROIs as nodes. In addition, we propose a new graph pooling method called FTPool which allows to measure node significance in terms of both node characteristics and graph topology. Finally, we further propose a dual-stream network (GCNet) combining GNN and CNN for the baseline-independent stress recognition. The experimental results show that the proposed GCNet can achieve state-of-the-art (SOTA) results with an accuracy of 78.57% on the original unbalanced database. © 2023 IEEE.

关键词： Graph pooling Graph structure Regions of Interest Stress recognition Tissue oxygen saturation

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Ingredient-Guided RGB-D Fusion Network for Nutritional Assessment

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2025年第1期3卷 156-166页

作者： Feng, Zhihui Xiong, Hao Min, Weiqing Hou, Sujuan Duan, Huichuan Liu, Zhonghua Jiang, Shuqiang Shandong Normal University School of Information Science and Enginering Jinan250358 China Macquarie University Centre for Health Informatics Macquarie ParkNSW2109 Australia Chinese Academy of Sciences Key Laboratory of Intelligent Information Processing Institute of Computing Technology Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Zhejiang Ocean University School of Information Engineering Zhoushan316022 China

The nutritional value of agricultural products is an important indicator for evaluating their quality, which directly affects people's dietary choices and overall well-being. Nutritional assessment studies provide a scientific basis for the production, processing, and marketing of food by analyzing the nutrients they contain. Traditional methods often struggle with suboptimal accuracy and can be time consuming, as well as a shortage of professionals. The progress in artificial intelligence has revolutionized dietary health by offering more accessible methods for food nutritional assessment using vision-based approaches. However, existing vision-based methods using RGB images often face challenges due to varying lighting conditions, impacting the accuracy of nutritional assessment. An alternative is the RGB-D fusion method, which combines RGB images and depth maps. Yet, these methods typically rely on simple fusion techniques that do not ensure precise assessment. Additionally, current vision-based methods struggle to detect small components like oils and sugars on food surfaces, crucial for determining ingredient information and ensuring accurate nutritional assessment. In this pursuit, we propose a novel ingredient-guided RGB-D fusion network that integrates RGB images with depth maps and enables more reliable nutritional assessment guided by ingredient information. Specifically, the multifrequency bimodality fusion module is designed to leverage the correlation between the RGB image and the depth map within the frequency domain. Furthermore, the progressive-fusion module and ingredient-guided module leverage ingredient information to explore the potential correlation between ingredients and nutrients, thereby enhancing the guidance for nutritional assessment learning. We evaluate our approach on a variety of ablation settings on Nutrition5k, where it consistently outperforms state-of-the-art methods. © 2023 IEEE.

关键词： Medicinal chemistry

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A Faster Parameterized Algorithm for Bipartite 1-Sided Vertex Explosion 16th

A Faster Parameterized Algorithm for Bipartite 1-Sided Vert...

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16th Annual International Conference on Combinatorial Optimization and Applications, COCOA 2023

作者： Liu, Yunlong Xiao, Guang Liu, Ao Wu, Di Huang, Jingui College of Information Science and Engineering Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China Xiangtan University Xiangtan411101 China School of Computer Science and Engineering Central South University Changsha410083 China

ISBN: (纸本)9783031496134

Given a bipartite graph G = (T∪ B, E), the problem bipartite 1-sided vertex explosion is to decide whether there exists a planar 2-layer embedding of G after exploding at most k vertices of B. For this problem, which is known to be NP-complete, parameterized algorithms have received increasing attention more recently. In this paper, we focus on the problem parameterized by the number k of allowed exploded vertices of B and develop a faster algorithm for it. More specifically, we show that this parameterized problem admits a kernel of at most 10.5k vertices, and present a fixed-parameter tractable algorithm running in time O(2. 31 k· m), where m is the number of edges of G. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Embeddings

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Personality recognition based on ECG in response to VR emotional stimuli 23

Personality recognition based on ECG in response to VR emoti...

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3rd International Conference on Bioinformatics and intelligent computing, BIC 2023

作者： Lan, Ping Xie, Jialan Hu, Zhaonian Liu, Guangyuan College of Electronic and Information Engineering Southwest University Chongqing400715 China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Southwest University Chongqing400715 China Institute of Affective Computing and Information Processing Southwest University Chongqing400715 China Key Laboratory of Cognition and Personality Ministry of Education Faculty of Psychology Southwest University Chongqing400715 China

ISBN: (纸本)9798400700200

This paper explores the feasibility and effect of using ECG signals in response to virtual reality(VR) emotional stimuli for personality recognition. Personality represents an individual''s behavior pattern, so it has extensive research significance to identify personality. In the field of personality recognition, there have been researches on the use of physiological signals to recognize personality recently, but there have been no studies on personality recognition in VR scenarios. VR technology has been applied in many fields, and in the field of emotion recognition, the advantage of VR video as emotional arousal material has been proved. We designed the emotional stimulation experiment in VR scene to collect ECG signals, and constructed the personality recognition model. The results showed that the F1 scores of neuroticism, extraversion, openness, agreeableness and conscientiousness using only ECG were 0.79, 0.80, 0.81, 0.73 and 0.81, respectively, which were much higher than the random level. And we found that in VR scenes, negative emotional stimulation can achieve better personality recognition effect. Our results confirm the feasibility of personality recognition using ECG signals in response to VR emotional stimuli. © 2023 ACM.

关键词： Electrocardiograms

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ONE2SET + Large Language Model: Best Partners for keyphrase Generation

ONE2SET + Large Language Model: Best Partners for Keyphrase ...

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2024 Conference on Empirical Methods in Natural Language processing, EMNLP 2024

作者： Shao, Liangying Zhang, Liang Peng, Minlong Ma, Guoqi Yue, Hao Sun, Mingming Su, Jinsong School of Informatics Xiamen University China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Xiamen University Ministry of Culture and Tourism China Cognitive Computing Lab Baidu Research China Beijing Institute of Mathematical Sciences and Applications China

ISBN: (纸本)9798891761643

keyphrase generation (KPG) aims to automatically generate a collection of phrases representing the core concepts of a given document. The dominant paradigms in KPG include ONE2SEQ and ONE2SET. Recently, there has been increasing interest in applying large language models (LLMs) to KPG. Our preliminary experiments reveal that it is challenging for a single model to excel in both recall and precision. Further analysis shows that: 1) the ONE2SET paradigm owns the advantage of high recall, but suffers from improper assignments of supervision signals during training;2) LLMs are powerful in keyphrase selection, but existing selection methods often make redundant selections. Given these observations, we introduce a generate-then-select framework decomposing KPG into two steps, where we adopt a ONE2SET-based model as generator to produce candidates and then use an LLM as selector to select keyphrases from these candidates. Particularly, we make two important improvements on our generator and selector: 1) we design an Optimal Transport-based assignment strategy to address the above improper assignments;2) we model the keyphrase selection as a sequence labeling task to alleviate redundant selections. Experimental results on multiple benchmark datasets show that our framework significantly surpasses state-of-the-art models, especially in absent keyphrase prediction. We release our code at https://***/DeepLearnXMU/KPG-SetLLM. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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