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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Jiang, Hanjing Wang, Meineng Zhang, Luping Huang, Yu-An Li, Yangyuan Huang, Yabing Huazhong University of Science and Technology Key Laboratory of Image Information Processing and Intelligent Control Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Hubei Wuhan430074 China China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China Yichun University School of Mathematics and Computer Science Yichun336000 China East China University of Technology School of Information Engineering Nanchang330013 China Northwestern Polytechnical University School of Computer Science Xi'an710129 China Renmin Hospital Wuhan University Department of Pathology Wuhan430060 China

ISBN: (纸本)9798350386226

The advent of single-cell RNA sequencing (scRNA-seq) has facilitated the acquisition of high-resolution data regarding cell heterogeneity across various tissues. A fundamental and critical step in the analysis of scRNA-seq data is cell type identification. An appropriate graph construction strategy can reflect the topological structure between cells and allows for detailed exploration of the relationships between cells and genes. Given the sparsity of scRNA-seq data and the pronounced noise generated by shallow sequencing, choosing an appropriate graph construction strategy has become a significant challenge. In this paper, we propose a single-cell clustering method, named Sc-PNNMF, based on structural perturbation of nonnegative matrix factorization. Sc-PNNMF employs a structural perturbation algorithm to optimize the gene expression matrix. The optimized matrix guides the matrix factorization process, effectively overcoming the impact of data sparsity and significant noise in gene expression data on the graph construction strategy. We compared the clustering abilities of Sc-PNNMF with five other methods on ten real datasets. © 2024 IEEE.

关键词： Non-negative matrix factorization

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An Algorithm Board in Neural Decoding

arXiv

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arXiv 2025年

作者： Feng, Jingyi Yang, Kai School of Computer Science Wuhan University China School of Computer Science and Artificial Intelligence Wuhan Textile University China Hubei Luojia Laboratory Wuhan China

Understanding the mechanisms of neural encoding and decoding has always been a highly interesting research topic in fields such as neuroscience and cognitive intelligence. In prior studies, some researchers identified a symmetry in neural data decoded by unsupervised methods in motor scenarios and constructed a cognitive learning system based on this pattern (i.e., symmetry). Nevertheless, the distribution state of the data flow that significantly influences neural decoding positions still remains a mystery within the system, which further restricts the enhancement of the system’s interpretability. Based on this, this paper mainly explores changes in the distribution state within the system from the machine learning and mathematical statistics perspectives. In the experiment, we assessed the correctness of this symmetry using various tools and indicators commonly utilized in mathematics and statistics. According to the experimental results, the normal distribution (or Gaussian distribution) plays a crucial role in the decoding of prediction positions within the system. Eventually, an algorithm board similar to the Galton board was built to serve as the mathematical foundation of the discovered symmetry. Copyright © 2025, The Authors. All rights reserved.

关键词： Normal distribution

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Identifiable EEG Embeddings by Contrastive Learning from Differential Entropy Features 19th

Identifiable EEG Embeddings by Contrastive Learning from D...

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19th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2023

作者： Zhang, Zhen Liang, Feng Mo, Jiawei Hu, Wenxin Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Shenzhen MSU-BIT University Shenzhen China School of Information Science and Engineering Lanzhou University Lanzhou China School of Computer Science and Engineering Central South University Changsha China

ISBN: (纸本)9783031651250

Encoding EEG data into low-dimension latent embeddings greatly facilitates data analysis and interpretation in neuroscience studies, clinical diagnosis, and human-computer interaction. But generating informative and identifiable latent embeddings that are representative of the origin EEG is not an easy mission. Contrastive learning has the potential to utilize large amounts of unlabelled EEG data and extract informative and identifiable latent embeddings for a wide range of downstream tasks. We explore the feasibility of applying the contrastive learning method to train the EEG latent encoder from the feature of differential entropy of short-time window frequency domain signals. The encoder minimizes the noise-contrastive estimation loss by comparing the embeddings with positive and negative embedding samples, where the distinction of samples is guided by time nearness information or task-specific labels. We test encoders with different output dimensions and the outcome latent embeddings can be identifiable via visualization of a few dimensions. The decoding result also shows that the embeddings preserve information about the original EEG features and can be potentially used for a wide range of downstream tasks. The source code is available at: https://***/liangfengsid/deContrastiveLearning. © ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Contrastive Learning

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Covert Communication in Underlay Multi-Antenna Cognitive Radio Networks

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IEEE Transactions on Cognitive Communications and Networking 2025年第3期11卷 1493-1507页

作者： Fan, Hongbin Yang, Weiwei Shi, Hui Pan, Gaofeng Si, Jiangbo Wang, Yutao Rong, Chuanzhen Army Engineering University of PLA College of Communications Engineering Nanjing210007 China Xiangnan University College of Computer and Artificial Intelligence Chenzhou423000 China Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software Guilin541004 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Xidian University State Key Laboratory of Integrated Services Networks Xi’an710100 China

In this paper, we investigated a multi-antenna covert communication in underlay cognitive radio networks (UCRNs), by considering both cases that Willie has the perfect and statistical channel state information (CSI) of the link from the secondary transmitter (ST) to the primary receiver (PR), respectively. Specifically, the optimal antenna of the secondary receiver (SR) is selected to receive the private information from ST, and then one of the remaining antennas is randomly selected to transmit jamming signals. The closed-form expressions of Willie’s detection error probability, the average minimum detection error probability, the transmission outage probability and the maximum effective covert rate under both cases are derived. The maximum effective covert rate is obtained by optimising the maximum jamming power of SR. The numerical results show that the combined effect of the maximum interference tolerance of PR and the maximum interference power of SR provides the positive effective covert rate between ST and SR. It is further found that the maximum effective covert rate between ST and SR when Willie knows the perfect CSI of the ST-SR link is greater than that when Willie only knows the statistical CSI of the ST-SR link. © 2015 IEEE.

关键词： Jamming

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NEURAL COLLAPSE INSPIRED FEATURE-CLASSIFIER ALIGNMENT FOR FEW-SHOT CLASS INCREMENTAL LEARNING 11

NEURAL COLLAPSE INSPIRED FEATURE-CLASSIFIER ALIGNMENT FOR FE...

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11th International Conference on Learning Representations, ICLR 2023

作者： Yang, Yibo Yuan, Haobo Li, Xiangtai Lin, Zhouchen Torr, Philip Tao, Dacheng JD Explore Academy School of Computer Science Wuhan University China National Key Lab of General AI School of Intelligence Science and Technology Peking University China Institute for Artificial Intelligence Peking University China Peng Cheng Laboratory China University of Oxford United Kingdom

Few-shot class-incremental learning (FSCIL) has been a challenging problem as only a few training samples are accessible for each novel class in the new sessions. Finetuning the backbone or adjusting the classifier prototypes trained in the prior sessions would inevitably cause a misalignment between the feature and classifier of old classes, which explains the well-known catastrophic forgetting problem. In this paper, we deal with this misalignment dilemma in FSCIL inspired by the recently discovered phenomenon named neural collapse, which reveals that the last-layer features of the same class will collapse into a vertex, and the vertices of all classes are aligned with the classifier prototypes, which are formed as a simplex equiangular tight frame (ETF). It corresponds to an optimal geometric structure for classification due to the maximized Fisher Discriminant Ratio. We propose a neural collapse inspired framework for FSCIL. A group of classifier prototypes are pre-assigned as a simplex ETF for the whole label space, including the base session and all the incremental sessions. During training, the classifier prototypes are not learnable, and we adopt a novel loss function that drives the features into their corresponding prototypes. Theoretical analysis shows that our method holds the neural collapse optimality and does not break the feature-classifier alignment in an incremental fashion. Experiments on the miniImageNet, CUB-200, and CIFAR-100 datasets demonstrate that our proposed framework outperforms the state-of-the-art performances. Code address: https://***/NeuralCollapseApplications/FSCIL. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Alignment

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AI Idea Bench 2025: AI Research Idea Generation Benchmark

arXiv

引用

arXiv 2025年

作者： Qiu, Yansheng Zhang, Haoquan Xu, Zhaopan Li, Ming Song, Diping Wang, Zheng Zhang, Kaipeng School of Computer Science Wuhan University China Shanghai Artificial Intelligence Laboratory China Harbin Institute of Technology China

Large-scale Language Models (LLMs) have revolutionized human-AI interaction and achieved significant success in the generation of novel ideas. However, current assessments of idea generation overlook crucial factors such as knowledge leakage in LLMs, the absence of open-ended benchmarks with grounded truth, and the limited scope of feasibility analysis constrained by prompt design. These limitations hinder the potential of uncovering groundbreaking research ideas. In this paper, we present AI Idea Bench 2025, a framework designed to quantitatively evaluate and compare the ideas generated by LLMs within the domain of AI research from diverse perspectives. The framework comprises a comprehensive dataset of 3,495 AI papers and their associated inspired works, along with a robust evaluation methodology. This evaluation system gauges idea quality in two dimensions: alignment with the ground-truth content of the original papers and judgment based on general reference material. AI Idea Bench 2025’s benchmarking system stands to be an invaluable resource for assessing and comparing idea-generation techniques, thereby facilitating the automation of scientific discovery. Copyright © 2025, The Authors. All rights reserved.

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Discounted Iterative Adaptive Critic Designs With Novel Stability Analysis for Tracking Control

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1262-1272页

作者： Mingming Ha Ding Wang Derong Liu School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083China Faculty of Information Technology the Beijing Key Laboratory of Computational Intelligence and Intelligent Systemthe Beijing Laboratory of Smart Environmental Protectionand the Beijing Institute of Artificial IntelligenceBeijing University of TechnologyBeijing 100124China Department of Electrical and Computer Engineering University of Illinois at ChicagoChicago IL 60607 USA IEEE

The core task of tracking control is to make the controlled plant track a desired *** traditional performance index used in previous studies cannot eliminate completely the tracking error as the number of time steps *** this paper,a new cost function is introduced to develop the value-iteration-based adaptive critic framework to solve the tracking control *** the regulator problem,the iterative value function of tracking control problem cannot be regarded as a Lyapunov function.A novel stability analysis method is developed to guarantee that the tracking error converges to *** discounted iterative scheme under the new cost function for the special case of linear systems is ***,the tracking performance of the present scheme is demonstrated by numerical results and compared with those of the traditional approaches.

关键词： Adaptive critic design adaptive dynamic programming(ADP) approximate dynamic programming discrete-time nonlinear systems reinforcement learning stability analysis tracking control value iteration(VI)

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ralphi: A Deep Reinforcement Learning Framework for Haplotype Assembly 29th

ralphi: A Deep Reinforcement Learning Framework for Haplot...

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29th International Conference on Research in Computational Molecular Biology, RECOMB 2025

作者： Battistella, Enzo Maheshwari, Anant Ekim, Barış Berger, Bonnie Popic, Victoria Broad Clinical Labs CambridgeMA United States Broad Institute of MIT and Harvard CambridgeMA United States Computer Science and Artificial Intelligence Laboratory MIT CambridgeMA United States Department of Mathematics MIT CambridgeMA United States

ISBN: (纸本)9783031902512

Haplotype assembly is the problem of reconstructing the combination of alleles on the maternally and paternally inherited chromosome copies. Individual haplotypes are essential to our understanding of how combinations of different variants impact phenotype. In this work, we focus on read-based haplotype assembly of individual diploid genomes, which reconstructs the two haplotypes directly from read alignments at variant loci. We introduce ralphi, a novel deep reinforcement learning framework for haplotype assembly, which integrates the representational power of deep learning with reinforcement learning to accurately partition read fragments into their respective haplotype sets. To set the reward objective for reinforcement learning, our approach uses the classic reduction of the problem to the maximum fragment cut formulation on fragment graphs, where nodes correspond to reads and edge weights capture the conflict or agreement of the reads at shared variant sites. We trained ralphi on a diverse dataset of fragment graph topologies derived from genomes of different populations included in the 1000 Genomes Project. We show that ralphi consistently achieves lower error rates at comparable or longer haplotype block lengths over the state of the art for short and long ONT reads at varying coverage in standard human genome benchmarks. ralphi is available at https://***/PopicLab/ralphi. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep reinforcement learning

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Benchmarking Fish Dataset and Evaluation Metric in Keypoint Detection - Towards Precise Fish Morphological Assessment in Aquaculture Breeding

arXiv

引用

arXiv 2024年

作者： Liu, Weizhen Tan, Jiayu Lan, Guangyu Li, Ao Li, Dongye Zhao, Le Yuan, Xiaohui Dong, Nanqing School of Computer Science and Artificial Intelligence Wuhan University of Technology China Shanghai Artificial Intelligence Laboratory China Yazhouwan National Laboratory China Sanya Boruiyuan Technology Co. Ltd China

Accurate phenotypic analysis in aquaculture breeding necessitates the quantification of subtle morphological phenotypes. Existing datasets suffer from limitations such as small scale, limited species coverage, and inadequate annotation of keypoints for measuring refined and complex morphological phenotypes of fish body parts. To address this gap, we introduce FishPhenoKey, a comprehensive dataset comprising 23,331 high-resolution images spanning six fish species. Notably, FishPhenoKey includes 22 phenotype-oriented annotations, enabling the capture of intricate morphological phenotypes. Motivated by the nuanced evaluation of these subtle morphologies, we also propose a new evaluation metric, Percentage of Measured Phenotype (PMP). It is designed to assess the accuracy of individual keypoint positions and is highly sensitive to the phenotypes measured using the corresponding keypoints. To enhance keypoint detection accuracy, we further propose a novel loss, Anatomically-Calibrated Regularization (ACR), that can be integrated into keypoint detection models, leveraging biological insights to refine keypoint localization. Our contributions set a new benchmark in fish phenotype analysis, addressing the challenges of precise morphological quantification and opening new avenues for research in sustainable aquaculture and genetic studies. Our dataset and code are available at https://***/WeizhenLiuBioinform/Fish-Phenotype-Detect. Copyright © 2024, The Authors. All rights reserved.

关键词： Fish

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Contrastive Learning Consistent and Identifiable Latent Embeddings for EEG 19th

Contrastive Learning Consistent and Identifiable Latent Emb...

引用

19th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2023

作者： Liang, Feng Zhang, Zhen Mo, Jiawei Hu, Wenxin Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Shenzhen MSU-BIT University Shenzhen China School of Information Science and Engineering Lanzhou University Lanzhou China School of Computer Science and Engineering Central South University Changsha China

ISBN: (纸本)9783031651250

Extracting informative EEG data into low-dimension latent embeddings is important for storing and analyzing these neuron signals and applying them to various applications, such as modern human-computer interaction (HCI) techniques. We use the contrastive learning algorithm on time-domain features of EEG in both discovery-driven (self-supervised) and hypothesis (supervised) manners to encode the EEG data into latent embeddings that are proven consistent and identifiable. The self-supervised embeddings have the potential to be used for a range of downstream tasks, while the supervised embeddings have very high decoding accuracy for specific tasks. With embeddings encoded from EEG features collected within every 0.5-s window, the accuracy of recognizing the identities of persons by decoding the self-supervised and supervised embeddings is as high as 96.2% and 99.6%, respectively. Our method and results can promote new HCI techniques, e.g., automatically connecting users to their roles in AR games once they wear EEG-capable devices. The source code is available at: https://***/liangfengsid/timeEegContrastive. © ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Contrastive Learning

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