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38th Conference on Neural information processing Systems, NeurIPS 2024

作者： Wu, Junxian Ke, Xinyi Jiang, Xiaoming Wu, Huanwen Kong, Youyong Shao, Lizhi School of Internet Anhui University China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University China Department of Pathology State Key Laboratory of Complex Severe and Rare Disease Molecular Pathology Research Center Peking Union Medical College Hospital Chinese Academy of Medical Sciences Peking Union Medical College Beijing China School of Bioinformatics Chongqing University of Post and Telecommunications China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education China

Survival prediction is a significant challenge in cancer management. Tumor microenvironment is a highly sophisticated ecosystem consisting of cancer cells, immune cells, endothelial cells, fibroblasts, nerves and extracellular matrix. The intratumor heterogeneity and the interaction across multiple tissue types profoundly impact the prognosis. However, current methods often neglect the fact that the contribution to prognosis differs with tissue types. In this paper, we propose ProtoSurv, a novel heterogeneous graph model for WSI survival prediction. The learning process of ProtoSurv is not only driven by data but also incorporates pathological domain knowledge, including the awareness of tissue heterogeneity, the emphasis on prior knowledge of prognostic-related tissues, and the depiction of spatial interaction across multiple tissues. We validate ProtoSurv across five different cancer types from TCGA (i.e., BRCA, LGG, LUAD, COAD and PAAD), and demonstrate the superiority of our method over the state-of-the-art methods. Source code is available at https://***/wjx-error/ProtoSurv. © 2024 Neural information processing systems foundation. All rights reserved.

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FaceGCN: Structured Priors Inspired Graph Convolutional Networks for Blind Face Restoration

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Yan, Weidan Shao, Wenze Zhang, Dengyin Xiao, Liang Nanjing University of Posts and Telecommunications School of Internet of Things Nanjing210003 China Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Intelligent Information Processing and Communication Technology Nanjing210003 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

Facial image restoration has gained a tremendous progress since the increasing boom of the deep learning methods. Owing to its nature of strong ill-posedness, different categories of a-priori constraints have been harnessed or embedded in the existing deep architectures. While, as it turns to blind face restoration with more complicated degradations, the challenge becomes greater. In this paper, a further insightful step is taken by exploring the potentials of the graph convolutional networks (GCN) in conjunction with the structured priors for the blind problem. Specifically, a lightweight yet physically more intuitive model termed FaceGCN is proposed. On the one hand, a dynamic generator of facial adjacency matrices is constructed assisted by two self-supervised losses, allowing a sparse, accurate, and adaptive construction of case-specific face graphs with facial feature components as nodes. On the other hand, to model well the joint local-nonlocal correlations among various facial feature components, a kind of novel strip-attention GCN modules is correspondingly developed by splitting facial feature maps into intra- and inter-strips in both horizontal and vertical orientations, respectively. Extensive experimental results show that FaceGCN has achieved comparable or even superior performance to state-of-the-art methods, yet at a considerably less computational cost. © 1991-2012 IEEE.

关键词： Convolution

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Neural Network-Based Dimensionality Reduction for Large-Scale Binary Optimization With Millions of Variables

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Tian, Ye Wang, Luchen Yang, Shangshang Ding, Jinliang Jin, Yaochu Zhang, Xingyi School of Computer Science and Technology Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei China Institutes of Physical Science and Information Technology Anhui University Hefei China School of Artificial Intelligence Anhui University Hefei China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China School of Engineering Westlake University Hangzhou China

Binary optimization assumes a pervasive significance in the context of practical applications, such as knapsack problems, maximum cut problems, and critical node detection problems. Existing techniques including mathematical programming, heuristics, evolutionary computation, and neural networks have been employed to tackle binary optimization problems (BOPs), however, they grapple with the challenge of optimizing a large number of binary variables. In this paper, we propose a dimensionality reduction method to assist evolutionary algorithms in solving large-scale BOPs, which is achieved based on neural networks. The proposed method converts the optimization of a large number of binary variables into the optimization of a small number of network weights, resulting in a significant reduction in search space dimensionality. Crucially, the proposed method obviates the necessity for a training process, which eliminates the requirement for a priori knowledge and enhances the search efficiency. On six types of single-and multi-objective BOPs with up to 10 000 000 variables, the proposed method demonstrates superiority over top-tier evolutionary algorithms and neural network-based methods. IEEE

关键词： Neural networks

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Prototype-based classifier learning for long-tailed visual recognition

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Science China(information Sciences) 2022年第6期65卷 62-76页

作者： Xiu-Shen WEI Shu-Lin XU Hao CHEN Liang XIAO Yuxin PENG School of Computer Science and Engineering Nanjing University of Science and Technology State Key Laboratory of Integrated Services Networks Xidian University Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security Wangxuan Institute of Computer Technology Peking University

In this paper, we tackle the long-tailed visual recognition problem from the categorical prototype perspective by proposing a prototype-based classifier learning(PCL) method. Specifically, thanks to the generalization ability and robustness, categorical prototypes reveal their advantages of representing the category semantics. Coupled with their class-balance characteristic, categorical prototypes also show potential for handling data imbalance. In our PCL, we propose to generate the categorical classifiers based on the prototypes by performing a learnable mapping function. To further alleviate the impact of imbalance on classifier generation, two kinds of classifier calibration approaches are designed from both prototype-level and example-level aspects. Extensive experiments on five benchmark datasets, including the large-scale iNaturalist, Places-LT,and ImageNet-LT, justify that the proposed PCL can outperform state-of-the-arts. Furthermore, validation experiments can demonstrate the effectiveness of tailored designs in PCL for long-tailed problems.

关键词： long-tailed distribution categorical prototype classifier generation classifier calibration class imbalance

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DeepIA: An Interpretability Analysis based Test Data Generation Method for DNN

DeepIA: An Interpretability Analysis based Test Data Generat...

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IEEE International Conference on Software Quality, Reliability and Security (QRS)

作者： Qifan He Ruilin Xie Li Li Zhanqi Cui School of Computer Science Beijing Information Science and Technology University Beijing China Key Laboratory of Safety-Critical Software (Nanjing University of Aeronautics and Astronautics) Ministry of Industry and Information Nanjing Jiangsu China

Recently, deep neural networks (DNN) have been widely applied in various fields, such as image classification, even replace humans to make decisions in some specific tasks. However, like traditional software, DNNs inevitably contain defects. If defective DNN models are applied in safety-critical fields, such as autonomous driving and medical diagnosis, it may cause disastrous consequences. Therefore, effective testing methods are urgently needed to improve the reliability of DNNs. The existing DNN testing methods typically generate test data by either globally modifying the original data or taking adversarial approaches. The generated test data typically struggle to simultaneously achieve good performance in both the degree of difference from the original data and the Error-inducing Success Rate (ESR) with respect to the target DNN model. Moreover, the perturbation-based methods are difficult to be understood by humans. To address the above issue, this paper proposes DeepIA, an interpretability analysis based test data generation method for DNN. DeepIA analyzes the interpretability of decision-making behaviors for DNN. According to the interpretability analysis results, the original training data is split into different regions to evaluate their influences on decision-making results of the DNN. After that, the most significant regions of the original test data are transformed to generate new test data. Experimental results show that the interpretability method effectively enhances the misleading ability of DeepIA for the DNN model under test. Compared with DeepTest and DeepSearch, DeepIA can generate test data with minor permutations and greater ESR.

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Multimodal Spatiotemporal Drug Assessment Platform Based on a Microelectrode Neural Network Chip in Alzheimer's Disease

SSRN

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SSRN 2024年

作者： Gao, Fan Xu, Wei Xiao, Yan Jiang, Wei Chen, Mu Liu, Zekai Zhang, Peng Yang, Tianyi Qian, Zhiyu Gao, Keqiang Department of Biomedical Engineering Key Laboratory of Multi-modal Brain-Computer Precision Drive Ministry of Industry and Information Technology Key Laboratory of Digital Medical Equipment and Technology of Jiangsu Province Nanjing University of Aeronautics and Astronautics Nanjing210016 China School of Pharmacy Nanjing Medical University Nanjing211166 China

The pathogenesis of Alzheimer's disease (AD) remains elusive. The development of new drugs is protracted, costly, and fraught with failure. There is a dearth of in vitro high-throughput assay platforms capable of observing the therapeutic effects of AD drugs at the level of cellular and network. Consequently, an in vitro multimodal spatiotemporal drug assessment platform has devised based on a neuronal network chip. It has a bi-directional distribution of electrode points of different sizes, which are more suitable for the detection of neuronal connections. The multi-level neurophysiological properties, including single neuron firing pattern, intercellular connection, transmission speed, and neural network communication, were detected in real time. The results demonstrated that the differences between experimental groups in electrophysiological properties could significantly distinguish the effects of AD representative drug treatment after different levels of neurological injury. The administration of the drug at the initial stage of β-amyloid oligomers toxicity was found to effectively impede the deterioration of neurons and network. Furthermore, the neurophysiological mechanism of drug interaction was elucidated, which may provide the guidance for the optimal timing and dosage of drug administration. It is anticipated that this will become a novel platform for the preclinical evaluation and screening of AD drugs. © 2024, The Authors. All rights reserved.

关键词： Neural networks

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Experience report:investigating bug fixes in machine learning frameworks/libraries

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Frontiers of computer Science 2021年第6期15卷 43-58页

作者： Xiaobing SUN Tianchi ZHOU Rongcun WANG Yucong DUAN Lili BO Jianming CHANG School of Information Engineering Yangzhou UniversityYangzhou 225100China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China Jiangsu Engineering Research Center of Knowledge Management and Intelligent Service Yangzhou University Yangzhou 225127China School of Computer Science and Technology China University of Mining and TechnologyXuzhou 221116China School of Computer Science and Cyberspace Security Hainan UniversityHaikou 570228China

Machine learning(ML)techniques and algorithms have been successfully and widely used in various areas including software engineering *** other software projects,bugs are also common in ML projects and *** order to more deeply understand the features related to bug fixing in ML projects,we conduct an empirical study with 939 bugs from five ML projects by manually examining the bug categories,fixing patterns,fixing scale,fixing duration,and types of *** results show that(1)there are commonly seven types of bugs in ML programs;(2)twelve fixing patterns are typically used to fix the bugs in ML programs;(3)68.80%of the patches belong to micro-scale-fix and small-scale-fix;(4)66.77%of the bugs in ML programs can be fixed within one month;(5)45.90%of the bug fixes belong to corrective activity from the perspective of software ***,we perform a questionnaire survey and send them to developers or users of ML projects to validate the results in our empirical *** results of our empirical study are basically consistent with the feedback from *** findings from the empirical study provide useful guidance and insights for developers and users to effectively detect and fix bugs in MLprojects.

关键词： bug fixing machine learning project empirical study questionnaire survey

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Fuzzy Safety and Liveness Properties in Linear-time

Fuzzy Safety and Liveness Properties in Linear-time

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IEEE International Conference on Software Quality, Reliability and Security (QRS)

作者： Fan Shi Zhiqiu Huang Haiyu Pan Yuting Chang Heng Xu College of Computer Science and Technology/College of Software Nanjing University of Aeronautics and Astronautics Nanjing Jiangsu China Key Laboratory for Safety-critical Software Development and Verification Ministry of Industry and Information Technology Nanjing Jiangsu China School of Computer and Information Security/School of Software Guilin University of Electronic Technology Guilin Guangxi China Nokis Solutions and Networks System Technology (Beijing) Co. Ltd. Hangzhou Zhejiang China

ISBN: (数字)9798350365634

ISBN: (纸本)9798350365641

Safety and liveness are fundamental to many system verification paradigms. In contrast to existing approaches for extending safety and liveness properties of fuzzy systems, we first utilize ultrametric to measure the similarity of valuations, traces and properties in a fuzzy Kripke structure (FKS). A distance threshold $\alpha$ is introduced, which is used to define two quantitative extensions of safety and liveness properties, called $\alpha$-safety and $\alpha$-liveness properties. In addition, we provide characterizations of $\alpha$-safety and $\alpha$-liveness properties in terms of Büchi automata. These results will provide the foundation for the verification of fuzzy systems.

关键词： Uncertainty System verification Software algorithms Automata Software quality Safety Software reliability

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Discrete-time Formation Control With Attitude Alignment: A Geometric Approach under Ergodic Products

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IEEE Transactions on Control of Network Systems 2025年

作者： Li, Zhen Tang, Yang Zhang, Wenbing Fan, Yongqing Huang, Tingwen Xi-an University of Posts and Telecommunications School of Automation Xian710121 China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Shanghai200237 China Yangzhou University Department of Mathematics Jiangsu225002 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

Assuming agents possess sensing capabilities and dynamics relative to their body coordinate frames, this paper addresses a displacement-based strategy for discrete-time formation control through attitude synchronization. Unlike the Eulerian approximation, the first challenge lies in developing discrete-time models that capture both position and attitude motions, thereby complementing existing continuous-time systems with a discretized version. Then, two leader-following control protocols are designed, incorporating a jointly-connected topology to relax the connectivity requirements. The analysis of these systems often relies on the Lyapunov function, the existence of which may be hard to guarantee. Consequently, geometric properties of several polytopes are explored under the dynamic topologies and time-varying parameters. By extending the ergodicity results to leader-following scenarios, these properties are characterized as positively invariant sets. This invariance, which is independent of the existence of the Lyapunov function or the solvability of its associated criteria, provides a beneficial tool for analyzing time-variant systems. Utilizing this invariance and some mathematical techniques, the desired formation shape can be achieved in the cases where the attitudes of the followers synchronize with the leader. Finally, a numerical simulation validates the results. © 2014 IEEE.

关键词： Lyapunov functions

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MDDMamba: A Model for Multi-Modal Depression Detection with a Memory-Saving Cross-Modal Attention Mechanism Based on Mamba

MDDMamba: A Model for Multi-Modal Depression Detection with ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Mingzhou Liu Zheyu Wang Feng Liu School of Education Science and Technology Nanjing University of Posts and Telecommunications Nanjing China School of Computer and Artificial Intelligence Nanjing University of Science and Technology ZiJin College Nanjing China Jiangsu Key Laboratory of Intelligent Information Processing and Communication Technology Nanjing China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Depression is a severe mental health disorder that constitutes a significant global health challenge, necessitating efficient and accurate automated diagnostic tools. Traditional approaches to automated depression assessment using multi-modal data often face three significant difficulties: small sample sizes in available datasets, high computational cost, and difficulties in effectively integrating different modalities. In this paper, we first mitigate the problem of dataset quality by utilizing an estimated imputation strategy for missing values based on a known data augmentation method. Additionally, we introduce a multi-modal depression detection model named MDDMamba, designed to address the latter two challenges by integrating features from pre-trained emotion models with the memory-efficient selective state space model named Mamba. The MDDMamba model combines features from pre-trained emotion models for both text and audio, enhancing the extraction of emotional information. The core of the MDDMamba model is the CrossMamba module, which replaces the traditional Transformer’s quadratic computational complexity with a linear complexity alternative, facilitating efficient multi-modal fusion. Experimental results on CMDC and EATD-Corpus datasets show that the MDDMamba model achieves better classification and regression performance and stronger generalization capabilities in depression detection. Notably, the MDDMamba model achieves an F1 score of 0.94 and a Pearson correlation coefficient of 0.86 on the CMDC dataset while maintaining lower memory cost, outperforming most advanced multi-modal depression detection methods across various experimental scenarios.

关键词： Emotion recognition Computational modeling Biological system modeling Depression Brain modeling Feature extraction Data models Imputation Data mining Computational complexity

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