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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Yang, Guangzhengao Luo, Xingyu Zhao, Jie Fan, Fangfang Li, Haoshen Dong, Bin Zhang, Li Zhang, Yan Center for Data Science Peking University China Peking University First Hospital Department of Cardiology China National Engineering Laboratory for Big Data Analysis and Applications Peking University China Peking University Changsha Institute for Computing and Digital Economy China Beijing International Center for Mathematical Research Peking University China Center for Machine Learning Research Peking University China National Biomedical Imaging Center Peking University China

ISBN: (纸本)9798331520526

Hypertension and diabetes are known to potentially cause morphological changes in the retinal capillary system, yet quantifying these changes presents significant challenges. This research addresses this issue by designing and developing an automated system capable of analyzing retinal images to detect and measure these changes. Our system automatically segments vessels and the optic disc, calculates tortuosity and bifurcation angles, and performs disease prediction based on these measurements along with clinical features. For tortuosity calculation, we adopted a novel curve fitting method, which provides more stable convergence compared to traditional discrete numerical methods. The system integrates clinical data and conducts prediction experiments on hypertensive and diabetic patients with varying sample sizes. Our results demonstrate the system's strong performance and scalability: in the hypertension group, the AUC reached a maximum of 0.814 ± 0.023, with an ACC of 0.735 ± 0.033, while in the diabetes group, the AUC reached a maximum of 0.710 ± 0.043, with an ACC of 0.641 ± 0.052. These findings suggest that our automated system could serve as a valuable tool for early detection and risk assessment of hypertension and diabetes, potentially improving patient outcomes through timely intervention. © 2025 IEEE.

关键词： Bifurcation (mathematics)

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Stress Detection at Workplace by Multimodal Analysis

Stress Detection at Workplace by Multimodal Analysis

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International Conference on Information Networking

作者： Snigdha Mondal Arush Tripathi Debasis Das School of Artificial Intelligence and Data Sciences Indian Institute of Technology (IIT) Jodhpur India Department of Computer Science and Engineering Indian Institute of Technology (IIT) Jodhpur India

ISBN: (数字)9798350330946

ISBN: (纸本)9798350330953

Stress is a significant societal issue, as it is the cause of many health problems and huge economic losses for companies. Detecting stress in computer users is technically challenging and, however, of the utmost importance in the workplace, especially now that remote working scenarios are becoming omnipresent. Owing to heightened competitiveness within the sector, companies are increasingly seeking enhanced efficiency and extended work hours from their personnel. However, with the existing deadline stress and all, they also face a work-life balance problem. To prevent stress from becoming chronic and provoking irreversible damage, it is necessary to detect it in its early stages. In this paper, we are proposing a multimodal system to detect the seven emotions of employees at the workplace. This study presents a novel COMBINED-STRESS model for detecting and analyzing stress. In our model, we try to imbibe this approach by fusing the 3 modalities, i.e., stress review data, audio, and face data, and predicting an output regarding the mental and stress health of the patient. In the audio model, we achieved an accuracy of 95.26% on training and 87% on the validation set using Bi-LSTM, whereas in the facial emotion detection model, we achieved an accuracy of 80% using the ViT and Bi-LSTM models combined. In the third model, we have used sentiment analysis and the PSS stress questionnaire to detect the stress percentage. At the end, we have combined all three models using the weighted combination of all three and predicted the final stress score or percentage of the employee.

关键词： Training Analytical models Sentiment analysis Accuracy Reviews Employment Human factors

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MP-GAN: Cyber-Attack Detection and Localization for Cyber-Physical Systems with Multi-Process Generative Adversarial Networks*

MP-GAN: Cyber-Attack Detection and Localization for Cyber-Ph...

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Artificial Intelligence of Things and Systems (AIoTSys), International Conference on

作者： Yikui Zhou Jie Wang Junnan Tang Chao Gou Zigui Jiang Dan Li See-Kiong Ng School of Software Engineering Sun-Yat Sen University Zhuhai China School of Intelligent Systems Engineering Sun-Yat Sen University Shenzhen China Institute of Data Science/School of Computing National University of Singapore

Cyber-Physical System (CPS) integrates sensing, computation, cybernetics, and networking to control a hybrid physical system consisting of different functional subsystems, making the production process more intelligent and controllable. However, cyber-attacks during its operation will lead to abnormal system behaviors or even system breakdowns. In recent years, data-driven anomaly detection methods have been adopted to judge whether the CPS system is under cyber-attacks based on rich sensor measurements to avoid further economic losses or safety issues. However, the multi-process essence of CPS has not been adequately addressed in existing works to locate the processes or points being attacked for in-time actions. In this work, we proposed a Multi-Process Generative Adversarial Network (MP-GAN) framework to detect anomalous CPS statuses and locate cyber-attacks. Specifically, the Long-Short-Term-Memory Recurrent Neural Networks (LSTM-RNN) was adopted as the base model to capture the temporal correlation of time series distributions, and the data was transferred between latent space and data space in a bi-directional manner, which regulated the generator to generate more realistic samples and thus better grasping the underline principles of the system. Moreover, parallel generators were employed to capture system performances at different physical processes, thus localizing the attacked CPS processes. Experiments on three CPS datasets, two collected from the Secure Water Treatment (SWaT) system and one collected from the water Distribution (WADI) system, showed that the proposed MP-GAN framework effectively reported anomalies caused by various cyber-attacks inserted in these complex multiprocess CPSs, which outperforms the state-of-the-art methods and can also locate most of the detected irregularities at the corresponding stages where the attacks were inserted.

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Gradient and Variable Tracking with Multiple Local SGD for Decentralized Non-Convex Learning

arXiv

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arXiv 2023年

作者： Ge, Songyang Chang, Tsung-Hui The Shenzhen Research Institute of Big Data The School of Science and Engineering The Chinese University of Hong Kong Shenzhen518172 China

Stochastic distributed optimization methods that solve an optimization problem over a multi-agent network have played an important role in a variety of large-scale signal processing and machine leaning applications. Among the existing methods, the gradient tracking (GT) method is found robust against the variance between agents’ local data distribution, in contrast to the distributed stochastic gradient descent (SGD) methods which have a slowed convergence speed when the agents have heterogeneous data distributions. However, the GT method can be communication expensive due to the need of a large number of iterations for convergence. In this paper, we intend to reduce the communication cost of the GT method by integrating it with the local SGD technique. Specifically, we propose a new local stochastic GT (LSGT) algorithm where, within each communication round, the agents perform multiple SGD updates locally. Theoretically, we build the convergence conditions of the _LSGT algorithm and show that it can have an improved convergence rate of O(1/√ET), where E is the number of local SGD updates and T is the number of communication rounds. We further extend the LSGT algorithm to solve a more complex learning problem which has linearly coupled variables inside the objective function. Experiment results demonstrate that the proposed algorithms have significantly improved convergence speed even under heterogeneous data distribution. Copyright © 2023, The Authors. All rights reserved.

关键词： Signal processing

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Hyperbolic Neural Network Based Preselection for Expensive Multi-Objective Optimization

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Li, Bingdong Yang, Yanting Hong, Wenjing Yang, Peng Zhou, Aimin Shanghai Institute of AI for Education the School of Computer Science and Technology and the Shanghai Frontiers Science Center of Molecule Intelligent Syntheses East China Normal University Shanghai China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science and Engineering Guangdong Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

A series of surrogate-assisted evolutionary algorithms (SAEAs) have been proposed for expensive multi-objective optimization problems (EMOPs), building cheap surrogate models to replace the expensive real function evaluations. However, the search efficiency of these SAEAs is not yet satisfactory. More efforts are needed to further exploit useful information from the real function evaluations in order to better guide the search process. Facing this challenge, this paper proposes a Hyperbolic Neural Network (HNN) based preselection operator to accelerate the optimization process based on limited evaluated solutions. First, the preselection task is modeled as a multi-label classification problem where solutions are classified into different layers (ordinal categories) through -relaxed objective aggregation. Second, in order to resemble the hierarchical structure of candidate solutions, a hyperbolic neural network is applied to tackle the multi-label classification problem. The reason for using HNN is that hyperbolic spaces more closely resemble hierarchical structures than Euclidean spaces. Moreover, to alleviate the data deficiency issue, a data augmentation strategy is employed for training the HNN. In order to evaluate its performance, the proposed HNN-based preselection operator is embedded into two surrogate-assisted evolutionary algorithms. Experimental results on two benchmark test suites and three real-world problems with up to 11 objectives and 150 decision variables involving seven state-of-the-art algorithms demonstrate the effectiveness of the proposed method. IEEE

关键词： Multiobjective optimization

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Partial Homoscedasticity in Causal Discovery with Linear Models

arXiv

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arXiv 2023年

作者： Wu, Jun Drton, Mathias Technical University of Munich GermanyTUM School of Computation Information and Technology Department of Mathematics and Munich Data Science Institute

Recursive linear structural equation models and the associated directed acyclic graphs (DAGs) play an important role in causal discovery. The classic identifiability result for this class of models states that when only observational data is available, each DAG can be identified only up to a Markov equivalence class. In contrast, recent work has shown that the DAG can be uniquely identified if the errors in the model are homoscedastic, i.e., all have the same variance. This equal variance assumption yields methods that, if appropriate, are highly scalable and also sheds light on fundamental information-theoretic limits and optimality in causal discovery. In this paper, we fill the gap that exists between the two previously considered cases, which assume the error variances to be either arbitrary or all equal. Specifically, we formulate a framework of partial homoscedasticity, in which the variables are partitioned into blocks and each block shares the same error variance. For any such groupwise equal variances assumption, we characterize when two DAGs give rise to identical Gaussian linear structural equation models. Furthermore, we show how the resulting distributional equivalence classes may be represented using a completed partially directed acyclic graph (CPDAG), and we give an algorithm to efficiently construct this CPDAG. In a simulation study, we demonstrate that greedy search provides an effective way to learn the CPDAG and exploit partial knowledge about homoscedasticity of errors in structural equation models. © 2023, CC BY.

关键词： Errors

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Analysis of Product Sales Volume and Pricing Based on Multivariate Linear Regression Model and Time Series Forecasting Algorithm

Analysis of Product Sales Volume and Pricing Based on Multiv...

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Sensors, Electronics and Computer Engineering (ICSECE), IEEE International Conference on

作者： Linjie Zhou Haolei Yang SWUFE-UD Institute of Data Science Southwestern University of Finance and Economics Chengdu China School of Mathematics Southwestern University of Finance and Economics Chengdu China

ISBN: (数字)9798350373646

ISBN: (纸本)9798350373653

This paper presents an analysis of product sales and pricing based on a multi-linear regression model and time series forecasting algorithm. Initially, historical sales prices are considered cost-plus pricing. A multi-linear regression model is then established, with the monthly sales volume of each product category as the dependent variable and the cost-plus pricing of products within the category as the independent variable, to ascertain the relationship between sales volume, cost-plus pricing, and expected profit rates for each product category. Subsequently, utilizing data from the first seven days of July for the previous three years and considering the sales of individual products and historical wholesale prices, a time series forecasting algorithm is developed to predict the wholesale prices of products sold from July 1 to July 7, 2023. Finally, an objective programming model is constructed, with the expected profit rates as decision variables and the maximization of supermarket profits as the objective, to establish constraints and compute the pricing and sales volume for 31 saleable products. The maximum profit for the supermarket over 7 days is determined to be 12808.92568 RMB.

关键词： Solid modeling Analytical models Computational modeling Heuristic algorithms Time series analysis Pricing Predictive models Programming Strategic planning Forecasting

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Overview of SMP-CAIL2020-Argmine:The Interactive Argument-Pair Extraction in Judgement Document Challenge

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data Intelligence 2021年第2期3卷 287-307页

作者： Jian Yuan Zhongyu Wei Yixu Gao Wei Chen Yun Song Donghua Zhao Jinglei Ma Zhen Hu Shaokun Zou Donghai Li Xuanjing Huang School of Data Science Fudan UniversityShanghai 200433China Heilongjiang University Heilongjiang 150080China School of Mathematical Sciences Fudan UniversityShanghai 200433China China Judicial Big Data Institute Beijing 100043China THUNISOFT Co. Ltd.Beijing 100084China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China School of Computer Science Fudan UniversityShanghai 200433China

In this paper we present the results of the Interactive Argument-Pair Extraction in Judgement Document Challenge held by both the Chinese AI and Law Challenge(CAIL)and the Chinese National Social Media Processing Conference(SMP),and introduce the related data *** task challenged participants to choose the correct argument among five candidates proposed by the defense to refute or acknowledge the given argument made by the plaintiff,providing the full context recorded in the judgement documents of both *** received entries from 63 competing teams,38 of which scored higher than the provided baseline model(BERT)in the first phase and entered the second *** best performing system in the two phases achieved accuracy of 0.856 and 0.905,*** this paper,we will present the results of the competition and a summary of the systems,highlighting commonalities and innovations among participating *** SMP-CAIL2020-Argmine data set and baseline modelshave been already released.

关键词： Argumentation mining Judgement documents Natural language understanding Pretrained language model

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DETECTING OUT-OF-DISTRIBUTION SAMPLES VIA CONDITIONAL DISTRIBUTION ENTROPY WITH OPTIMAL TRANSPORT

arXiv

引用

arXiv 2024年

作者： Feng, Chuanwen Chen, Wenlong Ke, Ao Ren, Yilong Xie, Xike Zhou, S. Kevin School of Biomedical Engineering University of Science and Technology of China China Data Darkness Lab MIRACLE Center Suzhou Institute for Advanced Research USTC China

When deploying a trained machine learning model in the real world, it is inevitable to receive inputs from out-of-distribution (OOD) sources. For instance, in continual learning settings, it is common to encounter OOD samples due to the non-stationarity of a domain. More generally, when we have access to a set of test inputs, the existing rich line of OOD detection solutions, especially the recent promise of distance-based methods, falls short in effectively utilizing the distribution information from training samples and test inputs. In this paper, we argue that empirical probability distributions that incorporate geometric information from both training samples and test inputs can be highly beneficial for OOD detection in the presence of test inputs available. To address this, we propose to model OOD detection as a discrete optimal transport problem. Within the framework of optimal transport, we propose a novel score function known as the conditional distribution entropy to quantify the uncertainty of a test input being an OOD sample. Our proposal inherits the merits of certain distance-based methods while eliminating the reliance on distribution assumptions, a-prior knowledge, and specific training mechanisms. Extensive experiments conducted on benchmark datasets demonstrate that our method outperforms its competitors in OOD detection. Copyright © 2024, The Authors. All rights reserved.

关键词： Probability distributions

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Physics-aware cross-domain fusion aids learning-driven computer-generated holography

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Photonics Research 2024年第12期12卷 2747-2756页

作者： GANZHANGQIN YUAN MI ZHOU FEI LIU MU KU CHEN KUI JIANG YIFAN PENG ZIHAN GENG Institute of Data and Information Tsinghua Shenzhen International Graduate SchoolTsinghua UniversityShenzhen 518071China School of Optoelectronic Engineering Xidian UniversityXi’an 710071China Department of Electrical Engineering City University of Hong KongHong Kong SARChina School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001China Department of Electrical and Electronic Engineering The University of Hong KongHong Kong SARChina Department of Computer Science The University of Hong KongHong Kong SARChina

The rapid advancement of computer-generated holography has bridged deep learning with traditional optical principles in recent ***,a critical challenge in this evolution is the efficient and accurate conversion from the amplitude to phase domain for high-quality phase-only hologram(POH)*** computational models often struggle to address the inherent complexities of optical phenomena,compromising the conversion *** this study,we present the cross-domain fusion network(CDFN),an architecture designed to tackle the complexities involved in POH *** CDFN employs a multi-stage(MS)mechanism to progressively learn the translation from amplitude to phase domain,complemented by the deep supervision(DS)strategy of middle features to enhance task-relevant feature learning from the initial ***,we propose an infinite phase mapper(IPM),a phase-mapping function that circumvents the limitations of conventional activation functions and encapsulates the physical essence of *** simulations,our proposed method successfully reconstructs high-quality 2K color images from the DIV2K dataset,achieving an average PSNR of 31.68 dB and SSIM of ***,we realize high-quality color image reconstruction in optical *** experimental results highlight the computational intelligence and optical fidelity achieved by our proposed physics-aware cross-domain fusion.

关键词： computer cross holography

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