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Multi-Task Learning for Alzheimer's Disease Diagnosis and Mini-Mental State Examination Score Prediction

Big data Mining and Analytics 2024年第3期7卷 828-842页

作者： Jin Liu Xu Tian Hanhe Lin Hong-Dong Li Yi Pan Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of SoftwareXinjiang UniversityUrumqi 830091China School of Science and Engineering University of DundeeDundeeDD14HNUK Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Accurately diagnosing Alzheimer's disease is essential for improving elderly ***,accurate prediction of the mini-mental state examination score also can measure cognition impairment and track the progression of Alzheimer's ***,most of the existing methods perform Alzheimer's disease diagnosis and mini-mental state examination score prediction separately and ignore the relation between these two *** address this challenging problem,we propose a novel multi-task learning method,which uses feature interaction to explore the relationship between Alzheimer's disease diagnosis and minimental state examination score *** our proposed method,features from each task branch are firstly decoupled into candidate and non-candidate parts for ***,we propose feature sharing module to obtain shared features from candidate features and return shared features to task branches,which can promote the learning of each *** validate the effectiveness of our proposed method on multiple *** Alzheimer's disease neuroimaging initiative 1 dataset,the accuracy in diagnosis task and the root mean squared error in prediction task of our proposed method is 87.86%and 2.5,*** results show that our proposed method outperforms most state-of-the-art *** proposed method enables accurate Alzheimer's disease diagnosis and mini-mental state examination score ***,it can be used as a reference for the clinical diagnosis of Alzheimer's disease,and can also help doctors and patients track disease progression in a timely manner.

关键词： multi-task learning Alzheimer's disease diagnosis mini-mental state examination score prediction

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gTRAVEL+: Enhancing Ground Segmentation for 3D Point Cloud With Temporal Noise Removal and Adaptive Plane Fitting for Urban and Off-Road Environments

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IEEE Access 2025年 13卷 82431-82440页

作者： Dao, Truong Giang Tran, Dinh Tuan Nguyen, Duc Manh Lee, Joo-Ho Nguyen, Anh Quang Hanoi University of Science and Technology School of Electrical and Electronic Engineering Hanoi100000 Viet Nam Shiga University Faculty of Data Science Hikone522-8522 Japan Shiga University Data Science and AI Innovation Research Promotion Center Hikone522-8522 Japan Ritsumeikan University College of Information Science and Engineering Kyoto603-8577 Japan

Ground segmentation plays an increasingly important role as a preprocessing module for numerous applications such as Simultaneous Localization and Mapping (SLAM), place recognition, and point cloud registration. The efficiency and accuracy of ground segmentation can directly influence the performance of these applications. While several methods have been proposed, they continue to struggle to meet the comprehensive requirements of robust ground segmentation. To this end, we propose a ground segmentation method called gTRAVEL+, an improvement of TRAVEL that focuses exclusively on enhancing ground segmentation performance. Our method exploits a temporal and locally-aware noise removal strategy, termed Temporal Node-Wise Noise Removal (TNNR), which effectively eliminates noise points that compromise the ground plane fitting process. Additionally, Merge Node Plane Fitting (MNPF) is proposed to address issues of partial under-segmentation arising from suboptimal region sizing. Finally, Rejected Ground Node Revert (RNGR) ensures thorough geometric relationship verification, significantly improving true positives. Quantitative and qualitative experiments conducted on both urban and off-road environments show that our proposed method demonstrates superior performance, achieving a F1 -score of 95.16% and competitive speed compared to state-of-the-art methods. © 2013 IEEE.

关键词： Image segmentation

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A Reduced State Space Generation Method for Concurrent Systems Based on CPN-PR Model

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IEEE Transactions on computer-aided Design of Integrated Circuits and Systems 2024年第6期44卷 2328-2342页

作者： Zhong, Wenjie Sun, Tao Zhou, Jian-Tao Wang, Zhuowei Song, Xiaoyu Inner Mongolia Engineering Laboratory for Big Data Analysis Technology College of Computer Science Inner Mongolia University Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Hohhot010000 China Guangdong University of Technology School of Computer Science and Technology Guangzhou China Portland State University Department of Electrical and Computer Engineering PortlandOR97207 United States

Colored Petri Nets (CPNs) provide descriptions of the concurrent behaviors for software and hardware. Model checking based on CPNs is an effective method to simulate and verify the concurrent behavior in system design. However, the model checking method traverses the full state space, which suffers from the state space explosion problem. A reduced state space generation method related to the property of concurrent systems is proposed. Specifically, we extend CPNs to define a property-related model (CPN-PR) and give a property-related analysis method whose results can be used to generate the CPN-PR model. A reduced state space generation method is developed based on enabled binding element filtering rules. The stutter trace equivalence between the state spaces of CPN and CPN-PR has been proven by showing that the reduced state space may not change the model checking result. A comparison experiment is conducted to demonstrate the effectiveness of our method. © 1982-2012 IEEE.

关键词： Model checking

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Monte Carlo Simulation-Based Robust Workflow Scheduling for Spot Instances in Cloud Environments

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Tsinghua science and Technology 2024年第1期29卷 112-126页

作者： Quanwang Wu Jianzhao Fang Jie Zeng Junhao Wen Fengji Luo College of Computer Science Chongqing UniversityChongqing 400044China National Experimental Teaching Demonstration Center Chongqing UniversityChongqing 400044China College of Big Data and Software Engineering Chongqing UniversityChongqing 400044China School of Civil Engineering The University of SydneySydney 2006Australia

When deploying workflows in cloud environments,the use of Spot Instances(SIs)is intriguing as they are much cheaper than on-demand ***,Sls are volatile and may be revoked at any time,which results in a more challenging scheduling problem involving execution interruption and hence hinders the successful handling of conventional cloud workflow scheduling *** some scheduling methods for Sls have been proposed,most of them are no more applicable to the latest Sls,as they have evolved by eliminating bidding and simplifying the pricing *** study focuses on how to minimize the execution cost with a deadline constraint when deploying a workflow on volatile Sls in cloud *** on Monte Carlo simulation and list scheduling,a stochastic scheduling method called MCLS is devised to optimize a utility function introduced for this *** the Monte Carlo simulation framework,MCLS employs sampled task execution time to build solutions via deadline distribution and list scheduling,and then returns the most robust solution from all the candidates with a specific evaluation mechanism and selection *** results show that the performance of MCLS is more competitive comparedwithtraditionalalgorithms.

关键词： constrained optimization Monte Carlo simulation robustness Spot Instances(Sls) workflow scheduling

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BERD+: A Generic Sequential Recommendation Framework by Eliminating Unreliable data with Item- and Attribute-level Signals

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ACM Transactions on Information Systems 2023年第2期42卷 1-33页

作者： Sun, Yatong Yang, Xiaochun Sun, Zhu Wang, Bin School of Computer Science and Engineering Northeastern University School of Computing Macquarie University China Australia Institute of High Performance Computing Centre for Frontier AI Research A*STAR Singapore School of Computer Science and Engineering Northeastern University China National Frontiers Science Center for Industrial Intelligence and Systems Optimization China Key Laboratory of Data Analytics and Optimization for Smart Industry [Northeastern University Ministry of Education China

A preliminary work of our study was published at IJCai’21 [47], which is substantially extended in the following aspects: (1) In Section 1, we analyze the necessity of introducing item attributes for detecting unreliable instances, together with the problems and challenges that attributes may bring in. (2) In Section 2, we add discussions about the limitations of existing attribute-aware recommender systems (Section 2.2) and denoising methods (Section 2.3) in the context of detecting unreliable instances. (3) In Section 4.2, we further conduct an in-depth analysis at the attribute level to demonstrate the capability of attributes for rectifying instance loss and uncertainty, as well as the disturbance caused by attributes. (4) We generalize BERD to a generic framework BERD+ in Section 5.1, equipped with novel modules, i.e., HU-GCN (Section 5.2) and EPE (Section 5.4), which properly incorporate item attributes while reducing their disturbance for rectifying instance uncer tainty (Section 5.5) and loss (Section 5.6). The generic BERD+ can be flexibly plugged into existing SRSs for performance enhanced recommendation via eliminating unreliable data. (5) In Section 6.2, we apply our BERD+ framework to seven state-of-the-art SRSs on five real-world datasets to illustrate its superiority. (6) To avoid unfair comparison caused by item attributes, we build and compare with the baseline that combines the original BERD and an advanced attribute-aware recommender system, KSR [19]. (7) For more comprehensive comparison, in Section 6.2.2, we compare BRED+ with two state-of-the-art denoising approaches;in Section 6.2.3, to examine the efficacy of HU-GCN and EPE, we compare HU-GCN with various attribute embedding techniques, i.e., variants of graph neural networks, and compare EPE with different attribute fusing methods, i.e., adding, concatenation, and weighted sum. (8) In Section 6.2.4, a detailed ablation study is conducted to verify the effectiveness of each module of BERD+. (

关键词： Convolution

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Learned distributed image compression with decoder side information

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Digital Communications and Networks 2025年第2期11卷 349-358页

作者： Yankai Yin Zhe Sun Peiying Ruan Ruidong Li Feng Duan Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Nankai UniversityTianjin 300350China Institute of Natural Sciences Kanazawa UniversityIshikawa 9201164Japan Faculty of Health Data Science and Graduate School of Medicine Juntendo UniversityChiba 2790013Japan NVIDIA AI Technology Center NVIDIA JapanTokyo 1070052Japan

With the rapid development of digital communication and the widespread use of the Internet of Things,multi-view image compression has attracted increasing attention as a fundamental technology for image data ***-view image compression aims to improve compression efficiency by leveraging correlations between ***,the requirement of synchronization and inter-image communication at the encoder side poses significant challenges,especially for constrained *** this study,we introduce a novel distributed image compression model based on the attention mechanism to address the challenges associated with the availability of side information only during *** model integrates an encoder network,a quantization module,and a decoder network,to ensure both high compression performance and high-quality image *** encoder uses a deep Convolutional Neural Network(CNN)to extract high-level features from the input image,which then pass through the quantization module for further compression before undergoing lossless entropy *** decoder of our model consists of three main components that allow us to fully exploit the information within and between images on the decoder ***,we first introduce a channel-spatial attention module to capture and refine information within individual image feature ***,we employ a semi-coupled convolution module to extract both shared and specific information in ***,a cross-attention module is employed to fuse mutual information extracted from side *** effectiveness of our model is validated on various datasets,including KITTI Stereo and *** results highlight the superior compression capabilities of our method,surpassing state-of-the-art techniques.

关键词： Digital communication Image compression Side information Channel-spatial attention module Cross-attention module

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Edge Coalition Construction Based on a Novel Market Equilibrium Price

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China Communications 2025年第3期22卷 288-305页

作者： Wang Xiaolong Dang Jianwu Zhao Shuxu Wang Yangping Zhang Zhanping Hao Zhanjun School of Electronic and Information Engineering Lanzhou Jiaotong UniversityLanzhou 730070China Big Data Center of Gansu Province Lanzhou 730000China School of Mathematics and Information Engineering Longdong UniversityLanzhou 745000China College of Computer Science and Engineering Northwest Normal UniversityLanzhou 730070China

The emergence of multi-access edge computing(MEC)aims at extending cloud computing capabilities to the edge of the radio access *** the large-scale internet of things(IoT)services are rapidly growing,a single edge infrastructure provider(EIP)may not be sufficient to handle the data traffic generated by these *** of the existing work addressed the computing resource shortage problem by optimizing tasks schedule,whereas others overcome such issue by placing computing resources on ***,when considering a multiple EIPs scenario,an urgent challenge is how to generate a coalition structure to maximize each EIP’s gain with a suitable price for computing resource block corresponding to a *** this end,we design a scheme of EIPs collaboration with a market price for containers under a scenario that considers a collection of service providers(SPs)with different budgets and several EIPs distributed in geographical ***,we bring in the net profit market price model to generate a more reasonable equilibrium price and select the optimal EIPs for each SP by a convex *** we use a mathematical model to maximize EIP’s profits and form stable coalitions between EIPs by a distributed coalition formation *** results demonstrate that our proposed collaborative scheme among EIPs enhances EIPs’gain and increases users’surplus.

关键词： coalition formation computing resource block market equilibrium multi-access edge computing surplus

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Probabilistic time series forecasting with deep non-linear state space models

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CAai Transactions on Intelligence Technology 2023年第1期8卷 3-13页

作者： Heming Du Shouguo Du Wen Li School of Computer Science College of Engineering and Computer ScienceAustralian National UniversityCanberraAustralian Capital TerritoryAustralia Shanghai Municipal Big Data Center ShanghaiChina Department of Economic Statistics School of Statistics and InformationShanghai University of International Business and EconomicsShanghaiChina

Probabilistic time series forecasting aims at estimating future probabilistic distributions based on given time series *** is a widespread challenge in various tasks,such as risk management and decision *** investigate temporal patterns in time series data and predict subsequent probabilities,the state space model(SSM)provides a general *** of SSM achieve considerable success in many fields,such as engineering and ***,since underlying processes in real-world scenarios are usually unknown and complicated,actual time series observations are always irregular and ***,it is very difficult to determinate an SSM for classical statistical *** this paper,a general time series forecasting framework,called Deep Nonlinear State Space Model(DNLSSM),is proposed to predict the probabilistic distribution based on estimated underlying unknown processes from historical time series *** fuse deep neural networks and statistical methods to iteratively estimate states and network parameters and thus exploit intricate temporal patterns of time series *** particular,the unscented Kalman filter(UKF)is adopted to calculate marginal likelihoods and update distributions recursively for non-linear *** that,a non-linear Joseph form covariance update is developed to ensure that calculated covariance matrices in UKF updates are symmetric and positive ***,the authors enhance the tolerance of UKF to round-off errors and manage to combine UKF and deep neural *** this manner,the DNLSSM effectively models non-linear correlations between observed time series data and underlying dynamic *** in both synthetic and real-world datasets demonstrate that the DNLSSM consistently improves the accuracy of probability forecasts compared to the baseline methods.

关键词： Artificial Intelligence machine learning time series

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LCGen: Mining in Low-Certainty Generation for View-consistent Text-to-3D 38

LCGen: Mining in Low-Certainty Generation for View-consisten...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Tao, Zeng Yang, Tong Lin, Junxiong Mai, Xinji Wang, Haoran Wang, Beining Zhou, Enyu Wang, Yan Zhang, Wenqiang Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University Shanghai China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China School of Computer Science Fudan University Shanghai China Engineering Research Center of AI & Robotics Ministry of Education Academy for Engineering & Technology Fudan University Shanghai China

The Janus Problem is a common issue in SDS-based text-to-3D methods. Due to view encoding approach and 2D diffusion prior guidance, the 3D representation model tends to learn content with higher certainty from each perspective, leading to view inconsistency. In this work, we first model and analyze the problem, visualizing the specific causes of the Janus Problem, which are associated with discrete view encoding and shared priors in 2D lifting. Based on this, we further propose the LCGen method, which guides text-to-3D to obtain different priors with different certainty from various viewpoints, aiding in view-consistent generation. Experiments have proven that our LCGen method can be directly applied to different SDS-based text-to-3D methods, alleviating the Janus Problem without introducing additional information, increasing excessive training burden, or compromising the generation effect. Project page is [here]. © 2024 Neural information processing systems foundation. All rights reserved.

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Enhancing Lighter Neural Network Performance with Layer-wise Knowledge Distillation and Selective Pixel Attention 13

Enhancing Lighter Neural Network Performance with Layer-wise...

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13th International Conference on computer and Knowledge engineering, ICCKE 2023

作者： Zaravashan, Siavash Torabi, Sajjad Zaravashan, Hesam Iran University of Science and Technology Dept. Computer Engineering Tehran Iran Data Scientist Part Research Center of Ai Tehran Iran

ISBN: (纸本)9798350330151

Deep neural networks have made a revolution in different areas of Artificial Intelligence such as image classifi-cation and object detection. However, most of these models are heavy and time-consuming at the inference stage. If we desire to use real-time applications, it's necessary to lighten them up. However, this could negatively affect the performance of models. One suggested approach for enhancing the capabilities of smaller models involves a technique called knowledge distillation. In this method, a less advanced model, referred to as the student model, learns from and emulates the more advanced teacher model to enhance its performance. This method has been proven as an effective technique to compress the model and increase its accuracy. Previous methods mostly focus on proposing feature transformation and loss functions between the same level's features to improve the effectiveness. In this paper, we investigate the effect of connections between different layers of teacher and student networks and reveal their great importance. We have employed three different loss functions - attention-driven distillation, hierarchical context loss, and non-local loss - to enhance the classifier's overall performance. Extensive experiments on the CIFAR-100 dataset show that the proposed method has a great performance improvement. For the benefit of the research community, we will make the code for this study available on GitHub. © 2023 IEEE.

关键词： Image classification

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