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Dynamic interactive robust consensus reaching framework for maximum experts considering uncertain cost and adjustment strategy

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Expert Systems with Applications 2025年 289卷

作者： Jinpeng Wei Xuanhua Xu Qiuhan Wang Xiaoxia Xu Chengwei Zhao Francisco Javier Cabrerizo Business School Central South University Changsha 410083 China School of Business Jiangnan University Wuxi 214026 China School of Management Science and Engineering Nanjing University of Information Science and Technology Nanjing 210044 China Business School Central South University of Forestry and Technology Changsha 410004 China Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) Department of Computer Science and Artificial Intelligence University of Granada Granada 18071 Spain

Consensus optimization models often struggle to coordinate continuous updates of expert opinions and costs, easily falling into the illusion of temporary consensus. Moreover, accurately estimating the extent of opinion adjustments and unit adjustment costs is challenging, posing obstacles to reaching consensus. Based on the maximum expert consensus model (MECM), this study introduces a dynamic interactive robust consensus-reaching framework that considers uncertain costs and adjustment strategies. First, we construct three uncertainty sets to describe uncertain costs, which are then used to develop the robust consensus models. Simultaneously, we propose a dynamic adjustment strategy that uses the optimal adjustment opinions from the model as reference information, objectively indicating the direction and magnitude of expert opinion adjustments and the cost compensation provided by the moderator. This approach helps objectively indicate the direction and magnitude of expert opinion adjustments. This study combines the complementary strengths of identification and direction rule (IR-DR) along with optimization-based rule (OR), aiming to dynamically facilitate consensus among all experts under uncertain conditions. Finally, we design an improved genetic algorithm (GA) to handle the robust models and validate the performance of the proposed consensus framework through case studies and comparative analyses. Results reveal how experts can accelerate consensus through dynamic strategies.

关键词： Genetic algorithms

Unified gas-kinetic wave-particle methods VI: Disperse dilute gas-particle multiphase flow

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arXiv 2021年

作者： Yang, Xiaojian Liu, Chang Ji, Xing Shyy, Wei Xu, Kun Department of Mechanical and Aerospace Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Institute of Applied Physics and Computational Mathematics Beijing China Department of Mathematics Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications Hong Kong University of Science and Technology Hong Kong Shenzhen Research Institute Hong Kong University of Science and Technology Shenzhen Hong Kong

In this paper, a unified gas-kinetic wave-particle scheme (UGKWP) for the disperse dilute gas-particle multiphase flow is proposed. In this study, the gas phase is always in the hydrodynamic regime. However, the particle phase covers different flow regimes from particle trajectory crossing to the hydrodynamic wave interaction with the variation of local particle phase Knudsen number. The UGKWP is an appropriate method for the capturing of the multiscale transport mechanism in the particle phase through its coupled wave-particle formulation. In the regime with intensive particle collision, the evolution of solid particle will be followed by the analytic wave with quasi-equilibrium distribution;while in the rarefied regime the non-equilibrium particle phase will be captured through particle tracking and collision, which plays a decisive role in recovering particle trajectory crossing behavior. The gas phase in the multiphase system is assumed to be stay in the continuum flow regime, and its evolution is mainly controlled by the Navier-Stokes equations with the interaction with particle phase. The gas-kinetic scheme (GKS) is employed for the simulation of gas flow. In the highly collision regime for the particles, no particles will be sampled in UGKWP and the wave formulation for solid particle with the hydrodynamic gas phase will reduce the system to the two-fluid Eulerian model. On the other hand, in the collisionless regime for the solid particle, the free transport of solid particle will be followed in UGKWP, and coupled system will return to the Eulerian-Lagrangian formulation for the gas and particle. The scheme will be tested for in all flow regimes, which include the non-equilibrium particle trajectory crossing, the particle concentration under different Knudsen number, and the dispersion of particle flow with the variation of Stokes number. A experiment of shock-induced particle bed fluidization is simulated and the results are compared with experimental measu

关键词： Gases

Evaluation of Activated Sludge Settling Characteristics from Microscopy Images with Deep Convolutional Neural Networks and Transfer Learning

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arXiv 2024年

作者： Borzooei, Sina Scabini, Leonardo Miranda, Gisele Daneshgar, Saba Deblieck, Lukas De Langhe, Piet Bruno, Odemir De Baets, Bernard Nopens, Ingmar Torfs, Elena IVL Swedish Environmental Research Institute P.O. Box 210 60 StockholmSE-100 31 Sweden BIOMATH Department of Data Analysis and Mathematical Modelling Faculty of Bioscience Engineering Ghent University Coupure links 653 Gent9000 Belgium São Carlos Institute of Physics University of São Paulo SP São Carlos13560-970 Brazil KERMIT Department of Data Analysis and Mathematical Modelling Ghent University Coupure links 653 Gent9000 Belgium Division of Computational Science and Technology KTH Royal Institute of Technology Stockholm10044 Sweden CAPTURE Centre for Advanced Process Technology for Urban Resource Recovery Frieda Saeysstraat 1 Gent9000 Belgium Pantarein Water Egide Walschaertsstraat 22L Mechelen2800 Belgium Université Laval 1065 avenue de la Médecine QuébecQCG1V 0A6 Canada

Microbial communities play a key role in biological wastewater treatment processes. Activated sludge settling characteristics, for example, are affected by microbial community composition, varying by changes in operating conditions and influent characteristics of wastewater treatment plants (WWTPs). Timely assessment and prediction of changes in microbial composition leading to settling problems, such as filamentous bulking (FB), can prevent operational challenges, reduction of treatment efficiency, and adverse environmental impacts. This study presents an innovative computer vision-based approach to assess activated sludge-settling characteristics based on the morphological properties of flocs and filaments in microscopy images. Implementing the transfer learning of deep convolutional neural network (CNN) models, this approach aims to overcome the limitations of existing quantitative image analysis techniques. The offline microscopy image dataset was collected over two years, with weekly sampling at a full-scale industrial WWTP in Belgium. Multiple data augmentation techniques were employed to enhance the generalizability of the CNN models. Various CNN architectures, including Inception v3, ResNet18, ResNet152, ConvNeXt-nano, and ConvNeXt-S, were tested to evaluate their performance in predicting sludge settling characteristics. The sludge volume index (SVI) was used as the final prediction variable, but the method can easily be adjusted to predict any other settling metric of choice. The results showed that the suggested CNN-based approach provides less labourintensive, objective, and consistent assessments while transfer learning notably minimises the training phase, resulting in a generalizable system that can be employed in real-time applications. The model's capability to signal early indicators of FB events makes it a valuable tool for effective monitoring and preemptive management of WWTPs facing activated sludge settling problems. © 2024, CC BY-NC-ND.

关键词： Convolutional neural networks

Adaptive test allocation for outbreak detection and tracking in social contact networks

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arXiv 2020年

作者： Batlle, Pau Bruna, Joan Fernandez-Granda, Carlos Preciado, Victor M. Courant Institute of Mathematical Sciences Center for Data Science New York University NY United States Department of Electrical and Systems Engineering Applied Mathematics and Computational Science University of Pennsylvania PA United States

We present a general framework for adaptive allocation of viral tests in social contact networks. We pose and solve several complementary problems. First, we consider the design of a social sensing system whose objective is the early detection of a novel epidemic outbreak. In particular, we propose an algorithm to select a subset of individuals to be tested in order to detect the onset of an epidemic outbreak as fast as possible. We pose this problem as a hitting time probability maximization problem and use submodularity optimization techniques to derive explicit quality guarantees for the proposed solution. Second, once an epidemic outbreak has been detected, we consider the problem of adaptively distributing viral tests over time in order to maximize the information gained about the current state of the epidemic. We formalize this problem in terms of information entropy and mutual information and propose an adaptive allocation strategy with quality guarantees. For these problems, we derive analytical solutions for any stochastic compartmental epidemic model with Markovian dynamics, as well as efficient Monte-Carlo-based algorithms for non-Markovian dynamics. Finally, we illustrate the performance of the proposed framework in numerical experiments involving a model of Covid-19 applied to a real human contact network. Copyright © 2020, The Authors. All rights reserved.

关键词： Epidemiology

Biases in Inverse Ising Estimates of Near-Critical Behaviour

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arXiv 2023年

作者： Kloucek, Maximilian B. Machon, Thomas Kajimura, Shogo Royall, C. Patrick Masuda, Naoki Turci, Francesco H.H. Wills Laboratory School of Physics University of Bristol Bristol United Kingdom Bristol Centre for Functional Nanomaterials University of Bristol Bristol United Kingdom Faculty of Information and Human Sciences Kyoto Institute of Technology Kyoto606-8585 Japan Gulliver UMR CNRS 7083 ESPCI Paris Université PSL Paris75005 France Department of Mathematics State University of New York BuffaloNY14260-2900 United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY14260-5030 United States

Inverse Ising inference allows pairwise interactions of complex binary systems to be reconstructed from empirical correlations. Typical estimators used for this inference, such as Pseudo-likelihood maximization (PLM), are biased. Using the Sherrington-Kirkpatrick (SK) model as a benchmark, we show that these biases are large in critical regimes close to phase boundaries, and may alter the qualitative interpretation of the inferred model. In particular, we show that the small-sample bias causes models inferred through PLM to appear closer-to-criticality than one would expect from the data. data-driven methods to correct this bias are explored and applied to a functional magnetic resonance imaging (fMRI) dataset from neuroscience. Our results indicate that additional care should be taken when attributing criticality to real-world datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Criticality (nuclear fission)

Correction to: Mitigation strategies against cascading failures within a project activity network

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Journal of computational Social science 2021年第1期5卷 1097-1098页

作者： Ellinas, Christos Nicolaides, Christos Masuda, Naoki Nodes and Links Ltd Cambridge UK Department of Business and Public Administration University of Cyprus Nicosia Cyprus Initiative on the Digital Economy Massachusetts Institute of Technology Cambridge USA Department of Mathematics State University of New York at Buffalo Buffalo USA Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo USA Faculty of Management and Economics Dalian University of Technology Dalian China

Towards Efficient Large-Scale Network Slicing: An LP Dynamic Rounding-and-Refinement Approach

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arXiv 2021年

作者： Chen, Wei-Kun Liu, Ya-Feng Liu, Fan Dai, Yu-Hong Luo, Zhi-Quan School of Mathematics and Statistics Beijing Key Laboratory on MCAACI Beijing Institute of Technology Beijing100081 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen518055 China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen518172 China

In this paper, we propose an efficient algorithm for the network slicing problem which attempts to map multiple customized virtual network requests (also called services) to a common shared network infrastructure and allocate network resources to meet diverse service requirements. The problem has been formulated as a mixed integer linear programming (MILP) formulation in the literature. We first propose a novel linear programming (LP) relaxation of the MILP formulation. We show that compared with a natural LP relaxation of the MILP formulation, the novel LP relaxation is much more compact in terms of smaller numbers of variables and constraints, and much stronger in terms of providing a better LP bound, which makes it particularly suitable to be embedded in an LP relaxation based algorithm. Then we design an efficient two-stage LP dynamic rounding-and-refinement algorithm based on this novel LP relaxation. In the first stage, the proposed algorithm uses an LP dynamic rounding procedure to place the virtual network functions of all services into cloud nodes while taking traffic routing of all services into consideration;in the second stage, the proposed algorithm uses an iterative LP refinement procedure to obtain a solution for traffic routing of all services with their end-to-end delay constraints being satisfied. Compared with the existing algorithms which either have an exponential complexity or return a low-quality solution, our proposed algorithm achieves a better trade-off between the solution quality and the computational complexity. In particular, the worst-case complexity of our proposed algorithm is polynomial, which makes it suitable for solving large-scale problems. Numerical results demonstrate the effectiveness and efficiency of our proposed algorithm. Copyright © 2021, The Authors. All rights reserved.

关键词： Iterative methods

Parallel Planning:A New Motion Planning Framework for Autonomous Driving

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 236-246页

作者： Long Chen Xuemin Hu Wei Tian Hong Wang Dongpu Cao Fei-Yue Wang IEEE School of Data and Computer Science Sun Yat-sen University the School of Computer Science and Information Engineering Hubei University Institute of Measurement and Control Systems Karlsruhe Institute of Technology Department of Mechanical and Mechatronics Engineering University of Waterloo the State Key Laboratory of Management and Control for Complex Systems.Institute of Automation Chinese Academy of Sciences the Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology

Motion planning is one of the most significant technologies for autonomous driving. To make motion planning models able to learn from the environment and to deal with emergency situations, a new motion planning framework called as"parallel planning" is proposed in this paper. In order to generate sufficient and various training samples, artificial traffic scenes are firstly constructed based on the knowledge from the reality.A deep planning model which combines a convolutional neural network(CNN) with the Long Short-Term Memory module(LSTM) is developed to make planning decisions in an end-toend mode. This model can learn from both real and artificial traffic scenes and imitate the driving style of human ***, a parallel deep reinforcement learning approach is also presented to improve the robustness of planning model and reduce the error rate. To handle emergency situations, a hybrid generative model including a variational auto-encoder(VAE) and a generative adversarial network(GAN) is utilized to learn from virtual emergencies generated in artificial traffic scenes. While an autonomous vehicle is moving, the hybrid generative model generates multiple video clips in parallel, which correspond to different potential emergency scenarios. Simultaneously, the deep planning model makes planning decisions for both virtual and current real scenes. The final planning decision is determined by analysis of real observations. Leveraging the parallel planning approach, the planner is able to make rational decisions without heavy calculation burden when an emergency occurs.

关键词： Autonomous driving artificial traffic scene deep learning emergencies motion planning parallel planning

Stability of stochastic gradient descent on nonsmooth convex losses 20

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Stability of stochastic gradient descent on nonsmooth convex...

Proceedings of the 34th International Conference on Neural Information Processing Systems

作者： Raef Bassily Vitaly Feldman Cristóbal Guzmán Kunal Talwar Department of Computer Science & Engineering The Ohio State University Apple Pontificia Universidad Católica de Chile Institute for Mathematical and Computational Engineering ANID – Millennium Science Initiative Program Millennium Nucleus Center for the Discovery of Structures in Complex Data

ISBN: (纸本)9781713829546

Uniform stability is a notion of algorithmic stability that bounds the worst case change in the model output by the algorithm when a single data point in the dataset is replaced. An influential work of Hardt et al. [20] provides strong upper bounds on the uniform stability of the stochastic gradient descent (SGD) algorithm on sufficiently smooth convex losses. These results led to important progress in understanding of the generalization properties of SGD and several applications to differentially private convex optimization for smooth *** work is the first to address uniform stability of SGD on nonsmooth convex losses. Specifically, we provide sharp upper and lower bounds for several forms of SGD and full-batch GD on arbitrary Lipschitz nonsmooth convex losses. Our lower bounds show that, in the nonsmooth case, (S)GD can be inherently less stable than in the smooth case. On the other hand, our upper bounds show that (S)GD is sufficiently stable for deriving new and useful bounds on generalization error. Most notably, we obtain the first dimension-independent generalization bounds for multi-pass SGD in the nonsmooth case. In addition, our bounds allow us to derive a new algorithm for differentially private nonsmooth stochastic convex optimization with optimal excess population risk. Our algorithm is simpler and more efficient than the best known algorithm for the nonsmooth case [16].

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