Article
Mathematics
Francisco Javier Diez, Manuel Arias, Jorge Perez-Martin, Manuel Luque
Summary: OpenMarkov is an open-source software tool designed for probabilistic graphical models, primarily in medicine but also used in other fields and education in over 30 countries. This paper explains how OpenMarkov can be used as a pedagogical tool to teach the main concepts of Bayesian networks and influence diagrams, as well as various inference algorithms.
Review
Thermodynamics
Tingting Li, Yang Zhao, Ke Yan, Kai Zhou, Chaobo Zhang, Xuejun Zhang
Summary: Probabilistic graphical models are effective in addressing various issues in energy systems, with static models handling incomplete or uncertain information and dynamic models accurately predicting energy consumption, occupancy, and failures. A unified framework combining knowledge-driven and data-driven PGMs is suggested for better performance, with the need for universal PGM-based approaches adaptable to different energy systems and hybrid algorithms integrating advanced techniques for improved results.
BUILDING SIMULATION
(2022)
Article
Computer Science, Interdisciplinary Applications
Kuhwan Jeong, Minwoo Chae, Yongdai Kim
Summary: The Dirichlet process (DP) mixture model is commonly used for clustering and density estimation. Despite the development of Markov chain Monte Carlo algorithms, the computational costs of DP mixture models make them impractical for analyzing large data. To address this, we propose a novel mini-batch online learning algorithm based on assumed density filtering, which improves performance relative to existing online algorithms based on variational inference by leveraging available computing resources.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2023)
Article
Optics
Bowen Ma, Junfeng Zhang, Xing Li, Weiwen Zou
Summary: A noise-injection scheme is proposed to implement a GHz-rate stochastic photonic spiking neuron (S-PSN), which can achieve firing-probability encoding and Bayesian inference with unsupervised learning. In a breast diagnosis task, the stochastic photonic spiking neural network (S-PSNN) demonstrates high classification accuracy and the ability to evaluate diagnosis uncertainty. The S-PSN enables high-speed Bayesian inference for reliable information processing in error-critical scenarios.
Article
Engineering, Mechanical
Karthik Reddy Lyathakula, Fuh-Gwo Yuan
Summary: The paper presents an efficient and robust probabilistic fatigue life prediction framework for adhesively bonded joints, calibrating the fatigue life model with experimental data and utilizing probabilistic assessment and neural networks for prediction. This framework allows rapid simulation of fatigue degradation and quantification of uncertainties for probabilistic fatigue life prediction in various joint configurations.
INTERNATIONAL JOURNAL OF FATIGUE
(2021)
Article
Engineering, Mechanical
P. L. Green, L. J. Devlin, R. E. Moore, R. J. Jackson, J. Li, S. Maskell
Summary: This paper discusses the optimization of the 'L-kernel' in Sequential Monte Carlo samplers to improve performance, resulting in reduced variance of estimates and fewer resampling requirements.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Computer Science, Artificial Intelligence
Manuel Gomez-Olmedo, Rafael Cabanas, Andres Cano, Serafin Moral, Ofelia P. Retamero
Summary: This study introduces a new structure called value-based potentials (VBPs) to efficiently represent quantitative information in probabilistic graphical models. VBPs leverage repeated values to reduce memory requirements and outperform probability trees (PTs) by overcoming certain limitations.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2021)
Article
Engineering, Geological
Jiabao Xu, Lulu Zhang, Jinhui Li, Zijun Cao, Haoqing Yang, Xiangyu Chen
Summary: This study explores the ability of Bayesian inference using MCMC simulation to accurately estimate the variogram of geotechnical properties with a trend, presenting predictive uncertainty and total uncertainty bounds. Analysis of 200 measurements of q(c) data confirms the accuracy and feasibility of this method.
GEORISK-ASSESSMENT AND MANAGEMENT OF RISK FOR ENGINEERED SYSTEMS AND GEOHAZARDS
(2021)
Article
Geosciences, Multidisciplinary
Christoph Herbert, Adriano Camps, Florian Wellmann, Mercedes Vall-llossera
Summary: In this study, a Bayesian unsupervised learning algorithm is used to segment Arctic sea ice to recognize spatial patterns and analyze the temporal stability and separability of classes. This method is important for improving current sea ice thickness detection algorithms.
GEOPHYSICAL RESEARCH LETTERS
(2021)
Article
Construction & Building Technology
Hai-Bin Huang, Wei Zhang, Zhi-Guo Sun, Dong-Sheng Wang
Summary: In this study, a probabilistic approach is proposed to improve the predictive accuracy and uncertainty quantification of existing deterministic bond strength models for EB FRP-to-concrete joints. Through evaluation and calibration using an experimental database, the proposed probabilistic models are capable of accurately predicting the bond strength of EB FRP-to-concrete joints and quantifying the corresponding uncertainties.
CONSTRUCTION AND BUILDING MATERIALS
(2022)
Article
Mathematics
Pedro Bonilla-Nadal, Andres Cano, Manuel Gomez-Olmedo, Serafin Moral, Ofelia Paula Retamero
Summary: The computerization of tasks generates a large amount of data, leading to the development of machine-learning methods to extract useful information for decision-making processes. Fields like medicine and education are interested in obtaining relevant information from this data, but the complex nature of the problems requires efficient techniques and approximation methods to handle the high degree of interdependency between variables.
Article
Computer Science, Information Systems
Isabel Haasler, Rahul Singh, Qinsheng Zhang, Johan Karlsson, Yongxin Chen
Summary: In this study, a connection between multi-marginal optimal transport problems and probabilistic graphical models is pointed out, showing that an entropy regularized multi-marginal optimal transport is equivalent to a Bayesian marginal inference problem. This relation extends both optimal transport and probabilistic graphical model theories while enabling fast algorithms for multi-marginal optimal transport through leveraging Bayesian inference algorithms. Several numerical examples are provided to illustrate the results.
IEEE TRANSACTIONS ON INFORMATION THEORY
(2021)
Article
Construction & Building Technology
Qi-Sen Chen, Bo Yu, Bing Li
Summary: This study updated probabilistic models based on Bayesian theory and Markov Chain Monte Carlo, calibrated deterministic models for concrete axial compressive behavior, and evaluated performance under different conditions while individually analyzing different types of FRP, providing an efficient approach to enhance the confidence level and computational accuracy of deterministic models in literature.
CONSTRUCTION AND BUILDING MATERIALS
(2021)
Article
Automation & Control Systems
Rahul Singh, Isabel Haasler, Qinsheng Zhang, Johan Karlsson, Yongxin Chen
Summary: This article proposes a new inference algorithm based on optimal transport for solving inference problems over probabilistic graphical models with aggregate data. The algorithm is efficient and globally convergent, and performs well in specific cases like hidden Markov models.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Computer Science, Artificial Intelligence
Benoit Audelan, Herve Delingette
Summary: Monitoring the quality of image segmentation is crucial in clinical applications. This paper introduces an unsupervised approach based on a generic probabilistic model for quality assessment of segmented images, which can automatically detect challenging cases in a dataset and localize possible segmentation errors.
MEDICAL IMAGE ANALYSIS
(2021)
Article
Computer Science, Artificial Intelligence
Rui Lv, Dingheng Wang, Jiangbin Zheng, Zhao-Xu Yang
Summary: In this paper, the authors investigate tensor decomposition for neural network compression. They analyze the convergence and precision of tensor mapping theory, validate the rationality of tensor mapping and its superiority over traditional tensor approximation based on the Lottery Ticket Hypothesis. They propose an efficient method called 3D-KCPNet to compress 3D convolutional neural networks using the Kronecker canonical polyadic (KCP) tensor decomposition. Experimental results show that 3D-KCPNet achieves higher accuracy compared to the original baseline model and the corresponding tensor approximation model.
Article
Computer Science, Artificial Intelligence
Xiangkun He, Zhongxu Hu, Haohan Yang, Chen Lv
Summary: In this paper, a novel constrained multi-objective reinforcement learning algorithm is proposed for personalized end-to-end robotic control with continuous actions. The approach trains a single model using constraint design and a comprehensive index to achieve optimal policies based on user-specified preferences.
Article
Computer Science, Artificial Intelligence
Zhijian Zhuo, Bilian Chen, Shenbao Yu, Langcai Cao
Summary: In this paper, a novel method called Expansion with Contraction Method for Overlapping Community Detection (ECOCD) is proposed, which utilizes non-negative matrix factorization to obtain disjoint communities and applies expansion and contraction processes to adjust the degree of overlap. ECOCD is applicable to various networks with different properties and achieves high-quality overlapping community detection.
Article
Computer Science, Artificial Intelligence
Yizhe Zhu, Chunhui Zhang, Jialin Gao, Xin Sun, Zihan Rui, Xi Zhou
Summary: In this work, the authors propose a Contrastive Spatio-Temporal Distilling (CSTD) approach to improve the detection of high-compressed deepfake videos. The approach leverages spatial-frequency cues and temporal-contrastive alignment to fully exploit spatiotemporal inconsistency information.
Review
Computer Science, Artificial Intelligence
Laijin Meng, Xinghao Jiang, Tanfeng Sun
Summary: This paper provides a review of coverless steganographic algorithms, including the development process, known contributions, and general issues in image and video algorithms. It also discusses the security of coverless steganography from theoretical analysis to actual investigation for the first time.
Article
Computer Science, Artificial Intelligence
Yajie Bao, Tianwei Xing, Xun Chen
Summary: Visual question answering requires processing multi-modal information and effective reasoning. Neural-symbolic learning is a promising method, but current approaches lack uncertainty handling and can only provide a single answer. To address this, we propose a confidence based neural-symbolic approach that evaluates NN inferences and conducts reasoning based on confidence.
Article
Computer Science, Artificial Intelligence
Anh H. Vo, Bao T. Nguyen
Summary: Interior style classification is an interesting problem with potential applications in both commercial and academic domains. This project proposes a method named ISC-DeIT, which combines data-efficient image transformer architectures and knowledge distillation, to address the interior style classification problem. Experimental results demonstrate a significant improvement in predictive accuracy compared to other state-of-the-art methods.
Article
Computer Science, Artificial Intelligence
Shashank Kotyan, Danilo Vasconcellos Vargas
Summary: This article introduces a novel augmentation technique called Dynamic Scanning Augmentation to improve the accuracy and robustness of Vision Transformer (ViT). The technique leverages dynamic input sequences to adaptively focus on different patches, resulting in significant changes in ViT's attention mechanism. Experimental results demonstrate that Dynamic Scanning Augmentation outperforms ViT in terms of both robustness to adversarial attacks and accuracy against natural images.
Article
Computer Science, Artificial Intelligence
Hiba Alqasir, Damien Muselet, Christophe Ducottet
Summary: The article proposes a solution to improve the learning process of a classification network by providing shape priors, reducing the need for annotated data. The solution is tested on cross-domain digit classification tasks and a video surveillance application.
Article
Computer Science, Artificial Intelligence
Dexiu Ma, Mei Liu, Mingsheng Shang
Summary: This paper proposes a method using neural dynamics solvers to solve infinity-norm optimization problems. Two improved solvers are constructed and their effectiveness and superiority are demonstrated through theoretical analysis and simulation experiments.
Article
Computer Science, Artificial Intelligence
Francesco Gregoretti, Giovanni Pezzulo, Domenico Maisto
Summary: Active Inference is a computational framework that uses probabilistic inference and variational free energy minimization to describe perception, planning, and action. cpp-AIF is a header-only C++ library that provides a powerful tool for implementing Active Inference for Partially Observable Markov Decision Processes through multi-core computing. It is cross-platform and improves performance, memory management, and usability compared to existing software.
Article
Computer Science, Artificial Intelligence
Zelin Ying, Dawei Cheng, Cen Chen, Xiang Li, Peng Zhu, Yifeng Luo, Yuqi Liang
Summary: This paper proposes a novel stock market trends prediction framework called SMART, which includes a self-supervised stock technical data sequence embedding model S3E. By training with multiple self-supervised auxiliary tasks, the model encodes stock technical data sequences into embeddings and uses the learned sequence embeddings for predicting stock market trends. Extensive experiments on China A-Shares market and NASDAQ market prove the high effectiveness of our model in stock market trends prediction, and its effectiveness is further validated in real-world applications in a leading financial service provider in China.
Article
Computer Science, Artificial Intelligence
Hao Li, Hao Jiang, Dongsheng Ye, Qiang Wang, Liang Du, Yuanyuan Zeng, Liu Yuan, Yingxue Wang, C. Chen
Summary: DHGAT1, a dynamic hyperbolic graph attention network, utilizes hyperbolic metric properties to embed dynamic graphs. It employs a spatiotemporal self-attention mechanism and weighted node representations, resulting in excellent performance in link prediction tasks.
Article
Computer Science, Artificial Intelligence
Jiehui Huang, Zhenchao Tang, Xuedong He, Jun Zhou, Defeng Zhou, Calvin Yu-Chian Chen
Summary: This study proposes a progressive learning multi-scale feature blending model for image deraining tasks. The model utilizes detail dilation and texture extraction to improve the restoration of rainy images. Experimental results show that the model achieves near state-of-the-art performance in rain removal tasks and exhibits better rain removal realism.
Article
Computer Science, Artificial Intelligence
Lizhi Liu, Zilin Gao, Yinhe Wang, Yongfu Li
Summary: This paper proposes a novel discrete-time interconnected model for depicting complex dynamical networks. The model consists of nodes and edges subsystems, which consider the dynamic characteristic of both nodes and edges. By designing control strategies and coupling modes, the stabilization and synchronization of the network are achieved. Simulation results demonstrate the effectiveness of the proposed methods.