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Topology identification under spatially correlated noise

AUTOMATICA 2023年第1期156卷

作者： Veedu, Mishfad Shaikh V. Salapaka, Murti Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA

This article addresses the problem of reconstructing the topology of a network of agents interacting via linear dynamics, while being excited by exogenous stochastic sources that are possibly correlated across the agents, from time-series measurements alone. It is shown, under the assumption that the correlations are affine in nature, such network of nodal interactions is equivalent to a network with added agents. The added agents are represented by nodes that are latent, where no corresponding time-series measurements are available;however, here all the exogenous excitements are spatially (that is, across agents) uncorrelated. Generalizing affine correlations, it is shown that, under polynomial correlations, the latent nodes in the expanded networks can be excited by clusters of noise sources, where the clusters are uncorrelated with each other. The clusters can be replaced with a single noise source if the latent nodes are allowed to have non-linear interactions. Finally, using the sparse plus low-rank matrix decomposition of the imaginary part of the inverse power spectral density matrix (IPSDM) of the time-series data, the topology of the network is reconstructed. Under non conservative assumptions, the correlation graph of the noise sources is retrieved. & COPY;2023 Elsevier Ltd. All rights reserved.

关键词： Linear dynamical systems Time-series analysis probabilistic graphical model Network topology identification Power spectral density Sparse estimation Latent nodes Structure learning Learning and control Sensor placement

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WLFS: Weighted label fusion learning framework for glioma tumor segmentation in brain MRI

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2021年第0期68卷 102617-102617页

作者： Barzegar, Zeynab Jamzad, Mansour Sharif Univ Technol Dept Comp Engn Tehran Iran

Glioma is a common type of tumor that develops in the brain. Due to many differences in the shape and appearance, accurate segmentation of glioma for identifying all parts of the tumor and its surrounding tissues in cancer detection is a challenging task in cancer detection. In recent researches, the combination of atlas-based segmentation and machine learning methods have presented superior performance over other automatic brain MRI segmentation algorithms. To overcome the side effects of limited existing information on atlas-based segmentation, and the long training and the time consuming phase of learning methods, we proposed a semisupervised learning framework by introducing a probabilistic graph based method. It combines the advantages of label propagation and patch-based segmentation on a parametric graph. To evaluate the proposed framework, we apply it to publicly available BRATS datasets, including low and high-grade glioma tumors. The experimental results show that the proposed framework has accurate segmentation results. Compared with the state-of-the-art methods, the proposed framework could obtain the best dice score for segmenting the "whole tumor" (WT) and "tumor core" (TC) regions. The segmentation result of the "enhancing active tumor" (ET) region is similar to the most recent works compared in this paper.

关键词： Multi-modal brain MRI Glioma brain tumor Atlas-based segmentation Patch-based segmentation probabilistic graphical model Label propagation

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Quantifying scientists' research ability by taking institutions' scientific impact as priori information

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JOURNAL OF INFORMATION SCIENCE 2023年

作者： Huang, Shengzhi Lu, Wei Huang, Yong Luo, Zhuoran Wuhan Univ Sch Informat Management Wuhan Peoples R China Wuhan Univ Informat Retrieval & Knowledge Min Lab Wuhan Peoples R China Wuhan Univ Sch Informat Management Wuhan 430072 Peoples R China

Scholar performance evaluation is extremely important in research assessment decisions, such as funding allocation, academic rankings, and academic promotion. In this article, we propose the institution Q model (IQ) and its two variants (IQ-2 and IQ-3), which aim to evaluate the individual-level research ability to publish high-quality scientific papers. Specifically, our models integrate scientists' institutions, countries and collaborators as valuable prior information and jointly evaluate the research ability of scientists from different institutions. To estimate model parameters and hidden variables defined in our models, we propose a generic BBVI-EM algorithm. To test the effectiveness of our models, we examine their performance on the synthetic data and the empirical data (17,750/26,992 scientists in the computer science/physics field). We find that our models can more accurately quantify the research ability of scientists and institutions and more effectively predict scientists' scientific impact (the h-index and total citations) than the Q model and common machine learning models. In conclusion, our models are effective evaluation and prediction tools for quantifying research ability and predicting the scientific impact, and the BBVI-EM algorithm is an effective variational inference algorithm. This study makes a theoretical contribution to broaden the idea of incorporating the academic environment into scientific evaluation.

关键词： Citation analysis probabilistic graphical model research ability variational inference

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New graphical models for sequential data and the improved state estimations by data-conditioned driving noises

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2024年第1期2024卷 50页

作者： Lee, Wonjung Beijing Normal Univ Zhuhai Res Ctr Math Zhuhai 519087 Peoples R China BNU HKBU Dept Math Sci United Int Coll Zhuhai 519087 Peoples R China

A prevalent problem in statistical signal processing, applied statistics, and time series analysis arises from the attempt to identify the hidden state of Markov process based on a set of available noisy observations. In the context of sequential data, filtering refers to the probability distribution of the underlying Markovian system given the measurements made at or before the time of the estimated state. In addition to the filtering, the smoothing distribution is obtained from incorporating measurements made after the time of the estimated state into the filtered solution. This work proposes a number of new filters and smoothers that, in contrast to the traditional schemes, systematically make use of the process noises to give rise to enhanced performances in addressing the state estimation problem. In doing so, our approaches for the resolution are characterized by the application of the graphical models;the graph-based framework not only provides a unified perspective on the existing filters and smoothers but leads us to design new algorithms in a consistent and comprehensible manner. Moreover, the graph models facilitate the implementation of the suggested algorithms through message passing on the graph.

关键词： probabilistic graphical model Bayesian inference Gaussian approximation filter and smoother Cubature measure Expectation propagation (EP)

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Exploring the influence of climate change-induced drought propagation on wetlands

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ECOLOGICAL ENGINEERING 2020年 149卷 105799-105799页

作者： Jehanzaib, Muhammad Kim, Tae-Woong Hanyang Univ Dept Civil & Environm Engn Seoul 04763 South Korea Hanyang Univ Dept Civil & Environm Engn Ansan 15588 South Korea

Drought has been one of the most ominous natural hazards for the last century. However, it is a big challenge to identify the progression of drought, because, unlike other natural disasters, its progress is very slow. This study examined the impact of climate change on drought-related hydro-meteorological variables (precipitation, soil moisture, and streamflow) and drought propagation from meteorological to agricultural and hydrological drought based on the emission scenario representative concentration pathway (RCP) 8.5. The multi-model ensemble (MME) projection of five general circulation models (GCMs) was made, and Sen's slope test was used to identify the trend embedded in the hydro-meteorological variables. Drought characteristics (duration, severity, and frequency) of meteorological, agricultural, and hydrological drought were also investigated spatially and temporally under the climate change scenario. A probabilistic graphical model (PGM) was used to estimate the propagation probability of different states of meteorological drought to agricultural and hydrological droughts, whereas the joint propagation probability of meteorological and agricultural drought to hydrological drought was investigated in the Nakdong River basin of South Korea. The drought characteristics (duration and severity) of meteorological, agricultural, and hydrological drought were projected to increase across the basin, except for the north and northwestern parts, for the period of 2021 to 2060, whereas drought frequency was projected to increase in the whole basin in all future periods. The results of drought propagation showed that the propagation probability of severe and extreme states of meteorological drought resulting in severe and extreme states of agricultural and hydrological drought ranged from 0.36-0.57 and 0.48-0.72, respectively;whereas the propagation probability of severe and extreme states of agricultural drought resulting in severe and extreme states of hydrological dr

关键词： Climate change Multi-model ensemble Drought propagation probabilistic graphical model

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Unification of probabilistic graph model and deep reinforcement learning (UPGMDRL) for multi-intersection traffic signal control

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KNOWLEDGE-BASED SYSTEMS 2024年 305卷

作者： Sattarzadeh, Ali Reza Pathirana, Pubudu N. Deakin Univ Sch Engn Waurn Ponds Vic 3216 Australia

Traffic signals play a pivotal role in modern life by preventing collisions, regulating traffic flow, and ensuring a predictable and efficient transportation system. Adaptive traffic light signal control (ATSC) is a promising paradigm for mitigating traffic congestion in Intelligent Transportation Systems (ITS). Among various AI-based approaches, Deep Reinforcement Learning (DRL) has gained widespread application, demonstrating superior performance. This paper aims to develop a latent space reinforcement learning method for intelligent traffic control, with a focus on making explainable decisions. According to the latent model and hidden Markov mixed model, this method integrated both to develop an ATSC framework for traffic networks with multiple intersections. Given the challenges posed by high-dimensional data and a limited understanding of the task, traditional decision-making methods often struggle with understanding the environment. This paper aims to provide semantic information and an enhanced understanding of the environment by offering interpretable states. The latent model is employed to extract task-relevant information from underlying representations within a framework that unifies representation learning and DRL. The experimental results demonstrate how our approach effectively and efficiently balances traffic flow, leading to improved traffic management.

关键词： Traffic congestion Adaptive traffic signal control Intelligent transportation systems Deep reinforcement learning probabilistic graphical model

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Harshness-aware sentiment mining framework for product review

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 187卷 115887-115887页

作者： Wang, Xun Zhou, Ting Wang, Xiaoyang Fang, Yili Zhejiang Gongshang Univ Sch Comp & Informat Engn Hangzhou 310018 Peoples R China

Sentiment mining has been a helpful mechanism that targets to understand the market feedback on certain commodities by utilizing user comments. In general, the process of yielding each comment is essentially associated with his/her criteria for rating (i.e., the degree of harshness) , which makes users provide biased comments. For instance, for a tolerant user, although the user is extremely dissatisfied with the product, harshness still makes her yield a neutral comment which cannot indicate the product quality. Existing work straightforwardly removes the comments of harsh users and those of tolerant ones, which is not the best strategy. To this end, we propose a harshness-aware sentiment analysis framework for product review. First, we depict the process of providing comments from users as a probabilistic graphical model in which the harshness is incorporated. Second, we employ a Bayesian-based inference for sentiment mining. Extensive experimental evaluations have shown that the results of the proposed method are more consistent with the expert evaluations than those of the state-of-the-art methods, and even outperform the method which infers the final evaluations with the ground truth of comments without considering users' harshness.

关键词： Sentiment mining Bayesian inference probabilistic graphical model

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Face photo-sketch synthesis via intra-domain enhancement

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KNOWLEDGE-BASED SYSTEMS 2023年 259卷

作者： Peng, Chunlei Zhang, Congyu Liu, Decheng Wang, Nannan Gao, Xinbo Xidian Univ Sch Cyber Engn State Key Lab Integrated Serv Networks Xian 710071 Shaanxi Peoples R China Shanghai Key Lab Comp Software Evaluating & Testi Shanghai 201112 Peoples R China Xidian Univ Sch Telecommun Engn State Key Lab Integrated Serv Networks Xian 710071 Shaanxi Peoples R China Chongqing Univ Posts & Telecommun Chongqing Key Lab Image Cognit Chongqing 400065 Peoples R China

Face photo-sketch synthesis plays an increasingly important role in the field of law enforcement and entertainment. However, most of the existing methods only focus on how to cross the modality differences between the photo domain and the sketch domain, while neglecting the image quality enhancement of the generated faces. In this paper, we propose a novel intra-domain enhancement (IDE) method for the face photo-sketch synthesis task. Our method is composed of two steps to cope with the inter-domain generation gap and the intra-domain quality defect respectively, i.e. inter-domain face generation and intra-domain face enhancement. In the inter-domain face generation stage, we apply a probabilistic graphical model which is capable of synthesizing the coarse face sketch from photos with low quality. In the intra-domain face enhancement stage, a Multi-Layer feature Aggregation (MLA) based Generative Adversarial Networks (GAN) is designed to enhance facial details in the sketches and further refine the synthesized image quality. Our first phase helps bridge the modality gap between photos and sketches and ensures the shape consistency between them. The second phase aims to improve the detail-quality and resolution of the synthesized sketches. Extensive experimental results on public face sketch databases outperform the state-of-the-art face sketch synthesis methods. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Face sketch synthesis probabilistic graphical model Generative Adversarial Networks Intra-domain enhancement

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probabilistic modeling of Imaging, Genetics and Diagnosis

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2016年第7期35卷 1765-1779页

作者： Batmanghelich, Nematollah K. Dalca, Adrian Quon, Gerald Sabuncu, Mert Golland, Polina MIT Comp Sci & Artificial Intelligence Lab 77 Massachusetts Ave Cambridge MA 02139 USA MIT Dept Elect Engn & Comp Sci Cambridge MA 02139 USA Univ Calif Davis Davis CA 95616 USA Massachusetts Gen Hosp Martinos Ctr Biomed Imaging Charlestown MA 02129 USA

We propose a unified Bayesian framework for detecting genetic variants associated with disease by exploiting image-based features as an intermediate phenotype. The use of imaging data for examining genetic associations promises new directions of analysis, but currently the most widely used methods make sub-optimal use of the richness that these data types can offer. Currently, image features are most commonly selected based on their relevance to the disease phenotype. Then, in a separate step, a set of genetic variants is identified to explain the selected features. In contrast, our method performs these tasks simultaneously in order to jointly exploit information in both data types. The analysis yields probabilistic measures of clinical relevance for both imaging and genetic markers. We derive an efficient approximate inference algorithm that handles the high dimensionality of image and genetic data. We evaluate the algorithm on synthetic data and demonstrate that it outperforms traditional models. We also illustrate our method on Alzheimer's Disease Neuroimaging Initiative data.

关键词： Bayesian models imaging genetics probabilistic graphical model variational inference

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Mutual information for explainable deep learning of multiscale systems

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JOURNAL OF COMPUTATIONAL PHYSICS 2021年 444卷 110551-110551页

作者： Taverniers, Soren Hall, Eric J. Katsoulakis, Markos A. Tartakovsky, Daniel M. Stanford Univ Dept Energy Resources Engn Stanford CA 94305 USA Univ Dundee Div Math Dundee DD1 4HN Scotland Univ Massachusetts Dept Math & Stat Amherst MA 01003 USA

Timely completion of design cycles for complex systems ranging from consumer electronics to hypersonic vehicles relies on rapid simulation-based prototyping. The latter typically involves high-dimensional spaces of possibly correlated control variables (CVs) and quantities of interest (QoIs) with non-Gaussian and possibly multimodal distributions. We develop a model-agnostic, moment-independent global sensitivity analysis (GSA) that relies on differential mutual information to rank the effects of CVs on QoIs. The data requirements of this information-theoretic approach to GSA are met by replacing computationally intensive components of the physics-based model with a deep neural network surrogate. Subsequently, the GSA is used to explain the surrogate predictions, and the surrogate-driven GSA is deployed as an uncertainty quantification emulator to close design loops. Viewed as an uncertainty quantification method for interrogating the surrogate, this framework is compatible with a wide variety of black-box models. We demonstrate that the surrogate-driven mutual information GSA provides useful and distinguishable rankings via a validation step for applications of interest in energy storage. Consequently, our information-theoretic GSA provides an "outer loop" for accelerated product design by identifying the most and least sensitive input directions and performing subsequent optimization over appropriately reduced parameter subspaces. (C) 2021 Elsevier Inc. All rights reserved.

关键词： Surrogate model Mutual information Global sensitivity analysis Black box probabilistic graphical model Electrical double-layer capacitor

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