Pursuing a non-cooperative moving target through multiple unmanned aerial vehicles (multi-UAV) is still challenging, especially in complex environments with dynamic obstacles. This article proposes a self-organizing m...
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Pursuing a non-cooperative moving target through multiple unmanned aerial vehicles (multi-UAV) is still challenging, especially in complex environments with dynamic obstacles. This article proposes a self-organizing multi-UAV cooperative pursuit approach based on hierarchical probabilistic graphical models. Firstly, we establish the UAV double-integrator kinematic models and provide a mathematical description of the pursuit task. Subsequently, a task-specific hierarchical probabilisticgraphical model is designed for autonomous decision-making of UAVs. In the model, local perception states and individual motion capabilities are integrated to estimate the probability distribution parameters for each node. To enhance pursuit efficiency, the pursuit task is segmented into multiple phases and a "dispersed encirclement" strategy is devised inspired by wolf pack hunting behavior. Finally, numerical simulations and real-world experiments are conducted to validate the scalability, adaptability, and robustness of the proposed approach.
In this study, we formulate the task of Video Anomaly Detection as a probabilistic analysis of object bounding boxes. We hypothesize that the representation of objects via their bounding boxes only, can be sufficient ...
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In this work, we develop a graphical model to capture team dynamics. We analyze the model and show how to learn its parameters from data. Using our model we study the phenomenon of team collapse from a computational p...
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ISBN:
(纸本)9798400712456
In this work, we develop a graphical model to capture team dynamics. We analyze the model and show how to learn its parameters from data. Using our model we study the phenomenon of team collapse from a computational perspective. We use simulations and real-world experiments to find the main causes of team collapse. We also provide the principles of building resilient teams, i.e., teams that avoid collapsing. Finally, we use our model to analyze the structure of NBA teams and dive deeper into games of interest.
probabilistic graphical models have been widely recognized as a powerful formalism in the bioinformatics field, especially in gene expression studies and linkage analysis. Although less well known in association genet...
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probabilistic graphical models have been widely recognized as a powerful formalism in the bioinformatics field, especially in gene expression studies and linkage analysis. Although less well known in association genetics, many successful methods have recently emerged to dissect the genetic architecture of complex diseases. In this review article, we cover the applications of these models to the population association studies' context, such as linkage disequilibrium modeling, fine mapping and candidate gene studies, and genome-scale association studies. Significant breakthroughs of the corresponding methods are highlighted, but emphasis is also given to their current limitations, in particular, to the issue of scalability. Finally, we give promising directions for future research in this field.
This paper suggests a probabilistic graphical models' approach for finding optimal therapy. This approach is based on creating a network of dependencies using statistics patient treatment. We used Bayesian network...
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This paper suggests a probabilistic graphical models' approach for finding optimal therapy. This approach is based on creating a network of dependencies using statistics patient treatment. We used Bayesian networks for describing diabetes mellitus treatment. 4 networks were created, one of them with expert knowledge, and the other was created using different algorithms. Treatment outcomes include a set of treatment-goal values and a combination of drugs. Networks were trained and validated by the treatment dataset. Results of validation showed that this approach was high-quality for cases that had a wide representation of using medication. Most of the predictions were equal with the expert's opinion, therefore models could be used as part of Decision Support Systems for medical experts who work with patients suffering from T2DM (Type 2 Diabetes Mellitus). (C) 2021 The Authors. Published by ELSEVIER B.V.
We propose a probabilisticgraphical model (PGM) for prognosis and diagnosis of breast cancer. PGMs are suitable for building predictive models in medical applications, as they are powerful tools for making decisions ...
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ISBN:
(纸本)9781509002870
We propose a probabilisticgraphical model (PGM) for prognosis and diagnosis of breast cancer. PGMs are suitable for building predictive models in medical applications, as they are powerful tools for making decisions under uncertainty from big data with missing attributes and noisy evidence. Previous work relied mostly on clinical data to create a predictive model. Moreover, practical knowledge of an expert was needed to build the structure of a model, which may not be accurate. In our opinion, since cancer is basically a genetic disease, the integration of microarray and clinical data can improve the accuracy of a predictive model. However, since microarray data is high-dimensional, including genomic variables may lead to poor results for structure and parameter learning due to the curse of dimensionality and small sample size problems. We address these problems by applying manifold learning and a deep belief network (DBN) to microarray data. First, we construct a PGM and a DBN using clinical and microarray data, and extract the structure of the clinical model automatically by applying a structure learning algorithm to the clinical data. Then, we integrate these two models using softmax nodes. Extensive experiments using real-world databases, such as METABRIC and NKI, show promising results in comparison to Support Vector Machines (SVMs) and k-Nearest Neighbors (k-NN) classifiers, for classifying tumors and predicting events like recurrence and metastasis.
Reading is a complex cognitive process, errors in which may assume diverse forms. In this study, introducing a novel approach, we use two families of probabilistic graphical models to analyze patterns of reading error...
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ISBN:
(纸本)9781450336642
Reading is a complex cognitive process, errors in which may assume diverse forms. In this study, introducing a novel approach, we use two families of probabilistic graphical models to analyze patterns of reading errors made by dyslexic people: an LDA-based model and two Naive Bayes models which differ by their assumptions about the generation process of reading errors. The models are trained on a large corpus of reading errors. Results show that a Naive Bayes model achieves highest accuracy compared to labels given by clinicians (AUC = 0.801 +/- 0.05), thus providing the first automated and objective diagnosis tool for dyslexia which is solely based on reading errors data. Results also show that the LDA-based model best captures patterns of reading errors and could therefore contribute to the understanding of dyslexia and to future improvement of the diagnostic procedure. Finally, we draw on our results to shed light on a theoretical debate about the definition and heterogeneity of dyslexia. Our results support a model assuming multiple dyslexia subtypes, that of a heterogeneous view of dyslexia.
We study multi-marginal optimal transport problems from a probabilisticgraphical model perspective. We point out an elegant connection between the two when the underlying cost for optimal transport allows a graph str...
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We study multi-marginal optimal transport problems from a probabilisticgraphical model perspective. We point out an elegant connection between the two when the underlying cost for optimal transport allows a graph structure. In particular, an entropy regularized multi-marginal optimal transport is equivalent to a Bayesian marginal inference problem for probabilistic graphical models with the additional requirement that some of the marginal distributions are specified. This relation on the one hand extends the optimal transport as well as the probabilisticgraphical model theories, and on the other hand leads to fast algorithms for multi-marginal optimal transport by leveraging the well-developed algorithms in Bayesian inference. Several numerical examples are provided to highlight the results.
In this work, we quantify scalability of network resilience upon failures. We characterize resilience as the percentage of lost traffic upon failures and define scalability as the growth rate of the percentage of lost...
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In this work, we quantify scalability of network resilience upon failures. We characterize resilience as the percentage of lost traffic upon failures and define scalability as the growth rate of the percentage of lost traffic with respect to network size, link failure probability, and network traffic for given failure protection schemes. We apply probabilistic graphical models to characterize statistical dependence between physical-layer failures and the network-layer traffic, and analyze the scalability for large networks of different topologies. We first focus on the scalability of resilience for regular topologies under uniform deterministic traffic with independent and dependent link failures, with and without protection. For large networks with small probabilities of failures and without protection, we show that the scalability of network resilience grows linearly with the average route length and with the "effective" link failure probability. For large networks with 1 + 1 protection, we obtain lower and upper bound of the percentage of lost traffic. We derive approximations of the scalability for arbitrary topologies, and attain close-form analytical results for ring, star, and mesh-torus topologies. We then study network resilience under random traffic with Poisson arrivals. We find that when the network is under light load, the network resilience is reduced to that under uniform deterministic traffic. When the network load is under heavy load, the percentage of lost traffic approaches the marginal probability of link failure. Our scalability analysis shows explicitly how network resilience varies with different factors and provides insights for resilient network design.
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