Multifidelity Analysis for Predicting Rare Events in Stochastic Computational Models of Complex Biological Systems

Rare events such as genetic mutations or cell-cell interactions are important contributors to dynamics in complex biological systems, eg, in drug-resistant infections. Computational approaches can help analyze rare events that are difficult to study experimentally. However, analyzing the frequency and dynamics of rare events in computational models can also be challenging due to high computational resource demands, especially for high-fidelity stochastic computational models. To facilitate analysis of rare events in complex biological systems, we present a multifidelity analysis approach that uses medium-fidelity analysis (Monte Carlo simulations) and/or low-fidelity analysis (Markov chain models) to analyze high-fidelity stochastic model results. Medium-fidelity analysis can produce large numbers of possible rare event trajectories for a single high-fidelity model simulation. This allows prediction of both rare event dynamics and probability distributions at much lower frequencies than high-fidelity models. Low-fidelity analysis can calculate probability distributions for rare events over time for any frequency by updating the probabilities of the rare event state space after each discrete event of the high-fidelity model. To validate the approach, we apply multifidelity analysis to a high-fidelity model of tuberculosis disease. We validate the method against high-fidelity model results and illustrate the application of multifidelity analysis in predicting rare event trajectories, performing sensitivity analyses and extrapolating predictions to very low frequencies in complex systems. We believe that our approach will complement ongoing efforts to enable accurate prediction of rare event dynamics in high-fidelity computational models.

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A New Variable Fidelity Optimization Framework Based on Model Fusion and Objective-Oriented Sequential Sampling

Volume 6: 33rd Design Automation Conference, Parts A and B ◽

10.1115/detc2007-35782 ◽

2007 ◽

Cited By ~ 3

Author(s):

Ying Xiong ◽

Wei Chen ◽

Kwok-Leung Tsui

Keyword(s):

Computational Models ◽

Sequential Sampling ◽

Surrogate Models ◽

High Fidelity ◽

Design Decision ◽

Model Fusion ◽

Design Objective ◽

Variable Fidelity ◽

High Fidelity Simulations ◽

Fidelity Model

Computational models with variable fidelity have been widely used in engineering design. To alleviate the computational burden, surrogate models are used for optimization without recourse to expensive high-fidelity simulations. In this work, a model fusion technique based on Bayesian Gaussian process modeling is employed to construct cheap, surrogate models to integrate information from both low-fidelity and high-fidelity models, while the interpolation uncertainty of the surrogate model due to the lack of sufficient high-fidelity simulations is quantified. In contrast to space filling, the sequential sampling of a high-fidelity simulation model in our proposed framework is objective-oriented, aiming for improving a design objective. Strategy based on periodical switching criteria is studied which is shown to be effective in guiding the sequential sampling of a high-fidelity model towards improving a design objective as well as reducing the interpolation uncertainty. A design confidence (DC) metric is proposed to serves as the stopping criterion to facilitate design decision making against the interpolation uncertainty. Numerical and engineering examples are provided to demonstrate the benefits of the proposed methodology.

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Framework for rare event detection using Artificial Neural Network based context free grammar

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189164 ◽

2020 ◽

Vol 39 (6) ◽

pp. 8463-8475

Author(s):

Palanivel Srinivasan ◽

Manivannan Doraipandian

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Event Detection ◽

Performance Metrics ◽

Rare Events ◽

Rare Event ◽

Video Stream ◽

Context Free Grammar ◽

Artificial Neural ◽

Context Free

Rare event detections are performed using spatial domain and frequency domain-based procedures. Omnipresent surveillance camera footages are increasing exponentially due course the time. Monitoring all the events manually is an insignificant and more time-consuming process. Therefore, an automated rare event detection contrivance is required to make this process manageable. In this work, a Context-Free Grammar (CFG) is developed for detecting rare events from a video stream and Artificial Neural Network (ANN) is used to train CFG. A set of dedicated algorithms are used to perform frame split process, edge detection, background subtraction and convert the processed data into CFG. The developed CFG is converted into nodes and edges to form a graph. The graph is given to the input layer of an ANN to classify normal and rare event classes. Graph derived from CFG using input video stream is used to train ANN Further the performance of developed Artificial Neural Network Based Context-Free Grammar – Rare Event Detection (ACFG-RED) is compared with other existing techniques and performance metrics such as accuracy, precision, sensitivity, recall, average processing time and average processing power are used for performance estimation and analyzed. Better performance metrics values have been observed for the ANN-CFG model compared with other techniques. The developed model will provide a better solution in detecting rare events using video streams.

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A novel geo-hierarchical population mobility model for spatial spreading of resurgent epidemics

Scientific Reports ◽

10.1038/s41598-021-93810-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Alexandru Topîrceanu ◽

Radu-Emil Precup

Keyword(s):

Computational Models ◽

Human Mobility ◽

Discrete Event ◽

Mobility Model ◽

Hierarchical Organization ◽

Population Mobility ◽

Mobility Patterns ◽

Fine Grained ◽

Epidemic Size ◽

Control Of Epidemics

AbstractComputational models for large, resurgent epidemics are recognized as a crucial tool for predicting the spread of infectious diseases. It is widely agreed, that such models can be augmented with realistic multiscale population models and by incorporating human mobility patterns. Nevertheless, a large proportion of recent studies, aimed at better understanding global epidemics, like influenza, measles, H1N1, SARS, and COVID-19, underestimate the role of heterogeneous mixing in populations, characterized by strong social structures and geography. Motivated by the reduced tractability of studies employing homogeneous mixing, which make conclusions hard to deduce, we propose a new, very fine-grained model incorporating the spatial distribution of population into geographical settlements, with a hierarchical organization down to the level of households (inside which we assume homogeneous mixing). In addition, population is organized heterogeneously outside households, and we model the movement of individuals using travel distance and frequency parameters for inter- and intra-settlement movement. Discrete event simulation, employing an adapted SIR model with relapse, reproduces important qualitative characteristics of real epidemics, like high variation in size and temporal heterogeneity (e.g., waves), that are challenging to reproduce and to quantify with existing measures. Our results pinpoint an important aspect, that epidemic size is more sensitive to the increase in distance of travel, rather that the frequency of travel. Finally, we discuss implications for the control of epidemics by integrating human mobility restrictions, as well as progressive vaccination of individuals.

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Interspecies Chromosome Mapping in Caprimulgiformes, Piciformes, Suliformes, and Trogoniformes (Aves): Cytogenomic Insight into Microchromosome Organization and Karyotype Evolution in Birds

Cells ◽

10.3390/cells10040826 ◽

2021 ◽

Vol 10 (4) ◽

pp. 826

Author(s):

Rafael Kretschmer ◽

Marcelo Santos de Souza ◽

Ivanete de Oliveira Furo ◽

Michael N. Romanov ◽

Ricardo José Gunski ◽

...

Keyword(s):

Convergent Evolution ◽

Karyotype Evolution ◽

Rare Events ◽

Rare Event ◽

Chromosome Mapping ◽

The Other ◽

Other Hand ◽

Interchromosomal Rearrangement ◽

Insight Into ◽

Phalacrocorax Brasilianus

Interchromosomal rearrangements involving microchromosomes are rare events in birds. To date, they have been found mostly in Psittaciformes, Falconiformes, and Cuculiformes, although only a few orders have been analyzed. Hence, cytogenomic studies focusing on microchromosomes in species belonging to different bird orders are essential to shed more light on the avian chromosome and karyotype evolution. Based on this, we performed a comparative chromosome mapping for chicken microchromosomes 10 to 28 using interspecies BAC-based FISH hybridization in five species, representing four Neoaves orders (Caprimulgiformes, Piciformes, Suliformes, and Trogoniformes). Our results suggest that the ancestral microchromosomal syntenies are conserved in Pteroglossus inscriptus (Piciformes), Ramphastos tucanus tucanus (Piciformes), and Trogon surrucura surrucura (Trogoniformes). On the other hand, chromosome reorganization in Phalacrocorax brasilianus (Suliformes) and Hydropsalis torquata (Caprimulgiformes) included fusions involving both macro- and microchromosomes. Fissions in macrochromosomes were observed in P. brasilianus and H. torquata. Relevant hypothetical Neognathae and Neoaves ancestral karyotypes were reconstructed to trace these rearrangements. We found no interchromosomal rearrangement involving microchromosomes to be shared between avian orders where rearrangements were detected. Our findings suggest that convergent evolution involving microchromosomal change is a rare event in birds and may be appropriate in cytotaxonomic inferences in orders where these rearrangements occurred.

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Adaptive response prediction for aerodynamic shape optimization

Engineering Computations ◽

10.1108/ec-02-2016-0070 ◽

2017 ◽

Vol 34 (5) ◽

pp. 1485-1500

Author(s):

Leifur Leifsson ◽

Slawomir Koziel

Keyword(s):

Shape Optimization ◽

Adaptive Response ◽

Simulation Models ◽

Response Prediction ◽

High Fidelity ◽

High Fidelity Simulation ◽

Aerodynamic Shape Optimization ◽

Content Type ◽

Aerodynamic Shape ◽

Fidelity Model

Purpose The purpose of this paper is to reduce the overall computational time of aerodynamic shape optimization that involves accurate high-fidelity simulation models. Design/methodology/approach The proposed approach is based on the surrogate-based optimization paradigm. In particular, multi-fidelity surrogate models are used in the optimization process in place of the computationally expensive high-fidelity model. The multi-fidelity surrogate is constructed using physics-based low-fidelity models and a proper correction. This work introduces a novel correction methodology – referred to as the adaptive response prediction (ARP). The ARP technique corrects the low-fidelity model response, represented by the airfoil pressure distribution, through suitable horizontal and vertical adjustments. Findings Numerical investigations show the feasibility of solving real-world problems involving optimization of transonic airfoil shapes and accurate computational fluid dynamics simulation models of such surfaces. The results show that the proposed approach outperforms traditional surrogate-based approaches. Originality/value The proposed aerodynamic design optimization algorithm is novel and holistic. In particular, the ARP correction technique is original. The algorithm is useful for fast design of aerodynamic surfaces using high-fidelity simulation data in moderately sized search spaces, which is challenging using conventional methods because of excessive computational costs.

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High Fidelity Microsurgical Simulation: The Thiel Model and Evaluation Instrument

Plastic Surgery ◽

10.1177/2292550318800324 ◽

2018 ◽

Vol 27 (2) ◽

pp. 118-124 ◽

Cited By ~ 1

Author(s):

Andrei Odobescu ◽

Isak Goodwin ◽

Djamal Berbiche ◽

Joseph BouMerhi ◽

Patrick G. Harris ◽

...

Keyword(s):

External Validity ◽

Internal Validity ◽

High Fidelity ◽

Evaluation Instrument ◽

Intra Class Correlation ◽

Validity Measures ◽

The Difference ◽

Fidelity Model ◽

Junior Residents ◽

Internal And External Validity

Background: The Thiel embalmment method has recently been used in a number of medical simulation fields. The authors investigate the use of Thiel vessels as a high fidelity model for microvascular simulation and propose a new checklist-based evaluation instrument for microsurgical training. Methods: Thirteen residents and 2 attending microsurgeons performed video recorded microvascular anastomoses on Thiel embalmed arteries that were evaluated using a new evaluation instrument (Microvascular Evaluation Scale) by 4 fellowship trained microsurgeons. The internal validity was assessed using the Cronbach coefficient. The external validity was verified using regression models. Results: The reliability assessment revealed an excellent intra-class correlation of 0.89. When comparing scores obtained by participants from different levels of training, attending surgeons and senior residents (Post Graduate Year [PGY] 4-5) scored significantly better than junior residents (PGY 1-3). The difference between senior residents and attending surgeons was not significant. When considering microsurgical experience, the differences were significant between the advanced group and the minimal and moderate experience groups. The differences between minimal and moderate experience groups were not significant. Based on the data obtained, a score of 8 would translate into a level of microsurgical competence appropriate for clinical microsurgery. Conclusions: Thiel cadaveric vessels are a high fidelity model for microsurgical simulation. Excellent internal and external validity measures were obtained using the Microvascular Evaluation Scale (MVES).

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Parallel Network Simulation With OMNeT++

Research and Development on Information and Communication Technology ◽

10.32913/mic-ict-research.v3.n5.320 ◽

2009 ◽

Author(s):

András Varga ◽

Ahmet Y. Şekercioğlu Şekercioğlu

Keyword(s):

Communication Networks ◽

Large Scale ◽

Distributed Simulation ◽

Discrete Event ◽

Rare Event ◽

Telecommunication Networks ◽

Event Simulation ◽

Parallel And Distributed Simulation ◽

Simulation Protocol ◽

Parallel Network

This paper reports a new parallel and distributed simulation architecture for OMNeT++, an open-source discrete event simulation environment. The primary application area of OMNeT++ is the simulation of communication networks. Support for a conservative PDES protocol (the Null Message Algorithm) and the relatively novel Ideal Simulation Protocol has been implemented.Placeholder modules, a novel way of distributing the model over several logical processes (LPs) is presented. The OMNeT++ PDES implementation has a modular and extensible architecture, allowing new synchronization protocols and new communication mechanisms to be added easily, which makes it an attractive platform for PDES research, too. We intend touse this framework to harness the computational capacity of highperformance cluster computersfor modeling very large scale telecommunication networks to investigate protocol performance and rare event failure scenarios.

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Real time model-based diagnosis enabled by hybrid modeling

Annual Conference of the PHM Society ◽

10.36001/phmconf.2020.v12i1.1278 ◽

2020 ◽

Vol 12 (1) ◽

pp. 10

Author(s):

Ion Matei ◽

Alexander Feldman ◽

Johan De Kleer ◽

Alexandre Perez

Keyword(s):

Automatic Differentiation ◽

Complex Model ◽

Hybrid Modeling ◽

High Fidelity ◽

Time Model ◽

Order Of Magnitude ◽

Switch System ◽

Using Data ◽

Model Components ◽

Fidelity Model

In this paper we propose a hybrid modeling approach for generating reduced models of a high fidelity model of a physical system. We propose machine learning inspired representations for complex model components. These representations preserve in part the physical interpretation of the original components. Training platforms featuring automatic differentiation are used to learn the parameters of the new representations using data generated by the high-fidelity model. We showcase our approach in the context of fault diagnosis for a rail switch system. We generate three new model abstractions whose complexities are two order of magnitude smaller than the complexity of the high fidelity model, both in the number of equations and simulation time. Faster simulations ensure faster diagnosis solutions and enable the use of diagnosis algorithms relying heavily on large numbers of model simulations.

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