Journal articles on the topic 'Adaptive parametric sampling'
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Rafiq, Danish, and Mohammad Abid Bazaz. "Adaptive parametric sampling scheme for nonlinear model order reduction." Nonlinear Dynamics 107, no. 1 (November 2, 2021): 813–28. http://dx.doi.org/10.1007/s11071-021-07025-7.
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Azencott, R., A. Beri, and I. Timofeyev. "Adaptive Sub-sampling for Parametric Estimation of Gaussian Diffusions." Journal of Statistical Physics 139, no. 6 (May 1, 2010): 1066–89. http://dx.doi.org/10.1007/s10955-010-9975-y.
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Borggaard, Jeff, Kevin R. Pond, and Lizette Zietsman. "Parametric Reduced Order Models Using Adaptive Sampling and Interpolation." IFAC Proceedings Volumes 47, no. 3 (2014): 7773–78. http://dx.doi.org/10.3182/20140824-6-za-1003.02664.
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Liu, Ying, Hongguang Li, Huanyu Du, Ningke Tong, and Guang Meng. "An adaptive sampling procedure for parametric model order reduction by matrix interpolation." Journal of Low Frequency Noise, Vibration and Active Control 39, no. 4 (June 15, 2019): 821–34. http://dx.doi.org/10.1177/1461348419851595.
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An adaptive sampling approach for parametric model order reduction by matrix interpolation is developed. This approach is based on an efficient exploration of the candidate parameter sets and identification of the points with maximum errors. An error indicator is defined and used for fast evaluation of the parameter points in the configuration space. Furthermore, the exact error of the model with maximum error indicator is calculated to determine whether the adaptive sampling procedure reaches a desired error tolerance. To improve the accuracy, the orthogonal eigenvectors are utilized as the reduced-order basis. The proposed adaptive sampling procedure is then illustrated by application in the moving coil of electrical-dynamic shaker. It is shown that the new method can sample the parameter space adaptively and efficiently with the assurance of the resulting reduced-order models’ accuracy.
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Chen, Yi-Wen, and Wen-Hsiao Peng. "Parametric OBMC for Pixel-Adaptive Temporal Prediction on Irregular Motion Sampling Grids." IEEE Transactions on Circuits and Systems for Video Technology 22, no. 1 (January 2012): 113–27. http://dx.doi.org/10.1109/tcsvt.2011.2158341.
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Woudt, Edwin, Pieter-Tjerk de Boer, and Jan-Kees van Ommeren. "Improving Adaptive Importance Sampling Simulation of Markovian Queueing Models using Non-parametric Smoothing." SIMULATION 83, no. 12 (December 2007): 811–20. http://dx.doi.org/10.1177/0037549707087223.
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Jia, Gaofeng, and Alexandros A. Taflanidis. "Non-parametric stochastic subset optimization utilizing multivariate boundary kernels and adaptive stochastic sampling." Advances in Engineering Software 89 (November 2015): 3–16. http://dx.doi.org/10.1016/j.advengsoft.2015.06.014.
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Lu, Kuan, Haopeng Zhang, Kangyu Zhang, Yulin Jin, Shibo Zhao, Chao Fu, and Yushu Chen. "The Transient POD Method Based on Minimum Error of Bifurcation Parameter." Mathematics 9, no. 4 (February 16, 2021): 392. http://dx.doi.org/10.3390/math9040392.
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An invariable order reduction model cannot be obtained by the adaptive proper orthogonal decomposition (POD) method in parametric domain, there exists uniqueness of the model with different conditions. In this paper, the transient POD method based on the minimum error of bifurcation parameter is proposed and the order reduction conditions in the parametric domain are provided. The order reduction model equivalence of optimal sampling length is discussed. The POD method was applied for order reduction of a high-dimensional rotor system supported by sliding bearings in a certain speed range. The effects of speed, initial conditions, sampling length, and mode number on parametric domain order reduction are discussed. The existence of sampling length was verified, and two- and three-degrees-of-freedom (DOF) invariable order reduction models were obtained by proper orthogonal modes (POM) on the basis of optimal sampling length.
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Ourbih-Tari, Megdouda, and Mahdia Azzal. "Survival function estimation with non parametric adaptive refined descriptive sampling algorithm: A case study." Communications in Statistics - Theory and Methods 46, no. 12 (April 25, 2016): 5840–50. http://dx.doi.org/10.1080/03610926.2015.1065328.
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Morio, Jérôme. "Non-parametric adaptive importance sampling for the probability estimation of a launcher impact position." Reliability Engineering & System Safety 96, no. 1 (January 2011): 178–83. http://dx.doi.org/10.1016/j.ress.2010.08.006.
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Botev, Z. I., A. Ridder, and L. Rojas-Nandayapa. "Semiparametric cross entropy for rare-event simulation." Journal of Applied Probability 53, no. 3 (September 2016): 633–49. http://dx.doi.org/10.1017/jpr.2016.31.
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AbstractThe cross entropy is a well-known adaptive importance sampling method which requires estimating an optimal importance sampling distribution within a parametric class. In this paper we analyze an alternative version of the cross entropy, where the importance sampling distribution is selected instead within a general semiparametric class of distributions. We show that the semiparametric cross entropy method delivers efficient estimators in a wide variety of rare-event problems. We illustrate the favourable performance of the method with numerical experiments.
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Gautier, Athénaïs, David Ginsbourger, and Guillaume Pirot. "Goal-oriented adaptive sampling under random field modelling of response probability distributions." ESAIM: Proceedings and Surveys 71 (August 2021): 89–100. http://dx.doi.org/10.1051/proc/202171108.
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In the study of natural and artificial complex systems, responses that are not completely determined by the considered decision variables are commonly modelled probabilistically, resulting in response distributions varying across decision space. We consider cases where the spatial variation of these response distributions does not only concern their mean and/or variance but also other features including for instance shape or uni-modality versus multi-modality. Our contributions build upon a non-parametric Bayesian approach to modelling the thereby induced fields of probability distributions, and in particular to a spatial extension of the logistic Gaussian model. The considered models deliver probabilistic predictions of response distributions at candidate points, allowing for instance to perform (approximate) posterior simulations of probability density functions, to jointly predict multiple moments and other functionals of target distributions, as well as to quantify the impact of collecting new samples on the state of knowledge of the distribution field of interest. In particular, we introduce adaptive sampling strategies leveraging the potential of the considered random distribution field models to guide system evaluations in a goal-oriented way, with a view towards parsimoniously addressing calibration and related problems from non-linear (stochastic) inversion and global optimisation.
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Trampert, Patrick, Dmitri Rubinstein, Faysal Boughorbel, Christian Schlinkmann, Maria Luschkova, Philipp Slusallek, Tim Dahmen, and Stefan Sandfeld. "Deep Neural Networks for Analysis of Microscopy Images—Synthetic Data Generation and Adaptive Sampling." Crystals 11, no. 3 (March 5, 2021): 258. http://dx.doi.org/10.3390/cryst11030258.
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The analysis of microscopy images has always been an important yet time consuming process in materials science. Convolutional Neural Networks (CNNs) have been very successfully used for a number of tasks, such as image segmentation. However, training a CNN requires a large amount of hand annotated data, which can be a problem for material science data. We present a procedure to generate synthetic data based on ad hoc parametric data modelling for enhancing generalization of trained neural network models. Especially for situations where it is not possible to gather a lot of data, such an approach is beneficial and may enable to train a neural network reasonably. Furthermore, we show that targeted data generation by adaptively sampling the parameter space of the generative models gives superior results compared to generating random data points.
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Liu, Yang, and Balgobin Nandram. "Sampling methods for the concentration parameter and discrete baseline of the Dirichlet Process." Statistics in Transition New Series 23, no. 4 (December 1, 2022): 21–36. http://dx.doi.org/10.2478/stattrans-2022-0040.
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Abstract There are many models in the current statistical literature for making inferences based on samples selected from a finite population. Parametric models may be problematic because statistical inference is sensitive to parametric assumptions. The Dirichlet process (DP) prior is very flexible and determines the complexity of the model. It is indexed by two hyper-parameters: the baseline distribution and concentration parameter. We address two distinct problems in the article. Firstly, we review the current sampling methods for the concentration parameter, which use the continuous baseline distribution. We compare three different methods: the adaptive rejection method, the mixture of Gammas method and the grid method. We also propose a new method based on the ratio of uniforms. Secondly, in practice, some survey responses are known to be discrete. If a continuous distribution is adopted as the baseline distribution, the model is misspecified and standard inference may be invalid. We propose a discrete baseline approach to the DP prior and sample the unobserved responses from the finite population both using a Polya urn scheme and a Multinomial distribution. We applied our discrete baseline approach to a Phytophthora data set.
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S. Sureshkrishna, S. Varalakshmi, K. Senthil Kumar, A. K. Gnanasekar,. "An Effective Adaptive Threshold Based Compressive Spectrum Sensing in Cognitive Radio Networks." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 1 (March 18, 2021): 1220–24. http://dx.doi.org/10.17762/itii.v9i1.260.
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Spectrum sensing is playing a vital role in Cognitive Radio networks. Wideband spectrum sensing increases the speed of sensing but which in turn requires higher sampling rate and also increases the complexity of hardware and also power consumption. Compression based sensing reduces the sampling rate by using Sub-Nyquist sampling but the compression and the reconstruction problem exists. In compression based spectrum sensing, noise uncertainty is one of the major performance degradation factor. To reduce this degradation, compressive measurements based sensing with adaptive threshold is proposed. In this technique compressed signal is sensed without any reconstruction of the signal. When the nodes are mobile in the low SNR region, the noise uncertainty degrades the performance of spectrum sensing. To conquer this problem, noise variance is estimated using parametric estimation technique and the threshold is varied adaptively. In the low SNR region, this proposed technique reduces the effect of noise and improves the spectrum sensing performance.
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Скачков, Валерій, Віктор Чепкій, Олександр Єфимчиков, and Владислав Набок. "ПРОБЛЕМА ОПТИМАЛЬНОСТІ АДАПТИВНИХ РАДІОТЕХНІЧНИХ СИСТЕМ В СИТУАЦІЇ З НЕКЛАСИФІКОВАНОЮ НАВЧАЛЬНОЮ ВИБІРКОЮ СПОСТЕРЕЖЕНЬ." Collection of scientific works of Odesa Military Academy, no. 18 (March 3, 2023): 66–77. http://dx.doi.org/10.37129/2313-7509.2022.18.66-77.
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Statistical synthesis of adaptive systems for spatiotemporal signal processing in most radio engineering problems occurs under the condition of parametric a priori uncertainty. The prospective direction of overcoming parametric a priori uncertainty is affiliated with the prior learning procedure. As a result of the training, sufficient statistics of likelihood ratios are formed, which are used in decision-making. The asymptotic optimality of adaptive systems with alternative benchmarks, one of which is peculiar to the class of radio structures with an adaptive antenna array and the other with an adaptive interference compensator, is studied subjectively with unclassified training sampling. The optimality of the system is determined by the criterion of maximum signal-to-interference ratio. The energy parameter representing the signal-to-interference ratio at the output of adaptive radio systems with a generalized reference was chosen as an indicator of optimality. As a result of the research, analytical expressions for the estimation and comparative analysis of energy losses at the output of adaptive systems with different standards under conditions of classified and unclassified learning were obtained. The invariance of the system with an adaptive antenna array to any type of training with preservation of the asymptotic properties of the radio system in the situation with a finite size of the training sample is proved. This feature is considered as a basis for factorization of the transfer function of the adaptive system. This makes it possible to form an optimal hierarchical algorithm for compensation of combined passive and active interference, as well as active interference with an arbitrary spatial power spectrum. The obligatory classification of a training sample of observations for radio engineering systems with an adaptive interference compensator under conditions of signal-interference a priori uncertainty is substantiated. Keywords: asymptotic optimality, adaptive system, alternative benchmark, unclassified training sample, correlation matrix of observations, energy index.
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Vosniakos, G. C., and T. Giannakakis. "Reverse engineering of simple surfaces of unknown shape with touch probes: Scanning and compensation issues." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 217, no. 4 (April 1, 2003): 563–68. http://dx.doi.org/10.1243/095440503321628233.
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This work discusses issues concerning the implementation of scanning of unknown engineering objects containing just simple (i.e. no freeform) surfaces with touch probes on three-axis computer numerical control (CNC) measuring machines in order to reconstruct their shape in a computer aided design (CAD) system. Several ideas are put forward e.g. scanning along vertical slicing planes adaptive point sampling distances in-process ‘proactive’ segmentation of points into curve sections and probe radius compensation in two directions as well as limited remedy of edge scanning ambiguities. Most of the suggested algorithms are implemented as parametric numerical control (NC) programs on an OKUMA machining centre.
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Karwowski, Andrzej. "Performance evaluation of MoM-based wide-band EM simulation with adaptive frequency sampling and Stöer-Bulirsch algorithm." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 36, no. 4 (July 3, 2017): 859–70. http://dx.doi.org/10.1108/compel-07-2016-0318.
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Purpose The purpose of this paper is to examine the convergence, offered accuracy and efficiency of the bisectional adaptive frequency sampling (AFS) scheme combined with the Stöer-Bulirsch (SB) algorithm as a tool for supporting frequency-domain method-of-moments (MoM) in broadband electromagnetic (EM) simulations. Design/methodology/approach The AFS and SB procedures have been interfaced with the MoM code, and then, an extensive parametric study has been carried out to explore the performance of the numerical solution for the test problems of reconstructing frequency responses of the wire radiator and scatterer, respectively, over at least a decade bandwidth. Findings The results give evidence for the efficiency of the overall approach and its capability of constructing the approximation of multi-resonant responses with sharp resonant peaks from a substantially reduced number of EM samples (data points) compared to that of conventional uniform sampling. Originality/value Results of the study offer thorough insight into the performance of the AFS-SB technique, and the data given in this paper may be helpful in selecting the convergence criterion and the tolerance for the AFS-SB algorithm to achieve a possibly economical broadband simulation technique.
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Bellam Muralidhar, Nanda Kishore, Natalie Rauter, Andrey Mikhaylenko, Rolf Lammering, and Dirk A. Lorenz. "Parametric Model Order Reduction of Guided Ultrasonic Wave Propagation in Fiber Metal Laminates with Damage." Modelling 2, no. 4 (November 3, 2021): 591–608. http://dx.doi.org/10.3390/modelling2040031.
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This paper focuses on parametric model order reduction (PMOR) of guided ultrasonic wave propagation and its interaction with damage in a fiber metal laminate (FML). Structural health monitoring in FML seeks to detect, localize and characterize the damage with high accuracy and minimal use of sensors. This can be achieved by the inverse problem analysis approach, which employs the signal measurement data recorded by the embedded sensors in the structure. The inverse analysis requires us to solve the forward simulation of the underlying system several thousand times. These simulations are often exorbitantly expensive and trigger the need for improving their computational efficiency. A PMOR approach hinged on the proper orthogonal decomposition method is presented in this paper. An adaptive parameter sampling technique is established with the aid of a surrogate model to efficiently update the reduced-order basis in a greedy fashion. A numerical experiment is conducted to illustrate the parametric training of the reduced-order model. The results show that the reduced-order solution based on the PMOR approach is accurately complying with that of the high fidelity solution.
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Martín del Campo, Gustavo, Yuriy Shkvarko, Andreas Reigber, and Matteo Nannini. "TomoSAR Imaging for the Study of Forested Areas: A Virtual Adaptive Beamforming Approach." Remote Sensing 10, no. 11 (November 17, 2018): 1822. http://dx.doi.org/10.3390/rs10111822.
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Among the objectives of the upcoming space missions Tandem-L and BIOMASS, is the 3-D representation of the global forest structure via synthetic aperture radar (SAR) tomography (TomoSAR). To achieve such a goal, modern approaches suggest solving the TomoSAR inverse problems by exploiting polarimetric diversity and structural model properties of the different scattering mechanisms. This way, the related tomographic imaging problems are treated in descriptive regularization settings, applying modern non-parametric spatial spectral analysis (SSA) techniques. Nonetheless, the achievable resolution of the commonly performed SSA-based estimators highly depends on the span of the tomographic aperture; furthermore, irregular sampling and non-uniform constellations sacrifice the attainable resolution, introduce artifacts and increase ambiguity. Overcoming these drawbacks, in this paper, we address a new multi-stage iterative technique for feature-enhanced TomoSAR imaging that aggregates the virtual adaptive beamforming (VAB)-based SSA approach, with the wavelet domain thresholding (WDT) regularization framework, which we refer to as WAVAB (WDT-refined VAB). First, high resolution imagery is recovered applying the descriptive experiment design regularization (DEDR)-inspired reconstructive processing. Next, the additional resolution enhancement with suppression of artifacts is performed, via the WDT-based sparsity promoting refinement in the wavelet transform (WT) domain. Additionally, incorporation of the sum of Kronecker products (SKP) decomposition technique at the pre-processing stage, improves ground and canopy separation and allows for the utilization of different better adapted TomoSAR imaging techniques, on the ground and canopy structural components, separately. The feature enhancing capabilities of the novel robust WAVAB TomoSAR imaging technique are corroborated through the processing of airborne data of the German Aerospace Center (DLR), providing detailed volume height profiles reconstruction, as an alternative to the competing non-parametric SSA-based methods.
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Li, Tianyi, and Jean-François Luyé. "Optimization of Fiber Orientation Model Parameters in the Presence of Flow-Fiber Coupling." Journal of Composites Science 2, no. 4 (December 18, 2018): 73. http://dx.doi.org/10.3390/jcs2040073.
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In this paper, we propose a novel systematic procedure to minimize the discrepancy between the numerically predicted and the experimentally measured fiber orientation results on an injection-molded part. Fiber orientation model parameters are optimized simultaneously using Latin hypercube sampling and kriging-based adaptive surrogate modeling techniques. Via an adequate discrepancy measure, the optimized solution possesses correct skin–shell–core structure and global orientation evolution throughout the considered center-gated disk. Some non-trivial interaction between these parameters and flow-fiber coupling effects as well as their quantitative importance are illustrated. The parametric fine-tuning of orientation models mostly leads to a better agreement in the skin and shell regions, while the coupling effect via a fiber-dependent viscosity improves prediction in the core.
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Lang, Michael. "Control Limits for an Adaptive Self-Starting Distribution-Free CUSUM Based on Sequential Ranks." Technologies 7, no. 4 (October 1, 2019): 71. http://dx.doi.org/10.3390/technologies7040071.
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Since their introduction in 1954, cumulative sum (CUSUM) control charts have seen a widespread use beyond the conventional realm of statistical process control (SPC). While off-the-shelf implementations aimed at practitioners are available, their successful use is often hampered by inherent limitations which make them not easily reconcilable with real-world scenarios. Challenges commonly arise regarding a lack of robustness due to underlying parametric assumptions or requiring the availability of large representative training datasets. We evaluate an adaptive distribution-free CUSUM based on sequential ranks which is self-starting and provide detailed pseudo-code of a simple, yet effective calibration algorithm. The main contribution of this paper is in providing a set of ready-to-use tables of control limits suitable to a wide variety of applications where a departure from the underlying sampling distribution to a stochastically larger distribution is of interest. Performance of the proposed tabularized control limits is assessed and compared to competing approaches through extensive simulation experiments. The proposed control limits are shown to yield significantly increased agility (reduced detection delay) while maintaining good overall robustness.
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ROVEA, S. B., and RODOLFO FLESCH. "FAST ADAPTIVE GENERALIZED PREDICTIVE CONTROL FOR SYSTEMS WITH VARIABLE PARAMETERS." Journal of Engineering and Exact Sciences 5, no. 5 (December 20, 2019): 0408–14. http://dx.doi.org/10.18540/jcecvl5iss5pp0408-0414.
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This paper proposes a fast predictive control structure with online model update according to process parametric variations. The proposed controller is based on the Generalized Predictive Control (GPC) algorithm, but it integrates the recursive least squares identification method with a variable forgetting factor to estimate at each iteration the parameters of a linear structure model used for multi-step ahead prediction. For a system with constraints on the process variables, the resulting optimization problem of GPC is solved using quadratic programming based on the Alternate Direction Method of Multipliers, which allows the control signal to be obtained with small computational effort. In order to validate the proposed algorithm an experimental case study that considers the speed control of a direct current motor and the proposed controller embedded in a microcontroller STM32F303K8T6 is presented. Experimental results use as baseline the GPC with fixed model parameters and show that the proposed fast adaptive predictive control structure is able to keep almost the same transient response for all the considered operating points of the motor, while GPC presents high oscillations at operating conditions far from the one used to obtain the nominal model. Even though the proposed controller needs to solve two optimization problems at each sampling instant, it can run about 60 times in a second in the microcontroller used in this study
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Mukherjee, S., D. Kumar, L. Udpa, and Y. Deng. "Robust defect detection under uncertainties using spatially adaptive capacitive imaging." Journal of Applied Physics 131, no. 23 (June 21, 2022): 234901. http://dx.doi.org/10.1063/5.0088320.
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We develop a high-Q capacitive sensing based robust non-destructive evaluation (NDE) methodology that can be widely used in varied NDE applications. We show that the proposed method can detect defects in a host of robust regimes where uncertainties such as lift-off, probe tilt, fluctuations in sampling rates, and step sizes are inherent in the data collection process. We explicitly characterize the corruption in the capacitive sensing data due to various lift-off based uncertainties. We use a Bayesian decision theoretic approach to rigorously understand the impact of these corruptions on defect identification efficacy. Using an optimally tuned weighted classification loss, we prove that it is theoretically feasible to accurately detect defect location and sizes from capacitive sensing signals collected under the aforementioned uncertainties. The Bayesian decision theoretic study needs prior information for accurate detection that is not available in real NDE inspections. So, we develop a solely data driven algorithm that analyzes the capacitive sensing signals without any prior knowledge of defect or uncertainty types. The developed algorithm is non-parametric and uses spatially adaptive denoising to weed out uncertainty induced noises. By leveraging the spatial association in the capacitive sensing signals, our algorithm greatly improves on popular non-spatial approaches. Compared to popular thresholding methods and low-rank based denoising approaches, we demonstrate superior performance of the proposed method in terms of coverage and false positive metrics for defect identification. Using spatially adaptive denoising, we design a robust capacitive sensing method that can detect defects with high precision under various uncertainty regimes.
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Sarma, Sridevi V., David P. Nguyen, Gabriela Czanner, Sylvia Wirth, Matthew A. Wilson, Wendy Suzuki, and Emery N. Brown. "Computing Confidence Intervals for Point Process Models." Neural Computation 23, no. 11 (November 2011): 2731–45. http://dx.doi.org/10.1162/neco_a_00198.
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Characterizing neural spiking activity as a function of intrinsic and extrinsic factors is important in neuroscience. Point process models are valuable for capturing such information; however, the process of fully applying these models is not always obvious. A complete model application has four broad steps: specification of the model, estimation of model parameters given observed data, verification of the model using goodness of fit, and characterization of the model using confidence bounds. Of these steps, only the first three have been applied widely in the literature, suggesting the need to dedicate a discussion to how the time-rescaling theorem, in combination with parametric bootstrap sampling, can be generally used to compute confidence bounds of point process models. In our first example, we use a generalized linear model of spiking propensity to demonstrate that confidence bounds derived from bootstrap simulations are consistent with those computed from closed-form analytic solutions. In our second example, we consider an adaptive point process model of hippocampal place field plasticity for which no analytical confidence bounds can be derived. We demonstrate how to simulate bootstrap samples from adaptive point process models, how to use these samples to generate confidence bounds, and how to statistically test the hypothesis that neural representations at two time points are significantly different. These examples have been designed as useful guides for performing scientific inference based on point process models.
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Krisnawati, Endang, Adji Achmad Rinaldo Fernandes, and Solimun Solimun. "Development of Nonparametric Path Model using Multivariate Adaptive Regression Spline (MARS) Method In Compliance Behavior of Paying Credit In Bank." WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL 16 (December 20, 2021): 686–95. http://dx.doi.org/10.37394/23203.2021.16.62.
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The purpose of this study is to develop a Non-parametric Path with the MARS (Multivariate Adaptive Regression Spline) approach which is applied to the behavior of paying credit compliance at Bank. prospective debtor by a Bank. The data used in this study is primary data using a research instrument in the form of a questionnaire. There are 7 variables, namely 5 exogenous variables in the form of 5C variables (Character (X1), Capacity (X2), Capital (X3), Collateral (X4), Condition of Economy (X5)), and two endogenous variables, namely Punctual Payment (Y1), Obedient Paying Behavior (Y2). Variable measurement technique is done by calculating the average score on the items. Sampling in this study used a purposive sampling technique with the criteria of respondents in the study were mortgage debtors (House Ownership Credit) at Bank X. Respondents obtained in this study were 100 respondents. The analysis used is nonparametric path with Multivariate Adaptive Regression Spline (MARS) approach. The result of this research is the estimation of nonparametric Path function using MARS approach on various interactions. The best estimate of the function of obedient behavior in paying credit is when it involves 4 variables, namely Character (X1), Capacity (X2), Conditions of economy (X5), and On time pay (Y1) with a value of generalized cross-validation The smallest (GCV) obtained is 0.2496. The originality of this research is the development of a nonparametric path with the MARS approach that is able to capture interactions between existing variables and is also able to handle the limitations of the truncated spline to determine the position and number of knot points used when involving many predictor variables. There has been no previous research that has examined the development of a nonparametric path with the MARS approach.
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Levy, Jonathan, Mark van der Laan, Alan Hubbard, and Romain Pirracchio. "A fundamental measure of treatment effect heterogeneity." Journal of Causal Inference 9, no. 1 (January 1, 2021): 83–108. http://dx.doi.org/10.1515/jci-2019-0003.
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Abstract The stratum-specific treatment effect function is a random variable giving the average treatment effect (ATE) for a randomly drawn stratum of potential confounders a clinician may use to assign treatment. In addition to the ATE, the variance of the stratum-specific treatment effect function is fundamental in determining the heterogeneity of treatment effect values. We offer a non-parametric plug-in estimator, the targeted maximum likelihood estimator (TMLE) and the cross-validated TMLE (CV-TMLE), to simultaneously estimate both the average and variance of the stratum-specific treatment effect function. The CV-TMLE is preferable because it guarantees asymptotic efficiency under two conditions without needing entropy conditions on the initial fits of the outcome model and treatment mechanism, as required by TMLE. Particularly, in circumstances where data adaptive fitting methods are very important to eliminate bias but hold no guarantee of satisfying the entropy condition, we show that the CV-TMLE sampling distributions maintain normality with a lower mean squared error than TMLE. In addition to verifying the theoretical properties of TMLE and CV-TMLE through simulations, we highlight some of the challenges in estimating the variance of the treatment effect, which lack double robustness and might be biased if the true variance is small and sample size insufficient.
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Hermawan, Romy, M. Daniel Septian, and Hayat Hayat. "Impacts of Implementing the Learning Policy on Vocational Higher Education in Post Covid-19 Pandemic." AL-ISHLAH: Jurnal Pendidikan 14, no. 1 (April 9, 2022): 119–26. http://dx.doi.org/10.35445/alishlah.v14i1.1835.
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This study aimed to describe the influences of learning policies within the period of vocational higher education after the Covid-19 pandemic. The field research design applied the quantitative approach. The research population, 5.915 respondents, came from elements of the academic community at the Vocational Education Program, Brawijaya University, Malang, and the Vocational Faculty, Airlangga University, Surabaya. Then, the sampling technique determined 98 samples based on the Stratified Random Sampling approach. Data collection techniques were supported by questionnaires, documentation, and interviews. In order to facilitate the analysis, we used parametric inference statistical analysis techniques to test the validity and reliability. The results of the validity test through correlation (Pearson Correlation Coefficient) and the reliability test with the Cronbach Alpha method (Cronbach's Alpha) showed that all question items had been proven to be valid based on the sig value 0.05. From 18 items, the Cronbach alpha value was 0.785. As it was more than 0.6, the questionnaire was valid. Also, the reliability test of 16 items obtained from Cronbach's alpha value was 0.838. It was more than 0.6, meaning that the questionnaire was valid. The correlation value between items 1-18 had a fairly high correlation, and the view of the question value sig. 2 tailed overall was 0.05, meaning that each question item was valid. The findings from this study indicated that the existence of learning policies has a significant influence on the vocational learning process during the Covid-19 pandemic. This study recommends that in the post-Covid-19 pandemic, educational policies in higher education units of vocational type are more adaptive to digital-based learning by strengthening health principles and protocols.
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Carrilho, A. C., M. Galo, and R. C. Santos. "STATISTICAL OUTLIER DETECTION METHOD FOR AIRBORNE LIDAR DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1 (September 26, 2018): 87–92. http://dx.doi.org/10.5194/isprs-archives-xlii-1-87-2018.
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<p><strong>Abstract.</strong> Sampling the Earth’s surface using airborne LASER scanning (ALS) systems suffers from several factors inherent to the LASER system itself as well as external factors, such as the presence of particles in the atmosphere, and/or multi-path returns due to reflections. The resulting point cloud may therefore contain some outliers and removing them is an important (and difficult) step for all subsequent processes that use this kind of data as input. In the literature, there are several approaches for outlier removal, some of which require external information, such as spatial frequency characteristics or presume parametric mathematical models for surface fitting. A limitation on the height histogram filtering approach was identified from the literature review: outliers that lie within the ground elevation difference might not be detected. To overcome such a limitation, this paper proposes an adaptive alternative based on point cloud cell subdivision. Instead of computing a single histogram for the whole dataset, the method applies the filtering to smaller patches, in which the ground elevation difference can be ignored. A study area was filtered, and the results were compared quantitatively with two other methods implemented in both free and commercial software packages. The reference data was generated manually in order to provide useful quality measures. The experiment shows that none of the tested filters was able to reach a level of complete automation, therefore manual corrections by the operator are still necessary.</p>
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Fusco, Roberta, Adele Piccirillo, Mario Sansone, Vincenza Granata, Paolo Vallone, Maria Luisa Barretta, Teresa Petrosino, et al. "Radiomic and Artificial Intelligence Analysis with Textural Metrics, Morphological and Dynamic Perfusion Features Extracted by Dynamic Contrast-Enhanced Magnetic Resonance Imaging in the Classification of Breast Lesions." Applied Sciences 11, no. 4 (February 20, 2021): 1880. http://dx.doi.org/10.3390/app11041880.
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Purpose: The aim of the study was to estimate the diagnostic accuracy of textural, morphological and dynamic features, extracted by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images, by carrying out univariate and multivariate statistical analyses including artificial intelligence approaches. Methods: In total, 85 patients with known breast lesion were enrolled in this retrospective study according to regulations issued by the local Institutional Review Board. All patients underwent DCE-MRI examination. The reference standard was pathology from a surgical specimen for malignant lesions and pathology from a surgical specimen or fine needle aspiration cytology, core or Tru-Cut needle biopsy for benign lesions. In total, 91 samples of 85 patients were analyzed. Furthermore, 48 textural metrics, 15 morphological and 81 dynamic parameters were extracted by manually segmenting regions of interest. Statistical analyses including univariate and multivariate approaches were performed: non-parametric Wilcoxon–Mann–Whitney test; receiver operating characteristic (ROC), linear classifier (LDA), decision tree (DT), k-nearest neighbors (KNN), and support vector machine (SVM) were utilized. A balancing approach and feature selection methods were used. Results: The univariate analysis showed low accuracy and area under the curve (AUC) for all considered features. Instead, in the multivariate textural analysis, the best performance (accuracy (ACC) = 0.78; AUC = 0.78) was reached with all 48 metrics and an LDA trained with balanced data. The best performance (ACC = 0.75; AUC = 0.80) using morphological features was reached with an SVM trained with 10-fold cross-variation (CV) and balanced data (with adaptive synthetic (ADASYN) function) and a subset of five robust morphological features (circularity, rectangularity, sphericity, gleaning and surface). The best performance (ACC = 0.82; AUC = 0.83) using dynamic features was reached with a trained SVM and balanced data (with ADASYN function). Conclusion: Multivariate analyses using pattern recognition approaches, including all morphological, textural and dynamic features, optimized by adaptive synthetic sampling and feature selection operations obtained the best results and showed the best performance in the discrimination of benign and malignant lesions.
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Fusco, Roberta, Elio Di Bernardo, Adele Piccirillo, Maria Rosaria Rubulotta, Teresa Petrosino, Maria Luisa Barretta, Mauro Mattace Raso, et al. "Radiomic and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography and Dynamic Contrast Magnetic Resonance Imaging to Detect Breast Malignant Lesions." Current Oncology 29, no. 3 (March 13, 2022): 1947–66. http://dx.doi.org/10.3390/curroncol29030159.
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Purpose:The purpose of this study was to discriminate between benign and malignant breast lesions through several classifiers using, as predictors, radiomic metrics extracted from CEM and DCE-MRI images. In order to optimize the analysis, balancing and feature selection procedures were performed. Methods: Fifty-four patients with 79 histo-pathologically proven breast lesions (48 malignant lesions and 31 benign lesions) underwent both CEM and DCE-MRI. The lesions were retrospectively analyzed with radiomic and artificial intelligence approaches. Forty-eight textural metrics were extracted, and univariate and multivariate analyses were performed: non-parametric statistical test, receiver operating characteristic (ROC) and machine learning classifiers. Results: Considering the single metrics extracted from CEM, the best predictors were KURTOSIS (area under ROC curve (AUC) = 0.71) and SKEWNESS (AUC = 0.71) calculated on late MLO view. Considering the features calculated from DCE-MRI, the best predictors were RANGE (AUC = 0.72), ENERGY (AUC = 0.72), ENTROPY (AUC = 0.70) and GLN (gray-level nonuniformity) of the gray-level run-length matrix (AUC = 0.72). Considering the analysis with classifiers and an unbalanced dataset, no significant results were obtained. After the balancing and feature selection procedures, higher values of accuracy, specificity and AUC were reached. The best performance was obtained considering 18 robust features among all metrics derived from CEM and DCE-MRI, using a linear discriminant analysis (accuracy of 0.84 and AUC = 0.88). Conclusions: Classifiers, adjusted with adaptive synthetic sampling and feature selection, allowed for increased diagnostic performance of CEM and DCE-MRI in the differentiation between benign and malignant lesions.
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Kilic, Zeliha, Max Schweiger, Camille Moyer, and Steve Pressé. "Monte Carlo samplers for efficient network inference." PLOS Computational Biology 19, no. 7 (July 18, 2023): e1011256. http://dx.doi.org/10.1371/journal.pcbi.1011256.
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Accessing information on an underlying network driving a biological process often involves interrupting the process and collecting snapshot data. When snapshot data are stochastic, the data’s structure necessitates a probabilistic description to infer underlying reaction networks. As an example, we may imagine wanting to learn gene state networks from the type of data collected in single molecule RNA fluorescence in situ hybridization (RNA-FISH). In the networks we consider, nodes represent network states, and edges represent biochemical reaction rates linking states. Simultaneously estimating the number of nodes and constituent parameters from snapshot data remains a challenging task in part on account of data uncertainty and timescale separations between kinetic parameters mediating the network. While parametric Bayesian methods learn parameters given a network structure (with known node numbers) with rigorously propagated measurement uncertainty, learning the number of nodes and parameters with potentially large timescale separations remain open questions. Here, we propose a Bayesian nonparametric framework and describe a hybrid Bayesian Markov Chain Monte Carlo (MCMC) sampler directly addressing these challenges. In particular, in our hybrid method, Hamiltonian Monte Carlo (HMC) leverages local posterior geometries in inference to explore the parameter space; Adaptive Metropolis Hastings (AMH) learns correlations between plausible parameter sets to efficiently propose probable models; and Parallel Tempering takes into account multiple models simultaneously with tempered information content to augment sampling efficiency. We apply our method to synthetic data mimicking single molecule RNA-FISH, a popular snapshot method in probing transcriptional networks to illustrate the identified challenges inherent to learning dynamical models from these snapshots and how our method addresses them.
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Zhu, Fang, and Wei Liu. "A novel medical image fusion method based on multi-scale shearing rolling weighted guided image filter." Mathematical Biosciences and Engineering 20, no. 8 (2023): 15374–406. http://dx.doi.org/10.3934/mbe.2023687.
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<abstract><p>Medical image fusion is a crucial technology for biomedical diagnoses. However, current fusion methods struggle to balance algorithm design, visual effects, and computational efficiency. To address these challenges, we introduce a novel medical image fusion method based on the multi-scale shearing rolling weighted guided image filter (MSRWGIF). Inspired by the rolling guided filter, we construct the rolling weighted guided image filter (RWGIF) based on the weighted guided image filter. This filter offers progressive smoothing filtering of the image, generating smooth and detailed images. Then, we construct a novel image decomposition tool, MSRWGIF, by replacing non-subsampled shearlet transform's non-sampling pyramid filter with RWGIF to extract richer detailed information. In the first step of our method, we decompose the original images under MSRWGIF to obtain low-frequency subbands (LFS) and high-frequency subbands (HFS). Since LFS contain a large amount of energy-based information, we propose an improved local energy maximum (ILGM) fusion strategy. Meanwhile, HFS employ a fast and efficient parametric adaptive pulse coupled-neural network (AP-PCNN) model to combine more detailed information. Finally, the inverse MSRWGIF is utilized to generate the final fused image from fused LFS and HFS. To test the proposed method, we select multiple medical image sets for experimental simulation and confirm its advantages by combining seven high-quality representative metrics. The simplicity and efficiency of the method are compared with 11 classical fusion methods, illustrating significant improvements in the subjective and objective performance, especially for color medical image fusion.</p></abstract>
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Nugroho, Yusanto, Suyanto Suyanto, Gusti Syeransyah Rudy, Supandi Supandi, Yudha Hardiyanto Eka Saputra, Syamsu Alam, Jeriels Matatula, and Pandu Yudha Adi Putra Wirabuana. "A comparison of soil characteristics from four land covers around a coal mining concession area in South Kalimantan." Journal of Degraded and Mining Lands Management 10, no. 1 (October 1, 2022): 3883. http://dx.doi.org/10.15243/jdmlm.2022.101.3883.
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Understanding soil characteristics is important to determine the alternative strategies of land management, particularly those related to the scheme of soil and water conservation. This study investigated soil characteristics from four land covers around the coal mining concession area located in South Kalimantan. A soil survey was conducted using a purposive sampling method with three replicates in each land cover. Soil samples that were taken at depths of 0-10 cm, 11-20 cm, and 21-30 cm, were composited before being brought to the laboratory to quantify their characteristics, such as texture and organic carbon content. Data analysis was processed using a non-parametric test with a significant level of 5%. Comparison average of soil characteristics between land covers was evaluated using the Kruskal-Wallis test and followed by Nemenyi-test. Results found that soil characteristics from four land covers significantly differed in texture and organic carbon content. The highest sand fraction was noted in shrubs (67.23±0.86%), while the greatest silt fraction was recorded in plantation forests (29.71±2.84%). Compared to other land covers, the clay content in plantation forests and reclamation area was relatively equal by around 53-54%. On another side, The highest soil organic carbon was found in plantation forests with ranging of (4.44±0.14%) followed by natural forests (4.24±0.62%), shrubs (3.38±0.09%), and reclamation area (1.14±0.09%). These findings indicated there were high variations of soil characteristics from different land covers around the coal mining concession area. Therefore, it is recommended for managers to apply adaptive strategies in supporting soil conservation efforts based on the soil characteristics in each site.
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Fusco, Roberta, Adele Piccirillo, Mario Sansone, Vincenza Granata, Maria Rosaria Rubulotta, Teresa Petrosino, Maria Luisa Barretta, et al. "Radiomics and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography in the Breast Lesions Classification." Diagnostics 11, no. 5 (April 30, 2021): 815. http://dx.doi.org/10.3390/diagnostics11050815.
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The aim of the study was to estimate the diagnostic accuracy of textural features extracted by dual-energy contrast-enhanced mammography (CEM) images, by carrying out univariate and multivariate statistical analyses including artificial intelligence approaches. In total, 80 patients with known breast lesion were enrolled in this prospective study according to regulations issued by the local Institutional Review Board. All patients underwent dual-energy CEM examination in both craniocaudally (CC) and double acquisition of mediolateral oblique (MLO) projections (early and late). The reference standard was pathology from a surgical specimen for malignant lesions and pathology from a surgical specimen or fine needle aspiration cytology, core or Tru-Cut needle biopsy, and vacuum assisted breast biopsy for benign lesions. In total, 104 samples of 80 patients were analyzed. Furthermore, 48 textural parameters were extracted by manually segmenting regions of interest. Univariate and multivariate approaches were performed: non-parametric Wilcoxon–Mann–Whitney test; receiver operating characteristic (ROC), linear classifier (LDA), decision tree (DT), k-nearest neighbors (KNN), artificial neural network (NNET), and support vector machine (SVM) were utilized. A balancing approach and feature selection methods were used. The univariate analysis showed low accuracy and area under the curve (AUC) for all considered features. Instead, in the multivariate textural analysis, the best performance considering the CC view (accuracy (ACC) = 0.75; AUC = 0.82) was reached with a DT trained with leave-one-out cross-variation (LOOCV) and balanced data (with adaptive synthetic (ADASYN) function) and a subset of three robust textural features (MAD, VARIANCE, and LRLGE). The best performance (ACC = 0.77; AUC = 0.83) considering the early-MLO view was reached with a NNET trained with LOOCV and balanced data (with ADASYN function) and a subset of ten robust features (MEAN, MAD, RANGE, IQR, VARIANCE, CORRELATION, RLV, COARSNESS, BUSYNESS, and STRENGTH). The best performance (ACC = 0.73; AUC = 0.82) considering the late-MLO view was reached with a NNET trained with LOOCV and balanced data (with ADASYN function) and a subset of eleven robust features (MODE, MEDIAN, RANGE, RLN, LRLGE, RLV, LZLGE, GLV_GLSZM, ZSV, COARSNESS, and BUSYNESS). Multivariate analyses using pattern recognition approaches, considering 144 textural features extracted from all three mammographic projections (CC, early MLO, and late MLO), optimized by adaptive synthetic sampling and feature selection operations obtained the best results (ACC = 0.87; AUC = 0.90) and showed the best performance in the discrimination of benign and malignant lesions.
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Grivas, A., M. Grigoriou, P. Katsimpri, P. Verginis, and D. Boumpas. "POS0413 COMPREHENSIVE IMMUNE PROFILING OF PERIPHERAL BLOOD IN PSORIATIC ARTHRITIS (PsA) PATIENTS: EXPANSION OF INTERMEDIATE MONOCYTES AND DECREASED T REG AND CD8 T CELLS." Annals of the Rheumatic Diseases 80, Suppl 1 (May 19, 2021): 436.1–436. http://dx.doi.org/10.1136/annrheumdis-2021-eular.3540.
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Background:Psoriatic arthritis (PsA) is a heterogeneous inflammatory arthritis that develops in a subset of patients with psoriasis. According to the current paradigm, cells of the innate and adaptive immunity interact with resident tissue fibroblasts mounting an inflammatory response via complex cytokine networks in the skin and joints in which type 1 and type 17 T cells play a dominant role. The abundance and relative contribution of other peripheral blood immune cells to disease pathogenesis as well the molecular signature of peripheral blood mononuclear cells and tissue fibroblasts remain ill defined.Objectives:To comprehensively characterize immune cell subsets driving inflammation in the peripheral blood of patients with active PsA and their impact on psoriatic skin fibroblasts.Methods:Peripheral blood was collected from PsA patients (n=31) and age-/sex-matched healthy individuals (HI) (n=9), after informed consent. Psoriatic skin biopsies were acquired from a subset of 5 patients and 3 HI. All patients fulfilled the CASPAR criteria for the diagnosis and displayed peripheral polyarthritis of moderate- to high-disease activity. Patients’ demographic and clinical data were recorded at time of sampling. Disease activity was assessed using the Disease Activity Index for Psoriatic arthritis (DAPSA) score. Skin psoriasis activity indices, enthesitis and dactylitis were also recorded. Peripheral blood mononuclear cells (PBMCs) were isolated by ficoll density gradient centrifugation. Flow cytometry was performed using a BD FACS-Aria-III and analyzed using FlowJo software. The antibody staining panel utilized aimed at the identification of the following immune cell subsets: Monocyte subsets (HLA-DR+ CD14+/- CD16+/-), Plasmacytoid dendritic cells (HLA- DR+ CD123+), T helper (CD4+), cytotoxic T (CD8+), regulatory T (CD4+ CD25+ CD127-) and B cells (CD19+). Statistical analyses were performed using GraphPad Prism software. Differences between groups were compared using unpaired T test for parametric data; Mann-Whitney and Kruskal Wallis tests for non-parametric data. The level of significance was set at P<0.05.Results:9 males and 22 females PsA patients are included (mean age 50 years and the mean disease duration 19.2 years for skin disease and 5.9 years for arthritis). The mean DAPSA score was 43.4, suggestive of high disease activity, while 8 (26%) patients displayed clinical enthesitis at time of sampling. Flow cytometry analysis revealed aberrancies in peripheral blood immune cell populations. More specifically, PsA patients displayed a significant increase in intermediate monocyte subset (HLA-DR+ CD14+ CD16+) compared to HI with patients with clinical enthesitis demonstrating a more exaggerated expansion of intermediate monocytes compared to patients without enthesitis. A trend towards increased patrolling monocytes (HLA-DR+ CD14- CD16+) was also noted although this did not reach statistical significance. In contrast, both regulatory T cells and cytotoxic CD8+ T cells were significantly decreased probably due to their selective migration at the sites of inflammation. RNA-seq from whole blood and skin fibroblasts from affected skin are in progress.Conclusion:These data demonstrate significant expansion of intermediate monocytes -more pronounced in the enthesitis affected individuals- and decrease in T regulatory cells and T cytotoxic cells in PsA peripheral blood. Increased antigen presentation and co-stimulation mediated via intermediate monocytes in combination with their proangiogenic properties may contribute to disease pathogenesisReferences:[1]Veale, D. J. & Fearon, U. The pathogenesis of psoriatic arthritis. The Lancet (2018) doi:10.1016/S0140-6736(18)30830-4.Disclosure of Interests:None declared
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Komolafe, Adebayo Francis. "The Nexus Between Fatigue Indices and Coping Strategies in Sports Among Oyo State Special Athletes." European Scientific Journal, ESJ 14, no. 1 (January 31, 2018): 151. http://dx.doi.org/10.19044/esj.2018.v14n1p151.
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Disability sports have acquired an indispensable status in the life of many nations and the special athlete as a whole. Special athletes are exposed to a number of intense physical and psychosocial activities and they needed to make use of a set of cognitive and behavioural strategies in order to cope with these challenges and related fatigue. Hitherto, previous studies had focused largely on adaptive sports and effects of sporting activities on persons with disability without establishing a link between disabled athlete’s use of coping strategies and their achievement motivation. Therefore, it is on this premise that this study harps on the relationship between fatigue indices and coping strategies among Oyo State special athletes. Descriptive survey design using purposive sampling technique was employed. Participants of the study include all the registered special athletes in Oyo State comprising of one hundred and twenty three (123) athletes. Two standardized instruments, thus, Modified fatigue impact scale (r=0.384) and Athletic coping skills inventory (r=0.514) were both employed. Altogether, five hypotheses were tested at 0.05 significant level, and parametric statistics, Pearsons Product moment correlation, and multiple regression analysis were used to analyse the data. Findings revealed significant effect of two independent variables on the dependent variable: cognitive style (β=0.294, r=0.514, P<0.05) and psychosocial factors (β=0.236, r=0.499, P<0.05), while modified fatigue impact has no significant relative effect on coping strategies. The composite effect of independent variables (Modified Fatigue Impact, Cognitive style and Psychosocial Factor) on Athletic coping skills was significant ((F(3,119)=166.777) and about 30% of the variation was accounted for by the independent variables as explained by the adjusted R square. It is therefore recommended that the relationship between fatigue indices and coping strategies is high and the higher the individuals rated their coping with fatigue caused by their disabilities, the lower they scored on fatigue experiences. The more they used coping strategies, the more efficient they coped with fatigue.
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Petrillo, Antonella, Roberta Fusco, Elio Di Bernardo, Teresa Petrosino, Maria Luisa Barretta, Annamaria Porto, Vincenza Granata, et al. "Prediction of Breast Cancer Histological Outcome by Radiomics and Artificial Intelligence Analysis in Contrast-Enhanced Mammography." Cancers 14, no. 9 (April 25, 2022): 2132. http://dx.doi.org/10.3390/cancers14092132.
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Purpose: To evaluate radiomics features in order to: differentiate malignant versus benign lesions; predict low versus moderate and high grading; identify positive or negative hormone receptors; and discriminate positive versus negative human epidermal growth factor receptor 2 related to breast cancer. Methods: A total of 182 patients with known breast lesions and that underwent Contrast-Enhanced Mammography were enrolled in this retrospective study. The reference standard was pathology (118 malignant lesions and 64 benign lesions). A total of 837 textural metrics were extracted by manually segmenting the region of interest from both craniocaudally (CC) and mediolateral oblique (MLO) views. Non-parametric Wilcoxon–Mann–Whitney test, receiver operating characteristic, logistic regression and tree-based machine learning algorithms were used. The Adaptive Synthetic Sampling balancing approach was used and a feature selection process was implemented. Results: In univariate analysis, the classification of malignant versus benign lesions achieved the best performance when considering the original_gldm_DependenceNonUniformity feature extracted on CC view (accuracy of 88.98%). An accuracy of 83.65% was reached in the classification of grading, whereas a slightly lower value of accuracy (81.65%) was found in the classification of the presence of the hormone receptor; the features extracted were the original_glrlm_RunEntropy and the original_gldm_DependenceNonUniformity, respectively. The results of multivariate analysis achieved the best performances when using two or more features as predictors for classifying malignant versus benign lesions from CC view images (max test accuracy of 95.83% with a non-regularized logistic regression). Considering the features extracted from MLO view images, the best test accuracy (91.67%) was obtained when predicting the grading using a classification-tree algorithm. Combinations of only two features, extracted from both CC and MLO views, always showed test accuracy values greater than or equal to 90.00%, with the only exception being the prediction of the human epidermal growth factor receptor 2, where the best performance (test accuracy of 89.29%) was obtained with the random forest algorithm. Conclusions: The results confirm that the identification of malignant breast lesions and the differentiation of histological outcomes and some molecular subtypes of tumors (mainly positive hormone receptor tumors) can be obtained with satisfactory accuracy through both univariate and multivariate analysis of textural features extracted from Contrast-Enhanced Mammography images.
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Petrillo, Antonella, Roberta Fusco, Elio Di Bernardo, Teresa Petrosino, Maria Luisa Barretta, Annamaria Porto, Vincenza Granata, et al. "Prediction of Breast Cancer Histological Outcome by Radiomics and Artificial Intelligence Analysis in Contrast-Enhanced Mammography." Cancers 14, no. 9 (April 25, 2022): 2132. http://dx.doi.org/10.3390/cancers14092132.
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Purpose: To evaluate radiomics features in order to: differentiate malignant versus benign lesions; predict low versus moderate and high grading; identify positive or negative hormone receptors; and discriminate positive versus negative human epidermal growth factor receptor 2 related to breast cancer. Methods: A total of 182 patients with known breast lesions and that underwent Contrast-Enhanced Mammography were enrolled in this retrospective study. The reference standard was pathology (118 malignant lesions and 64 benign lesions). A total of 837 textural metrics were extracted by manually segmenting the region of interest from both craniocaudally (CC) and mediolateral oblique (MLO) views. Non-parametric Wilcoxon–Mann–Whitney test, receiver operating characteristic, logistic regression and tree-based machine learning algorithms were used. The Adaptive Synthetic Sampling balancing approach was used and a feature selection process was implemented. Results: In univariate analysis, the classification of malignant versus benign lesions achieved the best performance when considering the original_gldm_DependenceNonUniformity feature extracted on CC view (accuracy of 88.98%). An accuracy of 83.65% was reached in the classification of grading, whereas a slightly lower value of accuracy (81.65%) was found in the classification of the presence of the hormone receptor; the features extracted were the original_glrlm_RunEntropy and the original_gldm_DependenceNonUniformity, respectively. The results of multivariate analysis achieved the best performances when using two or more features as predictors for classifying malignant versus benign lesions from CC view images (max test accuracy of 95.83% with a non-regularized logistic regression). Considering the features extracted from MLO view images, the best test accuracy (91.67%) was obtained when predicting the grading using a classification-tree algorithm. Combinations of only two features, extracted from both CC and MLO views, always showed test accuracy values greater than or equal to 90.00%, with the only exception being the prediction of the human epidermal growth factor receptor 2, where the best performance (test accuracy of 89.29%) was obtained with the random forest algorithm. Conclusions: The results confirm that the identification of malignant breast lesions and the differentiation of histological outcomes and some molecular subtypes of tumors (mainly positive hormone receptor tumors) can be obtained with satisfactory accuracy through both univariate and multivariate analysis of textural features extracted from Contrast-Enhanced Mammography images.
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Liu, Jiajun, Michael Neely, Jeffrey Lipman, Fekade B. Sime, Jason Roberts, Patrick J. Kiel, and Marc H. Scheetz. "1573. Population Pharmacokinetic Analyses for Cefepime in Adult and Pediatric Patients." Open Forum Infectious Diseases 6, Supplement_2 (October 2019): S574—S575. http://dx.doi.org/10.1093/ofid/ofz360.1437.
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Abstract Background Cefepime (CEF) is commonly used for adult and pediatric infections. Several studies have examined CEF’s pharmacokinetics (PK) in various populations; however, a unifying PK model for adult and pediatric subjects does not yet exist. We developed a combined population model for adult and pediatric patients and validated the model. Methods The initial model includes adult and pediatric patients with a rich cefepime sampling design. All adults received 2 g CEF while pediatric subjects received a mean of 49 (SD 5) mg/kg. One- and two-compartment models were considered as base models and were fit using a non-parametric adaptive grid algorithm within the Pmetrics package 1.5.2 (Los Angeles, CA) for R 3.5.1. Compartmental model selection was based on Akaike information criteria (AIC). Covariate relationships with PK parameters were visually inspected and mathematically assessed. Predictive performance was evaluated using bias and imprecision of the population and individual prediction models. External validation was conducted using a separate adult cohort. Results A total of 45 subjects (n = 9 adults; n = 36 pediatrics) were included in the initial PK model build and 12 subjects in the external validation cohort. Overall, the data were best described using a two-compartment model with volume of distribution (V) normalized to total body weight (TBW/70 kg) and an allometric scaled elimination rate constant (Ke) for pediatric subjects (AIC = 4,138.36). Final model observed vs. predicted plots demonstrated good fit (population R2 = 0.87, individual R2 = 0.97, Figure 1a and b). For the final model, the population median parameter values (95% credibility interval) were V0 (total volume of distribution), 11.7 L (10.2–14.6); Ke for adult, 0.66 hour−1 (0.38–0.78), Ke for pediatrics, 0.82 hour−1 (0.64–0.85), KCP (rate constant from central to peripheral compartment), 1.4 hour−1 (1.3–1.8), KPC (rate constant from peripheral to central compartment), 1.6 hour−1 (1.2–1.8). The validation cohort has 12 subjects, and the final model fit the data well (individual R2 = 0.75). Conclusion In this diverse group of adult and pediatrics, a two-compartment model described CEF PK well and was externally validated with a unique cohort. This model can serve as a population prior for real-time PK software algorithms. Disclosures All authors: No reported disclosures.
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Lucor, Didier, and Olivier P. Le Maître. "Cardiovascular Modeling With Adapted Parametric Inference." ESAIM: Proceedings and Surveys 62 (2018): 91–107. http://dx.doi.org/10.1051/proc/201862091.
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Computational modeling of the cardiovascular system, promoted by the advance of fluid-structure interaction numerical methods, has made great progress towards the development of patient-specific numerical aids to diagnosis, risk prediction, intervention and clinical treatment. Nevertheless, the reliability of these models is inevitably impacted by rough modeling assumptions. A strong in-tegration of patient-specific data into numerical modeling is therefore needed in order to improve the accuracy of the predictions through the calibration of important physiological parameters. The Bayesian statistical framework to inverse problems is a powerful approach that relies on posterior sampling techniques, such as Markov chain Monte Carlo algorithms. The generation of samples re-quires many evaluations of the cardiovascular parameter-to-observable model. In practice, the use of a full cardiovascular numerical model is prohibitively expensive and a computational strategy based on approximations of the system response, or surrogate models, is needed to perform the data as-similation. As the support of the parameters distribution typically concentrates on a small fraction of the initial prior distribution, a worthy improvement consists in gradually adapting the surrogate model to minimize the approximation error for parameter values corresponding to high posterior den-sity. We introduce a novel numerical pathway to construct a series of polynomial surrogate models, by regression, using samples drawn from a sequence of distributions likely to converge to the posterior distribution. The approach yields substantial gains in efficiency and accuracy over direct prior-based surrogate models, as demonstrated via application to pulse wave velocities identification in a human lower limb arterial network.
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Hadka, David, and Patrick Reed. "Borg: An Auto-Adaptive Many-Objective Evolutionary Computing Framework." Evolutionary Computation 21, no. 2 (May 2013): 231–59. http://dx.doi.org/10.1162/evco_a_00075.
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This study introduces the Borg multi-objective evolutionary algorithm (MOEA) for many-objective, multimodal optimization. The Borg MOEA combines [Formula: see text]-dominance, a measure of convergence speed named [Formula: see text]-progress, randomized restarts, and auto-adaptive multioperator recombination into a unified optimization framework. A comparative study on 33 instances of 18 test problems from the DTLZ, WFG, and CEC 2009 test suites demonstrates Borg meets or exceeds six state of the art MOEAs on the majority of the tested problems. The performance for each test problem is evaluated using a 1,000 point Latin hypercube sampling of each algorithm's feasible parameteri- zation space. The statistical performance of every sampled MOEA parameterization is evaluated using 50 replicate random seed trials. The Borg MOEA is not a single algorithm; instead it represents a class of algorithms whose operators are adaptively selected based on the problem. The adaptive discovery of key operators is of particular importance for benchmarking how variation operators enhance search for complex many-objective problems.
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Tzortzis, Roxane, Andrea M. Doglioli, Stéphanie Barrillon, Anne A. Petrenko, Francesco d'Ovidio, Lloyd Izard, Melilotus Thyssen, et al. "Impact of moderately energetic fine-scale dynamics on the phytoplankton community structure in the western Mediterranean Sea." Biogeosciences 18, no. 24 (December 17, 2021): 6455–77. http://dx.doi.org/10.5194/bg-18-6455-2021.
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Abstract. Model simulations and remote sensing observations show that ocean dynamics at fine scales (1–100 km in space, day–weeks in time) strongly influence the distribution of phytoplankton. However, only a few in situ-based studies at fine scales have been performed, and most of them concern western boundary currents which may not be representative of less energetic regions. The PROTEVSMED-SWOT cruise took place in the moderately energetic waters of the western Mediterranean Sea (WMS), in the region south of the Balearic Islands. Taking advantage of near-real-time satellite information, we defined a sampling strategy in order to cross a frontal zone separating different water masses. Multi-parametric in situ sensors mounted on the research vessel, on a towed vehicle and on an ocean glider were used to sample physical and biogeochemical variables at a high spatial resolution. Particular attention was given to adapting the sampling route in order to estimate the vertical velocities in the frontal area also. This strategy was successful in sampling quasi-synoptically an oceanic area characterized by the presence of a narrow front with an associated vertical circulation. A multiparametric statistical analysis of the collected data identifies two water masses characterized by different abundances of several phytoplankton cytometric functional groups, as well as different concentrations of chlorophyll a and O2. Here, we focus on moderately energetic fronts induced by fine-scale circulation. Moreover, we explore physical–biological coupling in an oligotrophic region. Our results show that the fronts induced by the fine-scale circulation, even if weaker than the fronts occurring in energetic and nutrient-rich boundary current systems, maintain nevertheless a strong structuring effect on the phytoplankton community by segregating different groups at the surface. Since oligotrophic and moderately energetic regions are representative of a very large part of the world ocean, our results may have global significance when extrapolated.
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Kunah, O. N., E. V. Prokopenko, and A. V. Zhukov. "Ecomorphic organisation of the Ukraine steppe zone spider community." Fundamental and Applied Soil Science 15, no. 1-2 (March 14, 2014): 101–19. http://dx.doi.org/10.15421/041410.
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The conception of ecomorphes as ecological groups of living organisms has been developed by A. L. Belgard (1950) applicable for species of the highest plants of the southeast of a steppe zone of Ukraine. Conceptually close system of vital forms-biomorphs of animals and plants has been created by Mikhail Pavlovich Akimov (Akimov, 1955). A key task of an ecomorphic approach is the ecological analysis of ecosystems structure. The ecomorphic approach has been applied to various groups of animals: entomological fauna of wood plants (Apostolov, 1981), complexes of land arthropods (Barsov, 1996), communities of birds (Ponomarenko, 2002), soil mesofauna (Zhukov, 1996), coleoptera communities agrocoenosis (Sumarokov, 2007). To identify animal species as ecomorphes the expert approach was used: the expert in taxonomy group relying on the experience and knowledge of object identifies it ecomorphes. The algorithm of ecomorphes allocation of soil animals has been offered by Zhukov (Zhukov, 2009). With some changes this algorithm has been applied to allocation of spider species of the Dnepropetrovsk region (Prokopenko et al., 2011). A lack of the specified algorithm is that it yields satisfactory results only for abundant and frequent species in regional fauna. The shortcoming reason – it relies on parametrical statistics for which compliance of experimental data to the normal law of distribution is essentially important that actually can be established only for limited number of species. As result, for a number rather rare in regional fauna of species of spiders incorrect conclusions have been drawn on their ecological status which is known on data from spiders of regions more studied from the point of view of fauna. In our work nonparametric procedure of a multidimensional scaling which is tolerant to a statistical property of distribution of an abundance of types has been taken for a basis of ecomorphic classification of herpetobiont spiders. It including has allowed to integrate the data collected by various authors in a wide time and spatial span for the general analysis. Faunistic collection also differed and by a technique: the sizes of the traps, fixing liquid, number of traps, an exposition time. It is necessary to consider these circumstances objective as ecological classification of regional fauna should be based on considerable on coverage in time and space a material which cannot be collected on completely uniform procedure. The multidimensional scaling represents adaptive ordination procedure which assumes a choice of the final decision proceeding, first of all, ecological criteria, instead of especially mathematical. Such adaptability is reached by comparison of ordination decisions with markers of an ecological situation which are received at the biogeocoenosis description of places of sampling. These descriptions are presented in terms of typology of biogeocoenosis of Belgard (1950, 1971): their coenotic status, and also assessment of a mode of a fertility and humidity. The key material is received within the Prisamarsky biospheric station of the Dnepropetrovsk national university where within a monitoring profile reference types of wood biogeocenoses of a steppe zone of Ukraine are presented. Primary data of ecological-faunistic researches are represented in the form of matrixes (tables) where columns are presented by a species, and lines – sampling points. Sampling points may be ecologically processed on the basis of biogeocoenosis descriptions. If to find nature of compliance between sampling points and species, it is possible to make interpretation of the ecological status of species, i.e. to reveal key ecological groups (ecomorphes) and to establish belonging of species to them. The multidimensional scaling allows to estimate within one metric space an arrangement as species, and sampling points. Co-ordinates of sampling points in dimention of a multidimensional scaling are used as predictor of ecological characteristics of the environment in these points. Applying the obtained regression models it is possible to estimates of optimum conditions for species if in these models to use as predictor co-ordinates of species in those dimention of a multidimensional scaling. Species which are characterised by similar optimum values of ecological factors form ecological groups, or ecomorphes. Respectively for spiders we allocate coenomorphes, hygromorphes, trophocoenomorphes.
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Paananen, Topi, Juho Piironen, Paul-Christian Bürkner, and Aki Vehtari. "Implicitly adaptive importance sampling." Statistics and Computing 31, no. 2 (February 9, 2021). http://dx.doi.org/10.1007/s11222-020-09982-2.
Full textAbstract:
AbstractAdaptive importance sampling is a class of techniques for finding good proposal distributions for importance sampling. Often the proposal distributions are standard probability distributions whose parameters are adapted based on the mismatch between the current proposal and a target distribution. In this work, we present an implicit adaptive importance sampling method that applies to complicated distributions which are not available in closed form. The method iteratively matches the moments of a set of Monte Carlo draws to weighted moments based on importance weights. We apply the method to Bayesian leave-one-out cross-validation and show that it performs better than many existing parametric adaptive importance sampling methods while being computationally inexpensive.
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Weaver-Rosen, Jonathan, and Richard Malak. "Efficient Parametric Optimization for Expensive Single Objective Problems." Journal of Mechanical Design, January 5, 2021, 1–12. http://dx.doi.org/10.1115/1.4049519.
Full textAbstract:
Abstract Parametric optimization solves optimization problems as a function of uncontrollable or unknown parameters. Such an approach allows an engineer to gather more information than traditional optimization procedures during design. Existing methods for parametric optimization of computationally or monetarily expensive functions can be too time-consuming or impractical to solve. Therefore, new methods for the parametric optimization of expensive functions need to be explored. This work proposes a novel algorithm that leverages the advantages of two existing optimization algorithms. This new algorithm is called the efficient parametric optimization (EPO) algorithm. EPO enables adaptive sampling of a high-fidelity design space using an inexpensive low-fidelity response surface model. Such an approach largely reduces the required number of expensive high-fidelity computations. The proposed method is benchmarked using analytic test problems and used to evaluate a case study requiring finite element analysis. Results show that EPO performs as well as or better than the existing alternative, P3GA, for these problems given an allowable number of function evaluations.
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Chen, Ye, and Ilya O. Ryzhov. "Balancing Optimal Large Deviations in Sequential Selection." Management Science, September 14, 2022. http://dx.doi.org/10.1287/mnsc.2022.4527.
Full textAbstract:
In the ranking and selection problem, a sampling budget is allocated among a finite number of designs with the goal of efficiently identifying the best. Allocations of this budget may be static (with no dependence on the random values of the samples) or adaptive (decisions are made based on the results of previous decisions). A popular methodological strategy in the simulation literature is to first characterize optimal static allocations by using large deviations theory to derive a set of optimality conditions, and then to use these conditions to guide the design of adaptive allocations. We propose a new methodology that can be guaranteed to adaptively learn the solution to these optimality conditions in a computationally efficient manner, without any tunable parameters, and under a wide variety of parametric sampling distributions. This paper was accepted by Baris Ata, stochastic models and simulation.
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Hu, Yu, Yaolin Guo, Zhen Liu, Yifan Li, Zheyu Hu, Diwei Shi, Moran Bu, and Shiyu Du. "An adaptive parallel EI infilling strategy extended by non-parametric PMC sampling scheme for Efficient Global Optimization." IEEE Access, 2023, 1. http://dx.doi.org/10.1109/access.2023.3244996.
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Weaver-Rosen, Jonathan M., and Richard J. Malak. "Efficient Parametric Optimization for Expensive Single Objective Problems." Journal of Mechanical Design 143, no. 3 (January 27, 2021). http://dx.doi.org/10.1115/1.4049519.
Full textAbstract:
Abstract Parametric optimization solves optimization problems as a function of uncontrollable or unknown parameters. Such an approach allows an engineer to gather more information than traditional optimization procedures during design. Existing methods for parametric optimization of computationally or monetarily expensive functions can be too time-consuming or impractical to solve. Therefore, new methods for the parametric optimization of expensive functions need to be explored. This work proposes a novel algorithm that leverages the advantages of two existing optimization algorithms. This new algorithm is called the efficient parametric optimization (EPO) algorithm. EPO enables adaptive sampling of a high-fidelity design space using an inexpensive low-fidelity response surface model. Such an approach largely reduces the required number of expensive high-fidelity computations. The proposed method is benchmarked using analytic test problems and used to evaluate a case study requiring finite element analysis. Results show that EPO performs as well as or better than the existing alternative, Predictive Parameterized Pareto Genetic Algorithm (P3GA), for these problems given an allowable number of function evaluations.
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Hesthaven, Jan, Cecilia Pagliantini, and Nicolò Ripamonti. "Adaptive symplectic model order reduction of parametric particle-based Vlasov–Poisson equation." Mathematics of Computation, August 24, 2023. http://dx.doi.org/10.1090/mcom/3885.
Full textAbstract:
High-resolution simulations of particle-based kinetic plasma models typically require a high number of particles and thus often become computationally intractable. This is exacerbated in multi-query simulations, where the problem depends on a set of parameters. In this work, we derive reduced order models for the semi-discrete Hamiltonian system resulting from a geometric particle-in-cell approximation of the parametric Vlasov–Poisson equations. Since the problem’s nondissipative and highly nonlinear nature makes it reducible only locally in time, we adopt a nonlinear reduced basis approach where the reduced phase space evolves in time. This strategy allows a significant reduction in the number of simulated particles, but the evaluation of the nonlinear operators associated with the Vlasov–Poisson coupling remains computationally expensive. We propose a novel reduction of the nonlinear terms that combines adaptive parameter sampling and hyper-reduction techniques to address this. The proposed approach allows decoupling the operations having a cost dependent on the number of particles from those that depend on the instances of the required parameters. In particular, in each time step, the electric potential is approximated via dynamic mode decomposition (DMD) and the particle-to-grid map via a discrete empirical interpolation method (DEIM). These approximations are constructed from data obtained from a past temporal window at a few selected values of the parameters to guarantee a computationally efficient adaptation. The resulting DMD-DEIM reduced dynamical system retains the Hamiltonian structure of the full model, provides good approximations of the solution, and can be solved at a reduced computational cost.