Data-adaptive Method Research Articles

Versatile data-adaptive hyperelastic energy functions for soft materials

Applications of soft materials are customarily linked to complex deformation scenarios and material nonlinearities. In the bioengineering field, soft materials typically mimic the low stiffness of biological matter subjected to extreme deformations. Computational frameworks surge as a versatile tool to assist the design of functional applications. The constitutive model lies at the core of such frameworks. In this regard, the customary extreme non-linear behavior of elastomers poses an additional challenge to thoroughly capture the material behavior. Here, data-driven methodologies hold considerable promise for enhancing constitutive modeling when contrasted with phenomenological approaches. In this investigation, we introduce a versatile data-adaptive method tailored to the modeling of hyperelastic soft materials at finite strains. Specifically, our method substitutes an a priori chosen model for the strain energy function by a flexible interpolant defined on a discretized invariant space. Within this framework, the interpolation values assume the role of material parameters and are determined through finite element model updating to conform to measured experimental data — comprising full-field displacements coming from Digital-Image-Correlation and global reaction forces. We validate the method on uniaxial experimental tests of soft elastomers, encompassing ELASTOSILTM, DOWSILTM, and V HBTM. Overall, we aim to establish a new route for the construction of hyperelastic energy functions, untethered from any predefined existing models or assumptions regarding the shape of the energy.

Knockoffs-SPR: Clean Sample Selection in Learning With Noisy Labels.

A noisy training set usually leads to the degradation of the generalization and robustness of neural networks. In this article, we propose a novel theoretically guaranteed clean sample selection framework for learning with noisy labels. Specifically, we first present a Scalable Penalized Regression (SPR) method, to model the linear relation between network features and one-hot labels. In SPR, the clean data are identified by the zero mean-shift parameters solved in the regression model. We theoretically show that SPR can recover clean data under some conditions. Under general scenarios, the conditions may be no longer satisfied; and some noisy data are falsely selected as clean data. To solve this problem, we propose a data-adaptive method for Scalable Penalized Regression with Knockoff filters (Knockoffs-SPR), which is provable to control the False-Selection-Rate (FSR) in the selected clean data. To improve the efficiency, we further present a split algorithm that divides the whole training set into small pieces that can be solved in parallel to make the framework scalable to large datasets. While Knockoffs-SPR can be regarded as a sample selection module for a standard supervised training pipeline, we further combine it with a semi-supervised algorithm to exploit the support of noisy data as unlabeled data. Experimental results on several benchmark datasets and real-world noisy datasets show the effectiveness of our framework and validate the theoretical results of Knockoffs-SPR.

A data-adaptive method for investigating effect heterogeneity with high-dimensional covariates in Mendelian randomization

BackgroundMendelian randomization is a popular method for causal inference with observational data that uses genetic variants as instrumental variables. Similarly to a randomized trial, a standard Mendelian randomization analysis estimates the population-averaged effect of an exposure on an outcome. Dividing the population into subgroups can reveal effect heterogeneity to inform who would most benefit from intervention on the exposure. However, as covariates are measured post-“randomization”, naive stratification typically induces collider bias in stratum-specific estimates.MethodWe extend a previously proposed stratification method (the “doubly-ranked method”) to form strata based on a single covariate, and introduce a data-adaptive random forest method to calculate stratum-specific estimates that are robust to collider bias based on a high-dimensional covariate set. We also propose measures based on the Q statistic to assess heterogeneity between stratum-specific estimates (to understand whether estimates are more variable than expected due to chance alone) and variable importance (to identify the key drivers of effect heterogeneity).ResultWe show that the effect of body mass index (BMI) on lung function is heterogeneous, depending most strongly on hip circumference and weight. While for most individuals, the predicted effect of increasing BMI on lung function is negative, it is positive for some individuals and strongly negative for others.ConclusionOur data-adaptive approach allows for the exploration of effect heterogeneity in the relationship between an exposure and an outcome within a Mendelian randomization framework. This can yield valuable insights into disease aetiology and help identify specific groups of individuals who would derive the greatest benefit from targeted interventions on the exposure.

A data-adaptive method for outlier detection from functional data

A data-adaptive method for outlier detection from functional data

Further results on controlling the false discovery rate under some complex grouping structure of hypotheses

This article considers developing false discovery rate (FDR) controlling methods for testing multiple hypotheses under three different classification settings of the hypotheses into groups — simultaneous multi-way classification, hierarchical classification, and a combination of these two classifications. The methods are developed in their oracle forms by considering a weighted version of the Benjamini–Hochberg (BH, 1995) method, with the weights encoding the underlying structural information about the hypotheses. They control the FDR when the p-values involved are Positively Regression Dependent on the Subset (PRDS) of null p-values, and are more powerful than the BH procedure. Data-adaptive versions of these methods are also proposed by appropriately estimating the weights under the different types of classification. The proposed data-adaptive methods control the FDR at the desired level when the p-values are independent and, as simulations show, they can be more powerful FDR controlling methods, even under certain dependency, than some existing comparable multiple testing methods. We apply the data-adaptive method under the above-mentioned combined classification setting to analyze a publicly available neuro-imaging dataset. Such data typically have complex classification structures that were not addressed in previously available multiple testing methods, as far as we know. Our proposed method, with a flexible weighting scheme, is poised to utilize more information from the data in its decision making process, than other existing multiple testing methods.

PET AIF estimation when available ROI data is impacted by dispersive and/or background effects

Objective. Blood pool region of interest (ROI) data extracted from the field of view of a PET scanner can be impacted by both dispersive and background effects. This circumstance compromises the ability to correctly extract the arterial input function (AIF) signal. The paper explores a novel approach to addressing this difficulty. Approach. The method involves representing the AIF in terms of the whole-body impulse response (IR) to the injection profile. Analysis of a collection/population of directly sampled arterial data sets allows the statistical behaviour of the tracer’s impulse response to be evaluated. It is proposed that this information be used to develop a penalty term for construction of a data-adaptive method of regularisation estimator of the AIF when dispersive and/or background effects maybe impacting the blood pool ROI data. Main results. Computational efficiency of the approach derives from the linearity of the impulse response representation of the AIF and the ability to substantially rely on quadratic programming techniques for numerical implementation. Data from eight different tracers, used in PET cancer imaging studies, are considered. Sample image-based AIF extractions for brain studies with: 18F-labeled fluoro-deoxyglucose and fluoro-thymidine (FLT), 11C-labeled carbon dioxide (CO2) and 15O-labeled water (H2O) are presented. Results are compared to the true AIF based on direct arterial sampling. Formal numerical simulations are used to evaluate the performance of the AIF extraction method when the ROI data has varying amounts of contamination, in comparison to a direct approach that ignores such effects. It is found that even with quite small amounts of contamination, the mean squared error of the regularised AIF is significantly better than the error associated with direct use of the ROI data. Significance. The proposed IR-based AIF extraction scheme offers a practical methodological approach for situations where the available image ROI data may be contaminated by background and/or dispersion effects.

Data adaptive regularization with reference tissue constraints for liver quantitative susceptibility mapping.

To improve repeatability and reproducibility across acquisition parameters and reduce bias in quantitative susceptibility mapping (QSM) of the liver, through development of an optimized regularized reconstruction algorithm for abdominal QSM. An optimized approach to estimation of magnetic susceptibility distribution is formulated as a constrained reconstruction problem that incorporates estimates of the input data reliability and anatomical priors available from chemical shift-encoded imaging. The proposed data-adaptive method was evaluated with respect to bias, repeatability, and reproducibility in a patient population with a wide range of liver iron concentration (LIC). The proposed method was compared to the previously proposed and validated approach in liver QSM for two multi-echo spoiled gradient-recalled echo protocols with different acquisition parameters at 3T. Linear regression was used for evaluation of QSM methods against a reference FDA-approved -based LIC measure and measurements; repeatability/reproducibility were assessed by Bland-Altman analysis. The data-adaptive method produced susceptibility maps with higher subjective quality due to reduced shading artifacts. For both acquisition protocols, higher linear correlation with both - and -based measurements were observed for the data-adaptive method ( for , for ) than the standard method ( and ). For both protocols, the data-adaptive method enabled better test-retest repeatability (repeatability coefficients 0.19/0.30 ppm for the data-adaptive method, 0.38/0.47 ppm for the standard method) and reproducibility across protocols (reproducibility coefficient 0.28 vs. 0.53ppm) than the standard method. The proposed data-adaptive QSM algorithm may enable quantification of LIC with improved repeatability/reproducibility across different acquisition parameters as 3T.

The impact of dimensionality reduction of ion counts distributions on preserving moments, with applications to data compression

The field of space physics has a long history of utilizing dimensionality reduction methods to distill data, including but not limited to spherical harmonics, the Fourier Transform, and the wavelet transform. Here, we present a technique for performing dimensionality reduction on ion counts distributions from the Multiscale Mission/Fast Plasma Investigation (MMS/FPI) instrument using a data-adaptive method powered by neural networks. This has applications to both feeding low-dimensional parameterizations of the counts distributions into other machine learning algorithms, and the problem of data compression to reduce transmission volume for space missions. The algorithm presented here is lossy, and in this work, we present the technique of validating the reconstruction performance with calculated plasma moments under the argument that preserving the moments also preserves fluid-level physics, and in turn a degree of scientific validity. The method presented here is an improvement over other lossy compressions in loss-tolerant scenarios like the Multiscale Mission/Fast Plasma Investigation Fast Survey or in non-research space weather applications.

Application of big data adaptive semi-supervised clustering method based on deep learning

In order to solve the problems of high computational complexity, poor dimensionality reduction, and reduced clustering effect when the clustering task faces a large amount of big data, the application of a large data adaptive semi-supervised clustering method based on deep learning is proposed. Through the self-encoder of the deep clustering network, the analysis of the confrontation network is generated, and the semi-supervised deep clustering algorithm and algorithm of the adaptive strategy are optimized. Through the encoder layer structure of the deep coding network, different parameters are set for all data sets for algorithm experimental analysis. The results show that the data obtained by this method is faster, more accurate and more optimized than the traditional clustering method, which proves the effectiveness of the method.

A data-adaptive method for estimating density level sets under shape conditions

Given a random sample of points from some unknown density, we propose a method for estimating density level sets, for a positive threshold t, under the r-convexity assumption. This shape condition generalizes the convexity property and allows to consider level sets with more than one connected component. The main problem in practice is that r is an unknown geometric characteristic of the set related to its curvature, which may depend on t. A stochastic algorithm is proposed for selecting its value from data. The resulting reconstruction of the level set is able to achieve minimax rates for Hausdorff metric and distance in measure uniformly on the level t.

Data-adaptive binary neural networks for efficient object detection and recognition

• We reformulate 1-bit convolutional nerual networks via a data-driven manner. • A data-adaptive method is proposed to improve 1-bit convolutional neural networks. • A generic module is developed, which can be easily combined with other 1-bit convolutional neural networks. • An efficient binary object detection framework is formulated to balance efficiency and accuracy. • Performance of 1-bit convolutional neural networks on object detection and recognition are enhanced. Binary neural networks (BNNs) are promising for computational resource-limited devices, but the degradation of feature representation capacity stifles performance due to binarization. The reason is that existing methods fail to adapt to their input when approximating full-precision features. In this paper, we introduce the DA-BNN, a data-adaptive amplitude method based on spatial and channel attention. We generate an adaptive amplitude for a better feature approximation and minimize the gap between the real-valued and 1-bit convolution. Our adaptive amplitude introduces negligible storage but can significantly enhance the performance. Extensive experiments on object detection and recognition are conducted for the comprehensive evaluation of our methods. Our method achieves 64.0% on Pascal VOC with saving of the storage and computation by 18.62 × and 15.77 × , respectively. While on ImageNet, compared to the full-precision counterpart, 11.04 × and 10.80 × saving on storage and computation are obtained with just 3% drop on accuracy, demonstrating the effectiveness on both objective detection and recognition tasks.

Variable selection in finite mixture of location and mean regression models using skew-normal distribution

In this article, a variable selection method for the finite mixture of location regression (FMLR) and the finite mixture of mean regression (FMMR) models with a skew-normal error are discussed. The univariate skew-normal distribution was introduced by Sahu et al. will be used in this work, which is attractive because estimation of the skewness parameter does not present the same degree of difficulty as in the case with Azzalini one and, moreover, it allows easy implementation of the EM algorithm. A penalized likelihood approach for variable selection in FMLR and FMMR models was introduced in this article. With a data-adaptive method for selecting tuning parameters, we establish the theoretical properties of our procedure, including consistency in variable selection, the oracle property in estimation. The EM algorithm facilitated by Gauss–Newton method for efficient numerical computations are developed. Simulation studies and a real data set are used to illustrate the proposed methodologies.

Multi-source Heterogeneous Ecological Big Data Adaptive Fusion Method Based on Symmetric Encryption

Multi-source Heterogeneous Ecological Big Data Adaptive Fusion Method Based on Symmetric Encryption

Spotlight commentary: A role for real-world evidence to inform the clinical care of patients with diabetes mellitus.

Spotlight commentary: A role for real-world evidence to inform the clinical care of patients with diabetes mellitus.

Sources of Subseasonal Predictability over CONUS during Boreal Summer

AbstractThe predictability of the Unified Forecast System (UFS) Coupled Model Prototype 2 developed by the National Centers for Environmental Prediction is assessed for the boreal summer over the continental United States (CONUS). The retrospective forecasts of low-level horizontal wind, precipitation and 2-m temperature for 2011–17 are examined to determine the predictability at subseasonal time scale. Using a data-adaptive method, the leading modes of variability are obtained and identified to be related to El Niño–Southern Oscillation (ENSO), intraseasonal oscillation (ISO), and warming trend. In a new approach, the sources of enhanced predictability are identified by examining the forecast errors and correlations in the weekly averages of the leading modes of variability. During the boreal summer, the ISO followed by the trend in UFS are found to provide better predictability in weeks 1–4 compared to the ENSO mode and the total anomaly. The western CONUS seems to have better predictability on weekly time scale in all three modes.

Multi-source Heterogeneous Ecological Big Data Adaptive Fusion Method Based on Symmetric Encryption

In recent years, with the rapid development of the domestic economy, the concept of sustainable development has been paid more and more attention. Ecological environment protection is more and more important, and the ecological environment is closely related to economic development. How to measure the relationship between the two is very important. Whether it is to build ecological environment protection or to ensure sustainable development of the economy, we should take the green development concept as a guiding concept, promote ecological economic development, and study the integration of ecological data is of great significance for solving these problems. The research of this thesis studies the multi-source heterogeneous (MSH) ecological big data (BD)adaptive fusion based (FM) based on symmetric encryption. This paper sets up a comparative experiment, multi-sensor (MS) data fusion based (DFM) based on Rough set theory, MSH data fusion based on data information conversion. The method is compared with the symmetric fusion MSH BD adaptive FM proposed in this paper. The results show that the MSH DFM based on Rough set theory has the highest confidence of 0.812; the MSH DFM based on data information conversion has the highest confidence of 0.68; based on symmetric encryption MSH BD The fusion confidence of the adaptive FM is up to 0.965, and the MSH ecological BD adaptive FM based on symmetric encryption is superior.

On the Beta Prime Prior for Scale Parameters in High-Dimensional Bayesian Regression Models

We study high-dimensional Bayesian linear regression with a general beta prime distribution for the scale parameter. Under the assumption of sparsity, we show that appropriate selection of the hyperparameters in the beta prime prior leads to the (near) minimax posterior contraction rate when $p \gg n$. For finite samples, we propose a data-adaptive method for estimating the hyperparameters based on marginal maximum likelihood (MML). This enables our prior to adapt to both sparse and dense settings, and under our proposed empirical Bayes procedure, the MML estimates are never at risk of collapsing to zero. We derive efficient Monte Carlo EM and variational EM algorithms for implementing our model, which are available in the R package NormalBetaPrime. Simulations and analysis of a gene expression data set illustrate our model's self-adaptivity to varying levels of sparsity and signal strengths.

Ecological Big Data Adaptive Compression Method Combining 1D Convolutional Neural Network and Switching Idea

To address large data transmission requirements and high transmission power consumptions characterizing micro-environment monitoring systems that are commonly used in forest health and safety applications, we propose an ecological big data adaptive switching compression method based on a 1D convolutional neural network (1D CNN). First, to ensure that data samples apply to different compression dictionaries, a 1D CNN is used to classify the samples into two sets according to the characteristics of samples. Subsequently, based on the classification results, the switching factor $S$ is defined, such that the discrete cosine transform (DCT) predefined dictionary and the learning dictionary (K-SVD) can be used to adaptively achieve sparse expression and data compression. Finally, the orthogonal matching pursuit (OMP) algorithm reconstructs the sparse signal. To evaluate the feasibility and robustness of the proposed method, we conduct experiments on four types of data: air temperature (AT), air humidity (AH), soil temperature (ST), and soil humidity (SH). The results indicate that the proposed method, compared to K-SVD and DCT dictionary, exhibits excellent performance for all data samples having smaller sparse error (SE), smaller reconstruction error (RE), and larger compression ratio (CR) at different sparsity levels. In particular, when sparsity $K$ is 16, the reconstructed signal is the most similar to the original signal. In addition, the proposed method reduces power consumption by 79.90%, compared with uncompressed data transmission. Considering four factors, the adaptive switching compression method based on 1D CNN has higher reconstruction accuracy and lower power consumption than using only K-SVD or DCT dictionary.

Shrinking the eigenvalues of M-estimators of covariance matrix

A highly popular regularized (shrinkage) covariance matrix estimator is the shrinkage sample covariance matrix (SCM) which shares the same set of eigenvectors as the SCM but shrinks its eigenvalues toward the grand mean of the eigenvalues of the SCM. In this paper, a more general approach is considered in which the SCM is replaced by an M-estimator of scatter matrix and a fully automatic data adaptive method to compute the optimal shrinkage parameter with minimum mean squared error is proposed. Our approach permits the use of any weight function such as Gaussian, Huber's, Tyler's, or t weight functions, all of which are commonly used in M-estimation framework. Our simulation examples illustrate that shrinkage M-estimators based on the proposed optimal tuning combined with robust weight function do not loose in performance to shrinkage SCM estimator when the data is Gaussian, but provide significantly improved performance when the data is sampled from an unspecified heavy-tailed elliptically symmetric distribution. Also, real-world and synthetic stock market data validate the performance of the proposed method in practical applications.

Analysis of Rainfall and Temperature Data Using Ensemble Empirical Mode Decomposition

Climatic variables such as rainfall and temperature have nonlinear and non-stationary characteristics such that analysing them using linear methods inconclusive results are found. Ensemble empirical mode decomposition (EEMD) is a data-adaptive method that is best suitable for data with nonlinear and non-stationary characteristics. The average monthly rainfall and temperature data for a selected region in South Africa are decomposed into intrinsic mode functions (IMFs) at different time scales using EEMD. The IMFs exhibit an inter-annual to inter-decadal variability. The influence of climatic oscillations such as El-Nino Southern Oscillation (ENSO) and quasi-biennial oscillation (QBO) is identified. The influence of temperature variability on rainfall is also shown at different time scales. Based on the results obtained, the EEMD method is found to be suitable to identify different oscillations in the rainfall and temperature data.