Permutation Scheme Research Articles

Multilabel all-relevant feature selection using lower bounds of conditional mutual information

We consider a multilabel all-relevant feature selection task which is more general than the classical minimal-optimal subset task. Whereas the goal of the minimal-optimal methods is to find the smallest subset of features allowing accurate prediction of labels, the objective of the all-relevant methods is to identify all the features that are related to the target labels, including strongly and all weakly relevant features. The all-relevant task has received much interest in the fields where discovering the dependency structure between features and target variables is more important than the prediction itself, e.g., in medical and bioinformatics applications. In this paper, we formally describe the all-relevant problem for multi-label classification using an information-theoretic approach. We propose a relevancy score and an efficient method of its calculation based on the lower bounds of conditional mutual information. Another practical issue is how to separate the relevant features from irrelevant ones. To find a threshold, we propose a testing procedure based on a permutation scheme. Finally, empirical evaluation of all-relevant methods requires a specific approach. We consider a large variety of simulated datasets representing different dependency structures and containing various types of interactions. Empirical results on simulated datasets and a large clinical database demonstrate that the proposed method can successfully identify relevant features.

XtoE: A Novel Constructive and Camouflaged Adaptive Data Hiding and Image Encryption Scheme for High Dynamic Range Images

High dynamic range (HDR) image data hiding and encryption has attracted much interest in recent years due the benefits of providing high quality realistic images and versatile applications, such as copyright protection, data integrity, and covert communication. In this paper, we propose a novel constructive and camouflaged adaptive data hiding and image encryption scheme for HDR images. Our algorithm disguises hidden messages when converting an original OpenEXR format to the RGBE encoding, which contains the Red, Green, and Blue color channels and an exponent E channel. During the conversion process, we determine an optimal base for each pixel by considering the user’s demands and the exponent E channel information to achieve adaptive message concealment. To prevent inappropriate access to the stego image, we perform the bit-level permutation and confusion using a 2D Sine Logistic modulation map with hyperchaotic behavior and a random permutation scheme with the time complexity of ON. To the best of our knowledge, our algorithm is the first in HDR data hiding literature able to predict the image distortion and satisfy a user’s request for the embedding capacity. Our algorithm offers 18% to 32% larger embedding rate than that provided by the current state-of-the-art works without degrading the quality of the stego image. Experimental results confirm that our scheme provides high security superior to the competitors.

Local permutation tests for conditional independence

In this paper, we investigate local permutation tests for testing conditional independence between two random vectors X and Y given Z. The local permutation test determines the significance of a test statistic by locally shuffling samples, which share similar values of the conditioning variables Z, and it forms a natural extension of the usual permutation approach for unconditional independence testing. Despite its simplicity and empirical support, the theoretical underpinnings of the local permutation test remain unclear. Motivated by this gap, this paper aims to establish theoretical foundations of local permutation tests with a particular focus on binning-based statistics. We start by revisiting the hardness of conditional independence testing and provide an upper bound for the power of any valid conditional independence test, which holds when the probability of observing “collisions” in Z is small. This negative result naturally motivates us to impose additional restrictions on the possible distributions under the null and alternate. To this end, we focus our attention on certain classes of smooth distributions and identify provably tight conditions under which the local permutation method is universally valid, that is, it is valid when applied to any (binning-based) test statistic. To complement this result on type I error control, we also show that in some cases, a binning-based statistic calibrated via the local permutation method can achieve minimax optimal power. We also introduce a double-binning permutation strategy, which yields a valid test over less smooth null distributions than the typical single-binning method without compromising much power. Finally, we present simulation results to support our theoretical findings.

Hay meadows' overriding effect shapes ground beetle functional diversity in mountainous landscapes

AbstractMountain regions are hotspots of biodiversity, and are particularly sensitive to human activities and global changes. Characterizing biodiversity using trait‐based approaches may improve the understanding of the evolutionary and mechanistic basis of ecological patterns in species distribution. The investigation of trait–environment relationships, however, is challenging, especially when a hierarchical sampling design is used, as this may lead to misidentification of associations. Here, we investigate how functional traits in ground beetles (Coleoptera: Carabidae), a taxon often used as a bioindicator of climate and environmental changes, vary with environmental features in a mountainous landscape. The study was conducted in the Stelvio National Park (Central Italian Alps), collecting individuals with pitfall traps deployed within plots (small spatial scale), located along altitudinal transects (large spatial scale). To investigate the trait–environment association, we used double constrained correspondence analysis, which permits the selection of important traits and environmental variables while accounting for the hierarchical structure of the sampling design. The trait–environment association was largely one‐dimensional, with hay meadow acting as main environmental driver, negatively related to brachypterous wing‐form (indicator of poor dispersal ability) and, to a lesser extent, to specialized diet and (only for the large scale) body length. Secondarily, these traits were related negatively to soil pH and, for the larger spatial scale, positively to canopy cover and elevation. Double constrained correspondence analysis with specialized permutation schemes for statistical testing was effective and robust to analyze the data of the hierarchical sampling.

Pothole recognition using DeepCNN with layer permutation scheme

Pavements need frequent maintenance and management, for which one needs detailed information related to the road condition. At present, much of the condition monitoring around the world is done manually in situ or using high-end equipment. This is quite impractical, especially on a national or global scale because it takes time and involves high cost and effort. Pothole recognition using deep-learning-inspired image classification is currently being researched. Convolutional neural networks (CNNs) have drastically enhanced the techniques of image classification. We discuss an improved model based on CNN for pothole recognition. It introduces a neural architecture, namely layer permutation scheme for CNN (LPS-CNN). There is always a trade-off between network complexity and processing time efficiency in neural network architectures. The specific importance of the proposed neural network is that it learns a sparse architecture of the receptive neurons, which turns out to be fruitful in attaining maximum efficiency (for pothole recognition) with a reasonable processing time. The practical applicability of the proposed model is tested on the publicly available DSH 2017 pothole dataset. The proposed LPS-CNN outperforms all other existing architectures for pothole recognition and achieves an exceptional average accuracy of 99.6% on the benchmark pothole dataset.

Permutation tests under a rotating sampling plan with clustered data

The distribution of lumber strength of any grade may evolve, for example, due to climate change, forest fire, changes in processing methods, and other factors. So, in North America the forest products industry monitors the evolution of their means, percentiles, or other parameters to ensure the wood products meet the industrial standard. For administrative convenience and informativeness, one may adopt a rotating sampling plan by sampling 36 mills in the initial occasion and having six of them replaced in each successive occasion for the next five occasions. The strength data on a specified number, commonly 10 pieces of lumbers from each sampled mills, are obtained. Under such rotating plans the observations on pieces from the same mill are correlated, and the observations on samples from the same mill taken on different occasions are also correlated. Ignoring these correlations may lead to invalid inference procedures. Yet accommodating a cluster structure in parametric models is difficult and entails a high level of misspecification risk. In this paper we explore symmetry in the clustered data collected via a rotating sampling plan to develop a permutation scheme for testing various hypotheses of interest. We also introduce a semiparametric density ratio model to link the distributions of the response variable over time. The combination retains the validity of the inference methods while extracting maximum information from the sampling plan. A simulation study indicates that the proposed permutation tests firmly control the type I error whether or not the data are clustered. The use of the density ratio model improves the power of the tests. We also apply the proposed tests to data from the motivating application. The proposed permutation tests effectively address many real-world issues with trust worth inference conclusions.

A novel quantum image encryption technique based on improved controlled alternated quantum walks and hyperchaotic system

This paper proposes a novel quantum image encryption algorithm based on a local substitution and global permutation scheme. First, a plaintext-related secret key is generated by using a hyperchaotic system; second, the plain image is scrambled employing a block-based general Arnold transform and permutation-based special linear transform; third, S-box substitution and special linear transform are iterated multiple times until certain conditions are achieved. A novel controlled alternated quantum walks scheme is also proposed to generate substitution boxes. Numerical simulation shows that the proposed encryption scheme can effectively a encrypt plain image to an incomprehensible noise image, and is capable of resisting a variety of attacks.

Multi-source geographically weighted regression for regionalized ground-motion models

This work proposes a novel approach to the calibration of regionalized regression models, with particular reference to ground-motion models (GMMs), which are key for probabilistic seismic hazard analysis and earthquake engineering applications. A novel methodology, named multi-source geographically-weighted regression (MS-GWR), is developed, allowing one to (i) estimate regionalized regression models depending on multiple sources of non-stationarity (such as site- and event-dependent non-stationarities in GMMs), and (ii) make inference on the significance and stationarity of the regression coefficients. Unlike previous approaches to the problem, the proposed framework is non-parametric – in the sense of the distribution of the errors – the inference being based on a permutation scheme. MS-GWR is here used to calibrate a new regionalized ground-motion model for predicting peak ground acceleration in Italy, based on a large scale database of waveforms and metadata made available by the Italian Institute for Geophysics and Vulcanology (INGV).

Compact Message Permutation for a Fully Pipelined BLAKE-256/512 Accelerator

Developing a low-cost and high-performance BLAKE accelerator has recently become an attractive research trend because the BLAKE algorithm is important in widespread applications, such as cryptocurrencies, data security, and digital signatures. Unfortunately, the existing BLAKE circuits are limited in performance and hardware efficiency. Therefore, this paper introduces the first fully pipelined BLAKE-256/512 accelerator to improve throughput and hardware efficiency. Moreover, based on the rates of changed words in consecutive message inputs, a compact message permutation scheme is proposed to reduce the area and energy consumption of the fully pipelined BLAKE-256/512 accelerator. To achieve these goals, the compact message permutation scheme includes two novel optimization techniques: register optimization, reducing the number of registers used by over 80% compared to conventional message permutation in a theoretical evaluation, and XOR optimization, decreasing the number of XOR gates by 93.8%. An ASIC-based experiment shows that the proposed compact message permutation scheme helps reduce the area and power consumption by up to 11.35% and 21.10%, respectively, for the fully pipelined BLAKE-256 accelerator and by up to 9.86% and 20.32%, respectively, for the fully pipelined BLAKE-512 accelerator. The correctness of the compact message permutation scheme is verified on a real hardware platform (an Alveo U280 FPGA).

Spatially Lagged Covariate Model with Zero Inflated Conway-Maxwell-Poisson Distribution Model for the Analysis of Pedestrian Injury Counts

Spatial dependency is important to recognize because of the mapping of pedestrian injury counts analysis. Road safety has been a major issue in contemporary societies, with road crashes incurring major human and materials costs annually worldwide. South Korea’s pedestrian traffic accident rate is the highest among the Organization for Economic Cooperation and Development (OECD) countries. In this paper, we use spatially lagged covariate model with zero inflated Conway-Maxwell-Poisson distribution model to account for spatial autocorrelation of no. of pedestrian crashes with cars. Alternatively, the Conway-Maxwell-Poisson (CMP) distribution, first proposed by Conway and Maxwell (1962) has the flexibility to handle all levels of dispersion, including underdispersion. We test spatial autocorrelation of pedestrian injury counts at 2474 sites, with several weights matrices using Moran s I statistics, under permutation scheme. Then we fit different 20 models, and finally choose the best model by the AIC and SBC values.

Multi-target regressor chains with repetitive permutation scheme for characterization of built environments with remote sensing

Multi-task learning techniques allow the beneficial joint estimation of multiple target variables. Here, we propose a novel multi-task regression (MTR) method called ensemble of regressor chains with repetitive permutation scheme. It belongs to the family of problem transformation-based MTR methods which foresee the creation of an individual model per target variable. Subsequently, the combination of the separate models allows obtaining an overall prediction. Our method builds upon the concept of so-called ensemble of regressor chains which align single-target models along a flexible permutation, i.e., chain. However, in order to particularly address situations with a small number of target variables, we equip ensemble of regressor chains with a repetitive permutation scheme. Thereby, estimates of the target variables are cascaded to subsequent models as additional features when learning along a chain, whereby one target variable can occupy multiple elements of the chain. We provide experimental evaluation of the method by jointly estimating built-up height and built-up density based on features derived from Sentinel-2 data for the four largest cities in Germany in a comparative setup. We also consider single-target stacking, multi-target stacking, and ensemble of regressor chains without repetitive permutation. Empirical results underline the beneficial performance properties of MTR methods. Our ensemble of regressor chain with repetitive permutation scheme approach achieved most frequently the highest accuracies compared to the other MTR methods, whereby mean improvements across the experiments of 14.5% compared to initial single-target models could be achieved.

Inference for spatial regression models with functional response using a permutational approach

The aim of this work is to introduce an approach to null hypothesis significance testing in a functional linear model for spatial data. The proposed method is capable of dealing with the spatial structure of data by building a permutation testing procedure on spatially filtered residuals of a spatial regression model. Indeed, due to the spatial dependence existing among the data, the residuals of the regression model are not exchangeable, breaking the basic assumptions of the Freedman and Lane permutation scheme. Instead, it is proposed here to estimate the variance–covariance structure of the residuals by variography, remove this correlation by spatial filtering residuals and base the permutation test on these approximately exchangeable residuals. A simulation study is conducted to evaluate the performance of the proposed method in terms of empirical size and power, examining its behavior under different covariance settings. We show that neglecting the residuals spatial structure in the permutation scheme (thus permuting the correlated residuals directly) yields a very liberal testing procedures, whereas the proposed procedure is close to the nominal size of the test. The methodology is demonstrated on a real world data set on the amount of waste production in the Venice province of Italy.

CASANOVA: Permutation inference in factorial survival designs.

We propose inference procedures for general factorial designs with time-to-event endpoints. Similar to additive Aalen models, null hypotheses are formulated in terms of cumulative hazards. Deviations are measured in terms of quadratic forms in Nelson-Aalen-type integrals. Different from existing approaches, this allows to work without restrictive model assumptions as proportional hazards. In particular, crossing survival or hazard curves can be detected without a significant loss of power. For a distribution-free application of the method, a permutation strategy is suggested. The resulting procedures' asymptotic validity is proven and small sample performances are analyzed in extensive simulations. The analysis of a data set on asthma illustrates theapplicability.

A Novel Invariant Based Commutative Encryption and Watermarking Algorithm for Vector Maps

Commutative encryption and watermarking (CEW) is an emerging method that combines encryption technology with digital watermarking technology. It has the dual capability of secure transmission and copyright protection. However, the existing CEW methods for vector maps have good robustness in resisting geometric attacks but poor resistance to vertex attacks (e.g., addition, deletion, etc.). To solve this problem, here we propose a novel invariant-based CEW algorithm for vector maps, which consists of permutation-based encryption scheme and coordinates-based watermarking scheme. In the encryption scheme, the encryption key is generated via the Gaussian distribution method combined with the SHA-512 hash method; then, the double random position permutation strategy is applied to the vector map encryption. In watermarking embedding scheme, the original watermark image is scrambled via logistic chaotic encryption before embedding, and the coordinates of all the vertices are normalized. Then, the scrambled watermark image is embedded into the normalized coordinates. Results show that: proposed method is more robust to conventional attacks (e.g., vertex addition and deletion, reordering and data format conversion) and geometric attacks (e.g., scaling and translation). In addition, compared with the existing CEW methods for vector maps, the proposed method has higher security and stronger robustness against vertex attacks.

Combinatorial analysis of a permutation scheme for a certain type of series

The article deals with a permutation scheme of size n with r successive series of increasing elements. I solve the problems of finding the number of outcomes of the scheme, and design the procedure for their enumeration using r successive schemes of combinations of an array of different compositions of series of increasing serial numbers of permutation elements. The numbering problem for outcomes of the scheme is solved, and their simulation is discussed.

Analysis of correlations between local geographic atrophy growth rates and local OCT angiography-measured choriocapillaris flow deficits.

The purpose of this study is to quantitatively assess correlations between local geographic atrophy (GA) growth rates and local optical coherence tomography angiography (OCTA)-measured choriocapillaris (CC) flow deficits. Thirty-eight eyes from 27 patients with GA secondary to age-related macular degeneration (AMD) were imaged with a commercial 1050 nm swept-source OCTA instrument at 3 visits, each separated by ∼6 months. Pearson correlations were computed between local GA growth rates, estimated using a biophysical GA growth model, and local OCTA CC flow deficit percentages measured along the GA margins of the baseline visits. The p-values associated with the null hypothesis of no Pearson correlation were estimated using a Monte Carlo permutation scheme that incorporates the effects of spatial autocorrelation. The null hypothesis (Pearson's ρ = 0) was rejected at a Benjamini-Hochberg false discovery rate of 0.2 in 15 of the 114 visit pairs, 11 of which exhibited positive correlations; even amongst these 11 visit pairs, correlations were modest (r in [0.30, 0.53]). The presented framework appears well suited to evaluating other potential imaging biomarkers of local GA growth rates.

Combining hematoxylin and eosin (H&E) stained images and RNA sequencing (RNA-Seq) data to predict overall survival (OS) in patients with non-small cell lung cancer (NSCLC).

1547 Background: IMpower150 was a phase 3 clinical trial that evaluated the efficacy of atezolizumab in patients with metastatic nonsquamous NSCLC; it demonstrated no significant OS benefit in the ACP (atezolizumab+carboplatin+paclitaxel) arm vs the BCP (bevacizumab+carboplatin+paclitaxel) control arm (hazard ratio [HR]=0.85; 95%CI, 0.71-1.03). The objective of this analysis was to identify a subpopulation of patients that benefits from ACP using H&E stained images and RNA-Seq data. Methods: Spatial statistics algorithms were applied to the coordinates of tumor cells and lymphocytes of the H&E stained images to capture spatial heterogeneity of the tumor microenvironment. The normalized and log-transformed RNA-Seq data underwent a nested feature selection procedure using a Cox proportional hazard model with L1 regularization and stability selection. Cutoffs for gene selection were determined using a permutation strategy with a false discovery rate <0.001. To investigate the association between the 41 derived spatial features, significant genes and OS, a Cox proportional hazard model with L2 regularization was fitted only for the ACP group. Survival groups were further identified using nested Monte Carlo Cross Validation to prevent over-fitting. Results: A total of 236 ACP and 235 BCP patients who had both H&E stained images and RNA-Seq data were analyzed. In the predicted long survival group, ACP patients had significantly longer median OS vs BCP patients based on H&E stained images (HR=0.61; 95%CI, 0.41-0.90; P=0.013) and RNA-Seq data (HR=0.64; 95%CI, 0.41-0.99; P=0.042). The combination of both modalities further improved the OS benefit between the arms (HR=0.44; 95%CI, 0.27-0.73; P=0.001). Data-driven selection of genes relevant for the prediction of OS included MAML3, AC024475.4, RGPD1, LCE3D and AC004156.1. Conclusions: Our approach was able to stratify a subpopulation of patients that significantly benefited from ACP compared with BCP treatment, particularly when integrating both H&E stained images and RNA-Seq data, which demonstrated the complementary value of both modalities. Our results could inform the development of a companion diagnostic that predicts individualized treatment response. Clinical trial information: NCT02366143.

Vector Map Encryption Algorithm Based on Double Random Position Permutation Strategy

Encryption of vector maps, used for copyright protection, is of importance in the community of geographic information sciences. However, some studies adopt one-to-one mapping to scramble vertices and permutate the coordinates one by one according to the coordinate position in a plain map. An attacker can easily obtain the key values by analyzing the relationship between the cipher vector map and the plain vector map, which will lead to the ineffectiveness of the scrambling operation. To solve the problem, a vector map encryption algorithm based on a double random position permutation strategy is proposed in this paper. First, the secret key sequence is generated using a four-dimensional quadratic autonomous hyperchaotic system. Then, all coordinates of the vector map are encrypted using the strategy of double random position permutation. Lastly, the encrypted coordinates are reorganized according to the vector map structure to obtain the cipher map. Experimental results show that: (1) one-to-one mapping between the plain vector map and cipher vector map is prevented from happening; (2) scrambling encryption between different map objects is achieved; (3) hackers cannot obtain the permutation key value by analyzing the pairs of the plain map and cipher map.

Cross-validation and permutations in MVPA: Validity of permutation strategies and power of cross-validation schemes

Multi-Voxel Pattern Analysis (MVPA) is a well established tool to disclose weak, distributed effects in brain activity patterns. The generalization ability is assessed by testing the learning model on new, unseen data. However, when limited data is available, the decoding success is estimated using cross-validation. There is general consensus on assessing statistical significance of cross-validated accuracy with non-parametric permutation tests. In this work we focus on the false positive control of different permutation strategies and on the statistical power of different cross-validation schemes.With simulations, we show that estimating the entire cross-validation error on each permuted dataset is the only statistically valid permutation strategy. Furthermore, using both simulations and real data from the HCP WU-Minn 3T fMRI dataset, we show that, among the different cross-validation schemes, a repeated split-half cross-validation is the most powerful, despite achieving slightly lower classification accuracy, when compared to other schemes. Our findings provide additional insights into the optimization of the experimental design for MVPA, highlighting the benefits of having many short runs.

Estimating Transfer Entropy in Continuous Time Between Neural Spike Trains or Other Event-Based Data.

Transfer entropy (TE) is a widely used measure of directed information flows in a number of domains including neuroscience. Many real-world time series for which we are interested in information flows come in the form of (near) instantaneous events occurring over time. Examples include the spiking of biological neurons, trades on stock markets and posts to social media, amongst myriad other systems involving events in continuous time throughout the natural and social sciences. However, there exist severe limitations to the current approach to TE estimation on such event-based data via discretising the time series into time bins: it is not consistent, has high bias, converges slowly and cannot simultaneously capture relationships that occur with very fine time precision as well as those that occur over long time intervals. Building on recent work which derived a theoretical framework for TE in continuous time, we present an estimation framework for TE on event-based data and develop a k-nearest-neighbours estimator within this framework. This estimator is provably consistent, has favourable bias properties and converges orders of magnitude more quickly than the current state-of-the-art in discrete-time estimation on synthetic examples. We demonstrate failures of the traditionally-used source-time-shift method for null surrogate generation. In order to overcome these failures, we develop a local permutation scheme for generating surrogate time series conforming to the appropriate null hypothesis in order to test for the statistical significance of the TE and, as such, test for the conditional independence between the history of one point process and the updates of another. Our approach is shown to be capable of correctly rejecting or accepting the null hypothesis of conditional independence even in the presence of strong pairwise time-directed correlations. This capacity to accurately test for conditional independence is further demonstrated on models of a spiking neural circuit inspired by the pyloric circuit of the crustacean stomatogastric ganglion, succeeding where previous related estimators have failed.