Group Sparsity Research Articles

Federated Bayesian network learning from multi-site data.

Identifying functional connectivity biomarkers of major depressive disorder (MDD) patients is essential to advance the understanding of disorder mechanisms and early intervention. Multi-site data arise naturally which could enhance the statistical power of single-site-based methods. However, the main concern is the inter-site heterogeneity and data sharing barriers between different sites. Our objective is to overcome these barries to learn multiple Bayesian networks (BNs) from rs-fMRI data. We propose a federated joint estimator and the corresponding optimization algorithm, called NOTEARS-PFL. Specifically, we incorporate both shared and site-specific information into NOTEARS-PFL by utilizing the sparse group lasso penalty. Addressing data-sharing constraint, we develop the alternating direction method of multipliers for the optimization of NOTEARS-PFL. This entails processing neuroimaging data locally at each site, followed by the transmission of the learned network structures for central global updates. The effectiveness and accuracy of the NOTEARS-PFL method are validated through its application on both synthetic and real-world multi-site resting-state functional magnetic resonance imaging (rs-fMRI) datasets. This demonstrates its superior efficiency and precision in comparison to alternative approaches. We proposed a toolbox called NOTEARS-PFL to learn the heterogeneous brain functional connectivity in MDD patients using multi-site data efficiently and with the data sharing constraint. The comprehensive experiments on both synthetic data and real-world multi-site rs-fMRI datasets with MDD highlight the excellent efficacy of our proposed method.

Fast and interpretable mortality risk scores for critical care patients.

Prediction of mortality in intensive care unit (ICU) patients typically relies on black box models (that are unacceptable for use in hospitals) or hand-tuned interpretable models (that might lead to the loss in performance). We aim to bridge the gap between these 2 categories by building on modern interpretable machine learning (ML) techniques to design interpretable mortality risk scores that are as accurate as black boxes. We developed a new algorithm, GroupFasterRisk, which has several important benefits: it uses both hard and soft direct sparsity regularization, it incorporates group sparsity to allow more cohesive models, it allows for monotonicity constraint to include domain knowledge, and it produces many equally good models, which allows domain experts to choose among them. For evaluation, we leveraged the largest existing public ICU monitoring datasets (MIMIC III and eICU). Models produced by GroupFasterRisk outperformed OASIS and SAPS II scores and performed similarly to APACHE IV/IVa while using at most a third of the parameters. For patients with sepsis/septicemia, acute myocardial infarction, heart failure, and acute kidney failure, GroupFasterRisk models outperformed OASIS and SOFA. Finally, different mortality prediction ML approaches performed better based on variables selected by GroupFasterRisk as compared to OASIS variables. Group Faster Risk's models performed better than risk scores currently used in hospitals, and on par with black box ML models, while being orders of magnitude sparser. Because GroupFasterRisk produces a variety of risk scores, it allows design flexibility-the key enabler of practical model creation. Group Faster Risk is a fast, accessible, and flexible procedure that allows learning a diverse set of sparse risk scores for mortality prediction.

Acoustic Resolution Photoacoustic Microscopy Imaging Enhancement: Integration of Group Sparsity With Deep Denoiser Prior

Acoustic Resolution Photoacoustic Microscopy Imaging Enhancement: Integration of Group Sparsity With Deep Denoiser Prior

Skeleton action recognition via group sparsity constrained variant graph auto-encoder

Skeleton action recognition via group sparsity constrained variant graph auto-encoder

Fully-connected tensor network decomposition and group sparsity for multitemporal images cloud removal

Fully-connected tensor network decomposition and group sparsity for multitemporal images cloud removal

Research on lightweight detection methods for the golden snub-nosed monkey based on YOLOv5n

To enable rapid detection of golden snub-nosed monkeys in complex environments, reduce the human costs associated with tracking and observing these monkeys, and accelerate the development of intelligent forest monitoring, we propose the PCB-YOLOv5n-prune model. This model is designed for lightweight devices and is based on channel pruning and module reconstruction. First, we constructed a dataset that combines annotations of the golden snub-nosed monkey's face and body, with some data converted to grayscale. We mixed and expanded five data styles to decrease reliance on color and enhance the informational content. Next, we applied the Sparse Group Lasso selection operator method to slim down the YOLOv5n primitive model for golden snub-nosed monkey detection, improving the detection speed of the underlying network. We then introduced a lightweight convolutional module, PConv, to create the improved residual branching module, CPB, which reduces model computation and memory access. Additionally, we incorporated a lightweight attention module, ECA, to adaptively weight channel features, facilitating local cross-channel information interaction. Finally, we integrated the ByteTrack multi-target tracking algorithm to enable continuous tracking of golden snub-nosed monkeys and visualize detection results. Experimental results demonstrate that the PCB-YOLOv5n-prune model reduces the number of parameters, floating point operations, and model weight by 61 %, 56 %, and 55 %, respectively, compared to the original YOLOv5n model, while significantly improving detection speed.

Multi-Task Learning for Gaussian Graphical Regressions with High Dimensional Covariates

Gaussian graphical regression is a powerful approach for regressing the precision matrix of a Gaussian graphical model on covariates, which permits the response variables and covariates to outnumber the sample size. However, traditional approaches of fitting the model via separate node-wise lasso regressions overlook the network-induced structure among these regressions, leading to high error rates, particularly when the number of nodes is large. To address this issue, we propose a multi-task learning estimator for fitting Gaussian graphical regression models, which incorporates a cross-task group sparsity penalty and a within-task element-wise sparsity penalty to govern the sparsity of active covariates and their effects on the graph, respectively. We also develop an efficient augmented Lagrangian algorithm for computation, which solves subproblems with a semi-smooth Newton method. We further prove that our multi-task learning estimator has considerably lower error rates than the separate node-wise regression estimates, as the cross-task penalty enables borrowing information across tasks. We examine the utility of our method through simulations and an application to a gene co-expression network study with brain cancer patients. Supplementary materials for this article are available online.

A hybrid overlapping group sparsity denoising model with fractional-order total variation and non-convex regularizer

A hybrid overlapping group sparsity denoising model with fractional-order total variation and non-convex regularizer

Sparse groups need not be semisparse

AbstractIn 1999 Michael Hartley showed that any abstract polytope can be constructed as a double coset poset, by means of a C-group $$\mathcal {W}$$ W and a subgroup $$N \le \mathcal {W}$$ N ≤ W . Subgroups $$N \le \mathcal {W}$$ N ≤ W that give rise to abstract polytopes through such a construction are called sparse. If, further, the stabilizer of a base flag of the poset is precisely N, then N is said to be semisparse. In [4, Conjecture 5.2] Hartely conjectures that sparse groups are always semisparse. In this paper, we show that this conjecture is in fact false: there exist sparse groups that are not semisparse. In particular, we show that such groups are always obtained from non-faithful maniplexes that give rise to polytopes. Using this, we show that Hartely’s conjecture holds for rank 3, but we construct examples to disprove the conjecture for all ranks $$n\ge 4$$ n ≥ 4 .

Hybrid plug-and-play CT image restoration using nonconvex low-rank group sparsity and deep denoiser priors

Objective. Low-dose computed tomography (LDCT) is an imaging technique that can effectively help patients reduce radiation dose, which has attracted increasing interest from researchers in the field of medical imaging. Nevertheless, LDCT imaging is often affected by a large amount of noise, making it difficult to clearly display subtle abnormalities or lesions. Therefore, this paper proposes a multiple complementary priors CT image reconstruction method by simultaneously considering both the internal prior and external image information of CT images, thereby enhancing the reconstruction quality of CT images.Approach. Specifically, we propose a CT image reconstruction method based on weighted nonconvex low-rank regularized group sparse and deep image priors under hybrid plug-and-play framework by utilizing the weighted nonconvex low rankness and group sparsity of dictionary domain coefficients of each group of similar patches, and a convolutional neural network denoiser. To make the proposed reconstruction problem easier to tackle, we utilize the alternate direction method of multipliers for optimization.Main results. To verify the performance of the proposed method, we conduct detailed simulation experiments on the images of the abdominal, pelvic, and thoracic at projection views of 45, 65, and 85, and at noise levels of1×105and1×106, respectively. A large number of qualitative and quantitative experimental results indicate that the proposed method has achieved better results in texture preservation and noise suppression compared to several existing iterative reconstruction methods.Significance. The proposed method fully considers the internal nonlocal low rankness and sparsity, as well as the external local information of CT images, providing a more effective solution for CT image reconstruction. Consequently, this method enables doctors to diagnose and treat diseases more accurately by reconstructing high-quality CT images.

Method for Reconstructing Velocity Field Images of the Internal Structures of Bridges Based on Group Sparsity

Non-destructive testing (NDT) enables the determination of internal defects and flaws in concrete structures without damaging them, making it a common application in current bridge concrete inspections. However, due to the complexity of the internal structure of this type of concrete, limitations regarding measurement point placement, and the extensive detection area, accurate defect detection cannot be guaranteed. This paper proposes a method that combines the Simultaneous Algebraic Reconstruction Technique with Group Sparsity Regularization (SART-GSR) to achieve tomographic imaging of bridge concrete under sparse measurement conditions. Firstly, a mathematical model is established based on the principles of the tomographic imaging of bridge concrete; secondly, the SART algorithm is used to solve for its velocity values; thirdly, on the basis of the SART results, GSR is applied for optimized solution processing; finally, simulation experiments are conducted to verify the reconstruction effects of the SART-GSR algorithm compared with those of the SART and ART algorithms. The results show that the SART-GSR algorithm reduced the relative error to 1.5% and the root mean square error to 89.76 m/s compared to the SART and ART algorithms. This improvement in accuracy makes it valuable for the tomographic imaging of bridge concrete and provides a reference for defect detection in bridge concrete.

Flexible Krylov methods for group sparsity regularization

Abstract This paper introduces new solvers for efficiently computing solutions to large-scale inverse problems with group sparsity regularization, including both non-overlapping and overlapping groups. Group sparsity regularization refers to a type of structured sparsity regularization, where the goal is to impose additional structure in the regularization process by assigning variables to predefined groups that may represent graph or network structures. Special cases of group sparsity regularization include ℓ 1 and isotropic total variation regularization. In this work, we develop hybrid projection methods based on flexible Krylov subspaces, where we first recast the group sparsity regularization term as a sequence of 2-norm penalization terms using adaptive regularization matrices in an iterative reweighted norm fashion. Then we exploit flexible preconditioning techniques to efficiently incorporate the weight updates. The main advantages of these methods are that they are computationally efficient (leveraging the advantages of flexible methods), they are general (and therefore very easily adaptable to new regularization term choices), and they are able to select the regularization parameters automatically and adaptively (exploiting the advantages of hybrid methods). Extensions to multiple regularization terms and solution decomposition frameworks (e.g. for anomaly detection) are described, and a variety of numerical examples demonstrate both the efficiency and accuracy of the proposed approaches compared to existing solvers.

High-dimensional macroeconomic stress testing of corporate recovery rate

We investigate macroeconomic stress testing frameworks for corporate bond recovery rate analysis using machine learning techniques. In doing so, we simulate the macroeconomic effect of a broad range of 182 macroeconomic variables extracting key factors with methods such as (sparse) principal component analysis and sparse group least absolute selection and shrinkage operation (LASSO). Using the adverse stress testing scenario from the US Federal Reserve as the benchmark, we demonstrate that our least squares-support vector regression model produces sensible and potentially valuable risk measures such as value-at-risk and conditional value-at-risk for recovery rates during periods of macroeconomic stress.

Screening the characteristic hyperspectral wavelength variables of peanut leaves based on coupled algorithms to predict SPAD value in peanut

ABSTRACT The accurate acquisition and prediction of the chlorophyll content in peanuts can provide reference for scientifically sound planting and management of peanut crops. In this study, peanut canopy leaves analysed during the flowering and pegging period were taken as the research object, and the spectral data collected by a portable hyperspectral spectrometer were used as the data source to study the inversion model of peanut leaf soil and plant analyser development (SPAD). To address the issues of randomness, the need for multiple iterations, and strong subjectivity in threshold selection in the initial variable set of the random frog (RF) algorithm, an improved RF algorithm was developed based on the sparse group lasso tool. By analysing and comparing the results of variable extraction of the improved RF algorithm and six characteristic wavelength screening algorithms, three algorithms were selected for pairwise coupling. These six algorithms were the correlation coefficient method (CC), successive projections algorithm, iteratively retains informative variables (IRIV), uninformative variable elimination (UVE), competitive adaptive reweighed sampling, and a genetic algorithm. The results show that: (1) the results of CC, UVE, and IRIV algorithms are better in a single algorithm experiment; and (2) in the coupled algorithms experiment, CC-UVE, CC-IRIV, and UVE-IRIV methods can effectively reduce dimensions, and the model has a certain level of stability. Among them, the UVE-IRIV-Extreme gradient boosting model had the highest accuracy, R 2 = 0.652, RMSE = 1.867, and the variable compression rate was as high as 97.84%. The research results can provide a theoretical basis for rapid and accurate monitoring of the SPAD values of peanut leaves.

Hardware-Friendly 3-D CNN Acceleration With Balanced Kernel Group Sparsity

Hardware-Friendly 3-D CNN Acceleration With Balanced Kernel Group Sparsity

Exploring the critical factors influencing the severity of maritime accidents via multinomial logit model with adaptive sparse group lasso penalty

Identifying the most influential factors affecting the severity of maritime accidents is crucial for developing effective strategies to reduce accident losses. However, this task is challenging due to the complex interactions between influencing factors and the presence of numerous irrelevant and redundant factors. As a new effort, this paper proposes a multinomial logit model with adaptive sparse group lasso penalty (MLASGL) to identify critical factors influencing the severity of maritime accidents. To fully illustrate the effect of complex interactions on severity, factor importance is initially evaluated using association rule mining and complex networks. Subsequently, adaptive weights are constructed based on the factor importance evaluation, followed by proposing an adaptive sparse group lasso that can impose a greater penalty on the noise factors. Additionally, considering that severity as a decision-making objective is often a multi-categorical variable, the multinomial logit is utilized to portray the relationship between the influencing factors and severity. Simultaneously, the negative log-likelihood function of the multinomial logit model is incorporated into the proposed adaptive sparse group lasso to eliminate irrelevant and redundant factors, thereby realizing the identification of critical factors. Finally, the feasibility and superiority of the proposed method are demonstrated by analyzing a real maritime accident dataset.

Exploring predictors of teacher efficacy among elementary and secondary school teachers in Seoul using Sparse group lasso: focusing on differences across school levels

Exploring predictors of teacher efficacy among elementary and secondary school teachers in Seoul using Sparse group lasso: focusing on differences across school levels

TIPS: a novel pathway-guided joint model for transcriptome-wide association studies.

In the past two decades, genome-wide association studies (GWAS) have pinpointed numerous SNPs linked to human diseases and traits, yet many of these SNPs are in non-coding regions and hard to interpret. Transcriptome-wide association studies (TWAS) integrate GWAS and expression reference panels to identify the associations at gene level with tissue specificity, potentially improving the interpretability. However, the list of individual genes identified from univariate TWAS contains little unifying biological theme, leaving the underlying mechanisms largely elusive. In this paper, we propose a novel multivariate TWAS method that Incorporates Pathway or gene Set information, namely TIPS, to identify genes and pathways most associated with complex polygenic traits. We jointly modeled the imputation and association steps in TWAS, incorporated a sparse group lasso penalty in the model to induce selection at both gene and pathway levels and developed an expectation-maximization algorithm to estimate the parameters for the penalized likelihood. We applied our method to three different complex traits: systolic and diastolic blood pressure, as well as a brain aging biomarker white matter brain age gap in UK Biobank and identified critical biologically relevant pathways and genes associated with these traits. These pathways cannot be detected by traditional univariate TWAS + pathway enrichment analysis approach, showing the power of our model. We also conducted comprehensive simulations with varying heritability levels and genetic architectures and showed our method outperformed other established TWAS methods in feature selection, statistical power, and prediction. The R package that implements TIPS is available at https://github.com/nwang123/TIPS.

Porous CrO[formula omitted]: A ferromagnetic half-metallic member in sparse hollandite oxide family

A stable polymorph of CrO2 is predicted using PBE+U method. The porous material is isostructural with α−MnO2 making it the second transition metal oxide in sparse hollandite group of materials. However, unlike the anti-ferromagnetic semiconducting character of the α−MnO2, it is found to be a ferromagnetic half-metal. At Fermi level, the hole pocket has ample contribution from O−2p orbital, though, the electron pocket is mostly contributed by Cr−3dxy and Cr−3dx2−y2. A combination of negative charge transfer through orbital mixing and extended anti-bonding state near Fermi level is responsible for the half-metallic ferromagnetic character of the structure. A comparative study of rutile and hollandite CrO2 and hollandite MnO2 structures delineate the interplay between structural, electronic and magnetic properties. The material shows a robust magnetic character under hydrothermal pressure, as well as, the band topology is conserved under uniaxial strain. Moderate magneto-crystalline anisotropy is observed and it shows a correspondence with the anisotropy of elastic constants. Occurrence of type−II Weyl nodes and their evolution under pressure is explored.

Evaluation of droplet deposition parameters based on the Genetic-Otsu algorithm.

Pesticide spraying is a cost-effective way to control crop pests and diseases. The effectiveness of this method relies on the deposition and distribution of the spray droplets within the targeted application area. There is a critical need for an accurate and stable detection algorithm to evaluate the liquid droplet deposition parameters on the water-sensitive paper (WSP) and reduce the impact of image noise. This study acquired 90 WSP samples with diverse coverage through field spraying experiments. The droplets on the WSP were subsequently isolated, and the coverage and density were computed, employing the fixed threshold method, the Otsu threshold method, and our Genetic-Otsu threshold method. Based on the benchmark of manually measured data, an error analysis was conducted on the accuracy of three methods, and a comprehensive evaluation was carried out. The relative error results indicate that the Genetic-Otsu method proposed in this research demonstrates superior performance in detecting droplet coverage and density. The relative errors of droplet density in the sparse, medium, and dense droplet groups are 2.7%, 1.5%, and 2.0%, respectively. The relative errors of droplet coverage are 1.5%, 0.88%, and 1.2%, respectively. These results demonstrate that the Genetic-Otsu algorithm outperforms the other two algorithms. The proposed algorithm effectively identifies small-sized droplets and accurately distinguishes the multiple independent contours of adjacent droplets even in dense droplet groups, demonstrating excellent performance. Overall, the Genetic-Otsu algorithm offered a reliable solution for detecting droplet deposition parameters on WSP, providing an efficient tool for evaluating droplet deposition parameters in UAV pesticide spraying applications.