Recently, learned indexes are assisting and are being adopted to replace traditional indexes for their low memory usage and high query performance. However, existing learned indexes suffer in query efficiency when dealing with locally skewed data distributions which may be caused or exacerbated by ubiquitous updates. Frequent model retraining and reconstruction is required under this circumstance. To address this issue, we present Chameleon, an adaptive learned index for locally skewed data especially in the context of frequent updates. We propose a metric for measuring local skewness, based on which we employ Multi-Agent Reinforcement Learning to assist in locating locally skewed regions and optimizing index structures. Additionally, to reduce the blocking time caused by index model retraining, we propose a lightweight lock named the Interval Lock to achieve a non-blocking retraining. Extensive experiments demonstrate that, without costing more memory, Chameleon outperforms the state-of-the-art learned indexes by up to 3.75 x and 4.37 x in lookup times for read-only and mixed workloads, respectively, and it accelerates update processing by up to 2.92 x.

Ground Penetrating Radar (GPR) is an efficient non-destructive testing tool used for detecting and locating buried pipelines. It helps to avoid interference with existing pipelines and determine optimal layouts, resulting in time and cost savings. However, applications in this domain often require the joint observation of sequential images, mapping from 2D B-scans to 3D spatial structures. Complex underground environments, equipment orientations, noise, and data deviations can introduce visual distortions, blurriness, and unclear structures in the collected data. Therefore, there is a need for a method to rapidly comprehend and visually analyze the true conditions of underground pipeline structures. GPR data is typically collected and stored in the form of two-dimensional B-scan sequences. In this paper, we propose a network framework that takes sparse original 2D B-scan sequences as input and outputs a dense three-dimensional target model. We first employ a Transformer model to interpolate the B-scan slice collection, generating dense 3D B-scan volume data. Subsequently, a from-coarse-to-fine back-projection strategy, based on the Transformer model, constructs a 3D volume data inversion-mapping model to transform 2D hyperbolic waves into three-dimensional pipeline information. Additionally, we apply a clutter removal mechanism based on Conditional Generative Adversarial Networks (CGAN) to Declutter and enhance the visualization of the desired hyperbolic wave structures, improving the accuracy of 3D visual imaging. Experimental results demonstrate that the proposed method is better suited for structural analysis of GPR pipeline data, particularly in complex real-world data experiments, affirming the effectiveness and practicality of the approach presented in this paper.

In recent years, person Re-IDentification (ReID) has rapidly progressed with wide real-world applications but is also susceptible to various forms of attack, including proven vulnerability to adversarial attacks. In this paper, we focus on the backdoor attack on deep ReID models. Existing backdoor attack methods follow an all-to-one or all-to-all attack scenario, where all the target classes in the test set have already been seen in the training set. However, ReID is a much more complex fine-grained open-set recognition problem, where the identities in the test set are not contained in the training set. Thus, previous backdoor attack methods for classification are not applicable to ReID. To ameliorate this issue, we propose a novel backdoor attack on deep ReID under a new all-to-unknown scenario, called Dynamic Triggers Invisible Backdoor Attack (DT-IBA). Instead of learning fixed triggers for the target classes from the training set, DT-IBA can dynamically generate new triggers for any unknown identities. Specifically, an identity hashing network is proposed to first extract target identity information from a reference image, which is then injected into the benign images by image steganography. We extensively validate the effectiveness and stealthiness of the proposed attack on benchmark datasets and evaluate the effectiveness of several defense methods against our attack.

Underground pipelines are often detected and identified using ground-penetrating radar (GPR), which requires an inversion process to obtain specific pipeline properties. Unfortunately, this process is an ill-posed problem that involves inferring complex subsurface structures from a small number of observations, which leads to diverse and complex descriptions of the target. To address this challenge, this study proposes an inversion method based on a coupled-learning generative adversarial network. First, we extract hyperbolic waves from GPR buried-object B-scan images and filter out non-pipe and non-homogeneous media information from the resulting dielectric constant predictions. The training set comprises two data pairs: simulated clutter-free data with simulated clutter data pairing and simulated clutter-free data with dielectric constant data pairing. An experiment with real measured B-scan sequences showed that our proposed method provides clear and visually analyzed results about the location and direction of underground pipes. This approach enhances the accuracy and reliability of the inversion process and improves the clarity of subsurface pipeline property descriptions.

The acquisition of Ground Penetrating Radar (GPR) data is often impeded by clutter, which poses a significant obstacle to the effectiveness of target detection algorithms. This paper presents a novel approach to address this challenge by developing a flexibility-residual BiSeNetV2 for clutter suppression of GPR images. Our proposed network incorporates the flexibility-residual block into BiSeNetV2, allowing for adaptively selected convolutional kernel sizes based on the number of channels and network parameters required for different tasks, thereby ensuring effective mitigation of network degradation while minimizing the impact on time complexity. Moreover, we integrate an ECA attention mechanism into the network, which employs 1-dimensional convolutional local cross-channel interaction to extract inter-channel dependencies efficiently. As a result, the size of the 1-dimensional convolutional kernel can be adaptively selected according to the number of channels, determining the coverage of cross-channel interactions. Additionally, we adjust the ratio of multiple output losses in the network to optimize its suitability for our task. Experimental results demonstrate the effectiveness of our network for clutter suppression of cluttered images, and the network trained with the simulated dataset also performs better when processing measured GPR data.

Support vector machine (SVM) is a popular classification method for the analysis of a wide range of data including big biomedical data. Many SVM methods with feature selection have been developed under the frequentist regularization or Bayesian shrinkage frameworks. On the other hand, the value of incorporating a priori known biological knowledge, such as those from functional genomics and functional proteomics, into statistical analysis of -omic data has been recognized in recent years. Such biological information is often represented by graphs. We propose a novel method that assigns Laplace priors to the regression coefficients and incorporates the underlying graph information via a hyper-prior for the shrinkage parameters in the Laplace priors. This enables smoothing of shrinkage parameters for connected variables in the graph and conditional independence between shrinkage parameters for disconnected variables. Extensive simulations demonstrate that our proposed methods achieve the best performance compared to the other existing SVM methods in terms of prediction accuracy. The proposed method are also illustrated in analysis of genomic data from cancer studies, demonstrating its advantage in generating biologically meaningful results and identifying potentially important features.

Support vector machine (SVM) is a powerful classification tool for analysis of high dimensional data such as genomics. Regularized linear and nonlinear SVM methods with feature selection have been developed. On the other hand, there is a growing body of literature showing that incorporating prior biological knowledge such as functional genomics, which are typically represented by graphs, into the analysis of genomic data can improve feature selection and prediction. In practice, however, such biological knowledge can often be inaccurate or unavailable. To attack this problem, we propose a Bayesian modeling approach which enables us to learn the graph structure among features and perform feature selection simultaneously. Our approach employs a Gaussian graphical model for inferring the graphical information and exploits the inferred graph to guide feature selection for SVM. An efficient MCMC algorithm is developed and our numerical analysis demonstrates that the proposed method has advantages over existing methods in feature selection and prediction via simulations and an application to the analysis of glioblastoma patient data.

The unmanned surface vehicle (USV) cluster, during marine task execution, works in a challenging communication environment. The cluster's network topology and states of wireless channel change rapidly with time. And the computing resources of fog nodes may be shared by several task requests simultaneously. Therefore, it's necessary to find an effective load balancing mechanism to cope with the ever-changing adverse factors. The load balancing problems of vehicular fog computing of USV cluster are investigated in this work. And furthermore, the corresponding mathematical models, including marine vehicular fog computing networks, wireless channels, and several typical scheduling mechanisms, are established. The analytical models and simulation results show that the proposed scheduling algorithm based on minimum response time performs better than other selected algorithms and can significantly reduce the response time and blocking probability of task requests.

Support vector machine (SVM) is a popular classification method for analysis of high dimensional data such as genomics data. Recently a number of linear SVM methods have been developed to achieve feature selection through either frequentist regularization or Bayesian shrinkage, but the linear assumption may not be plausible for many real applications. In addition, recent work has demonstrated that incorporating known biological knowledge, such as those from functional genomics, into the statistical analysis of genomic data offers great promise of improved predictive accuracy and feature selection. Such biological knowledge can often be represented by graphs. In this article, we propose a novel knowledge-guided nonlinear Bayesian SVM approach for analysis of high-dimensional data. Our model uses graph information that represents the relationship among the features to guide feature selection. To achieve knowledge-guided feature selection, we assign an Ising prior to the indicators representing inclusion/exclusion of the features in the model. An efficient MCMC algorithm is developed for posterior inference. The performance of our method is evaluated and compared with several penalized linear SVM and the standard kernel SVM method in terms of prediction and feature selection in extensive simulation studies. Also, analyses of genomic data from a cancer study show that our method yields a more accurate prediction model for patient survival and reveals biologically more meaningful results than the existing methods.

Support vector machine (SVM) is a popular classification method for the analysis of wide range of data including big data. Many SVM methods with feature selection have been developed under frequentist regularization or Bayesian shrinkage frameworks. On the other hand, the importance of incorporating a priori known biological knowledge, such as gene pathway information which stems from the gene regulatory network, into the statistical analysis of genomic data has been recognized in recent years. In this article, we propose a new Bayesian SVM approach that enables the feature selection to be guided by the knowledge on the graphical structure among predictors. The proposed method uses the spike-and-slab prior for feature selection, combined with the Ising prior that encourages group-wise selection of the predictors adjacent to each other on the known graph. Gibbs sampling algorithm is used for Bayesian inference. The performance of our method is evaluated and compared with existing SVM methods in terms of prediction and feature selection in extensive simulation settings. In addition, our method is illustrated in the analysis of genomic data from a cancer study, demonstrating its advantage in generating biologically meaningful results and identifying potentially important features.