This article presents a novel method for causal discovery with generalized structural equation models suited for analyzing diverse types of outcomes, including discrete, continuous, and mixed data. Causal discovery often faces challenges due to unmeasured confounders that hinder the identification of causal relationships. The proposed approach addresses this issue by developing two peeling algorithms (bottom-up and top-down) to ascertain causal relationships and valid instruments. This approach first reconstructs a super-graph to represent ancestral relationships between variables, using a peeling algorithm based on nodewise GLM regressions that exploit relationships between primary and instrumental variables. Then, it estimates parent-child effects from the ancestral relationships using another peeling algorithm while deconfounding a child's model with information borrowed from its parents' models. The article offers a theoretical analysis of the proposed approach, establishing conditions for model identifiability and providing statistical guarantees for accurately discovering parent-child relationships via the peeling algorithms. Furthermore, the article presents numerical experiments showcasing the effectiveness of our approach in comparison to state-of-the-art structure learning methods without confounders. Lastly, it demonstrates an application to Alzheimer's disease (AD), highlighting the method's utility in constructing gene-to-gene and gene-to-disease regulatory networks involving Single Nucleotide Polymorphisms (SNPs) for healthy and AD subjects.

This paper introduces a novel Perturbation-Assisted Inference (PAI) framework utilizing synthetic data generated by the Perturbation-Assisted Sample Synthesis (PASS) method. The framework focuses on uncertainty quantification in complex data scenarios, particularly involving unstructured data while utilizing deep learning models. On one hand, PASS employs a generative model to create synthetic data that closely mirrors raw data while preserving its rank properties through data perturbation, thereby enhancing data diversity and bolstering privacy. By incorporating knowledge transfer from large pre-trained generative models, PASS enhances estimation accuracy, yielding refined distributional estimates of various statistics via Monte Carlo experiments. On the other hand, PAI boasts its statistically guaranteed validity. In pivotal inference, it enables precise conclusions even without prior knowledge of the pivotal's distribution. In non-pivotal situations, we enhance the reliability of synthetic data generation by training it with an independent holdout sample. We demonstrate the effectiveness of PAI in advancing uncertainty quantification in complex, data-driven tasks by applying it to diverse areas such as image synthesis, sentiment word analysis, multimodal inference, and the construction of prediction intervals.

An exciting recent development is the uptake of deep neural networks in many scientific fields, where the main objective is outcome prediction with a black-box nature. Significance testing is promising to address the black-box issue and explore novel scientific insights and interpretations of the decision-making process based on a deep learning model. However, testing for a neural network poses a challenge because of its black-box nature and unknown limiting distributions of parameter estimates while existing methods require strong assumptions or excessive computation. In this article, we derive one-split and two-split tests relaxing the assumptions and computational complexity of existing black-box tests and extending to examine the significance of a collection of features of interest in a dataset of possibly a complex type, such as an image. The one-split test estimates and evaluates a black-box model based on estimation and inference subsets through sample splitting and data perturbation. The two-split test further splits the inference subset into two but requires no perturbation. Also, we develop their combined versions by aggregating the p -values based on repeated sample splitting. By deflating the bias-sd-ratio, we establish asymptotic null distributions of the test statistics and the consistency in terms of Type 2 error. Numerically, we demonstrate the utility of the proposed tests on seven simulated examples and six real datasets. Accompanying this article is our python library dnn-inference (https://dnn-inference.readthedocs.io/en/latest/) that implements the proposed tests.

This paper presents FlowSUM, a normalizing flows-based variational encoder-decoder framework for Transformer-based summarization.Our approach tackles two primary challenges in variational summarization: insufficient semantic information in latent representations and posterior collapse during training.To address these challenges, we employ normalizing flows to enable flexible latent posterior modeling, and we propose a controlled alternate aggressive training (CAAT) strategy with an improved gate mechanism.Experimental results show that FlowSUM significantly enhances the quality of generated summaries and unleashes the potential for knowledge distillation with minimal impact on inference time.Furthermore, we investigate the issue of posterior collapse in normalizing flows and analyze how the summary quality is affected by the training strategy, gate initialization, and the type and number of normalizing flows used, offering valuable insights for future research.

Statistical inference of directed relations given some unspecified interventions (i.e., the intervention targets are unknown) is challenging. In this article, we test hypothesized directed relations with unspecified interventions. First, we derive conditions to yield an identifiable model. Unlike classical inference, testing directed relations requires identifying the ancestors and relevant interventions of hypothesis-specific primary variables. To this end, we propose a peeling algorithm based on nodewise regressions to establish a topological order of primary variables. Moreover, we prove that the peeling algorithm yields a consistent estimator in low-order polynomial time. Second, we propose a likelihood ratio test integrated with a data perturbation scheme to account for the uncertainty of identifying ancestors and interventions. Also, we show that the distribution of a data perturbation test statistic converges to the target distribution. Numerical examples demonstrate the utility and effectiveness of the proposed methods, including an application to infer gene regulatory networks.

Self-driving cars have become a popular research topic since the 1990s. Autonomous vehicles significantly benefit from seamless wireless connections between cars or between cars and the infrastructure. Vehicular visible light communications (V-VLC) can be considered as a complementary technology to radio frequency-based (RF-based) communications. V-VLC can use existing headlights and tail lights in cars. Moreover, it benefits from enhanced security and draws upon the unregulated bandwidth in the visible light spectrum. There have been many studies in this area, but to the best of our knowledge, there has been no investigation on the impact of road surface irregularities or the road terrain on the variations in the received signal-to-noise ratio (SNR). In this paper, a channel model is developed which includes the car suspension system regarding the angular relations and the vertical movement because of the car's vibrations on irregular road surfaces. The SNR ratios from different scenarios are compared using the model.

In spite of its urgent importance in the era of big data, testing high-dimensional parameters in generalized linear models (GLMs) in the presence of high-dimensional nuisance parameters has been largely under-studied, especially with regard to constructing powerful tests for general (and unknown) alternatives. Most existing tests are powerful only against certain alternatives and may yield incorrect Type 1 error rates under high-dimensional nuisance parameter situations. In this paper, we propose the adaptive interaction sum of powered score (aiSPU) test in the framework of penalized regression with a non-convex penalty, called truncated Lasso penalty (TLP), which can maintain correct Type 1 error rates while yielding high statistical power across a wide range of alternatives. To calculate its p-values analytically, we derive its asymptotic null distribution. Via simulations, its superior finite-sample performance is demonstrated over several representative existing methods. In addition, we apply it and other representative tests to an Alzheimer's Disease Neuroimaging Initiative (ADNI) data set, detecting possible gene-gender interactions for Alzheimer's disease. We also put R package “aispu” implementing the proposed test on GitHub.

MOTIVATION: Enhancer-promoter interactions (EPIs) in the genome play an important role in transcriptional regulation. EPIs can be useful in boosting statistical power and enhancing mechanistic interpretation for disease- or trait-associated genetic variants in genome-wide association studies. Instead of expensive and time-consuming biological experiments, computational prediction of EPIs with DNA sequence and other genomic data is a fast and viable alternative. In particular, deep learning and other machine learning methods have been demonstrated with promising performance. RESULTS: First, using a published human cell line dataset, we demonstrate that a simple convolutional neural network (CNN) performs as well as, if no better than, a more complicated and state-of-the-art architecture, a hybrid of a CNN and a recurrent neural network. More importantly, in spite of the well-known cell line-specific EPIs (and corresponding gene expression), in contrast to the standard practice of training and predicting for each cell line separately, we propose two transfer learning approaches to training a model using all cell lines to various extents, leading to substantially improved predictive performance. AVAILABILITY AND IMPLEMENTATION: Computer code is available at https://github.com/zzUMN/Combine-CNN-Enhancer-and-Promoters. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Recommender systems predict users' preferences over a large number of items by pooling similar information from other users and/or items in the presence of sparse observations. One major challenge is how to utilize user-item specific covariates and networks describing user-item interactions in a high-dimensional situation, for accurate personalized prediction. In this article, we propose a smooth neighborhood recommender in the framework of the latent factor models. A similarity kernel is utilized to borrow neighborhood information from continuous covariates over a user-item specific network, such as a user's social network, where the grouping information defined by discrete covariates is also integrated through the network. Consequently, user-item specific information is built into the recommender to battle the `cold-start” issue in the absence of observations in collaborative and content-based filtering. Moreover, we utilize a “divide-and-conquer” version of the alternating least squares algorithm to achieve scalable computation, and establish asymptotic results for the proposed method, demonstrating that it achieves superior prediction accuracy. Finally, we illustrate that the proposed method improves substantially over its competitors in simulated examples and real benchmark data--Last.fm music data.

In this paper, we consider the problem of data association in feature cloud matching. While Joint Compatibility (JC) test is a widely adopted technique for searching the global optimal data association, it becomes less restrictive as more features are well matched. The early well-matched features contribute little to total matching cost while the gating threshold increases in the chi-square test, which allows the acceptance of bad feature pairings in the last step. In this paper, we propose the Conditional Compatibility (CC) test, which is not only more restrictive than JC test, but also probabilistically sound. The proposed test of a new feature pairing is based on the conditional probability distribution of feature locations given the early pairings. CC test can be added into any JC test based search algorithm, such as Joint Compatibility Branch and Bound (JCBB), Incremental Posterior Joint Compatibility (IPJC) and FastJCBB, without increasing much computational complexity. The more restrictive criterion of accepting a feature pairing, not only helps to reject bad associations, but also bounds the search space, which substantially improves the search efficiency. The real matching experiments justify that our algorithm produces better feature cloud matching results in a more efficient manner.

We present a real-time vehicle detection and tracking system to accomplish the complex task of driving behavior analysis in urban environments. We propose a robust fusion system that combines a monocular camera and a 2D Lidar. This system takes advantage of three key components: robust vehicle detection using deep learning techniques, high precision range estimation from Lidar, and road context from the prior map knowledge. The camera and Lidar sensor fusion, data association and track management are all performed in the global map coordinate system by taking into account the sensors' characteristics. Lastly, behavior reasoning is performed by examining the tracked vehicle states in the lane coordinate system in which the road context is encoded. We validated our approach by tracking a leading vehicle while it performed usual urban driving behaviors such as lane keeping, stop-and-go at intersections, lane changing, overtaking and turning. The leading vehicle was tracked consistently throughout the 2.3 km route and its behavior was classified reliably.

In this work, we address the problem of robust data association for feature cloud matching. For matching two feature clouds observed at two different poses, we discover that the covariance matrix of the measurement prediction error can be written as the sum of a low rank matrix and a block diagonal matrix, if we assume that the features are observed independently at each pose. This special structure of the covariance matrix allows us to compute its inverse analytically and efficiently. Together with a good bookkeeping strategy, the complexity of the Joint Compatibility (JC) test is reduced to O(1). Contrary to the approximated JC test, ours is both exact and fast. Based on the efficient JC test algorithm and a branch and bound search procedure, we devise an algorithm, called Fast Joint Compatibility Branch and Bound (FastJCBB), to quickly obtain robust data association. The FastJCBB algorithm is essentially modified from the conventional Joint Compatibility Branch and Bound (JCBB) algorithm and both of these algorithms are able to produce exactly the same data association results. However, with the substantial improvement in the efficiency of JC tests, our FastJCBB algorithm is much faster than the conventional JCBB, especially when matching two large feature clouds. It is reported that our FastJCBB algorithm is more than 740 times faster than the conventional JCBB in carrying out one million JC tests when matching two clouds with about 100 features each. Since both FastJCBB and JCBB share the same branch and bound procedure in exploring the interpretation tree, the search complexity remains exponential. Our main contribution is the significant improvement in the efficiency of exploring each node of the interpretation tree.

In many real-world applications, the samples/features acquired are in spatial or temporal order. In such cases, the magnitudes of adjacent samples/features are typically close to each other. Meanwhile, in the high-dimensional scenario, identifying the most relevant samples/features is also desired. In this paper, we consider a regularized model which can simultaneously identify important features and group similar features together. The model is based on a penalty called Absolute Fused Lasso (AFL). The AFL penalty encourages sparsity in the coefficients as well as their successive differences of absolute values' i.e., local constancy of the coefficient components in absolute values. Due to the non-convexity of AFL, it is challenging to develop efficient algorithms to solve the optimization problem. To this end, we employ the Difference of Convex functions (DC) programming to optimize the proposed non-convex problem. At each DC iteration, we adopt the proximal algorithm to solve a convex regularized sub-problem. One of the major contributions of this paper is to develop a highly efficient algorithm to compute the proximal operator. Empirical studies on both synthetic and real-world data sets from Genome-Wide Association Studies demonstrate the efficiency and effectiveness of the proposed approach in simultaneous identifying important features and grouping similar features.

Clustering is unsupervised and exploratory in nature. Yet, it can be performed through penalized regression with grouping pursuit, as demonstrated in Pan et al. (2013). In this paper, we develop a more efficient algorithm for scalable computation and a new theory of clustering consistency for the method. This algorithm, called DC-ADMM, combines difference of convex (DC) programming with the alternating direction method of multipliers (ADMM). This algorithm is shown to be more computationally efficient than the quadratic penalty based algorithm of Pan et al. (2013) because of the former's closed-form updating formulas. Numerically, we compare the DC- ADMM algorithm with the quadratic penalty algorithm to demonstrate its utility and scalability. Theoretically, we establish a finite-sample mis- clustering error bound for penalized regression based clustering with the $L_0$ constrained regularization in a general setting. On this ground, we provide conditions for clustering consistency of the penalized clustering method. As an end product, we put R package prclust implementing PRclust with various loss and grouping penalty functions available on GitHub and CRAN.

We detail the design of autonomous golf cars which were used in public trials in Singapore's Chinese and Japanese Gardens, for the purpose of raising public awareness and gaining user acceptance of autonomous vehicles. The golf cars were designed to be robust, reliable, and safe, while operating under prolonged durations. Considerations that went in to the overall system design included the fact that any member of the public had to not only be able to easily use the system, but to also not have the option to use the system in an unintended manner. This paper details the hardware and software components of the golf cars with these considerations, and also how the booking system and mission planner facilitated users to book for a golf car from any of ten stations within the gardens. We show that the vehicles performed robustly throughout the prolonged operations with a small localization variance, and that users were very receptive from the user survey results.

Vehicle-to-vehicle (V2V) communication enables intention sharing among neighboring vehicles and thereby vehicles' motion can be coordinated to incorporate collision (or conflict) avoidance. In this paper, we propose a general framework to distribute the computational burden for coordinating multiple vehicles' stop-or-go motion. We formulate the multi-vehicle motion coordination problem as a total stopping time minimization problem under the constraint of mutual collision avoidance. The minimal stopping time solution, if such exists, is found using the A* search algorithm in the coordination diagram. The solution is executed efficiently by placing temporary virtual obstacles on the desired path. The communication latency is analyzed for practical applications. The simulations show the correctness and efficacy of our algorithm. An on-road experiment involving two autonomous vehicles, each equipped with V2V communication devices, was performed to demonstrate how deadlocks are successfully avoided.

In this paper, we present a multivehicle cooperative driving system architecture using cooperative perception along with experimental validation. For this goal, we first propose a multimodal cooperative perception system that provides see-through, lifted-seat, satellite and all-around views to drivers. Using the extended range information from the system, we then realize cooperative driving by a see-through forward collision warning, overtaking/lane-changing assistance, and automated hidden obstacle avoidance. We demonstrate the capabilities and features of our system through real-world experiments using four vehicles on the road.

This paper proposes a spatio-temporal motion feature detection and tracking method using range sensors working on a moving platform. The proposed spatio-temporal motion features are similar to optical flow but are extended on a moving platform with fusion of odometry and show much better classification accuracy with consideration of different uncertainties. In the proposal, the ego motion is compensated by odometry sensors and the laser scan points are accumulated and represented as space-time point clouds, from which the velocities and moving directions can be extracted. Based on these spatio-temporal features, a supervised learning technique is applied to classify the points as static or moving and Kalman filters are implemented to track the moving objects. A real experiment is performed during day and night on an autonomous vehicle platform and shows promising results in a crowded and dynamic environment.

In this paper, we attempt to develop a reusable framework of cooperative perception for vehicle control on the road that can extend perception range beyond line-of-sight and beyond field-of-view. For this goal, the following problems are addressed: map merging, vehicle identification, sensor multi-modality, impact of communications, and impact on path planning. We provide experimental results using a self-driving vehicle and manned vehicles equipped with the cooperative perception systems that we propose and implement.

In multivariate analysis, rank minimization emerges when a low-rank structure of matrices is desired as well as a small estimation error. Rank minimization is nonconvex and generally NP-hard, imposing one major challenge. In this paper, we consider a nonconvex least squares formulation, which seeks to minimize the least squares loss function with the rank constraint. Computationally, we develop efficient algorithms to compute a global solution as well as an entire regularization solution path. Theoretically, we show that our method reconstructs the oracle estimator exactly from noisy data. As a result, it recovers the true rank optimally against any method and leads to sharper parameter estimation over its counterpart. Finally, the utility of the proposed method is demonstrated by simulations and image reconstruction from noisy background.

High-dimensional regression/classification continues to be an important and challenging problem, especially when features are highly correlated. Feature selection, combined with additional structure information on the features has been considered to be promising in promoting regression/classification performance. Graph-guided fused lasso (GFlasso) has recently been proposed to facilitate feature selection and graph structure exploitation, when features exhibit certain graph structures. However, the formulation in GFlasso relies on pairwise sample correlations to perform feature grouping, which could introduce additional estimation bias. In this paper, we propose three new feature grouping and selection methods to resolve this issue. The first method employs a convex function to penalize the pairwise l∞ norm of connected regression/classification coefficients, achieving simultaneous feature grouping and selection. The second method improves the first one by utilizing a non-convex function to reduce the estimation bias. The third one is the extension of the second method using a truncated l1 regularization to further reduce the estimation bias. The proposed methods combine feature grouping and feature selection to enhance estimation accuracy. We employ the alternating direction method of multipliers (ADMM) and difference of convex functions (DC) programming to solve the proposed formulations. Our experimental results on synthetic data and two real datasets demonstrate the effectiveness of the proposed methods.

MOTIVATION: Model-based clustering has been widely used, e.g. in microarray data analysis. Since for high-dimensional data variable selection is necessary, several penalized model-based clustering methods have been proposed tørealize simultaneous variable selection and clustering. However, the existing methods all assume that the variables are independent with the use of diagonal covariance matrices. RESULTS: To model non-independence of variables (e.g. correlated gene expressions) while alleviating the problem with the large number of unknown parameters associated with a general non-diagonal covariance matrix, we generalize the mixture of factor analyzers to that with penalization, which, among others, can effectively realize variable selection. We use simulated data and real microarray data to illustrate the utility and advantages of the proposed method over several existing ones.

BACKGROUND: The importance of network-based approach to identifying biological markers for diagnostic classification and prognostic assessment in the context of microarray data has been increasingly recognized. To our knowledge, there have been few, if any, statistical tools that explicitly incorporate the prior information of gene networks into classifier building. The main idea of this paper is to take full advantage of the biological observation that neighboring genes in a network tend to function together in biological processes and to embed this information into a formal statistical framework. RESULTS: We propose a network-based support vector machine for binary classification problems by constructing a penalty term from the Finfinity-norm being applied to pairwise gene neighbors with the hope to improve predictive performance and gene selection. Simulation studies in both low- and high-dimensional data settings as well as two real microarray applications indicate that the proposed method is able to identify more clinically relevant genes while maintaining a sparse model with either similar or higher prediction accuracy compared with the standard and the L1 penalized support vector machines. CONCLUSION: The proposed network-based support vector machine has the potential to be a practically useful classification tool for microarrays and other high-dimensional data.

As a requisite of content-based multimedia technologies, video object (VO) extraction is of great importance. In recent years, approaches have been proposed to handle VO extraction directly as a classification problem. This type of methods calls for state-of-the-art classifiers because the extraction performance is directly related to the accuracy of classification. Promising results have been reported for single object extraction using support vector machines (SVM) and its extensions such as psi-learning. Multiple object extraction, on the other hand, still imposes great difficulty as multi-category classification is an ongoing research topic in machine learning. This paper introduces the newly developed multi-category psi-learning as the multiclass classifier for multiple VO extraction, and demonstrates its effectiveness and advantages by experiments

Binary support vector machines (SVM) have proven effective in classification. However, problems remain with respect to feature selection in multi-class classification. This article proposes a novel multi-class SVM, which performs classification and feature selection simultaneously via L1-norm penalized sparse representations. The proposed methodology, together with our developed regularization solution path, permits feature selection within the framework of classification. The operational characteristics of the proposed methodology is examined via both simulated and benchmark examples, and is compared to some competitors in terms of the accuracy of prediction and feature selection. The numerical results suggest that the proposed methodology is highly competitive

MOTIVATION: It is biologically interesting to address whether human blood outgrowth endothelial cells (BOECs) belong to or are closer to large vessel endothelial cells (LVECs) or microvascular endothelial cells (MVECs) based on global expression profiling. An earlier analysis using a hierarchical clustering and a small set of genes suggested that BOECs seemed to be closer to MVECs. By taking advantage of the two known classes, LVEC and MVEC, while allowing BOEC samples to belong to either of the two classes or to form their own new class, we take a semi-supervised learning approach; for high-dimensional data as encountered here, we propose a penalized mixture model with a weighted L1 penalty to realize automatic feature selection while fitting the model. RESULTS: We applied our penalized mixture model to a combined dataset containing 27 BOEC, 28 LVEC and 25 MVEC samples. Analysis results indicated that the BOEC samples appeared to form their own new class. A simulation study confirmed that, compared with the standard mixture model with or without initial variable selection, the penalized mixture model performed much better in identifying relevant genes and forming corresponding clusters. The penalized mixture model seems to be promising for high-dimensional data with the capability of novel class discovery and automatic feature selection.

Summary One central problem in science and engineering is predicting unseen outcome via relevant knowledge gained from data, where accuracy of generalization is the key. In the context of classification, we argue that higher generalization accuracy is achievable via ψ-learning, when a certain class of non-convex rather than convex cost functions are employed. To deliver attainable higher generalization accuracy, we propose two computational strategies via a global optimization technique–difference convex programming, which relies on a decomposition of the cost function into a difference of two convex functions. The first strategy solves sequential quadratic programs. The second strategy, combining this with the method of Branch-and-Bound, is more computationally intensive but is capable of producing global optima. Numerical experiments suggest that the algorithms realize the desired generalization ability of ψ-learning.