Advances in Learning Bayesian Networks of Bounded Treewidth

This work presents novel algorithms for learning Bayesian network structures with bounded treewidth. Both exact and approximate methods are developed. The exact method combines mixed-integer linear programming formulations for structure learning and treewidth computation. The approximate method consists in uniformly sampling $k$-trees (maximal graphs of treewidth $k$), and subsequently selecting, exactly or approximately, the best structure whose moral graph is a subgraph of that $k$-tree. Some properties of these methods are discussed and proven. The approaches are empirically compared to each other and to a state-of-the-art method for learning bounded treewidth structures on a collection of public data sets with up to 100 variables. The experiments show that our exact algorithm outperforms the state of the art, and that the approximate approach is fairly accurate.Comment: 23 pages, 2 figures, 3 table

Marine Target Detection from Nonstationary Sea-Clutter Based On Topological Data Analysis

AbstractDue to the instinct complexity and the large scale non-stationary of so-called sea-clutter, radar backscatters from ocean surface, it is always challenging to detect the weak marine target. In classical statistical approaches, the seaclutter is modeled as several kinds of stochastic processes, which are found inadequate, especially in high sea-state circumstances. Therefore it is reasonable to discover the underlying dynamics that is responsible for generating the time series of sea-clutter. In this work, we take into account of the marine target detection from the X-Band seaclutter datasets with low Signal-Clutter-Ratio, and propose adequate methods to process these non-stationary data, including Empirical Mode Decomposition and Topological Data Analysis. Both theoretical simulation and experimental results indicate the proposed method's usefulness of for marine target detection, which is implemented by extract different structural features from measured sea-clutter data

Efficient learning of Bayesian networks with bounded tree-width

Learning Bayesian networks with bounded tree-width has attracted much attention recently, because low tree-width allows exact inference to be performed efficiently. Some existing methods [24,29] tackle the problem by using k-trees to learn the optimal Bayesian network with tree-width up to k. Finding the best k-tree, however, is computationally intractable. In this paper, we propose a sampling method to efficiently find representative k-trees by introducing an informative score function to characterize the quality of a k-tree. To further improve the quality of the k-trees, we propose a probabilistic hill climbing approach that locally refines the sampled k-trees. The proposed algorithm can efficiently learn a quality Bayesian network with tree-width at most k. Experimental results demonstrate that our approach is more computationally efficient than the exact methods with comparable accuracy, and outperforms most existing approximate methods

Image Quality Is Not All You Want: Task-Driven Lens Design for Image Classification

In computer vision, it has long been taken for granted that high-quality images obtained through well-designed camera lenses would lead to superior results. However, we find that this common perception is not a "one-size-fits-all" solution for diverse computer vision tasks. We demonstrate that task-driven and deep-learned simple optics can actually deliver better visual task performance. The Task-Driven lens design approach, which relies solely on a well-trained network model for supervision, is proven to be capable of designing lenses from scratch. Experimental results demonstrate the designed image classification lens (``TaskLens'') exhibits higher accuracy compared to conventional imaging-driven lenses, even with fewer lens elements. Furthermore, we show that our TaskLens is compatible with various network models while maintaining enhanced classification accuracy. We propose that TaskLens holds significant potential, particularly when physical dimensions and cost are severely constrained.Comment: Use an image classification network to supervise the lens design from scratch. The final designs can achieve higher accuracy with fewer optical element

Surface Roughness Gradients Reveal Topography‐Specific Mechanosensitive Responses in Human Mesenchymal Stem Cells

The topographic features of an implant, which mechanically regulate cell behaviors and functions, are critical for the clinical success in tissue regeneration. How cells sense and respond to the topographical cues, e.g., interfacial roughness, is yet to be fully understood and even debatable. Here, the mechanotransduction and fate determination of human mesenchymal stem cells (MSCs) on surface roughness gradients are systematically studied. The broad range of topographical scales and high‐throughput imaging is achieved based on a catecholic polyglycerol coating fabricated by a one‐step‐tilted dip‐coating approach. It is revealed that the adhesion of MSCs is biphasically regulated by interfacial roughness. The cell mechanotransduction is investigated from focal adhesion to transcriptional activity, which explains that cellular response to interfacial roughness undergoes a direct force‐dependent mechanism. Moreover, the optimized roughness for promoting cell fate specification is explored