Synthetic Aperture Radar (SAR) provides all-weather imaging, yet small-scale, densely clustered ships remain difficult to detect because coherent speckle noise and coastal clutter often mask target echoes. Current detectors, derived mainly from optical imaging methods, fail to extract weak signatures from minute vessels and to separate closely spaced targets from background clutter, leading to frequent missed detections and elevated false-alarm rates. This paper presents SAR-D-FINE, a context-aware detection framework tailored for SAR imagery. Extending the D-FINE architecture, we design a hybrid backbone with a StarStage module that strengthens nonlinear feature extraction under heavy noise. A Focusing Diffusion Encoder, integrating a Multi-Kernel Aggregation Module (MKAM) and a parameter-free Shuffle-and-Shift Upsampling (SSU) unit, is adopted to aggregate multi-scale features without sacrificing fine spatial details. Experimental results on SSDD and HRSID indicate that SAR-D-FINE surpasses existing methods, including dedicated SAR detectors such as YOLO-SARSI and SW-Net, achieving AP improvements of 2.0% and 1.8% over the baseline, respectively. The results confirm the advantages of the proposed model, particularly for detecting small, densely distributed vessels.

With the development of biomedical techniques in the past decades, causal gene identification has become one of the most promising applications in human genome-based business, which can help doctors to evaluate the risk of certain genetic diseases and provide further treatment recommendations for potential patients. When no controlled experiments can be applied, machine learning techniques like causal inference-based methods are generally used to identify causal genes. Unfortunately, most of the existing methods detect disease-related genes by ranking-based strategies or feature selection techniques, which generally return a superset of the corresponding real causal genes. There are also some causal inference-based methods that can identify a part of real causal genes from those supersets, but they are just able to return a few causal genes. This is contrary to our knowledge, as many results from controlled experiments have demonstrated that a certain disease, especially cancer, is usually related to dozens or hundreds of genes. In this work, we present an effective approach for identifying causal genes from gene expression data by using a new search strategy based on non-linear regression-based independence tests, which is able to greatly reduce the search space, and simultaneously establish the causal relationships from the candidate genes to the disease variable. Extensive experiments on real-world cancer datasets show that our method is superior to the existing causal inference-based methods in three aspects: 1) our method can identify dozens of causal genes, and 1/3 ∼ 1/2 of the discovered causal genes can be verified by existing works that they are really directly related to the corresponding disease; 2) The discovered causal genes are able to distinguish the status or disease subtype of the target patient; 3) Most of the discovered causal genes are closely relevant to the disease variable.

This article addresses two important issues of causal inference in the high-dimensional situation. One is how to reduce redundant conditional independence (CI) tests, which heavily impact the efficiency and accuracy of existing constraint-based methods. Another is how to construct the true causal graph from a set of Markov equivalence classes returned by these methods. For the first issue, we design a recursive decomposition approach where the original data (a set of variables) are first decomposed into two small subsets, each of which is then recursively decomposed into two smaller subsets until none of these subsets can be decomposed further. Redundant CI tests can be reduced by inferring causalities from these subsets. The advantage of this decomposition scheme lies in two aspects: 1) it requires only low-order CI tests and 2) it does not violate d -separation. The complete causality can be reconstructed by merging all the partial results of the subsets. For the second issue, we employ regression-based CI tests to check CIs in linear non-Gaussian additive noise cases, which can identify more causal directions by [Formula: see text] (or [Formula: see text]). Consequently, causal direction learning is no longer limited by the number of returned V -structures and consistent propagation. Extensive experiments show that the proposed method can not only substantially reduce redundant CI tests but also effectively distinguish the equivalence classes.

In recent years, many methods have been developed for discovering causal relationships from observed data. However, as an important kind of causes existing in many causal systems, combined causes (e.g. multi-factor causes consisting of two or more component variables that individually might not be a cause) have not received enough attention. The existing approach includes both individual and combined variables in the causal discovery process using constraint-based methods, can neither distinguish a set of Markov equivalence classes nor identify a combined cause containing one (or more) individual cause(s), therefore can output only some combined causes, instead of all combined causes. In this paper, we first subsume all possible combined causes into three types and give them formal definitions, then extend the additive noise model (ANM) to infer combined causes. We show that if a candidate variable set X w.r.t. a target Y satisfies: (1) allowing ANM for only the forward direction X→Y, and (2) no disturbance variable is contained in X, i.e., removing any component of X will weaken the causal relationship between X and Y, then X forms a combined cause. Based on this finding, we develop an efficient method to discover combined causes. Furthermore, we also conduct extensive experiments to validate the proposed method on both synthetic and real-world data sets.

Recovering causal relationships from observed data is crucial to a variety of applications. Due to the curse of dimensionality, general causal discovery methods such as constraint-based methods and functional model based methods are not quite effective and efficient for large and high-dimensional data sets. Thus, some causal partitioning methods have been proposed to handle this problem. However, existing causal partitioning methods rely on high-order conditional independent (CI) tests, which makes them inefficient in handling dense causal graphs. Therefore, high-dimensionality is still a big challenge to these methods. In this work, we propose a new split-and-merge strategy to enable effective and scalable causality discovery. Different from the existing methods, our method uses only low-order CI tests, can get more accurate results and is applicable to various scenarios. We provide both theoretic analysis and empirical evaluation on the proposed method. Experiments on various real-world causal graphs show that the proposed method outperforms the stat-of-the-art method in terms of accuracy, efficiency and scalability. For high-dimensional cases, our method is much faster than the counterpart by one to three orders of magnitudes.

This paper addresses two important issues in causality inference. One is how to reduce redundant conditional independence (CI) tests, which heavily impact the efficiency and accuracy of existing constraint-based methods. Another is how to construct the true causal graph from a set of Markov equivalence classes returned by these methods.For the first issue, we design a recursive decomposition approach where the original data (a set of variables) is first decomposed into three small subsets, each of which is then recursively decomposed into three smaller subsets until none of subsets can be decomposed further. Consequently, redundant CI tests can be reduced by inferring causality from these subsets. Advantage of this decomposition scheme lies in two aspects: 1) it requires only low-order CI tests, and 2) it does not violate d-separation. Thus, the complete causality can be reconstructed by merging all the partial results of the subsets.For the second issue, we employ regression-based conditional independence test to check CIs in linear non-Gaussian additive noise cases, which can identify more causal directions by x−E(x|Z)⊥z (or y−E(y|Z)⊥z). Therefore, causal direction learning is no longer limited by the number of returned Vstructures and the consistent propagation.Extensive experiments show that the proposed method can not only substantially reduce redundant CI tests but also effectively distinguish the equivalence classes, thus is superior to the state of the art constraint-based methods in causality inference.