Camouflaged object detection (COD) is a challenging task aimed at identifying and segmenting camouflaged targets that are difficult to distinguish from complex backgrounds. To address the issues of incomplete detection and missing edges in camouflaged targets, this paper proposes a Bilateral Enhanced Complementary Network (BECNet) for COD. The network adopts a two-branch detection method, which is used for object recognition, edge recognition, and texture supervision respectively, to alleviate the feature ambiguity of features extracted from a single branch. Additionally, we introduce a Semantic Amplification Module (SAM) to further extract multi-scale semantic features. To effectively aggregate the discriminative features generated by both branches, we designed a Semantic-Texture Interaction Module (SIM). Finally, we incorporate an Edge Complementary Dual Attention Module (ECDA) during the decoding process to refine the model using edge information. Extensive experiments demonstrate the effectiveness and robustness of BECNet.

Advances in omics technologies provide unprecedented opportunities for systems biology, yet integrating multi-omics data remains challenging due to its complexity, heterogeneity, and the sparsity of prior knowledge networks. Here, we introduce a multi-omics data integration analysis (MODA) framework that fully incorporates prior knowledge to identify hub molecules and pathways, and elucidate biological mechanisms. By leveraging multiple machine learning approaches, MODA transforms raw omics data into a feature importance matrix that is mapped onto a biological knowledge graph to mitigate omics data noise. Then, it uses graph convolutional networks with attention mechanisms to capture intricate molecular relationships and rank molecules via a feature-selective layer. Ultimately, MODA transcends the limitations of predefined pathway annotations by employing an overlapping community detection algorithm to extract core functional modules that are involved in multiple pivotal disease pathways. Systematic evaluations show that MODA outperforms seven existing multi-omics integration methods in classification performance while maintaining biological interpretability. Moreover, MODA achieves superior stability in pan-cancer datasets. Application to the multi-omics datasets of prostate cancer reveals a key role for carnitine and palmitoylcarnitine, regulated by BBOX1 in the progression of prostate cancer. Population samples and in vitro experiments further validate these findings. With high data utilization efficiency and low computational cost, MODA serves as a robust tool for uncovering novel disease mechanisms and advancing precision medicine.

The rapid growth of high-dimensional omics datasets in biomedical research has created an urgent need for computational frameworks that are both robust and adaptable to diverse data complexities. Although a wide range of specialized tools and algorithms are available, researchers often rely on trial-and-error approaches to select suitable analytical workflows, compromising both efficiency and reproducibility. In this study, we systematically benchmarked hundreds of algorithms-preprocessing combinations across three common biomedical data challenges, including small sample sizes, missing values, and class imbalance. Our results show that tree-based models (e.g. Gradient Boosting Decision Tree, XGBoost, and Random Forest) consistently perform well in scenarios involving small-sample and missing-data, while partial least squares discriminant analysis (PLS-DA) is more effective in addressing imbalanced classes. Unsupervised cluster methods such as K-means and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) remain robust under moderate missingness, but their performance declines when missingness exceeds 10%. To support data-driven decision-making, we developed SysML, a web-based platform that recommends data-adaptive workflows based on dataset-specific characteristics. Validated on multiple real-world biomedical datasets, SysML demonstrated improvements in both model performance and workflow efficiency. Our findings underscore that adaptive data preprocessing, rather than algorithm choice alone, is critical for achieving reliable and reproducible machine learning applications in biomedicine.

The fusion of infrared and visible images is hard due to their different modalities. Different from existing methods using the integer-order gradient, we design an optimization model to fuse infrared and visible images using fractional-order gradient information. In this way, the complementary information of the source images can be better preserved. In order to better highlight the target and retain effective details, we use the results of the optimization model as pre-fusion images to guide the final image fusion. For better highlighting the target, we use MDLatLRR to extract the base layer of the pre-fusion image and use it as the base layer of the fused image. In addition, for getting more effective details, we use the pre-fusion image to calculate the weight map, which is used as the tradeoff parameter of the norm optimization problem to get the fused detail layers. Experimental results show that our method can highlight the target better while maintaining effective details. Compared with the current state-of-the-art image fusion methods, our method shows better fusion performance in both subjective and objective evaluation.

Mapping and Semantic mapping are the important research areas for the autonomous navigation of mobile robots. However, realising point cloud information extraction in a dynamic environment is still a challenge in semantic mapping. To solve this problem, We propose a method called PMGAT-SM(semantic mapping of 3D point clouds based on multi-hop graph attention network) for achieving semantic mapping in 3D point cloud environments. In PMGAT-SM, we designed a point cloud classifier PMGAT, which extracts semantic information of unordered point clouds by constructing a graph. We combined PMGAT with dynamic growing clustering to achieve instance segmentation in complex environments. Our extensive experiments in the KITTI scene show the effectiveness of the semantic mapping model. Meanwhile, compared with popular semantic information extraction models, the PMGAT performs better in fine-grained feature extraction of point cloud segments.

Cross-modal information fusion plays a vital role in the RGB-T salient object detection. Due to RGB and thermal images come from different domains, the modality difference will lead to the unsatisfactory effect of simple feature fusion. How to explore and integrate useful information is the key to the RGB-T saliency detection methods. In this paper, we introduce an Enhancement and Fusion Network. In detail, we propose a Self-modality Feature Enhancement Module that effectively integrate the feature representation of a single modality through global context information. And we propose a Cross-modality Feature Dynamic Fusion Module to realize the effective fusion of cross-modal features in the way of dynamic weighting. Experiments on public datasets show that the proposed method achieves satisfactory results compared with other state-of-the-art salient object detection approaches.

BACKGROUND: Plant secondary metabolites are highly valued for their applications in pharmaceuticals, nutrition, flavors, and aesthetics. It is of great importance to elucidate plant secondary metabolic pathways due to their crucial roles in biological processes during plant growth and development. However, understanding plant biosynthesis and degradation pathways remains a challenge due to the lack of sufficient information in current databases. To address this issue, we proposed a transfer learning approach using a pre-trained hybrid deep learning architecture that combines Graph Transformer and convolutional neural network (GTC) to predict plant metabolic pathways. RESULTS: GTC provides comprehensive molecular representation by extracting both structural features from the molecular graph and textual information from the SMILES string. GTC is pre-trained on the KEGG datasets to acquire general features, followed by fine-tuning on plant-derived datasets. Four metrics were chosen for model performance evaluation. The results show that GTC outperforms six other models, including three previously reported machine learning models, on the KEGG dataset. GTC yields an accuracy of 96.75%, precision of 85.14%, recall of 83.03%, and F1_score of 84.06%. Furthermore, an ablation study confirms the indispensability of all the components of the hybrid GTC model. Transfer learning is then employed to leverage the shared knowledge acquired from the KEGG metabolic pathways. As a result, the transferred GTC exhibits outstanding accuracy in predicting plant secondary metabolic pathways with an average accuracy of 98.30% in fivefold cross-validation and 97.82% on the final test. In addition, GTC is employed to classify natural products. It achieves a perfect accuracy score of 100.00% for alkaloids, while the lowest accuracy score of 98.42% for shikimates and phenylpropanoids. CONCLUSIONS: The proposed GTC effectively captures molecular features, and achieves high performance in classifying KEGG metabolic pathways and predicting plant secondary metabolic pathways via transfer learning. Furthermore, GTC demonstrates its generalization ability by accurately classifying natural products. A user-friendly executable program has been developed, which only requires the input of the SMILES string of the query compound in a graphical interface.

Abstract Transformer has shown its effectiveness and advantage in many computer vision tasks, for example, image classification and object re‐identification (ReID). However, existing vision transformers are stacked layer by layer, lacking direct information exchange among every layer. Inspired by DenseNet, we propose a dense transformer framework (termed Denseformer) that connects each layer to every other layer through class tokens. We demonstrate that Denseformer can consistently achieve better performance on person ReID tasks across datasets (Market‐1501, DukeMTMC, MSMT17, and Occluded‐Duke), only at a negligible increase of computation. We show that Denseformer has several compelling advantages: it pays more attention to the main parts of human bodies and obtains discriminative global features.

Abstract Fully convolutional neural networks have achieved great success in salient object detection, in which the effective use of multi‐layer features plays a critical role. Based on this advantage, many saliency detectors have emerged in recent years, and most of them designed a series of network structures to integrate the multi‐level features generated by the backbone network. However, information in different layer play different roles in saliency object detection, how to integrate them effectively is still a great challenge. In this article, a selective feature fusion network which consists of a selective feature fusion module (SFM) and an attention‐guide hierarchical feature emphasis module (AEM) is proposed. Most of the previous works mainly integrate multi‐level feature by addition and concatenation, as a difference, SFM adaptively selects the important information from the input features in the fusion, which effectively avoids introducing too much redundant information. Besides, AEM combines spatial attention and channel attention to enhance features simply and effectively by hierarchical iteration, and further improve the accuracy of salient object detection. Experiments on five datasets show that the proposed selective feature fusion method achieve satisfactory results when comparing to other state‐of‐the‐art salient object detection approaches.

Abstract Fully convolutional neural networks‐based salient object detection has recently achieved great success with its performance benefits from the effective use of multi‐layer features. Based on this, most of the existing saliency detectors designed complex network structures to fuse the multi‐level features generated by the backbone network. However, the variable scale and complex shape of the target are always a great challenge for saliency detection tasks. In this paper, the authors propose a Rich‐scale Feature Fusion Network (RFFNet) for salient object detection. The authors design a rich‐scale feature interactive fusion module to obtain more efficient features from the multi‐scale features. Moreover, the global feature enhance module is used to extract features with better characterization for the final saliency prediction. Extensive experiments performed on five benchmark datasets demonstrate that the proposed method can achieve satisfactory results on different evaluation metrics compared to other state‐of‐the‐art salient object detection approaches.

Modern deep neural networks suffer from performance degradation when evaluated on testing data under different distributions from training data. The goal of out-of-distribution generalization is to solve this problem by learning transferable knowledge from source domains to generalize to invisible target domains. This paper presents a data augmentation method for out-of-distribution generalization. The main assumption is that the main data distribution of an image mostly contains domain-related information, such as color, illumination, texture content, etc, which hurts the domain shifts. To force the model to pay less attention to this part of the information, we propose a new data augmentation method based on the main distribution transition. Extensive experiments on two data set have demonstrated that the proposed method is able to achieve state-of-the-art performance for domain generalization. At the same time, our method can not only combine with other methods to produce a superposition generalization effect but also generate obfuscation data cheaply.

A critical challenge in genetic diagnostics is the assessment of genetic variants associated with diseases, specifically variants that fall out with canonical splice sites, by altering alternative splicing. Several computational methods have been developed to prioritize variants effect on splicing; however, performance evaluation of these methods is hampered by the lack of large-scale benchmark datasets. In this study, we employed a splicing-region-specific strategy to evaluate the performance of prediction methods based on eight independent datasets. Under most conditions, we found that dbscSNV-ADA performed better in the exonic region, S-CAP performed better in the core donor and acceptor regions, S-CAP and SpliceAI performed better in the extended acceptor region and MMSplice performed better in identifying variants that caused exon skipping. However, it should be noted that the performances of prediction methods varied widely under different datasets and splicing regions, and none of these methods showed the best overall performance with all datasets. To address this, we developed a new method, machine learning-based classification of splice sites variants (MLCsplice), to predict variants effect on splicing based on individual methods. We demonstrated that MLCsplice achieved stable and superior prediction performance compared with any individual method. To facilitate the identification of the splicing effect of variants, we provided precomputed MLCsplice scores for all possible splice sites variants across human protein-coding genes (http://39.105.51.3:8090/MLCsplice/). We believe that the performance of different individual methods under eight benchmark datasets will provide tentative guidance for appropriate method selection to prioritize candidate splice-disrupting variants, thereby increasing the genetic diagnostic yield.

Abstract Multi‐focus image fusion aims to generate a clear image by fusing multiple source images. Existing deep learning‐based fusion methods often neglect the context information resulting in the loss of detail information. To address this issue, a context fusion network to merge multi‐focus images, namely CFNet, is proposed. Specifically, a context fusion module is proposed to make full use of low‐level pixels and high‐level semantic features. Particularly, the pyramid fusion mechanism and cross‐scale transfer strategy are adopted to ensure the visual and semantic consistency of the fused image. Meanwhile, to extract salient features more effectively, a spatial attention mechanism is introduced to enhance these features. Further, the pyramid loss is used to progressively refine the fused features at each scale. Experimental results show that the proposed method is superior to some existing methods in both qualitative and quantitative evaluation.

Fully convolutional neural network-based salient object detection has recently achieved great success with its performance benefits from the effective use of multi-layer features. Based on this, most of the existing saliency detectors design complex network structures to fuse the multi-level features of the backbone feature network. However, information in different layer play different roles in saliency object detection, how to integrate them is still an open problem. In this paper, a cascaded bottom-up feature aggregation module is designed to retain and strengthen more spatial details in the low-level features, and embed attention mechanism in the process of feature aggregation to filter more effective features. Extensive experiments show that the proposed networks can consistently improve saliency detection performance. The experimental results on five public datasets prove that this network is competitive in saliency detection.

The combination of a small unmanned ground vehicle (UGV) and a large unmanned carrier vehicle allows more flexibility in real applications such as rescue in dangerous scenarios. The autonomous recovery system, which is used to guide the small UGV back to the carrier vehicle, is an essential component to achieve a seamless combination of the two vehicles. This study proposes a novel autonomous recovery framework with a low‐cost monocular vision system to provide accurate positioning and attitude estimation of the UGV during navigation. First, the authors introduce a light‐weight convolutional neural network called UGV‐KPNet to detect the keypoints of the small UGV form the images captured by a monocular camera. UGV‐KPNet is computationally efficient with a small number of parameters and provides pixel‐level accurate keypoints detection results in real‐time. Then, six degrees of freedom (6‐DoF) pose is estimated using the detected keypoints to obtain positioning and attitude information of the UGV. Besides, they are the first to create a large‐scale real‐world keypoints data set of the UGV. The experimental results demonstrate that the proposed system achieves state‐of‐the‐art performance in terms of both accuracy and speed on UGV keypoint detection, and can further boost the 6‐DoF pose estimation for the UGV.

RGB images differentiate from depth as they carry more details about the color and texture information, which can be utilized as a vital complement to depth for boosting the performance of 3D semantic scene completion (SSC). SSC is composed of 3D shape completion (SC) and semantic scene labeling while most of the existing approaches use depth as the sole input which causes the performance bottleneck. Moreover, the state-of-the-art methods employ 3D CNNs which have cumbersome networks and tremendous parameters. We introduce a light-weight Dimensional Decomposition Residual network (DDR) for 3D dense prediction tasks. The novel factorized convolution layer is effective for reducing the network parameters, and the proposed multi-scale fusion mechanism for depth and color image can improve the completion and segmentation accuracy simultaneously. Our method demonstrates excellent performance on two public datasets. Compared with the latest method SSCNet, we achieve 5.9% gains in SC-IoU and 5.7% gains in SSC-IOU, albeit with only 21% network parameters and 16.6% FLOPs employed compared with that of SSCNet.

Road detection from images is a key task in autonomous driving. The recent advent of deep learning (and in particular, CNN or convolutional neural networks) has greatly improved the performance of road detection algorithms. In this paper, we show how to fuse multiple different cues under the same convolutional network framework. Specifically, we adopt a pre-trained Resnet-lOl to extract feature maps from RGB images; we then connect it with three extra deconvolution layers. These deconvolution layers is trained conditioning on appropriate image cues, and in our case they are a height image (i.e. elevation map obtained by e.g. Lidar scanner), image gradient, and position map. We also design two skip layers to speed up the convergence. Experiments on KITTI benchmark show competitive performance of our new networks.

Road detection is a key component of autonomous driving; however, most fully supervised learning road detection methods suffer from either insufficient training data or high costs of manual annotation. To overcome these problems, we propose a semisupervised learning (SSL) road detection method based on generative adversarial networks (GANs) and a weakly supervised learning (WSL) method based on conditional GANs. Specifically, in our SSL method, the generator generates the road detection results of labeled and unlabeled images, and then they are fed into the discriminator, which assigns a label on each input to judge whether it is labeled. Additionally, in WSL method we add another network to predict road shapes of input images and use them in both generator and discriminator to constrain the learning progress. By training under these frameworks, the discriminators can guide a latent annotation process on the unlabeled data; therefore, the networks can learn better representations of road areas and leverage the feature distributions on both labeled and unlabeled data. The experiments are carried out on KITTI ROAD benchmark, and the results show our methods achieve the state-of-the-art performances.

Recent works have proposed two L1-norm distance measure-based linear discriminant analysis (LDA) methods, L1-LD and LDA-L1, which aim to promote the robustness of the conventional LDA against outliers. In LDA-L1, a gradient ascending iterative algorithm is applied, which, however, suffers from the choice of stepwise. In L1-LDA, an alternating optimization strategy is proposed to overcome this problem. In this paper, however, we show that due to the use of this strategy, L1-LDA is accompanied with some serious problems that hinder the derivation of the optimal discrimination for data. Then, we propose an effective iterative framework to solve a general L1-norm minimization-maximization (minmax) problem. Based on the framework, we further develop a effective L1-norm distance-based LDA (called L1-ELDA) method. Theoretical insights into the convergence and effectiveness of our algorithm are provided and further verified by extensive experimental results on image databases.

Hyperspectral unmixing is an important technique for identifying the constituent spectra and estimating their corresponding fractions in an image. Nonnegative Matrix Factorization (NMF) has recently been widely used for hyperspectral unmixing. However, due to the complex distribution of hyperspectral data, most existing NMF algorithms cannot adequately reflect the intrinsic relationship of the data. In this paper, we propose a novel method, Structured Discriminative Nonnegative Matrix Factorization (SDNMF), to preserve the structural information of hyperspectral data. This is achieved by introducing structured discriminative regularization terms to model both local affinity and distant repulsion of observed spectral responses. Moreover, considering that the abundances of most materials are sparse, a sparseness constraint is also introduced into SDNMF. Experimental results on both synthetic and real data have validated the effectiveness of the proposed method which achieves better unmixing performance than several alternative approaches.

Given a hyperspectral image, unmixing tries to estimate the spectral responses of the latent constituent materials and their corresponding fractions. Recently, Nonnegative Matrix Factorization (NMF) has been widely applied to solve the hyper-spectral unmixing problem because of its plausible physical interpretation. In this paper, we propose a novel method, Multilayer Manifold and Sparsity constrained Nonnegative Matrix Factorization (MMSNMF), for hyperspectral unmixing. In this approach, Multilayer NMF decomposes a hyperspectral image iteratively at several layers. In order to consider both the manifold structure of hyperspectral image and the sparsity of abundance matrix, we impose a graph regularization term and a sparsity regularization term on both the spectral signature matrix and the abundance matrix. Experimental results on both synthetic and real data validate the effectiveness of the proposed method in hyperspectral unmixing.

For a given data set, exploring their multi-view instances under a clustering framework is a practical way to boost the clustering performance. This is because that each view might reflect partial information for the existing data. Furthermore, due to the noise and other impact factors, exploring these instances from different views will enhance the mining of the real structure and feature information within the data set. In this paper, we propose a multiple kernel spectral clustering algorithm through the multi-view instances on the given data set. By combining the kernel matrix learning and the spectral clustering optimization into one process framework, the algorithm can determine the kernel weights and cluster the multi-view data simultaneously. We compare the proposed algorithm with some recent published methods on real-world datasets to show the efficiency of the proposed algorithm.

In this paper, we propose a recursive soft margin (RSM) subspace learning framework for dimension reduction of high-dimensional data, which has strong recognition ability. RSM is motivated by the soft margin criterion of support vector machines (SVMs), which allows some training samples to be misclassified for a certain cost to achieve higher recognition results. Instead of maximizing the sum of squares of Euclidean interclass (called intracluster in unsupervised learning) pairwise distances over all the similar points in previous work, RSM seeks to maximize every pairwise interclass distance between two similar points, and this distance is represented in absolute. Then, we introduce a symmetrical Hingle loss function into the RSM framework. Doing so is to allow some pairwise interclass distances to violate the maximization constraint, such that we can get satisfactory classification performance by losing some training performance. To find multiple projection vectors, a recursive procedure is designed. Our framework is illustrated with Graph Embedding (GE). For any dimension reduction method expressible by the GE, it can thus be generalized by the proposed framework to boost their recognition power by reformulating the original problems.

Orthogonalized variant of Linear Discriminant Analysisis (LDA) is an effective statistical learning tool for dimension reduction. However, existing orthogonalized LDA algorithms suffer from various drawbacks, including the requirement for expensive computing time. This paper develops an efficient algorithm for dimension reduction, referred to as Fast Orthogonal Linear Discriminant Analysis (FOLDA), which adopts an iterative procedure to extract the orthogonal projection vectors. Different from previous efforts, this new approach applies QR decomposition and regression to solve for a new projection vector in each time of iterations, leading to the by far cheaper computational cost. FOLDA can achieve comparable recognition rates to existing orthogonal LDA algorithms. Experimental results on image databases, such as MNIST, COIL20, MEPG-7, and OUTEX, show the effectiveness and efficiency of FOLDA.

In order to resolve the problem that the denoising performance has a sharp drop when noise standard deviation reaches 40, proposed to replace the wavelet transform by the DCT. In this comment, we argue that this replacement is unnecessary, and that the problem can be solved by adjusting some numerical parameters. We also present this parameter modification approach here. Experimental results demonstrate that the proposed modification achieves better results in terms of both peak signal-to-noise ratio and subjective visual quality than the original method for strong noise.

Abstract:Using a conjugate gradient method, a novel iterative support vector machine (FISVM) is proposed, which is capable of generating a new non‐linear classifier. We attempt to solve a modified primal problem of proximal support vector machine (PSVM) and show that the solution of the modified primal problem reduces to solving just a system of linear equations as opposed to a quadratic programming problem in SVM. This algorithm not only has no requirement for special optimization solvers, such as linear or quadratic programming tools, but also guarantees fast convergence. The full algorithm merely needs four lines of MATLAB codes, which gives results that are similar to or better than that of several new learning algorithms, in terms of classification accuracy. Besides, the proposed stand‐alone approach is capable of dealing with instability of classification performance of smooth support vector machine, generalized proximal support vector machine, PSVM and reduced support vector machine. Experiments carried out on UCI datasets show the effectiveness of our approach.

In this paper, a new particle swarm based stereo algorithm is presented. Our motivation is to improve the accuracy of the disparity map by removing the mismatches caused by both occlusions and false targets. In our approach, the stereo matching problem is divided into two steps, including partial matching of segmented image and particle swarm optimization of the rest. The algorithm first takes advantage of SAD and Dynamic Programming to remove the mismatches mainly caused by visibility problems; after the first step, the algorithm selects all the rest image segmented regions, takes them as a particle and uses particle swarm to optimization it. In the second step, the cost function is defined on the pixel level, as well as on the segmented level, while the pixel level measures the data similarity based the current disparity map, the segmented level incorporates a smooth term. Results obtained for benchmark indicate that the proposed method is able to get rather accurate disparity maps.

The neighborhood structure of a pixel in an image can be described more accurately by its two principal curvatures than its gradient or mean curvature-based estimation. Based on this idea, we propose a novel method -- minimum principal curvature-driven diffusion, in which the two principal curvatures are used in a curvature-driven diffusion equation for image filtering. The main advantage of the proposed method over the existing methods is that it preserves not only conventional structures, such as edges, but also some fine structures such as ridges or thin lines.

Propose a road-surface abstraction algorithm which suitable for structured and semi-structured road environments. Algorithm uses fuzzy cluster method which based on maximum entropy theory to cluster ladar points that belong to a scan line. After fitting clustered data linearly, one can abstract straight lines that belong to road-surface by their location and slope angle. We can acquire a current referenced horizontal by comparing several continuous ladar scan lines and then the algorithm abstracts obstacles on road-surface area. Experiments show our algorithm works well in spite of the road-boundary's shape is regular or not, and free from the impact of complex texture or irregular illumination of the road.

In cross-country navigation, autonomous land vehicles (ALVs) must traverse harsh natural terrains, which are uneven, rough, and sloping. One of challenges is to evaluate the terrain's characteristics quantitatively so as to prepare for smooth and stable trajectory planning subsequently. In this paper, we proposed a separate-and-integrate model to analysis rugged terrains, and developed a more universal and robust untraversable regions detection method on elevation maps. When separate, we extract the necessary and sufficient terrain characteristics such as slope, roll variance and roughness from elevation maps respectively and when integrate, the fuzzy inference is applied to combine the above terrain features in order to obtain its traversability assessment and local quantitative evaluations. Experimental results show the method can accurately evaluate terrains' characters and properly classify rugged terrains, and the classification results are robust to the uncertainty and imprecision of the terrain information. And because it's based on terrains' geometry clues, the method provides a more generic framework for rugged terrain analysis.