Imitation learning has shown strong potential for enabling robots to acquire dexterous manipulation skills by integrating visual observations with proprioceptive states. However, common approaches typically use visual encoders pretrained in computer vision domains, which mainly aim to extract generic representations without emphasizing the precise spatial and semantic structures that are crucial for robotic manipulation. In this work, we propose the Joint-Guided Spatial and Semantic Sensitive Diffusion Policy (S3D), which effectively fuses structured and generic features by incorporating depth and semantic maps with RGB and proprioceptive inputs to strengthen spatial–semantic understanding in manipulation. However, naively incorporating these multimodal representations inevitably introduces additional computational overhead. Thus, we introduce a Joint-Guided Dynamic Attention module that generates joint-conditioned queries to extract behavior-specific representations with controlled complexity. Experiments across a variety of simulated and real-world robotic manipulation tasks demonstrate that S3D yields consistent performance gains over state-of-the-art methods.

Recent research has demonstrated that artificial intelligence (AI) can assist electronic design automation (EDA) in improving both the quality and efficiency of chip design. But current AI for EDA (AI-EDA) infrastructures remain fragmented, lacking comprehensive solutions for the entire data pipeline from design execution to AI integration. Key challenges include fragmented flow engines that generate raw data, heterogeneous file formats for data exchange, non-standardized data extraction methods, and poorly organized data storage. This work introduces a unified open-source library for EDA (AiEDA) that addresses these issues. AiEDA integrates multiple design-to-vector data representation techniques that transform diverse chip design data into universal multi-level vector representations, establishing an AI-aided design (AAD) paradigm optimized for AI-EDA workflows. AiEDA provides complete physical design flows with programmatic data extraction and standardized Python interfaces that bridge EDA datasets and AI frameworks. Leveraging the AiEDA library, we generate iDATA, a 600GB dataset of structured data derived from 50 real chip designs (28nm), and validate its effectiveness through five representative AAD tasks spanning prediction, generation, and optimization. The code of AiEDA is publicly available at https://github.com/OSCC-Project/AiEDA, providing a foundation for future AI-EDA research.

Accurate vascular segmentation is essential for coronary visualization and the diagnosis of coronary heart disease. This task involves the extraction of sparse tree-like vascular branches from volumetric space. However, existing methods have faced significant challenges due to discontinuous vascular segmentation and missing endpoints. To address this issue, a 3D vision graph neural network framework, named ViG3D-UNet, was introduced. This method integrates 3D graph representation and aggregation within a U-shaped architecture to facilitate continuous vascular segmentation. The ViG3D module captures volumetric vascular connectivity and topology, while the convolutional module extracts fine vascular details. These two branches are combined through channel attention to form the encoder feature. Subsequently, a paperclip-shaped offset decoder minimizes redundant computations in the sparse feature space and restores the feature map size to match the original input dimensions. To evaluate the effectiveness of the proposed approach for continuous vascular segmentation, evaluations were performed on two public datasets, ASOCA and ImageCAS. The segmentation results show that the ViG3D-UNet surpassed competing methods in maintaining vascular segmentation connectivity while achieving high segmentation accuracy.

We address a fundamental challenge in coordinating large-scale 3D drone swarms: how to achieve rapid collective response to environmental stimuli while ensuring group stability and safety. Existing swarm navigation modals often rely on sophisticated individual perception and communication capabilities, which can be computationally expensive and impractical for large swarms. In this paper, we propose the Non-reciprocal Collective Emergent Navigation model (NRCE), a decentralized approach designed for real-world drone flocking in complex environments. Unlike traditional models, our approach leverages localized non-reciprocal interactions, where boundary drones detect environmental stimuli and propagate this information throughout the swarm without directly controlling individual trajectories. Through extensive numerical simulations and physical experiments with up to 28 drones, we demonstrate how this model achieves coordinated collective motion while effectively balancing stability with responsiveness. Our findings reveal two notable insights: (1) intermediate cohesion levels (ωc) optimize collective response—a "Goldilocks zone" where individuals are neither too tightly coupled nor too independent, challenging the conventional wisdom that stronger cohesion always improves coordination; and (2) swarm queue configuration significantly affects optimal interaction parameters, with divergent trends observed between attraction- and repulsion-based coordination mechanisms as layer count increases. These discoveries provide critical design principles for cost-effective, high-density swarm systems while advancing the theoretical understanding of collective dynamics in both artificial and biological systems.

Ant Colony Optimization (ACO) is a prominent swarm intelligence algorithm extensively applied to path planning. However, traditional ACO methods often exhibit shortcomings, such as blind search behavior and slow convergence within complex environments. To address these challenges, this paper proposes the Pheromone-Focused Ant Colony Optimization (PFACO) algorithm, which introduces three key strategies to enhance the problem-solving ability of the ant colony. First, the initial pheromone distribution is concentrated in more promising regions based on the Euclidean distances of nodes to the start and end points, balancing the trade-off between exploration and exploitation. Second, promising solutions are reinforced during colony iterations to intensify pheromone deposition along high-quality paths, accelerating convergence while maintaining solution diversity. Third, a forward-looking mechanism is implemented to penalize redundant path turns, promoting smoother and more efficient solutions. These strategies collectively produce the focused pheromones to guide the ant colony’s search, which enhances the global optimization capabilities of the PFACO algorithm, significantly improving convergence speed and solution quality across diverse optimization problems. The experimental results demonstrate that PFACO consistently outperforms comparative ACO algorithms in terms of convergence speed and solution quality.

Efficient feature learning is considered indispensable for maximizing the representation of scarce information in tiny datasets. However, existing methods are often unable to fully exploit local features and contextual dependencies when dealing with tiny datasets. To overcome this shortcoming, a Cross-Feature Vision Transformer (CF-ViT) was proposed, which decouples local feature refinement from global context modeling and leverages the complementary strengths of CNNs and Transformers. Specifically, a Cross-Scale Fusion (CSF) module was introduced to integrate features from multiple scales, ensuring that cross-scale information is globally embedded. In addition, a Feature Enhancement and Reorganization (FER) module was incorporated into CF-ViT, whereby Transformer outputs are reorganized into 2D feature maps for convolution-based detail enhancement to thoroughly exploit local information. Extensive experiments have demonstrated that CF-ViT consistently surpasses baselines across 4 tiny datasets, reaching a 96.87% (KSDD) Top-1 accuracy with only 29.19 million parameters and 2.67 billion FLOPs. Moreover, a Top-1 accuracy of 85.03% is attained on a real-world tiny dataset of wood surface defect detection, exceeding all baselines. These findings underscore the effectiveness and generalization capability of CF-ViT in capturing fine-grained local details and global context, offering a promising and deployable solution for vision tasks in tiny datasets.

In logistics systems with multiagent collaboration, one of the prevailing focus lies on modeling as the dynamic multiperiod vehicle routing problem (DMPVRP). This work introduces modifications to DMPVRP to align with the requirements of real factory operations, particularly with dynamic supply demands. A self-established multiagent dynamic scheduling framework has been proposed to adapt to dynamic environmental changes and make timely adjustments, which consists of two modules: dynamic path planning and machine assignment. The first module utilizes a self-designed multioperator two-stage evolutionary algorithm to dynamically update the routes for vehicles. The second module maintains the workload balance among vehicles in real time. Experimental results demonstrate that the proposed algorithm achieves optimal outcomes compared to three state-of-the-art algorithms, surpassing others by 20% in machine output and exhibiting 5% lower transportation costs. In addition, a case study from a steel cord manufacturing factory is conducted, demonstrating its capability to promptly enhance efficiency.

Vision transformers have achieved remarkable success in image classification. The dual-branch vision transformer generates more features by taking advantage of feature fusion. Inspired by this, a dual-branch vision transformer with Real-Time Share feature was proposed during the encoding process for retinal image classification tasks. The approach processes image patches of varying sizes (base and large) through two independent branches and implements multi-stage Real-Time feature fusion via the Real-Time Share feature encoder. This encoder enables the branches to complement each other's features at each encoding stage, facilitating finer feature learning and enhancing the self-attention information passed to subsequent stages. It significantly boosts feature representation and classification performance. Additionally, a straightforward and effective feature fusion method, L-Times Attention Fusion, was proposed: vector concatenation for Real-Time Share feature in the earlier (L-1) encoding stages and element-wise addition for overall feature fusion at the L-th stage, achieving more efficient feature integration. The method was validated on a retinal image dataset. Results show that the approach outperforms the recent Cross-ViT average TOP-1 Acc by 5.61% with lower FLOPs and model parameters, without relying on pre-trained weights, highlighting stronger self-learning feature capabilities and reduced reliance on extensive pre-training data.

Human activity recognition (HAR) is a task designed to identify and classify physical activities or behaviors in people’s daily lives. This field relies on data collected from various sensors. Due to differences in environment, user posture and habits, as well as equipment placement, the data exhibits severe heterogeneity. The channel information from different sensors exhibits more complex and uncertain characteristics in terms of shape, noise level, and data quality. These properties can lead to poor generalization of deep learning models, posing challenges for the effectiveness of deep learning algorithms and the widespread use of specific embedded devices in this field. Therefore, this article proposes an adaptive channel signal scaling method to calibrate channel characteristics from the perspective of sensor channel information. Additionally, we propose a stable feature completion strategy to enrich the data feature information using different fusion strategies at the data level. Extensive experiments were conducted on three publicly available HAR datasets, and the experimental results demonstrate that our proposed method significantly improves the performance of state-of-the-art deep learning methods.

Lesion segmentation plays a crucial role in various medical image analyses, which not only improves the efficiency in clinical diagnosis but also assists in detecting early symptoms of various diseases. Most existing studies focus on directly extracting lesion information from specific types of medical images with pre-trained weights, often neglecting the underlying topological and pathological causes which lead to these lesions. Furthermore, they overlook to capture general anatomical features among lesions, which are related to the distribution of lesions, and thus the model is poorly generalized in different medical datasets. Inspired by these insights, we propose a Triple Fusion Net (TF-Net), a network structure divided into three branches: left, middle and right. The left and right branches are designed to extract lesion features and associated topological style features within various medical images, respectively. And these features are further fused and modeled in the middle branch. The proposed structure of triple branches for features fusing effectively learns multi-feature information and improves the performance of TF-Net. And our work experiments validate various feature fusion methods in the middle branch, including channel-wise concatenation, element-wise addition, attention gate, and transformer encoder block. Without using pre-trained weights in our network, the transformer encoder block performs best on some tasks of DDR and surpasses other pre-trained models. Channel concatenation exhibits performance close to other pre-trained models in both the IDRiD, Kvasir-Seg and TN3K. Attention gate fusion also shows competitive results in thyroid ultrasound segmentation. Our approach, leveraging a unique network structure and four different feature fusion methods, demonstrates remarkable generality across a spectrum of medical image segmentation tasks.

Peripheral waveform analysis (PWA), which is generally used to reveal hidden health status information from peripheral pulse signals, typically involves three procedures: signal preprocessing, feature extraction, and pattern classification. With the advancement of data-driven deep neural network methodologies, feature extraction and pattern classification have progressively converged into end-to-end neural networks, where the final layer of the network is equivalent to conventional pattern classifiers. However, the performance of deep learning models heavily relies on the quality of the dataset, rendering data signal preprocessing a crucial component. This study proposes a framework that integrates signal preprocessing, feature extraction, and pattern classification into a unified learning approach using heuristic algorithms, enabling the automatic discovery of optimal data encoding methods and their corresponding models. Initially, the search space is defined based on parameters relevant to signal preprocessing, and a fitness function is constructed utilizing CNN. Subsequently, the optimal combination of data preprocessing and CNN is determined through the heuristic algorithm Particle Swarm Optimization (PSO). The proposed method was evaluated in the dataset comprising authentic clinical cases of type 2 diabetes screening involving approximately 200 volunteers. The model derived from this framework demonstrates the capability to effectively discriminate between healthy volunteers and those with diabetes, achieving the highest accuracy of 93.6%. Compared to state-of-the-art algorithms, the proposed model was shown to be competitive in both accuracy and time cost.

Color constancy is the ability to remove the effect of illumination on color. Since color constancy is an ill-posed problem, many methods have been proposed based on assumptions to constraint the solution space. However, most existing assumptions require specular pixels or abundant colors, and fail to produce satisfactory results for different scenarios. According to extensive experiments, we observe that the chromaticity distribution of pixels within main color under canonical illumination, which we called canonical pixels, is linear and can also locate the position of illumination under the non-canonical illumination. Therefore, this paper proposes a chromaticity-line prior (CLP) as an additional linear constraint on the ill-posed problem of color constancy. In the calculation of CLP, the simple linear iterative clustering is firstly employed to segment an image into several super-pixel blocks. And the random sampling consensus is utilized to remove non-primary color points and fit the chromaticity-line. Based on the proposed CLP, a color constancy algorithm is implemented correspondingly. Since the main idea of the CLP is to extract the canonical pixels, which is the inherent property of image, the proposed CLP is more general and adaptive in real scenes. The experiments on two public datasets demonstrate that the proposed algorithm not only outperforms state-of-the-art learning-free algorithms, but also achieves results that are competitive to those of learning-based algorithms.

MOTIVATION: Identifying new therapeutic effects for the approved drugs is beneficial for effectively reducing the drug development cost and time. Most of the recent computational methods concentrate on exploiting multiple kinds of information about drugs and disease to predict the candidate associations between drugs and diseases. However, the drug and disease nodes have neighboring topologies with multiple scales, and the previous methods did not fully exploit and deeply integrate these topologies. RESULTS: We present a prediction method, multi-scale topology learning for drug-disease (MTRD), to integrate and learn multi-scale neighboring topologies and the attributes of a pair of drug and disease nodes. First, for multiple kinds of drug similarities, multiple drug-disease heterogenous networks are constructed respectively to integrate the similarities and associations related to drugs and diseases. Moreover, each heterogenous network has its specific topology structure, which is helpful for learning the corresponding specific topology representation. We formulate the topology embeddings for each drug node and disease node by random walking on each heterogeneous network, and the embeddings cover the neighboring topologies with different scopes. Because the multi-scale topology embeddings have context relationships, we construct Bi-directional long short-term memory-based module to encode these embeddings and their relationships and learn the neighboring topology representation. We also design the attention mechanisms at feature level and at scale level to obtain the more informative pairwise features and topology embeddings. A module based on multi-layer convolutional networks is constructed to learn the representative attributes of the drug-disease node pair according to their related similarity and association information. Comprehensive experimental results indicate that MTRD achieves the superior performance than several state-of-the-art methods for predicting drug-disease associations. MTRD also retrieves more actual drug-disease associations in the top-ranked candidates of the prediction result. Case studies on five drugs further demonstrate MTRD's ability in discovering the potential candidate diseases for the interested drugs.

Haze has a serious impact on the outdoor optical imaging systems, and it will result in image blurring, color shift, and saturation reduction. Recently, many single image dehazing algorithms have been proposed for practical applications, such as surveillance. However, since the widely-used global atmospheric light in image dehazing fails to well describe the local illumination differences of images, these algorithms fail to well adapt to scenes with different haze concentrations and lighting conditions. Therefore, this paper proposes an adaptive single image dehazing algorithm using joint local-global illumination adjustment. A local illumination estimation for hazy image is proposed to replace the global atmospheric light constant in the atmospheric scattering model, and it can better adapt to the local differences of image illumination. Correspondingly, the global atmospheric light constant is proposed to be utilized to adaptively compensate the illumination intensity, which may better overcome the dark illumination problem within the dehazed image. The experimental results demonstrate that the proposed algorithm can outperform the state-of-the-art algorithms in terms of not only the dehazing effect but also the adaptability.

Place recognition and loop closure detection are challenging for long-term visual navigation tasks. SeqSLAM is considered to be one of the most successful approaches to achieve long-term localization under varying environmental conditions and changing viewpoints. SeqSLAM uses a brute-force sequential matching method, which is computationally intensive. In this work, we introduce a multi-resolution sampling-based global visual place recognition method (MRS-VPR), which can significantly improve the matching efficiency and accuracy in sequential matching. The novelty of this method lies in the coarse-to-fine searching pipeline and a particle filter-based global sampling scheme, that can balance the matching efficiency and accuracy in the long-term navigation task. Moreover, our model works much better than SeqSLAM when the testing sequence is over a much smaller time scale than the reference sequence. Our experiments demonstrate that MRSVPR is efficient in locating short temporary trajectories within long-term reference ones without compromising on the accuracy compared to SeqSLAM.

Laser SLAM can be implemented using ROS and Ubuntu system. However, it cannot be run in Windows operating system which is more stable than Ubuntu. To implement the laser SLAM in Windows system, the main program of laser SLAM in ROS is carefully analyzed and modified to make it adapt to Windows system. The main programs of laser processing, coordinate transformation and map construction are rewritten and reorganized. To verify the effectiveness of our work, experiments were conducted in real-world environments. The results of experiments validated that laser SLAM can be implemented in Windows system by rewriting and reorganizing these main programs.

Hand gesture recognition (HGR) is a natural way of Human Machine Interaction and has been applied on different areas. In this paper, we discuss works done in the area of applications of HGR in industrial robots where focus is on the processing steps and techniques in gesture-based Human Robot Interaction (HRI), which can provide useful information for other researchers. We review several related works in the area of HGR based on different approaches including sensor based approach and vision approach. After comparing the two approaches, we found that 3D vision-based HGR method is a challenging but promising researching area. Then, concerning works of implementation of HGR in industrial scenario are discussed in detail. Pattern recognition algorithms that effectively used in HGR like k-means, DTW etc. are briefly introduced as well.

In this paper, we present a new and effective dimensionality reduction method called locality sparsity preserving projections (LSPP). Locality preserving projections (LPP) and sparsity preserving projections (SPP) only focus on an aspect of local structure and sparse reconstructive information of the dataset, respectively. The proposed method integrates the sparse reconstructive information and local structure of data. The projection of LSPP is sought such that the sparse reconstructive weights and local preserving weights can be best preserved and integrated. Extensive experiments on ORL, Yale, Yale B, AR and CMU PIE face databases show the effectiveness of the proposed LSPP.