Recent years have witnessed an explosion of Multi- view Subspace Classification (MSCla) and Multi-view Subspace Clustering (MSClu) methods for various applications. However, their theoretical foundation have not been well explored and understood. In this paper, we investigate the multi-view subspace-preserving recovery theory, which is the theoretical underpinnings for MSCla and MSClu methods. Specifically, we derive novel geometrically interpretable conditions for the success of multi-view subspace-preserving recovery. Compared with prior related works, we make the following innovations: First, our theory does not require the equality constraint, which is a common requirement in prior theoretical works and may be too restrictive in reality. Second, we provide both Individual Theoretical Guarantee (ITG) and Universal Theoretical Guarantee (UTG) for multi-view subspace-preserving recovery while prior works only give the UTG. Third, we also apply the proposed theory to establish theoretical guarantees for MSCla and MSClu, respectively. Numerical results validate the proposed theory for multi-view subspace-preserving recovery.

Road surface condition monitoring is crucial for enhancing transportation safety and efficiency, with applications in autonomous driving and urban infrastructure management. Existing methods often rely on single-camera setups or manual inspections, which are either insufficient for real-time monitoring or labor-intensive. This system focuses on two critical factors: road slope and surface damage, both significantly impacting driving safety and experience, highlighting the need for timely detection. To ensure accuracy and robustness, the system employs a binocular camera for detailed road environment insights and integrates urban sensing techniques. Its hardware deployment processes stereo vision data on embedded platforms, ensuring compatibility with urban IoT networks. This approach surpasses single-camera systems in detecting road surface variations. The research motivation stems from the pressing need to enhance road safety and driving conditions in urban areas. By analyzing binocular camera data and urban sensing technologies, the system offers real-time road condition analysis for effective decision-making. Regarding results, the system showed robust performance in detecting both road slope and surface damage. Slope detection achieved high accuracy with minimal error, and road damage detection reached an overall accuracy of 84%. The system remained stable across diverse conditions, including adverse weather and varying lighting.

The insurance industry plays a crucial role of the national financial system, and the operating efficiency of insurance companies has always been a significant subject of academic research. This study proposes a multiperiod DEA model to dynamically evaluate the operating efficiency of insurance companies, which not only overcomes the defect of the traditional DEA method ignoring the internal structure of decision-making units, but also extends the limitation of the leader–follower model in evaluating single-period efficiency. By analyzing the efficiency of seven listed insurance companies in China from 2009 to 2018, the following conclusions are drawn. The multiperiod DEA model demonstrates advantages over the leader–follower model. The profitability of insurance companies during the first five years is higher compared with the last five years. There is a significant correlation between premium financing efficiency and overall efficiency. Throughout both periods, the loss ratio, loss reserve ratio, and consumer price index (CPI) are always positively correlated with the efficiency of the insurer, while the gearing ratio is negatively related to the efficiency of the insurer. The correlation among gross domestic product (GDP), total insurance value, tradable financial assets, and corporate efficiency varies over time.

The structural information detection of road conditions, which is adopted for improving driving comfort, patrol inspection, road maintenance, and accident rescue. In order to improve the trustworthiness of road condition detection, a real-time artificial intelligence road detection system based on binocular vision sensors is investigated in this article. The system is deployed on the low-power edge computing platform, which can upload the processing results to the cloud through the Internet-of-Things devices. The authors use binocular disparity information and image-based lightweight deep segmentation network to enhance the detection robustness and accuracy in the industrial Internet-of-Things application scenarios. Considering the small training dataset, a special data labeling regularization and training strategy have also been proposed for training this network. In addition, we employ multiframes feature matching and measurement data filtering to enhance the measurement accuracy. The experimental results demonstrate that our monocular–binocular fusion framework is robust and efficient.

BACKGROUND: Nanoscale connectomics, which aims to map the fine connections between neurons with synaptic-level detail, has attracted increasing attention in recent years. Currently, the automated reconstruction algorithms in electron microscope volumes are in great demand. Most existing reconstruction methodologies for cellular and subcellular structures are independent, and exploring the inter-relationships between structures will contribute to image analysis. The primary goal of this research is to construct a joint optimization framework to improve the accuracy and efficiency of neural structure reconstruction algorithms. RESULTS: In this investigation, we introduce the concept of connectivity consensus between cellular and subcellular structures based on biological domain knowledge for neural structure agglomeration problems. We propose a joint graph partitioning model for solving ultrastructural and neuronal connections to overcome the limitations of connectivity cues at different levels. The advantage of the optimization model is the simultaneous reconstruction of multiple structures in one optimization step. The experimental results on several public datasets demonstrate that the joint optimization model outperforms existing hierarchical agglomeration algorithms. CONCLUSIONS: We present a joint optimization model by connectivity consensus to solve the neural structure agglomeration problem and demonstrate its superiority to existing methods. The intention of introducing connectivity consensus between different structures is to build a suitable optimization model that makes the reconstruction goals more consistent with biological plausible and domain knowledge. This idea can inspire other researchers to optimize existing reconstruction algorithms and other areas of biological data analysis.

In the present study, a two-stage data envelopment analysis (DEA) model and spatial econometric method were employed to evaluate and analyze the utilization efficiency of urban water resources and spatial–temporal differences in cities of China. The traditional DEA model was enhanced by adopting the Shannon entropy in the first stage. After selecting variables based on the previous step and the Bayes information criterion (BIC), redundant variables were removed. In the meanwhile, a comprehensive efficiency score (CES) was generated to rank the efficiency. Finally, spatial econometric analysis was applied to explore the spatial–temporal differences of urban water resource utilization efficiency. Results demonstrate that: 1) according to the calculations and analysis, communities should concentrate on increasing investment in equipment and technology that can help enhance water consumption efficiency, while overlooking some minor aspects, such as per capita gross domestic product (PCGDP); 2) most cities have poor water resource utilization efficiency (low CES). However, both input and output have much room for the improvement; 3) Lhasa, Beijing, Haikou, and Shanghai have high CES, indicating that the utilization efficiency of water resources is not entirely dependent on economic development; and 4) through performing the Lagrange multiplier (LM) test, the spatial error model (SEM) test is passed at the significant level of 5%. Moreover, the water resource utilization efficiency of a city may be enhanced with the economic development in neighboring cities.

As an important national initiative, China’s public transport priority development strategy is conducive to the development of the public transport industry and urban economic construction. However, current public transport operation is inefficient due to the information asymmetry between the government and public transport enterprises, thereby inevitably generating losses. To address the issue of information asymmetry, this study designs a public transport subsidy allocation method from the perspective of incentives and discusses the allocation results using this method through a case study. The rationality of the proposed method and the scientificity and authenticity of the conclusions, are fully explained and demonstrated via the experimental simulation. Based on real published data of public transport enterprises, the proposed method can motivate public transport enterprises to improve their performance. In the case of reporting false data, the proposed method can motivate public transport enterprises to report accurate data.

Biologists often need to handle numerous video-based home-cage animal behavior analysis tasks that require massive workloads. Therefore, we develop an AI-based multi-species tracking and segmentation system, SiamBOMB, for real-time and automatic home-cage animal behavioral analysis. In this system, a background-enhanced Siamese-based network with replaceable modular design ensures the flexibility and generalizability of the system, and a user-friendly interface makes it convenient to use for biologists. This real-time AI system will effectively reduce the burden on biologists.

The microwave radiation imager (MWRI) located onboard the FengYun-3C (FY-3C) satellite provides a considerable amount of critical information for numerical weather predictions. Obtaining accurate geolocation results from the FY-3C MWRI data is of great importance. In this letter, we improve the traditional coastline inflection method (CIM) and propose an$\ell _{0}$sparse approximation model for geolocation error estimation and correction. Specifically, we propose using the jump point of the step function to estimate the true coastline point. This approach can characterize the geolocation errors more accurately than the CIM, which further improves the geolocation accuracy. In the theoretical part, we provide a complete solution to obtain the step function through an iterative blind deconvolution. For a practical use, we demonstrate the effectiveness of the proposed method for geolocation error estimation through quantitative results obtained on the FY-3C MWRI data. The experimental results show that the proposed method can achieve an improvement of up to 33.33% in the standard deviation of geolocation errors (approximately 0.00030) compared to the traditional CIM (approximately 0.00045). Furthermore, we also apply the proposed method to the FY-3C satellite and improve the geolocation accuracy of the MWRI data through geolocation error correction.

Image registration of serial electron microscopy (EM) sections is a feasible way to reveal 3D structure of the biological tissue. However, the image registration proves difficult, as it is hard to find reliable correspondences between the adjacent sections and the section distortion may occur during the sample preparation. In this paper, we propose a non-linear image registration method for serial EM sections, which is composed of pairwise correspondences extraction, correspondences position adjustment and image warping. The proposed method is highly automatic, and retains the morphology of the original electron microscopic images as much as possible. We demonstrate that our method outperforms the state-of-the-art approaches on several datasets of serial EM sections images including a synthetic test case.

BACKGROUND: The locations and shapes of synapses are important in reconstructing connectomes and analyzing synaptic plasticity. However, current synapse detection and segmentation methods are still not adequate for accurately acquiring the synaptic connectivity, and they cannot effectively alleviate the burden of synapse validation. RESULTS: We propose a fully automated method that relies on deep learning to realize the 3D reconstruction of synapses in electron microscopy (EM) images. The proposed method consists of three main parts: (1) training and employing the faster region convolutional neural networks (R-CNN) algorithm to detect synapses, (2) using the z-continuity of synapses to reduce false positives, and (3) combining the Dijkstra algorithm with the GrabCut algorithm to obtain the segmentation of synaptic clefts. Experimental results were validated by manual tracking, and the effectiveness of our proposed method was demonstrated. The experimental results in anisotropic and isotropic EM volumes demonstrate the effectiveness of our algorithm, and the average precision of our detection (92.8% in anisotropy, 93.5% in isotropy) and segmentation (88.6% in anisotropy, 93.0% in isotropy) suggests that our method achieves state-of-the-art results. CONCLUSIONS: Our fully automated approach contributes to the development of neuroscience, providing neurologists with a rapid approach for obtaining rich synaptic statistics.

Origindestination (OD) trip matrices reflect demand patterns of traffic networks and play important roles in traffic engineering. Existing approaches for traffic flow forecast such as ARIMA, SVR, and neural network perform well in modeling and predicting each OD pair separately, but they cause inefficiency when dealing with multidimensional OD demand data. In this paper, to solve the problemgiven OD trip matrices for T moments, how can we predict the overall OD trip matrices at time T+1, T+2, or even T+L, we model temporal vehicle-based OD trip matrix as a four-order tensor consisting of four attributes: origin, destination, vehicle type and time. By resorting to CANDECOMP/PARAFAC (CP) tensor decomposition, we show how a prediction method can be used to forecast future traffic demand in time factor matrix. Experiments based on a real highway tolling dataset demonstrate that our method effectively reduces the time for prediction and meanwhile makes the prediction accuracy competitive with those of other methods.

Smartphone camera is a very powerful sensor for many intelligent applications. But it is difficult to obtain a stable video quality in an unstable motion environment. In this case, robust and fast video stabilization algorithm is necessary for some intelligent applications on smartphone. A novel rapid and efficient video stabilization algorithm is proposed in this paper. The proposed algorithm not only obtains good visual effect, but can also be implemented in real-time on mobile platforms with limited computational resource. Our algorithm can process about 2.9ms per frame with QVGA video format on the mobile platform. The experimental results show the proposed method has similar video stabilization effect with the Optical Image Stabilization (OIS) hardware. The proposed algorithm can support many camera applications for smartphone.

This paper proposes an image fusion algorithm based on a sparse linear equation system, which uses local extreme of high resolution image and intensity of multi-spectral image to construct the system. Based on this sparse system, the multi-scale image decomposition algorithm can be implemented. This algorithm extracts the details of the high-resolution image and integrates with the multi-spectral information to derive the fused image.

Empirical-mode decomposition (EMD) provides a powerful tool for adaptive multiscale analysis of nonstationary signals. Aiming at the intrinsic mode function (IMF) criteria in sifting process and the scale mixing problem in EMD, this paper proposes a bandwidth criterion for IMF. By analyzing the simulated signal, it is confirmed that the IMFs obtained with the bandwidth criterion approximate the real components better and reflect the intrinsic information of the analyzed signal. Furthermore, the criterion based on bandwidth can weaken the scale mixing problem.