High spatial resolution (HSR) remote sensing images inevitably pose the challenge of multi-scale transformation, as small objects such as cars and helicopters may occupy only a few pixel points. This incurs a significant hurdle for global context modeling, particularly in backbone networks with large downsampling coefficients. Simple summation or concatenation techniques, such as skip connections, fail to address semantic gaps and even impose negative impacts on multi-scale feature fusion. Meanwhile, due to the complexity of foreground objects, the boundary details of HSR remote sensing images are easy to lose in sampling operations. To overcome these challenges, we propose a Multi-scale Channel-wise Cross Attention Network (MCCANet) assisted by boundary supervision. Technically, MCCA captures the channel attention with various scales, which allows dynamic and adaptive feature fusion in a contextual scale-aware manner and focuses on both large and small objects distributed throughout the inputs. Besides, a Channel and Context Strainer (CCS) module is proposed and embedded in MCCA, filtering channels and contexts for the mitigation of intra-class differences. In addition, we apply a Boundary Supervision (BS) module to recover boundary contour, avoiding the blurring effect during the construction of contextual information. The refined boundary allows for the effective recognition of surrounding pixels, ensuring a better segmentation performance. Extensive experiments on iSAlD, ISPRS Potsdam, and LoveDA datasets demonstrate that our proposed MCCANet achieves a good balance of high accuracy and efficiency. Code will be available at: https://github.com/ZhengJianwei2/MCCANet.

Objective: Preterm birth is the leading cause of neonatal morbidity and mortality. Early identification of high-risk patients followed by medical interventions is essential to the prevention of preterm birth. Based on the relationship between uterine contraction and the fundamental electrical activities of muscles, we extracted effective features from EHG signals recorded from pregnant women, and use them to train classifiers with the purpose of providing high precision in classifying term and preterm pregnancies. Methods: To characterize changes from irregularity to coherence of the uterine activity during the whole pregnancy, network representations of the original electrohysterogram (EHG) signals are established by applying the Horizontal Visibility Graph (HVG) algorithm, from which we extract network degree density and distribution, clustering coefficient and assortativity coefficient. Concerns on the interferences of different noise sources embedded in the EHG signal, we apply Short-Time Fourier Transform (STFT) to expand the original signal in the time-frequency domain. This allows a network representation and the extraction of related features on each frequency component. Feature selection algorithms are then used to filter out unrelated frequency components. We further apply the proposed feature extraction method to EHG signals available in the Term-Preterm EHG database (TPEHG), and use them to train classifiers. We adopt the Partition-Synthesis scheme which splits the original imbalanced dataset into two sets, and synthesizes artificial samples separately within each subset to solve the problem of dataset imbalance. Results: The optimally selected network-based features, not only contribute to the identification of the essential frequency components of uterine activities related to preterm birth, but also to improved performance in classifying term/preterm pregnancies, i.e., the SVM (Support Vector Machine) classifier trained with the available samples in the TPEHG gives sensitivity, specificity, overall accuracy, and $auc$ values as high as 0.89, 0.93, 0.91, and 0.97, respectively.

Traffic flow prediction is the foundation of many applications in smart cities, and the granular precision of traffic flow prediction has to be enhanced with refined applications. However, most of the existing researches cannot meet these requirements. In this paper, we propose a spatial-temporal attention based fusion network (ST-AFN), for lane-level precise prediction. This seq2seq model consists of three parts, namely speed process network, spatial encoder, and temporal decoder. In order to exploit the dynamic dependencies among lanes, attention mechanism blocks are embedded in those networks. The application of deep spatial-temporal information matrix results in progresses in term of reliability. Furthermore, a specific ground lane selection method is also proposed to ST-AFN. To evaluate the proposed model, four months of real-world traffic data are collected in Xiaoshan District, Hangzhou, China. Experimental results demonstrate that ST-AFN can achieve more accurate and stable results than the benchmark models. To the best of our knowledge, this is the first time that a deep learning method has been applied to forecast traffic flow at the lane level on urban ground roads instead of expressways or elevated roads.

In this letter, we present a hyperspectral image (HSI) mixed-noise removal method named Moreau-enhanced total variation (TV) regularized local low-rank matrix recovery (LLRMTV). The rank-fixed matrix recovery is first adopted to separate the low-rank clean HSI patches from the sparse noise. Then, a Moreau-enhanced TV regularized image reconstruction strategy is utilized to ensure the piecewise smoothness of the reconstructed image from the low-rank patches. The proposed Moreau-enhanced TV restoration method involves a nonconvex penalty designed to maintain the convexity of the objective function. Moreover, the proposed model is integrated into an augmented Lagrange multiplier (ALM) algorithm to produce final results, leading to a complete HSI restoration framework. Examples of restoration illustrate the improvement over the typical TV regularization.