Global contextual dependency is of significance for semantic segmentation from light fields. However, previous works mostly exploit attention mechanisms to model spatial context dependency and angular context dependency separately, since a light field capture is very data-intensive. Considering that self-attention is resource-consuming, existing methods only use it to capture spatial contextual dependency for the central view of light fields, and employ resource-efficient attention mechanisms to model angular view dependencies instead. Inspired by the recent success of State Space Sequence model (SSM), we present a novel SSM-based encoder for light field semantic segmentation, which models spatio-angular contextual dependency together. The proposed method is evaluated on the existing light field semantic segmentation datasets, and experimental results show that the proposed method significantly outperforms previous state-of-the-art methods. The code and models used in this work are available in https://github.com/YanWQ/EPI-Mamba

Electrocardiogram (ECG) signals reflect heart electrical activity and are essential for diagnosing cardiovascular diseases. The typical ECG waveform consists of P-waves, QRS complexes, and T-waves, whose morphological variations reflect cardiac function. However, it is challenging to delineate the boundaries of these waveforms accurately due to signal overlap between consecutive heartbeats and the periodic complexity of the waveforms. Therefore, this study introduces a novel delineation method that integrates sample point pre-classification with a conditional random field (CRF) model, enabling precise identification of ECG waveform onset and offset points. The method begins with R-peak-based segmentation to isolate individual cardiac cycles and reduce inter-beat interference. An encoder-decoder structure is then employed to capture both local waveform features and global contextual relationships. Finally, the CRF incorporates segment dependencies to optimize boundary identification. Experimental results demonstrate that the approach achieves an average accuracy of 95.47%, outperforming state-of-the-art methods, particularly in detecting QRS complexes and T-waves.

Fetal heart rate (FHR) monitoring is essential for diagnosing hypoxic-ischemic encephalopathy, but traditional cardiotocography (CTG) suffers from interpretative variability, leading to suboptimal clinical decisions. Non-invasive fetal electrocardiography (fECG), which collects fetal heart electrical activity via maternal abdominal electrodes, offers a more effective alternative. However, deep-learning methods for fetal R-peak detection often require abundant reference signals invasively collected fetal scalp. The scarcity of labeled fECGs at the pregnant stage limits application during pregnancy. To overcome these limitations, we propose an unsupervised domain adaptation approach to enhance detection accuracy during pregnancy. This method incorporates a multiview enhancement strategy to analyze both time- and frequency-domain representations, improving adaptability across pregnancy stages. We further integrate FHR physiological characteristics and propose a misclassification correction mechanism to boost detection accuracy. Evaluations on multiple real datasets confirm the robustness and superior performance of our approach, establishing it as a reliable solution for fECG analysis across different pregnancy stages.

Change detection of multi-temporal remote sensing images is mushrooming with the innovations of neural networks, whose daunting challenge lies in locating sporadically distributed spatial-temporal changes given sophisticated scenes and various imaging conditions. Unfortunately, instead of devoting full attention to changes, most existing solutions often expend unnecessary resources yet derive task-irrelevant features. To relieve this issue, we propose a collective-attention network, which enjoys lightweight model architecture yet guarantees high performance. Specifically, an inter-temporal collective-attention module is developed for efficient interaction of bi-temporal features, in which a shared attention distribution is derived via the multiplication of temporal-concatenated queries and spatial-subtracted keys. Additionally, we present a non-change consistency-constraint, enforcing a change-oriented attention distribution and a noise-suppressed treatment. With the learned interaction features, bi-temporal differences are captured simply using the operations of spatial absolute error and temporal concatenation. Finally, decoding multi-scale differences is accomplished by lightweight temporal self-attention and spatial self-attention. Experiments on four datasets demonstrate that our model achieves state-of-the-art performance, yet requires only 1.71M parameters and 1.98G FLOPs.

The topic of identification for sparse vector in a distributed way has triggered great interest in the area of adaptive filtering. Grouping components in the sparse vector has been validated to be an efficient way for enhancing identification performance for sparse parameter. The technique of pairwise fused lasso, which can promote similarity between each possible pair of nonnegligible components in the sparse vector, does not require that the nonnegligible components have to be distributed in one or multiple clusters. In other words, the nonnegligible components may be randomly scattered in the unknown sparse vector. In this article, based on the technique of pairwise fused lasso, we propose the novel pairwise fused lasso diffusion least mean-square (PFL-DLMS) algorithm, to identify sparse vector. The objective function we construct consists of three terms, i.e., the mean-square error (MSE) term, the regularizing term promoting the sparsity of all components, and the regularizing term promoting the sparsity of difference between each pair of components in the unknown sparse vector. After investigating mean stability condition of mean-square behavior in theoretical analysis, we propose the strategy of variable regularizing coefficients to overcome the difficulty that the optimal regularizing coefficients are usually unknown. Finally, numerical experiments are conducted to verify the effectiveness of the PFL-DLMS algorithm in identifying and tracking sparse parameter vector.

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.

Barrier coverage problem is one of hot research topics in directional sensor networks (DSNs). Directional sensors could zoom their sensing ranges, within which the event detection probability decreases with the increase of the distance between the location of event and the sensor. Although the probability that an event is detected by a single sensor may be below the required criteria, the detection probability achieved jointly by two sensors can be above the required criteria. In this work, we study the barrier coverage problem of DSNs, taking into account the directional nodes' ability of adjusting their working directions and sensing ranges. We mainly propose a barrier construction scheme, which schedules the nodes to form multiple barriers by jointly determining which nodes jointly forming a barrier, and their respective working directions and sensing ranges. Comparing to existing works, the proposed scheme is able to form more barriers, leading to the increase of service lifetime.

Uncertainty prediction for ocean and climate predictions is essential for multiple applications today. Many-Task Computing can play a significant role in making such predictions feasible. In this manuscript, we focus on ocean uncertainty prediction using the Error Subspace Statistical Estimation (ESSE) approach. In ESSE, uncertainties are represented by an error subspace of variable size. To predict these uncertainties, we perturb an initial state based on the initial error subspace and integrate the corresponding ensemble of initial conditions forward in time, including stochastic forcing during each simulation. The dominant error covariance (generated via SVD of the ensemble) is used for data assimilation. The resulting ocean fields are used as inputs for predictions of underwater sound propagation. ESSE is a classic case of Many Task Computing: It uses dynamic heterogeneous workflows and ESSE ensembles are data intensive applications. We first study the execution characteristics of a distributed ESSE workflow on a medium size dedicated cluster, examine in more detail the I/O patterns exhibited and throughputs achieved by its components as well as the overall ensemble performance seen in practice. We then study the performance/usability challenges of employing Amazon EC2 and the Teragrid to augment our ESSE ensembles and provide better solutions faster.