Spatial transcriptomics (ST) technologies offer an unprecedented opportunity to resolve complex tissue microenvironments. The accurate identification of spatial domains is still a pivotal and challenging task in spatial transcriptomics studies. Although numerous computational methods have been developed for spatial domain detection, prevailing methods struggle with multi-modal data fusion, noise robustness, and clustering stability. To address these limitations, we introduce DPST, an end-to-end deep learning model, which integrates gene expression, spatial coordinates, and histology images with an attention mechanism. DPST leverages the self-supervised bootstrap your own latent (BYOL) framework to extract robust feature embeddings from histology images without requiring negative samples. At its core, DPST employs a MASK-REMASK dual-view decoding strategy that simultaneously corrects for noise in masked data while recovering details from unmasked data. Furthermore, we use the breaking the reclustering barriers mechanism. This mechanism incorporates weight resets, reclustering, and momentum resets. It helps deep embedded clustering algorithms overcome performance bottlenecks. The experimental results show that DPST outperforms state-of-the-art methods consistently in several tasks, including spatial clustering and trajectory inference.

The dysfunction of biological systems caused by disease-related genes is one of the inducements of complex diseases. To understand molecular mechanisms of complex diseases, the identification of disease-related gene modules in biological networks through community detection is emerging as a promising approach. However, most community detection methods are not suitable for biological networks because their topological structures are complex and the scale of biologically relevant modules are small. In this paper, a novel community detection method called MPSO-CD was proposed based on multi-objective particle swarm optimization, in which negative ratio association and ratio cut were employed as objective functions. Highlights of MPSO-CD are a mutation strategy based on clustering coefficient and the procedure of disease module screening referring to the internal connection density and functional similarity. Experimental results of social and synthetic complex networks indicate that MPSO-CD is comparable and often superior to four compared methods. Eventually, MPSO-CD is applied to the asthma gene co-expression network for identifying potential disease modules that provide the molecular mechanism information about asthma. Most of the captured modules have been proven to be associated with asthma through Gene Ontology and pathway enrichment analysis.

Recent developments in spatial transcriptomics (ST) technologies have afforded us a profound understanding of gene expression patterns in the tissue microenvironment. Recently, several prominent spatial domain identification methods have been introduced to employ both spatial and expression information for precisely deciphering tissue structures. However, existing methods only focus on information from immediate neighbors, failing to capture the mixed relationships of neighbors at various scales and learn a general mixed feature from neighbors at different distances. To this end, we propose ST-HNHG, which fuses gene expression profiles, spatial information, and morphological images for deciphering spatial domains. Specifically, the high-order neighbor feature hybrid graph convolutional network (HNHGCN) is designed to capture feature representations between neighbors at different distances and learn their linear mixing. A data augmentation module is also proposed to enhance data diversity and model robustness. The attention mechanism is also introduced to integrate the embeddings learned from morphological and expression information, obtaining the latent representation for spatial domain identification. We test ST-HNHG on two ST datasets. The results indicate that ST-HNHG outperforms most existing methods, and considering the linear mixing between neighbors at various scales is beneficial for improving the accuracy of recognizing spatial domains.

The advent and development of single-cell RNA sequencing (scRNA-seq) have provided new avenues for exploring cellular heterogeneity. Although many researchers have designed and developed efficient models to address cell heterogeneity and diversity by clustering cells into several groups, the performance of these methods may need improvement due to the characteristics of scRNA-seq data, such as high dimensionality, sparsity, and high dropout rates. In this paper, we propose a new method that couples low-rank representation (LRR) and concept factorization (CF) to learn a better clustering assignment matrix from both global and local perspectives, named SLRRGCF. Specifically, the LRR with similarity constraints based on tired random walk (TRW) can reduce the dimensionality of high-dimensional data while capturing more comprehensive global structure. At the same time, hypergraph regularization and CF are utilized to capture the local structure of the data further and directly obtain the clustering assignment matrix. We evaluated the performance of SLRRGCF on several real datasets, and comparisons with other competitive methods validated the effectiveness of our approach.

Predicting the petrophysical properties of rocks from seismic data is challenging because the relationship between petrophysical properties and their seismic response is nonlinear and multisolution. A targeted framework is presented to enhance the effectiveness of petrophysical properties prediction based on deep learning (DL) in this study. We utilize statistical rock physics and geological methods to tackle the challenge of the limited availability of labeled data. Moreover, in response to the issue of multiple solutions, we propose a multitask inversion neural network architecture for predicting petrophysical properties from multiinformation guided by a physical model. To evaluate the validity of the framework, we built a numerical model and carried out a quantitative analysis using threefold cross-validation and comparison of single trace predictive results. The framework is finally applied to a real work area of a deep tight dolomite reservoir in Southwest China, demonstrating promising prospects for practical application.

The existence of coal seams suppresses other useful information, especially the below-thin layers, and is unfavorable for delineating the target reservoirs beneath them. The matching pursuit (MP) based methods are commonly used for removing strong reflections caused by coal seams. They first decompose a seismic trace into several wavelets based on a user-defined wavelet dictionary and then separate the most similar wavelet with the coal seam. However, how to define a complete wavelet dictionary and how to maintain horizontal continuity are two unsolved issues. We propose a multi-scale attention deep learning (MSADL) model for separating and removing seismic strong reflections. First, we suggest a workflow to generate a synthetic data set for model training based on the characteristics of field data and well logs. Next, we build an MSADL model by integrating the discrete wavelet transform (DWT) and convolutional block attention module (CBAM) into the widely used Unet. After model training, we apply the well-trained MSADL model to 3D field data in the Sichuan Basin, China for the separation and removal of strong reflections and characterization of the beneath target thin layers.

In order to solve the key problems of secondary surveillance radar signal debugging on special devices such as DSP and FPGA, a parallel processing scheme of secondary surveillance radar response signal is proposed based on CPU–GPU architecture. It can effectively reduce the difficulty of code development and improve the portability of program. The parallel optimization design of each processing model of response signal is made through the characteristics of shared memory in CPU–GPU architecture to improve the processing speed of the algorithm. The proposed scheme is tested and analyzed on different graphics cards by the secondary surveillance radar Mode-5 response signal in this paper. The experimental results showed that it takes 8390.52960 us to implement the signal processing algorithm based on NVIDIA Tesla K40c graphics card, which can save 51.98% of time than NVIDIA Quadro K4200 graphics card, and makes it possible to do the real-time processing of the secondary surveillance radar signal.

Seismic attenuation estimation is a significant task for characterizing reservoirs and improving the resolution of seismic data. The logarithmic spectral ratio (LSR) is one of the widely used tools for seismic attenuation estimation. However, how to select a suitable frequency bandwidth for the LSR is a difficult task, especially for field data. Moreover, field data are often contained kinds of noises, which makes seismic attenuation estimation difficult and unstable. We proposed an enhanced LSR (ELSR) workflow to estimate seismic attenuation. First, we built a sparse S-transform (SST) to obtain a sparse time-frequency (TF) spectrum of the analyzed seismic trace. Then, based on the SST spectrum, we introduced an ELSR workflow to estimate seismic attenuation. It should be noticed that we provided an adaptive frequency band selection for the LSR. To demonstrate the effectiveness of the proposed workflow, we apply it on both synthetic and field data. Compared with the results from several traditional attenuation estimation methods, the proposed ELSR provides a more stable and more accurate attenuation estimation result and offers the potentials in improving the resolution of post-stacked seismic data.

Although there are plenty of approaches proposed for addressing seismic volumetric dip estimation, it still suffers from several limitations, for example, the expensive computation cost, the perturbations from sequence stratigraphic anomalies, and the difficulty for handling the complicated geologic structures. Recently, deep learning (DL) based models have been proposed for seismic dip estimation, which utilize seismic dips calculated by using the traditional methods as the training labels. Apparently, these DL based models can effectively improve the computational efficiency, however, it still subjects to the limitations of the traditional algorithms. We propose a multi-channel deep learning (MCDL) model for implementing seismic volumetric dip estimation, mainly including share module (SM), particular module (PM), and fused module (FM). First, we calculate seismic dips by using several traditional methods based on 3D real seismic data as the training labels, which are used to pre-train SM and PM. Then, we propose a workflow to create synthetic seismic data and ground truth dip labels, which are utilized to fine-tune SM/PM and train FM. In this way, we can obtain a DL model by considering both the features of synthetic ground truth dips and the calculated dips from real data. Moreover, we can effectively enhance the generalization ability of the MCDL by pre-training with the estimated dip volumes from real data. To demonstrate its validity and availability, we apply the MCDL to synthetic data and two 3D real seismic volumes. The qualitative and quantitative comparisons illustrate the superiority of the proposed model over the traditional methods.

Cancer genome data generally consists of multiple views from different sources. These views provide different levels of information about gene activity, as well as more comprehensive cancer information. The low-rank representation (LRR) method, as a powerful subspace clustering method, has been extended and applied in cancer data research. Although the multi-view learning methods based on low rank representation have achieved good results in cancer multi-omics analysis because they fully consider the consistency and complementarity between views, these methods have some shortcomings in mining the potential local geometry of data. In view of this, this paper proposes a new method named Multi-view Random-walk Graph regularization Low-Rank Representation (MRGLRR) to comprehensively analyze multi-view genomics data. This method uses multi-view model to find the common centroid of view. By constructing a joint affinity matrix to learn the low-rank subspace representation of multiple sets of data, the hidden information of each view is fully obtained. In addition, this method introduces random walk graph regularization constraint to obtain more accurate similarity between samples. Different from the traditional graph regularization constraint, after constructing the KNN graph, we use the random walk algorithm to obtain the weight matrix. The random walk algorithm can retain more local geometric information and better learn the topological structure of the data. What's more, a feature gene selection strategy suitable for multi-view model is proposed to find more differentially expressed genes with research value. Experimental results show that our method is better than other representative methods in terms of clustering and feature gene selection for cancer multi-omics data.

Recently, there has been a pressing need to manage high-dimensional vector data in data science and AI applications. This trend is fueled by the proliferation of unstructured data and machine learning (ML), where ML models usually transform unstructured data into feature vectors for data analytics, e.g., product recommendation. Existing systems and algorithms for managing vector data have two limitations: (1) They incur serious performance issue when handling large-scale and dynamic vector data; and (2) They provide limited functionalities that cannot meet the requirements of versatile applications.

Based on the relative researches, in order to solve the problem that the parameters of impedance gradient change medium are difficult to be optimized and generalized in different environments, an optimization method of the parameters of the impedance gradient change medium based on reinforcement learning was proposed. First, the propagation principle of sound wave in impedance gradient medium was analyzed. The sound field distribution in the medium was also studied, in order to master its acoustic characteristics. Second, the parameters of sound velocity and impedance distribution were optimized by DQN algorithm to reduce the sound reflection. Finally, the effectiveness of the proposed reinforcement learning model was verified by the traditional method. The experimental results show that the method presented in this paper was superior to the traditional method. The trained parameters are effective to reduce the acoustic reflection to a lower level.

Instantaneous seismic attributes are commonly used in assisting seismic interpretation and stratigraphy analysis. We compute the instantaneous seismic attributes using 1-D seismic traces and the corresponding quadrature (Hilbert transformed) traces. However, the 1-D seismic trace and the corresponding quadrature trace are sensitive to noise and seismic processing artifacts. To improve the lateral continuity of instantaneous seismic attributes, we propose to compute instantaneous attributes using multichannel seismic traces. Dynamic time warping (DTW) is used to align the seismic traces centered at the analysis point which mitigates the effect of structure dip on the multichannel complex seismic trace analysis (MCSTA). We fine interpolate the 1-D seismic traces to minimize the possible error in the computation of the error matrix of DTW. We also define a constraint in the backtracking of DTW to avoid severe strain (stretch or squeeze) between seismic traces. We obtain the “robust” 1-D complex seismic trace by applying Gaussian smoothing to the aligned complex seismic traces. We finally obtain instantaneous seismic attributes from the smoothed 1-D seismic trace and the corresponding quadrature trace. We show the superiority of new instantaneous attributes by applying our method to real seismic data.

Recently, Cyber-Physical-Social Systems (CPSS) have been introduced as a new information physics system, which enables personnel organizations to control physical entities in a reliable, real-time, secure, and collaborative manner through cyberspace. Moreover, with the maturity of edge computing technology, the data generated by physical entities in CPSS are usually sent to edge computing nodes for effective processing. Nevertheless, it remains a challenge to ensure that edge nodes maintain load balance while minimizing the completion time in the event of the edge node outage. Given these problems, a Unique Task Offloading Method (UTOM) for CPSS is designed in this paper. Technically, the system model is constructed firstly and then a multi-objective problem is defined. Afterward, Improving the Strength Pareto Evolutionary Algorithm (SPEA2) is utilized to generate the feasible solutions of the above problem, whose aims are optimizing the propagation time and achieving load balance. Furthermore, the normalization method has been leveraged to produce standard data and select the global optimal solution. Finally, several necessary experiments of UTOM are introduced in detail.

The deep exploration has become the focus of attention in the field of earth sciences. Coherence is a routine measure to identify structural and stratigraphic anomalies, such as faults, channels, and fractures in subsurface. However, deep seismic data typically suffer from a low signal-to-noise ratio and a weak reflection amplitude, thus it may not provide a better insight for seismic attribute analysis. The phase information has the ability to detect subtle changes in subsurface but it is sensitive to noise, thereby masking some stratigraphic features in the full-bandwidth data. To address these two issues, we propose a multispectral phase-based geosteering coherence method by combining coherence and spectral decomposition for deep stratigraphic feature characterization. The proposed method can effectively select and utilize the phase components of favorable spectral bands, which can detect different scale geologic discontinuities and reduce or avoid the effect of random noise in deep seismic data. Furthermore, corendering the coherence images of three different frequency components using red-green-blue blending can detect more geologic details in subsurface. The examples including 3-D physical modeling data and real seismic data set of carbonate reservoir from western deep formation are employed to demonstrate the effectiveness of the proposed method. The coherence attributes obtained from the proposed method can detect the weak or hidden geologic details clearer than the geosteering coherence calculated from the broadband seismic data, and it may serve as a future tool for detecting the distribution of geologic abnormalities in deep exploration.