Trajectory representation learning transforms complex spatio-temporal features of trajectories into dense, low-dimensional embeddings, enabling applications in intelligent transportation systems. With advances in this field and the availability of large-scale traffic data, intelligent urban systems have been widely deployed in major cities. However, existing methods heavily rely on large volumes of trajectory data, limiting their transferability to cities with sparse data, especially small or less-developed ones. Moreover, most current approaches learn representations within a single city, overlooking the shared travel patterns across regions and cities with similar geographic contexts. To address these issues, we propose MetaTRL, a self-supervised cross-city trajectory representation learning method based on meta-learning. Specifically, we introduce a Shared and Private Parameterized Cross-city Meta-learning Framework to support knowledge sharing and transfer across cities. We further design a Meta-knowledge Enhanced Road Segment Encoder and a Trajectory Encoder that integrates private and shared knowledge to learn and fuse spatio-temporal trajectory features. Extensive experiments on two real-world datasets and multiple downstream tasks demonstrate the significant superiority of MetaTRL over state-of-the-art baselines and achieves a remarkable average improvement of 134.66% in Macro-F1 on destination prediction task.

Trajectory representation learning transforms the complex spatio-temporal features of trajectories into a dense, low-dimensional embedding, which supports various downstream analytics tasks such as trajectory classification, travel time estimation, and similar trajectory search. Existing trajectory representation learning methods treat trajectories merely as general point sequences and use sequence models to learn the correlations between points. However, the complex spatio-temporal features of trajectories are multi-scale, meaning they are not only reflected in the correlations between trajectory points but also in the correlations between trajectory segments. Moreover, most existing methods do not sufficiently capture the multi-faceted temporal features within trajectories. To fill these gaps, we propose a novel self-supervised Trajectory$R$epresentation$L$earning model with multi-scale spatio-temporal features exploration called TrajRL. Specifically, we utilize trajectory augmentation to generate two views to achieve self-supervised pre-training exploiting multiple self-supervisory signals. In each view, we can learn the multi-scale spatio-temporal correlations both within and between road segments and road segment sequences in trajectories through the proposed multi-scale trajectory encoder. Additionally, we perform multi-faceted temporal information encoding, especially leveraging time intervals to learn multi-scale context-aware time patterns within the trajectories. Extensive experiments demonstrate the superiority of our TrajRL as compared to state-of-the-art baselines on two real-world datasets across various downstream tasks. The source code of our model is available at https://github.com/Xfc30/TrajRL.

The role of cell-cell communications (CCCs) is increasingly recognized as being important to differentiation, invasion, metastasis, and drug resistance in tumoral tissues. Developing CCC inference methods using traditional experimental methods are time-consuming, labor-intensive, cannot handle large amounts of data. To facilitate inference of CCCs, we proposed a computational framework, called CellMsg, which involves two primary steps: identifying ligand-receptor interactions (LRIs) and measuring the strength of LRIs-mediated CCCs. Specifically, CellMsg first identifies high-confident LRIs based on multimodal features of ligands and receptors and graph convolutional networks. Then, CellMsg measures the strength of intercellular communication by combining the identified LRIs and single-cell RNA-seq data using a three-point estimation method. Performance evaluation on four benchmark LRI datasets by five-fold cross validation demonstrated that CellMsg accurately captured the relationships between ligands and receptors, resulting in the identification of high-confident LRIs. Compared with other methods of identifying LRIs, CellMsg has better prediction performance and robustness. Furthermore, the LRIs identified by CellMsg were successfully validated through molecular docking. Finally, we examined the overlap of LRIs between CellMsg and five other classical CCC databases, as well as the intercellular crosstalk among seven cell types within a human melanoma tissue. In summary, CellMsg establishes a complete, reliable, and well-organized LRI database and an effective CCC strength evaluation method for each single-cell RNA-seq data. It provides a computational tool allowing researchers to decipher intercellular communications. CellMsg is freely available at https://github.com/pengsl-lab/CellMsg.

Cell-cell communications (CCCs) mediated by ligand-receptor interactions (LRIs) play a pivotal role in coordination and function of biological systems. The two primary steps of CCC inference methods are usually filtering significant LRIs and measuring the intercellular communication strength. Biological language models have demonstrated notable progress in bioinformatics across several biological problems, inspiring by the success of language models in the field of natural language processing. Here, we proposed a computational approach for CCC inference called SeqCCC. First, SeqCCC employed two sophisticated protein language models and the protein sequences of ligands and receptors to predict potential LRIs with the help of existing LRIs. Then, SeqCCC employed permutation test to filter significantly expressed LRIs from the single-cell expression matrix based on these known and predicted LRIs. Finally, SeqCCC calculated communication strength by applying the molecular diffusion and law of mass action in chemistry, and then it improves this by eliminating non-specific CCCs using a permutation test. In the results, SeqCCC yielded an average AUC of 0.93 (mean of the five folds, with a standard deviation of 0.02). The validity and reliability of SeqCCC are further supported by a comparative analysis with common CCC inference methods, which reveals high concordance in inference results. Additionally, SeqCCC provided different visualization methods for CCC results, including Circos Plot, Heatmap Plot, Dot Plot and Heatmap for LRIs. In conclusion, SeqCCC provides a new option for CCC inference by fusing biological language models with sequence information to improve our comprehension of intercellular communications.

Currently, single-cell foundation models, which are based on extensive single-cell sequencing data, are highly effective in extracting crucial biological information about genes and cells. They consistently demonstrate exceptional performance across a wide range of downstream applications. However, when it comes to spatially resolved transcriptomic data (ST), the availability of methods for inferring cell-cell communications (CCCs), in conjunction with foundation models, remains limited. In this work, we present SpaChat, which integrating a fine-tuned single-cell foundation model with a cell graph network for spatially resolved CCCs inference. SpaChat defines two scoring strategies to infer significant ligand-receptor (LR) pairs, including intracellular score based on gene-level attention of fine-tuned single-cell foundation model and inter-cellular score based on the KNN algorithm in a cell-cell graph network. On this basis, SpaChat employs the law of molecular diffusion and mass action in chemistry and the strategy of permutation test to calculate communication strengths and filter out communications with low specificity. The benchmarked performance of SpaChat on public spatial transcriptomic dataset is superior to that of existing inference methods. Furthermore, SpaChat subsequently identifies the communication patterns of specific cell types and provides a variety of options for visualizing the results of CCC analyses. In summary, SpaChat enables the inference of spatially resolved CCCs from spatial transcriptomic data, providing valuable insights into understanding CCCs in tissues.

Large deployment of wireless sensor networks in various fields bring great benefits. With the increasing volume of sensor data, traditional data collection and processing schemes gradually become unable to meet the requirements in actual scenarios. As data quality is vital to data mining and value extraction, this paper presents a distributed anomaly detection framework which combines cloud computing and edge computing. The framework consists of three major components: k-nearest neighbors, locality sensitive hashing, and cosine similarity. The traditional k-nearest neighbors algorithm is improved by locality sensitive hashing in terms of computation cost and processing time. An initial anomaly detection result is given by the combination of k-nearest neighbors and locality sensitive hashing. To further improve the accuracy of anomaly detection, a second test for anomaly is provided based on cosine similarity. Extensive experiments are conducted to evaluate the performance of our proposal. Six popular methods are used for comparison. Experimental results show that our model has advantages in the aspects of accuracy, delay, and energy consumption.

As the volume of data grows exponentially, big data brings an unprecedented burden to the current computing infrastructure. How to deal with big data efficiently and concisely and reduce the burden of computing infrastructure has always been a big challenge. Therefore, this paper proposes a high-quality core data extraction method in edge computing nodes. Firstly, heterogeneous data are fused into a unified model, the data characteristics of the original data are retained. Then, a Lanzcos-based incremental tensor decomposition method is proposed to extracted the high quality core tensor dynamically. Finally, the model algorithm is verified using real data. The experimental results show that the approximate tensor reconstructed from the tensor containing 15% of the core data can guarantee 90% accuracy. At the same time, IncLHOSVD is significantly better than non-incremental HOSVD in execution time in guaranteeing the accuracy of approximate equal error.

This paper considers the event-based containment control problem for second-order multi-agent systems. A novel event-triggered containment control protocol is proposed so as to carry out intermittent examination of the event-triggering condition at sampling instants. One important feature of the designed protocol is that only the sampled position data are used with no utilization of velocity measurements. It is shown that the realization of containment control is guaranteed under a sufficient condition which depends upon the control gains, the sampling period, and the spectrum of the Laplacian matrix among the followers. The effectiveness of the proposed event-triggered containment control protocol is demonstrated by a simulation example.

Tumor samples are usually heterogeneous, containing admixture of more than one kind of tumor subclones. Studies of genomic aberrations from heterogeneous tumor data are hindered by the mixed signal of tumor subclone cells. Most of the existing algorithms cannot distinguish contributions of different subclones from the measured single nucleotide polymorphism (SNP) array signals, which may cause erroneous estimation of genomic aberrations. Here, we have introduced a computational method, Cancer Heterogeneity Analysis from SNP-array Experiments (CHASE), to automatically detect subclone proportions and genomic aberrations from heterogeneous tumor samples. Our method is based on HMM, and incorporates EM algorithm to build a statistical model for modeling mixed signal of multiple tumor subclones. We tested the proposed approach on simulated datasets and two real datasets, and the results show that the proposed method can efficiently estimate tumor subclone proportions and recovery the genomic aberrations.

A novel multi-objective optimization based particle swarm optimization algorithm is presented to solve the global optimization problem for based services selecting in Web services composition technology. This algorithm takes Web services selection as a multi-objective constrained optimization problem with constraints. It introduces multi-objective PSO intelligent theory to optimize multi parameters simultaneously, and produces a set of constraints to meet the Pareto optimal solution. The experiments show that the algorithm is a feasible and efficient method for Web services selection.

The current trend of Web service research is towards automatic service composition. Undoubtedly, service matchmaking is one of the critical problem to achieve the intention. Previous methods concerns service matchmaking considers the problem in a context free manner , in another word, they are domain independent. Obviously, without the consideration of context information, the degree of recall and precision regarding service matchmaking can be significantly declined. In this paper, we formally analyzed the problem using description logic and present a complete solution to resolve it.

Matchmaking is one of the key issues in the field of Web services research community because it is the basis of doing service discovery and composition. Using ontology semantically express the capabilities of services, accurately match, discovery and composition service. In this paper we explore using concept and attribute of Web services to construct the ontology. Also, we present a novel technique for merging ontology between different services using concept lattice. This enables construct and merge small-scale domain ontology convenient.

The relationship between vegetation development and water condition is helpful for understanding the climate change impacts on terrestrial ecosystem, however, the relationship is so complex that it has not been studied adequately. In this paper, we choose the Normalized Difference Vegetation Index (NDVI), which is widely used for monitoring vegetation development and the Standard Precipitation Index (SPI), which is a multiple-time scale meteorological-drought index based on precipitation and took them as the proxy of vegetation vigor and moisture availability respectively. We conducted a correlation analysis on time series of monthly NDVI (1983-2002) during the growing season from May to October and the 1-month SPI of the corresponding month in the Yin mountain area of northern ectone in China. The result indicates that vegetation development is correlated with the moisture availability significantly. Moreover the correlation coefficient is seasonal changed, generally the highest correlation occurs at the beginning of the growing season. When comparing the Velocity of the NDVI change (VNDVI) and the correlations between NDVI and 1-month SPI, we found that VNDVI can impact the relationship between vegetation development and moisture availability.

This paper, based on surface energy balance theory, examined the feasibility of drought monitoring by MODIS LST product. Maximum Value Compositing method, which is widely used in cloud-contaminated-data compositing, was examined with other two land surface temperature products, that is, maximum land surface temperature deviation from air temperature and standardized thermal index (STI). All three products were calculated and correlated with soil moisture condition. And we found that STI was the best choice for drought monitoring.

Desertification, which is affecting more and more of the world, has been a major problem throughout the past decades in the north of China. Desertification and its spatio-temporal evolution information are very important to confirm stratagem and measure of desertification and implement macro-management effectively, which is helpful to promote environmental resources sustainable development. Remote sensing has been shown to be a powerful tool in monitoring and assessment desertification. This paper focuses on reviewing the theory and methodology of monitoring and assessment desertification by using high resolution remotely sensed data. On the basis of these theories and methods, the desertification in some counties at Hunshandake sandy desertification area was evaluated. The result indicates that quantitative monitoring and assessment of desertification in large area can be carried out by using high resolution remotely sensed image. It is vital to take reasonable estimation principle, establish scientific evaluation index and remotely sensed image interpreter symbol system, constitute feasible sand desertification classification project, and make use of appropriate interpreter means and technique routes. Compared with and the result from the low-resolution data, the evaluation to this method is more accurate, specific and valuable in application. The result of this method to evaluate regional desertification at Hunshandake desertification area indicates that, from 1970s, the acreage and proportion of the desertification at this area has augmented significantly, and the situation of sandy desertification is very serious.

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