Unmanned aerial vehicle (UAV)-assisted ground robot networks (UGRNets) are playing an increasingly critical role in a wide range of time-sensitive and mission-critical applications, such as environmental monitoring, infrastructure inspection, and emergency response. UGRNets require not only low-latency communication but also high spatial awareness to ensure effective coordination and decision-making. This paper proposes a unified spatiotemporal framework that evaluates and enhances the quality of updated information in UGRNets from both temporal and spatial dimensions. On the temporal side, we develop a martingale-theory-based prediction method for the delay violation probability bound (DVPB), coupled with a novel joint decay rate model to accurately characterize latency violations in heterogeneous multi-hop communication UGRNets. On the spatial side, we introduce the use of Wasserstein distance to quantify and improve the spatial completeness of robotic coverage. By integrating these metrics, we formulate a spatiotemporal optimization problem that jointly minimizes DVPB and maximizes spatial completeness, enabling robotic agents to adapt their information collection strategies accordingly. Numerical results demonstrate that the proposed framework significantly improves information timeliness and spatial completeness in heterogeneous and dynamic UGRNets scenarios, thereby providing practical insights for real-world deployment.

Reliable and ultra-low-latency communication is essential for multiagent communication involving autonomous ground robots and unmanned aerial vehicles (UAVs). These mobile platforms form dynamic, multi-hop heterogeneous networks where timely delivery of critical information, such as health status or hazard detection, is vital. While average delay is commonly used, it fails to reflect the risk of rare but critical delay violations, which delay violation probability bound (DVPB) quantifies and helps predict for better planning and control. In this paper, we propose a martingale-based framework to predict the DVPB in the UAV-assisted ground robot communication networks. We specifically introduce a joint decay rate derivation method and define a stability condition to derive closed-form expressions for end-to-end DVPB in multi-hop heterogeneous networks. Simulation results demonstrate that the proposed method significantly outperforms conventional moment generating function in stochastic network calculus (MGF SNC) approaches under various network loads, hop counts, and data types. The proposed martingale-based DVPB offers accurate and reliable delay guarantees for real-world emergency communication networks.

Satellite networks hold great promise for future 6G communications because of their benefits such as wide coverage and large capacity. Since the end-to-end (e2e) queuing delay is regarded as one key factor affecting the quality of service (QoS) in satellite networks, accurate delay prediction is a critically important topic. However, the delay prediction is complicated due to the irregular and time-varying features of inter-satellite links (ISLs) and satellite-ground links (SGLs), such as discontinuity and alternation. In this paper, we propose to establish the multi-node satellite-to-ground communication procedure as a multi-hop tandemly queuing model and present a heterogeneous heterogeneous multi-hop martingale model for queuing delay analysis. Due to the unique time-varying characteristics, we propose to model the SGL and ISL services as the stationary Markov processes using the Markov chain Monte Carlo approach. To match the intermittency and burstiness of traffic, the data arrival and service processes are handled using the Markov process. We propose to use a scaling factor for reflecting the heterogeneity of data processing capability, and then present a novel approach to ensure the stability condition requirement of the multi-hop system. Using the multi-hop heterogeneous martingale approach, the tight upper bounds of the delay and backlog in heterogeneous links are derived, and then precise delay prediction can be obtained. Finally, numerous simulations are conducted to demonstrate the effectiveness and accuracy of the proposed martingale method in analyzing the system delay and backlog when compared to the existing stochastic network calculus method.

Finding a desirable sampling estimator has a profound impact on the development of static word embedding models, such as continue-bag-of-words (CBOW) and skip gram (SG), which have been generally accepted as popular low-resource algorithms to generate task-agnostic word representations. Due to the prevalence of large-scale pretrained models, less attention has been paid to these static models in the recent years. However, compared with the dynamic embedding models (e.g., BERT), these static models are straightforward to interpret, cost effective to train, and out-of-box to deploy, thus are still widely used in various downstream models until now. Therefore, it is still of considerable significance to study and improve them, especially the crucial components shared by these static models. In this article, we focus on negative sampling (NS), a key component shared by the sampling-based static models, by investigating and mitigating some critical problems of the sampling core. Concretely, we propose Seeds, a sampling enhanced embedding framework, to learn static word embeddings by a new algorithmic innovation for replacing the NS estimator, in which multifactor global priors are considered dynamically for different training pairs. Then, we implement this framework by four concrete models. For the first two implementations, namely CBOW-GP and SG-GP, both negative words and positive auxiliaries are sampled. And for the other two implementations, CBOW-GN and SG-GN, estimations are simplified by sampling only the negative instances. Extensive experimental results across a variety of standard intrinsic and extrinsic tasks demonstrate that embeddings learned by the proposed models outperform their NS-based counterparts, such as CBOW-NS and SG-NS, as well as other strong baselines.

Simple linear models, which usually learn word-level representations that are later combined to form document representations, have recently shown impressive performance. To improve the performance of document-level classification, it is crucial to explore the factors affecting the quality of the document vector. In this paper, we propose the concept of containers and further explore the properties of word containers and document containers by experiments and theoretical demonstrations. We find that the document container has a fixed capacity and that the document vector obtained by a simple average of too many word embeddings undoubtedly cannot be fully loaded by the container and will lose some semantic and syntactic information on very large text datasets. We also propose an efficient approach for document representation, using clustering algorithms to divide a document container into several subcontainers and establishing the relationship between the subcontainers. We additionally report and discuss the properties of two methods of clustering algorithms, DVEM-Kmeans and DVEM-Random, on large text datasets by sentiment analysis and topic classification tasks. Compared to simple linear models, the results show that our models outperform the existing state-of-the-art in generating high-quality document representations for document-level classification relatedness tasks. Our approaches can also be introduced to other models based on neural networks, such as convolutional neural networks, recurrent neural networks and generative adversarial networks, in supervised or semisupervised settings.

BACKGROUND: Inferring gene regulatory networks (GRNs) from gene expression data remains a challenge in system biology. In past decade, numerous methods have been developed for the inference of GRNs. It remains a challenge due to the fact that the data is noisy and high dimensional, and there exists a large number of potential interactions. RESULTS: We present a novel method, namely priori-fused boosting network inference method (PFBNet), to infer GRNs from time-series expression data by using the non-linear model of Boosting and the prior information (e.g., the knockout data) fusion scheme. Specifically, PFBNet first calculates the confidences of the regulation relationships using the boosting-based model, where the information about the accumulation impact of the gene expressions at previous time points is taken into account. Then, a newly defined strategy is applied to fuse the information from the prior data by elevating the confidences of the regulation relationships from the corresponding regulators. CONCLUSIONS: The experiments on the benchmark datasets from DREAM challenge as well as the E.coli datasets show that PFBNet achieves significantly better performance than other state-of-the-art methods (Jump3, GEINE3-lag, HiDi, iRafNet and BiXGBoost).

BACKGROUND: Inferring the topology of gene regulatory networks (GRNs) from microarray gene expression data has many potential applications, such as identifying candidate drug targets and providing valuable insights into the biological processes. It remains a challenge due to the fact that the data is noisy and high dimensional, and there exists a large number of potential interactions. RESULTS: We introduce an ensemble gene regulatory network inference method PLSNET, which decomposes the GRN inference problem with p genes into p subproblems and solves each of the subproblems by using Partial least squares (PLS) based feature selection algorithm. Then, a statistical technique is used to refine the predictions in our method. The proposed method was evaluated on the DREAM4 and DREAM5 benchmark datasets and achieved higher accuracy than the winners of those competitions and other state-of-the-art GRN inference methods. CONCLUSIONS: Superior accuracy achieved on different benchmark datasets, including both in silico and in vivo networks, shows that PLSNET reaches state-of-the-art performance.