Mobile Edge Computing (MEC) utilizes edge servers to offload the computational burden from cloud infrastructure. By providing low-latency and high-bandwidth services, MEC enables mobile users and IoT devices to efficiently offload and execute computational tasks at the network edge. However, optimizing communication and computational resources in a multi-user, multi-server MEC environment remains a significant challenge. In this paper, we propose TSAJS, an efficient multi-server joint task scheduling scheme designed to enhance the effectiveness of MEC offloading. We model the task offloading and resource allocation problem as a Mixed-Integer Nonlinear Programming (MINLP) problem, aiming to maximize user offloading gain by minimizing task completion time and energy consumption. A heuristic algorithm for offloading is introduced by combining threshold-triggering and simulated annealing to effectively avoid local optima and converge toward the global optimum. Meanwhile, the optimal solution for resource allocation is derived using the Karush-Kuhn-Tucker (KKT) conditions. Experimental results demonstrate that TSAJS delivers near-optimal performance, outperforming traditional methods in terms of user offloading effectiveness. Its efficiency enables solution finding within polynomial time, while also adapting to the preferences of users and service providers.

Although programmed death (PD)-1 inhibitors inhibitors have been clinically approved for the treatment of lung adenocarcinoma (LUAD), only a few patients benefit from anti-PD-1 therapy. We developed a semi-supervised biological sparse neural network (sBiosNet) based on transfer learning to fully utilize labeled and unlabeled patient data. The pathways from the Reactome database were used to sparse the sBiosNet and extract associated biological features by integrating patients' genomic mutations and copy number variation data. We assessed the performance of the sBiosNet against random forest and support vector machine using four cohorts and provided clear interpretations using the DeepLIFT algorithm. The sBiosNet achieved the best prediction with an area under the receiver operating characteristic curve (AUROC) of 0.888 and an area under the precision recall curve (AUPR) of 0.919 for responders versus non-responders on the validation cohort, and AUROC of 0.853 and AUPR of 0.894 on an independent external cohort. The ablation experiments demonstrated that biological sparsification and multi-omics data integration, transfer learning and semi-supervised learning all contributed to improving the sBiosNet's performance. We further confirmed that genes (such as TP53, FGF3, FGFR4, and EGFR) affected LUAD patients' response to PD-1 inhibitors by regulating pathways. Meanwhile, the Low-risk LUAD patients identified by the sBiosNet obtained significant longer overall survival and progression-free survival with anti-PD-1 therapy. In conclusion, the sBiosNet accurately predicts the response and survival of patients on anti-PD-1 therapy to reduce unnecessary treatment in non-responders.

Gesture is considered as an important approach to implement natural human-machine interaction, but the existing online gesture detection methods still suffer from the problem of unsatisfactory detection rate and false trigger rate that may lead to failed interactions. In this paper, we propose an improved Multi-View and Multi-Task(MVMT) model called Multi-View and Multi-Scale Fusion Network(MVMSFN) for the problem of inaccurate gesture segmentation from the background data. Firstly, for the adaptive feature fusion, we construct Multi-View Fusion Model(MVFM), which introduces spatial attention weight matrix to learn to enhance information interaction among multi-view branches, and Multi-Scale Fusion Model(MSFM), which extracts correlations among features from different layers with different scales to mitigate the effect of inconsistent gesture lengths. Then we design Decoupled Gesture Progress Module (DGPM), which sets up auxiliary tasks that enable the network to predict the execution progression of static and dynamic gestures respectively, to address the network performance degradation problem caused by applying uniform gesture modeling in the heterogeneous gesture environment(including dynamic and static gestures) where there is great difference in representation between the two main gesture categories. The experimental results on the latest benchmark show that MVMSFN achieves the best performance in metrics, in which the false positive score outperforms the state-of-the-art approach(OO-dMVMT). In summary, MVMSFN provides higher accuracy of gesture segmentation and lower false trigger rate while maintaining relatively low computational time for real-time applications. The code is available at https://github.com/miixcc/MVMSFN-Gesture.

Causal inference in the field of infectious disease attempts to gain insight into the potential causal nature of an association between risk factors and diseases. Simulated causality inference experiments have shown preliminary promise in improving understanding of the transmission of infectious diseases but still lack sufficient quantitative causal inference studies based on real-world data. Here, we investigate the causal interactions between three different infectious diseases and related factors, using causal decomposition analysis, to characterize the nature of infectious disease transmission. We show that the complex interactions between infectious disease and human behavior have a quantifiable impact on transmission efficiency of infectious diseases. Our findings, by shedding light on the underlying transmission mechanism of infectious diseases, suggest that causal inference analysis is a promising approach to determine epidemiological interventions.

This paper presents RealTIS, a Real-time emergent Topic Identification System for user-generated content on the web via social networking services such as Twitter, Weibo, and Facebook. Without user intervention, our proposed RealTIS system can efficiently collect necessary social media posts, construct a quality topic summarization from the vast sea of data, and then automatically identify whether the emerging topics will be out-breaking or just fading into silence. RealTIS uses a time-sliding window to compute the statistics about the basic structure (motifs) variation of the propagation network for a specific topic. These statistics are then used to predict unusual shifts in correlations, make early warning and detect outbreak. Besides, this work also illustrates the mechanism by which our proposed system makes early warning happen.

An extended greatest common divisor (GCD) algorithm for parametric univariate polynomials is presented in this paper. This algorithm computes not only the GCD of parametric univariate polynomials in each constructible set but also the corresponding representation coefficients (or multipliers) for the GCD expressed as a linear combination of these parametric univariate polynomials. The key idea of our algorithm is that for non-parametric case the GCD of arbitrary finite number of univariate polynomials can be obtained by computing the minimal Gröbner basis of the ideal generated by those polynomials. But instead of computing the Gröbner basis of the ideal generated by those polynomials directly, we construct a special module by adding the unit vectors which can record the representation coefficients, then obtain the GCD and representation coefficients by computing a Gröbner basis of the module. This method can be naturally generalized to the parametric case because of the comprehensive Gröbner systems for modules. As a consequence, we obtain an extended GCD algorithm for parametric univariate polynomials. More importantly, we apply the proposed extended GCD algorithm to the computation of Smith normal form, and give the first algorithm for reducing a univariate polynomial matrix with parameters to its Smith normal form.