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Abstract To address the challenges posed by imbalanced and limited battlefield data, which typically results in complex models prone to overfitting during training, we introduce a novel diffusion model grounded in Wasserstein distance (WDiffusion) tailored for the multi-categorical and multivariate characteristics inherent to intent recognition data. Subsequently, we propose a streamlined tactical intent recognition framework predicated on gate recurrent unit (GRU), designed to enhance model responsiveness, and train it on an innovative dataset. Comparative experimental analyses corroborate that the synthetic data generated via WDiffusion significantly outperforms other prevalent generation models. Furthermore, the WDiffusion-GRU model achieves a recognition accuracy of 97.09%, surpassing current aerial target intent recognition models by more than 1.07%. This advancement maintains high recognition precision while substantially curtailing model parameters, thereby amplifying the agility and reliability of battlefield commanders’ decision-making processes.

Abstract Checkpoint kinase 1 (CHK1), a key effector in the cellular response to DNA lesions, is a crucial component of all cell cycle checkpoints. Recent reports have revealed that CHK1 is highly expressed in numerous cancer types in the clinical settings. However, the mechanisms underlying the regulation of CHK1 expression in tumor cells remain unclear. Here, we report that CHK1 is negatively regulated by the bromodomain-containing protein 7 (BRD7). Specifically, BRD7 silencing increased CHK1 (but not CHK2) expression at both mRNA and protein levels, in a p53-independent manner in multiple tumor cell lines. Furthermore, BRD7 silencing stabilized CHK1 via reducing its ubiquitination. Mechanistically, BRD7 knockdown not only increased the levels of USP1, a deubiquitinase for CHK1, but also promoted the interaction between CHK1 and USP1, subsequently enhancing the de-ubiquitination of CHK1. USP1 knockdown abrogated BRD7 silencing-induced CHK1 induction. Biologically, the increased expression of CHK1 in tumor cells caused by BRD7 silencing significantly increased cell sensitivity to CHK1 inhibitors by enhancing tumor cell apoptosis, and this effect was reversed by the simultaneous knockdown of CHK1 or USP1. Taken together, our findings suggest that BRD7 is a potential genetic or drug target that may help to improve the efficacy of chemotherapeutic drugs targeting CHK1 in combinatorial therapy.

Multi-resource allocation is ubiquitous in datacenters. Usually, the datacenter scheduler associates each user with a utility function and then allocates multiple resources so as to maximize the social welfare, which is the sum of the utilities of the users. We refer to this problem as the Social Welfare Maximization (SWM) problem. Meanwhile, given the dynamic nature of datacenters, the scheduler is supposed to support non-preemptive scheduling, where jobs of users that are being processed would not be interrupted by the reconfiguration of resources. Non-preemptive solutions to SWM incorporating pricing considerations include dynamic pricing and bidding/auctions. However, both strategies are quite complex. Meanwhile, traditional Lagrange solutions to SWM does not support non-preemptive job scheduling. In this paper, we propose Adaptive Dominant Resource Fairness (ADRF), that provides near-optimal social welfare where each user is assigned an α-fair utility function and when 1

STAT1 is a critical transcription factor for regulating host antiviral defenses. STAT1 activation is largely dependent on phosphorylation at tyrosine 701 site of STAT1 (pY701-STAT1). Understanding how pY701-STAT1 is regulated by intracellular signaling remains a major challenge. Here we find that pY701-STAT1 is the major form of ubiquitinated-STAT1 induced by interferons (IFNs). While total STAT1 remains relatively stable during the early stages of IFNs signaling, pY701-STAT1 can be rapidly downregulated by the ubiquitin-proteasome system. Moreover, ubiquitinated pY701-STAT1 is located predominantly in the nucleus, and inhibiting nuclear import of pY701-STAT1 significantly blocks ubiquitination and downregulation of pY701-STAT1. Furthermore, we reveal that the deubiquitinase USP2a translocates into the nucleus and binds to pY701-STAT1, and inhibits K48-linked ubiquitination and degradation of pY701-STAT1. Importantly, USP2a sustains IFNs-induced pY701-STAT1 levels, and enhances all three classes of IFNs- mediated signaling and antiviral activity. To our knowledge, this is the first identified deubiquitinase that targets activated pY701-STAT1. These findings uncover a positive mechanism by which IFNs execute efficient antiviral signaling and function, and may provide potential targets for improving IFNs-based antiviral therapy.

This paper aims at minimizing the communication cost for collecting flow information in Software Defined Networks (SDN). Since flow-based information collecting method requires too much communication cost, and switch-based method proposed recently cannot benefit from controlling flow routing, jointly optimize flow routing and polling switch selection is proposed to reduce the communication cost. To this end, joint optimization problem is formulated as an Integer Linear Programming (ILP) model firstly. Since the ILP model is intractable in large size network, we also design an optimal algorithm for the multi-rooted tree topology and an efficient heuristic algorithm for general topology. According to extensive simulations, it is found that our method can save up to 55.76% communication cost compared with the state-of-the-art switch-based scheme.

Although timely access to information is becoming increasingly important and gaining such access is no longer a problem, the capacity for humans to assimilate such huge amounts of information is limited. Topic Detection(TD) is then a promising research area that addresses speedy access of desired information. However, ironically, the time complexity of existing TD algorithms themselves is usually up to the -th power of . Linear performance requirement of real world topic detection has not been significantly addressed. This paper reveals a new patented topic detection algorithm called that combines elevance odel with nformation etrieval technique to improve on time efficiency. Relevance Model(RM) is a theoretical extension of statistical language modeling that was developed for the task of document retrieval. To reduce the costs of fetching RM, we reduce the number of comparisons for stories by a query-based approach that makes similar stories exist in the top-k query results. We also build our query based on inverted indices, which have the complexity close to linear. The time cost of rest of operations in the topic detection process is a constant. Hence, the total complexity of topic detection algorithm should be close to linear as shown in experimental results. In addition, also gains better detection rates and robustness by relative entropy based topic model design

INTRODUCTION: Distance network teaching has been widely used in today's informationization era, and it also provides a new teaching mode for ideological and political education. However, evaluating the quality of ideological and political distance network teaching still needs to be solved. OBJECTIVES: To solve the problem of subjective solid dependence in its evaluation, the hierarchical analysis method and entropy weighting method are used to calculate the subjective and objective weights, and the principle of minimum information identification is used to determine the optimal comprehensive consequences of the indicators, taking into account the absolute subjectivity and absolute objectivity brought about by the calculation method of the weights of the hands. METHODS: Considering the incompatibility of grade determination in the evaluation results, the evaluation grade determination of Civics remote network teaching is completed through the table evaluation model, and the table evaluation model of Civics remote network teaching based on the principle of minimum information discrimination with combined weights is finally established, and the application of the model is carried out. RESULTS: The evaluation model is simple and practical and improves the efficiency and operability of comprehensive evaluation. CONCLUSION: Civics remote network teaching will further broaden the sharing and dissemination channels of educational resources and needs to improve the quality and effect of education continuously, and the study has a good application prospect for the evaluation of Civics remote network teaching. [ABSTRACT FROM AUTHOR]