A Joint Network Coding and Scheduling Algorithm in Wireless Network

Network coding (NC) is an emerging technique of packet forwarding thatencodes packets at relay node in order to increase network throughput. It is understoodthat the performance of NC is strongly dependent on the physical layer as well as theMAC layer, and greedy coding method may in fact reduce the network throughputowing to the reduction in the spatial reuse. In this paper, we propose a NC-awarescheduling method combining link aggregation to improve the network throughput byconsidering the interplay between NC and spatial reuse. Simulation resultsdemonstrate the effectiveness of our proposed link aggregation method compared withthe unicast transmission model

The global B−LB-L symmetry in the flavor-unified SU(N){\rm SU}(N) theories

We study the origin of the global $B-L$ symmetry in a class of flavor-unified theories with gauge groups of ${\rm SU}(N\geq 6)$. In particular, we focus on the ${\rm SU}(8)$ theory which can minimally embed three-generational SM fermions non-trivially. A reformulation of the third law for the flavor sector proposed by Georgi is useful to manifest the underlying global symmetries. The 't Hooft anomaly matching and the generalized neutrality conditions for Higgs fields play the key roles in defining the $B-L$ symmetry. Based on the global $B-L$ symmetry, we count the Higgs fields that can develop the VEVs and the massless sterile neutrinos in the ${\rm SU}(8)$ theory. We also prove that a global $B-L$ symmetry can always be defined in any ${\rm SU}(N\geq 6)$ theory when it is spontaneously broken to the SM gauge symmetry.Comment: 34 pages, 11 table

Deep Reinforcement Learning for Vehicular Edge Computing: An Intelligent Offloading System

The development of smart vehicles brings drivers and passengers a comfortable and safe environment. Various emerging applications are promising to enrich users' traveling experiences and daily life. However, how to execute computing-intensive applications on resource-constrained vehicles still faces huge challenges. In this article, we construct an intelligent offloading system for vehicular edge computing by leveraging deep reinforcement learning. First, both the communication and computation states are modelled by finite Markov chains. Moreover, the task scheduling and resource allocation strategy is formulated as a joint optimization problem to maximize users' Quality of Experience (QoE). Due to its complexity, the original problem is further divided into two sub-optimization problems. A two-sided matching scheme and a deep reinforcement learning approach are developed to schedule offloading requests and allocate network resources, respectively. Performance evaluations illustrate the effectiveness and superiority of our constructed system

Emergency warning messages dissemination in vehicular social networks: A trust based scheme

To ensure users' safety on the road, a plethora of dissemination schemes for Emergency Warning Messages (EWMs) have been proposed in vehicular networks. However, the issue of false alarms triggered by malicious users still poses serious challenges, such as disruption of vehicular traffic especially on highways leading to precarious effects. This paper proposes a novel Trust based Dissemination Scheme (TDS) for EWMs in Vehicular Social Networks (VSNs) to solve the aforementioned issue. To ensure the authenticity of EWMs, we exploit the user-post credibility network for identifying true and false alarms. Moreover, we develop a reputation mechanism by calculating a trust-score for each node based on its social-utility, behavior, and contribution in the network. We utilize the hybrid architecture of VSNs by employing social-groups based dissemination in Vehicle-to-Infrastructure (V2I) mode, whereas nodes' friendship-network in Vehicle-to-Vehicle (V2V) mode. We analyze the proposed scheme for accuracy by extensive simulations under varying malicious nodes ratio in the network. Furthermore, we compare the efficiency of TDS with state-of-the-art dissemination schemes in VSNs for delivery ratio, transmission delay, number of transmissions, and hop-count. The experimental results validate the significant efficacy of TDS in accuracy and aforementioned network parameters. © 2019 Elsevier Inc

LHPP promotes the intracellular reactive oxygen species accumulation and sensitivity of gastric cancer to cisplatin via JNK and p38 MAPK pathways

Background. Cisplatin is the first-line chemotherapy drug for the treatment of gastric cancer (GC) patients. However, GC patients who are resistant to cisplatin often do not benefit from it. Therefore, finding a key molecule that affects cisplatin sensitivity is expected to enhance the efficacy of cisplatin in GC treatment. Methods. The human GC cell lines SGC-7901 and BGC-823 were used. The protein chip array was used to screen the cisplatin-resistance genes from the complete response and non-complete response GC patients’ tissues, then, the differential gene expression analysis, GO function annotation analysis, and KEGG pathway enrichment analysis were performed. The GC tissue chip in the GEO database was analyzed to screen the target gene. Flow cytometry, Hoechst 33342 staining assay, Western Blot, MTT, tumor sphere formation, cell cycle, and apoptosis assays were performed to explore the effect of Phospholysine Phosphohistidine Inorganic Pyrophosphate Phosphatase (LHPP) on the apoptosis, stemness, and reactive oxygen species (ROS) accumulation of cisplatin-resistant GC cells treated with cisplatin. In vivo, the cisplatin-resistant GC cell lines transfected with pcDNA-LHPP or si-LHPP were injected subcutaneously into mice to construct GC subcutaneous xenograft GC models. Results. Based on protein chip array and bioinformatics analysis, it was found that LHPP is the core molecule in the cisplatin resistance regulatory network in GC, and its expression is down-regulated in GC cisplatin-resistant tissues and cells. In vitro and in vivo experimental results show that the up-regulated expression of LHPP is closely related to the increase in sensitivity of GC to cisplatin. Mechanically, we found that overexpression of LHPP may inhibit the activation of the JNK and p38 MAPK pathways, promote cisplatininduced ROS accumulation, suppress stemness, and enhance the sensitivity of GC to cisplatin. Conclusions. Up-regulation of LHPP may inhibit the activation of the JNK and p38 MAPK pathways, attenuate stemness, and enhance the accumulation of intracellular ROS, thereby promoting cisplatin-mediated GC cell apoptosis and enhancing cisplatin sensitivity

Trustworthy Edge Machine Learning: A Survey

The convergence of Edge Computing (EC) and Machine Learning (ML), known as Edge Machine Learning (EML), has become a highly regarded research area by utilizing distributed network resources to perform joint training and inference in a cooperative manner. However, EML faces various challenges due to resource constraints, heterogeneous network environments, and diverse service requirements of different applications, which together affect the trustworthiness of EML in the eyes of its stakeholders. This survey provides a comprehensive summary of definitions, attributes, frameworks, techniques, and solutions for trustworthy EML. Specifically, we first emphasize the importance of trustworthy EML within the context of Sixth-Generation (6G) networks. We then discuss the necessity of trustworthiness from the perspective of challenges encountered during deployment and real-world application scenarios. Subsequently, we provide a preliminary definition of trustworthy EML and explore its key attributes. Following this, we introduce fundamental frameworks and enabling technologies for trustworthy EML systems, and provide an in-depth literature review of the latest solutions to enhance trustworthiness of EML. Finally, we discuss corresponding research challenges and open issues.Comment: 27 pages, 7 figures, 10 table