The Internet of Things(IoTs)has become an essential component of the 5th Generation(5G)network and beyond,accelerating the transition to digital *** increasing signaling traffic generated by billions of IoT devices ha...
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The Internet of Things(IoTs)has become an essential component of the 5th Generation(5G)network and beyond,accelerating the transition to digital *** increasing signaling traffic generated by billions of IoT devices has placed significant strain on the 5G Core network(5GC)control *** address this issue,the 3rd Gener-ation Partnership Project(3GPP)first proposed a Service-Based Architecture(SBA),intending to create a flexible,scalable,and agile cloud-native ***,considering the coupling of protocol states and functions,there are still many challenges to fully utilize the benefits of the cloud computing and orchestrate the 5GC in a cloud-native *** propose a Message-Level StateLess Design(ML-SLD)to provide a cloud-native 5GC from an architectural standpoint in this ***,we propose an innovative mechanism for servitization of the N2 interface to maintain the connection between Radio Access network(RAN)and the 5GC,avoiding interruptions and dropouts of large-scale user ***,we propose an On-demand Message Forwarding(OMF)al-gorithm to reduce the impact of cloud fluctuations on the performance of cloud-native ***,we create a prototype that is based on the OpenAirInterface(OAI)5G core network projects,with all network Functions(NFs)packaged in dockers and deployed in a kubernetes-based cloud *** experiments have been built with UERANSIM and Chaosblade simulation *** findings demonstrate the viability and efficiency of our proposed methods.
Contrastive Learning-based models have shown impressive performance in text-image retrieval tasks. However, when applied in video retrieval, traditional contrastive learning strategies have faced challenges in achievi...
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Diffusion models have demonstrated remarkable success in generating continuous data, such as images and audios. Previous studies on text generation employing continuous diffusion models have revealed the potential of ...
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Cybertwin-enabled 6th Generation(6G)network is envisioned to support artificial intelligence-native management to meet changing demands of 6G ***-Agent Deep Reinforcement Learning(MADRL)technologies driven by Cybertwi...
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Cybertwin-enabled 6th Generation(6G)network is envisioned to support artificial intelligence-native management to meet changing demands of 6G ***-Agent Deep Reinforcement Learning(MADRL)technologies driven by Cybertwins have been proposed for adaptive task offloading ***,the existence of random transmission delay between Cybertwin-driven agents and underlying networks is not considered in related works,which destroys the standard Markov property and increases the decision reaction time to reduce the task offloading strategy *** order to address this problem,we propose a pipelining task offloading method to lower the decision reaction time and model it as a delay-aware Markov Decision Process(MDP).Then,we design a delay-aware MADRL algorithm to minimize the weighted sum of task execution latency and energy ***,the state space is augmented using the lastly-received state and historical actions to rebuild the Markov ***,Gate Transformer-XL is introduced to capture historical actions'importance and maintain the consistent input dimension dynamically changed due to random transmission ***,a sampling method and a new loss function with the difference between the current and target state value and the difference between real state-action value and augmented state-action value are designed to obtain state transition trajectories close to the real *** results demonstrate that the proposed methods are effective in reducing reaction time and improving the task offloading performance in the random-delay Cybertwin-enabled 6G networks.
Micro-bending with random curvature is usually used to model the mode coupling in few mode fiber (FMF). However, previous models overlooked the accompanied mode dependent loss (MDL) and neglected the order constraints...
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In this paper, we present the method proposed by our team for Track 2 of NLPCC 2023 Shared Task 7, which focuses on the extraction of paragraph-level and whole essay topic sentences in middle school student ...
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A generalized non-affine nonlinear power system model is presented for a single machine bus power system with a Static Var Compensator(SVC)or State Var system(SVS)for hybrid Unmanned Aerial Vehicles(UAVs).The model is...
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A generalized non-affine nonlinear power system model is presented for a single machine bus power system with a Static Var Compensator(SVC)or State Var system(SVS)for hybrid Unmanned Aerial Vehicles(UAVs).The model is constructed by differential algebraic equations on the MATlab-Simulink platform with the programming technique of its *** the inverse system method and the Linear Quadratic Regulation(LQR),an optimized SVC controller is *** simulations under three fault conditions show that the proposed controller can effectively improve the power system transient performance.
Diffusion models have demonstrated remarkable success in generating continuous data, such as images and audios. Previous studies on text generation employing continuous diffusion models have revealed the potential of ...
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ISBN:
(纸本)9798400708688
Diffusion models have demonstrated remarkable success in generating continuous data, such as images and audios. Previous studies on text generation employing continuous diffusion models have revealed the potential of the diffusion framework. However, challenges like embedding collapse persist, limiting the overall generation performance. In this paper we introduce LDSeq, a latent diffusion framework employing a two-stage training procedure for sequence-to-sequence text generation. In the proposed framework, we first train a Variational Auto-Encoder (VAE) on downstream datasets to compress the target text of samples into a continuous latent space, and then we train a conditional latent diffusion model in the fixed continuous latent space, where the latent vectors are iteratively sampled conditioned on the input source text. The disjoint training stages prevent the collapse of diffusion space. Experimental results on paraphrase generation and text summarization datasets show that LDSeq achieves comparable or superior performance in comparison to AR and NAR baselines while requiring lower training cost. Furthermore, We discuss some potential future directions for enhancing diffusion models in the text generation domain.
Contrastive Learning-based models have shown impressive performance in text-image retrieval tasks. However, when applied in video retrieval, traditional contrastive learning strategies have faced challenges in achievi...
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ISBN:
(纸本)9798400708688
Contrastive Learning-based models have shown impressive performance in text-image retrieval tasks. However, when applied in video retrieval, traditional contrastive learning strategies have faced challenges in achieving satisfactory results due to redundancy of video contents. We discern several potential reasons: (1)Current methodologies sometimes overlook the significant information imbalance between videos and query text, specifically neglecting the in-depth textual representation of the content within the videos. (2) Current video matching methodologies typically focus on cross-model alignment at general entity similarity level, without specific consideration for how entity pair preferences and similarity properties affect the task at hand. (3) Previous vectorized retrieval based on video content features have been somewhat flawed. They primarily focused on aligning overall features without having an video content tags feature for meaningful feature discrimination. Considering the shortcomings identified in the mentioned three aspects, we propose an ontology semantic labels augments retrieval model and introduce a method to integrate video ontology semantic labels into the contrastive learning framework. In particular, we have developed ontology semantic descriptions about entities encompassing both human figures and textual elements within the videos. Subsequently, we conducted training and testing on the CMIVQA dataset to assess the performance of our approach. The experimental results show that employing fine-grained ontology labels as sample pairs for contrastive learning leads to an increased level of precision in video retrieval tasks.
Reasoning and knowledge-related skills are considered as two fundamental skills for natural language understanding (NLU) tasks such as machine reading comprehension (MRC) and natural language inference (NLI). However,...
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