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ML-SLD:A message-level stateless design for cloud-native 5G core network

Digital Communications and networks 2023年第3期9卷 743-756页

作者： Keliang Du Luhan Wang Xiangming Wen Yu Liu Haiwen Niu Shaoxin Huang Beijing Laboratory of Advanced Information Networks China Beijing Key Lab of Network System Architecture and Convergence China School of Information and Communication Engineering Beijing Univ.of Posts&TelecomBeijing100876China

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.

关键词： Cloud-native 5G core network service Based architecture Stateless OpenAirInterface

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LDSeq: Latent Diffusion Models for Sequence to Sequence Text Generation 7

LDSeq: Latent Diffusion Models for Sequence to Sequence Text...

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7th International Conference on Computer Science and Artificial Intelligence, CSAI 2023

作者： Ding, Yizhou Tian, Jidong Mei, Shanxing Zhou, Yifan Dong, Yuwu He, Hao Hu, Weisheng State Key Lab of Advanced Optical Communication System and Network Shanghai Jiao Tong University Shanghai China

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. © 2023 ACM.

关键词： Diffusion

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Ontology-Semantic Alignment On Contrastive Video-Language Model for Multimodel Video Retrieval Task 7

Ontology-Semantic Alignment On Contrastive Video-Language Mo...

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7th International Conference on Computer Science and Artificial Intelligence, CSAI 2023

作者： Zhou, Yifan Ding, Yizhou Dong, Yuwu He, Hao Shanghai Jiao Tong University State Key Lab of Advanced Optical Communication System and Network Shanghai China

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. © 2023 ACM.

关键词： Ontology

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A pipelining task offloading strategy via delay-aware multi-agent reinforcement learning in Cybertwin-enabled 6G network

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Digital Communications and networks 2025年第1期11卷 92-105页

作者： Haiwen Niu Luhan Wang Keliang Du Zhaoming Lu Xiangming Wen Yu Liu Beijing Laboratory of Advanced Information Networks Beijing Univ.of Posts&TelecomBeijing 100876China Beijing Key Lab of Network System Architecture and Convergence Beijing Univ.of Posts&TelecomBeijing 100876China School of Information and Communication Engineering Beijing Univ.of Posts&TelecomBeijing100876China

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.

关键词： Cybertwin Multi-Agent Deep Reinforcement Learning(MADRL) Task offloading Pipelining Delay-aware

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Micro-Bending Influence on Mode-Dependent Loss 15

Micro-Bending Influence on Mode-Dependent Loss

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15th IEEE International Conference on advanced Infocomm Technology, ICAIT 2023

作者： Liang, Junpeng Qiao, Gang Wei, Jinlong Li, Juhao Yang, Yu Xu, Zhaopeng Liu, Lulu Wang, Shangcheng Wu, Qi Hu, Weisheng Peng Cheng Laboratory Dept. of Circuits and System Shenzhen China Peking University State Key Lab of Advanced Optical Communication Systems and Network Beijing China

ISBN: (纸本)9798350314120

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 of coupling loss between different modes. In this work, we calculate out the accompanied loss of each mode by using the finite element method. The results show that MDL always increases with bending curvature due to the high-order modes have larger loss than low-order modes. This observation has been confirmed by experiments. Finally, we compare the multi-segment FMFs model with and without considering the accompanied MDL, the results show that micro-bending would degrade the system performance especially when considering the accompanied MDL. © 2023 IEEE.

关键词： MEMS

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Two-Stage Topic Sentence Extraction for Chinese Student Essays 12th

Two-Stage Topic Sentence Extraction for Chinese Student Ess...

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12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023

作者： Dong, Yuwu Zheng, Feiran Chen, Hongyu Ding, Yizhou Zhou, Yifan He, Hao MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China State Key Lab of Advanced Optical Communication System and Network Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9783031446986

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 essays. This paper proposes a two-stage topic sentence extraction framework for each paragraph and the whole essay. In the first stage, we extract topic sentences for each paragraph, considering local semantic and contextual aspects. In the second stage, we derive the text topic sentence for the whole essay from the extracted paragraph-level topic sentences. Compared with the one-stage method, the two-stage method which can focus on the local semantic information of paragraphs related to the task has advantages in paragraph and full-text topic sentence extraction. Comparative experiments show that the extraction performance of the fine-tuned two-stage topic sentence extraction framework surpasses the few-shot large language models (GPT-3.5 et al.). The final comprehensive index also achieved the first-place result in this track. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantics

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LDSeq: Latent Diffusion Models for Sequence to Sequence Text Generation 23

LDSeq: Latent Diffusion Models for Sequence to Sequence Text...

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Proceedings of the 2023 7th International Conference on Computer Science and Artificial Intelligence

作者： Yizhou Ding Jidong Tian Shanxing Mei Yifan Zhou Yuwu Dong Hao He Weisheng Hu State Key Lab of Advanced Optical Communication System and Network Shanghai Jiao Tong University China

ISBN: (纸本)9798400708688

关键词： Diffusion probabilistic models Latent variable models Sequence-to-sequence text generation

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Ontology-Semantic Alignment On Contrastive Video-Language Model for Multimodel Video Retrieval Task 23

Ontology-Semantic Alignment On Contrastive Video-Language Mo...

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Proceedings of the 2023 7th International Conference on Computer Science and Artificial Intelligence

作者： Yifan Zhou Yizhou Ding Yuwu Dong Hao He Shanghai Jiao Tong University State Key Lab of Advanced Optical Communication System and Network China

ISBN: (纸本)9798400708688

关键词： Multimodal alignment Ontology description Video content understanding

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To What Extent Do Natural Language Understanding Datasets Correlate to Logical Reasoning? A Method for Diagnosing Logical Reasoning 29

To What Extent Do Natural Language Understanding Datasets Co...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Li, Yitian Tian, Jidong Chen, Wenqing Fan, Caoyun He, Hao Jin, Yaohui MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China State Key Lab of Advanced Optical Communication System and Network Shanghai Jiao Tong University China

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, it is not clear to what extent an NLU task defined on a dataset correlates to a specific NLU skill. On the one hand, evaluating the correlation requires an understanding of the significance of the NLU skill in a dataset. Significance judges whether a dataset includes sufficient material to help the model master this skill. On the other hand, it is also necessary to evaluate the dependence of the task on the NLU skill. Dependence is a measure of how much the task defined on a dataset depends on the skill. In this paper, we propose a systematic method to diagnose the correlations between an NLU dataset and a specific skill, and then take a fundamental reasoning skill, logical reasoning, as an example for analysis. The method adopts a qualitative indicator to indicate the significance while adopting a quantitative indicator to measure the dependence. We perform diagnosis on 8 MRC datasets (including two types) and 3 NLI datasets and acquire intuitively reasonable results. We then perform the analysis to further understand the results and the proposed indicators. Based on the analysis, although the diagnostic method has some limitations, it is still an effective method to perform a basic diagnosis of the correlation between the dataset and logical reasoning skill, which also can be generalized to other NLU skills. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

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Hypothesis Testing Prompting Improves Deductive Reasoning in Large Language Models

arXiv

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arXiv 2024年

作者： Li, Yitian Tian, Jidong He, Hao Jin, Yaohui MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China State Key Lab of Advanced Optical Communication System and Network China

Combining different forms of prompts with pre-trained large language models has yielded remarkable results on reasoning tasks (e.g. Chain-of-Thought prompting). However, along with testing on more complex reasoning, these methods also expose problems such as invalid reasoning and fictional reasoning paths. In this paper, we develop Hypothesis Testing Prompting, which adds conclusion assumptions, backward reasoning, and fact verification during intermediate reasoning steps. Hypothesis Testing prompting involves multiple assumptions and reverses validation of conclusions leading to its unique correct answer. Experiments on two challenging deductive reasoning datasets ProofWriter and RuleTaker show that hypothesis testing prompting not only significantly improves the effect, but also generates a more reasonable and standardized reasoning process. © 2024, CC BY-NC-ND.

关键词： Computational linguistics

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