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An All-Optical Metro Network Architecture and QoS-Aware Wavelength Allocation Study for Converged Fixed, Mobile, and Edge Computing Multi-Granular Traffic

arXiv

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arXiv 2025年

作者： Georgantas, David Liu, Zhaoyang Drainakis, Georgios Pan, Bitao Bogris, Adonis Baziana, Peristera Department of Informatics and Telecommunications University of Thessaly Greece State Key Lab of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications China School of Electrical and Computer Engineering National Technical University of Athens Greece Department of Informatics and Computer Engineering University of West Attica Greece

In this paper, we introduce an all-optical metro network architecture, called MOON, to serve converged multi-granular traffic from fixed, mobile, and edge computing services. Since traffic is characterized by high dynamicity and diverse access requirements, MOON uses network slicing to provide quality of service (QoS) aware wavelength allocation to fulfill the various applications traffic demands. MOON incorporates hybrid optical switching (HOS) combining optical circuit switching (OCS) and optical time slotted switching (OTS) capabilities that appropriately maps different traffic types to them. Specifically, the OCS network slice explicitly serves aggregated traffic of long duration and high volume, while OTS network slice serves short bursty traffic. In order to provide flexibility, separate sets of wavelengths are used for OCS and OTS traffic service, both within a metro-access network (MAN) (intra-MAN) and between different MANs (inter-MAN). We extensively study the required number of wavelengths to efficiently serve OCS and OTS traffic for intra- and inter-MAN communication scenarios, taking into account their specific traffic access requirements in an effort to optimize wavelengths utilization. In our study, we assume non-blocking OCS communication for immediate access;therefore the number of required OCS wavelengths depends only on the number of nodes, while the number of required OTS wavelengths to obtain a desired QoS and latency level is independent from the number for OCS wavelengths. Simulation results show that within an OTS intra-MAN we achieve end-to-end (E2E) latency in sub-milliseconds scale, suitable for dynamic bursty traffic, while it is an decreasing function of the number of used OTS wavelengths. © 2025, CC BY.

关键词： Wimax

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Delving into the Reversal Curse: How Far Can Large Language Models Generalize?

arXiv

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arXiv 2024年

作者： Lin, Zhengkai Fu, Zhihang Liu, Kai Xie, Liang Lin, Binbin Wang, Wenxiao Cai, Deng Wu, Yue Ye, Jieping State Key Lab of CAD&CG Zhejiang University China Alibaba Cloud College of Biomedical Engineering & Instrument Science Zhejiang University China College of Computer Science and Technology Zhejiang University of Technology China School of Software Technology Zhejiang University China Fullong Inc China

While large language models (LLMs) showcase unprecedented capabilities, they also exhibit certain inherent limitations when facing seemingly trivial tasks. A prime example is the recently debated "reversal curse", which surfaces when models, having been trained on the fact "A is B", struggle to generalize this knowledge to infer that "B is A". In this paper, we examine the manifestation of the reversal curse across various tasks and delve into both the generalization abilities and the problem-solving mechanisms of LLMs. This investigation leads to a series of significant insights: (1) LLMs are able to generalize to "B is A" when both A and B are presented in the context as in the case of a multiple-choice question. (2) This generalization ability is highly correlated to the structure of the fact "A is B" in the training documents. For example, this generalization only applies to biographies structured in "[Name] is [Description]" but not to "[Description] is [Name]". (3) We propose and verify the hypothesis that LLMs possess an inherent bias in fact recalling during knowledge application, which explains and underscores the importance of the document structure to successful learning. (4) The negative impact of this bias on the downstream performance of LLMs can hardly be mitigated through training alone. These findings offer a novel perspective on interpreting LLMs' generalization through their intrinsic mechanisms and provide insights for developing more effective learning methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Data description

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An Exploratory Study on Automatic Identification of Assumptions in the Development of Deep Learning Frameworks

SSRN

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SSRN 2024年

作者： Yang, Chen Liang, Peng Ma, Zinan School of Artificial Intelligence Shenzhen Polytechnic University Shenzhen518055 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer Science Wuhan University Wuhan430072 China Department of Computer Science and Engineering Chalmers University of Technology GothenburgS-412 96 Sweden

Context: Stakeholders constantly make assumptions in the development of deep learning (DL) frameworks. These assumptions are related to various types of software artifacts (e.g., requirements, design decisions, and technical debt) and can turn out to be invalid, leading to system failures. Existing approaches and tools for assumption management usually depend on manual identification of assumptions. However, assumptions are scattered in various sources (e.g., code comments, commits, pull requests, and issues) of DL framework development, and manually identifying assumptions has high costs (e.g., time and resources).Objective: The objective of the study is to evaluate different classification models for the purpose of identification with respect to assumptions from the point of view of developers and users in the context of DL framework projects (i.e., issues, pull requests, and commits) on ***: We constructed a new and largest dataset (i.e., AssuEval) of assumptions collected from the TensorFlow and Keras repositories on GitHub;explored the performance of seven traditional machine learning models (e.g., Support Vector Machine, Classification and Regression Trees), a popular DL model (i.e., ALBERT), and a large language model (i.e., ChatGPT) of identifying assumptions on the ***: The experiment results show that ALBERT achieves the best performance (f1-score: 0.9584) of identifying assumptions on the AssuEval dataset, which is much better than the other models (the 2nd best f1-score is 0.6211, achieved by ChatGPT). Though ChatGPT is the most popular large language model, we do not recommend using it to identify assumptions in DL framework development because of its low performance on the task. Fine-tuning ChatGPT specifically for assumption identification could improve the ***: This study provides researchers with the largest dataset of assumptions for further research (e.g., assumption classification, evaluation, and

关键词： Large datasets

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HeGTa: Leveraging Heterogeneous Graph-enhanced Large Language Models for Few-shot Complex Table Understanding 39

HeGTa: Leveraging Heterogeneous Graph-enhanced Large Languag...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Jin, Rihui Li, Yu Qi, Guilin Hu, Nan Li, Yuan-Fang Chen, Jiaoyan Wang, Jianan Chen, Yongrui Min, Dehai Bi, Sheng School of Computer Science and Engineering Southeast University China Key Laboratory of New Generation Artificial Intelligence Technology and its Interdisciplinary Applications (Southeast University) Ministry of Education China Monash University Australia The University of Manchester United Kingdom Alibaba Group China Law and Innovation Lab Law School Southeast University China State Key Laboratory for Novel Software Technology Nanjing University China

ISBN: (纸本)157735897X

Table Understanding (TU) has achieved promising advancements, but it faces the challenges of the scarcity of manually labeled tables and the presence of complex table structures. To address these challenges, we propose HeGTa, a heterogeneous graph (HG)-enhanced large language model (LLM) designed for few-shot TU tasks. This framework aligns structural table semantics with the LLM's parametric knowledge through soft prompts and instruction tuning. It also addresses complex tables with a multi-task pre-training scheme by incorporating novel multi-granularity self-supervised HG pretext tasks. We empirically demonstrate the effectiveness of HeGTa, showing that it outperforms the SOTA for few-shot complex TU on several benchmarks. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Access Authentication for Mega-Constellation based on Hierarchical Consensus

Access Authentication for Mega-Constellation based on Hierar...

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Satellite Computing (Satellite), IEEE International Conference on

作者： Xin Lai Zhiyuan Wang Shan Zhang Qingkai Meng Hongbin Luo School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China State Key Laboratory of Virtual Reality Technology and Systems State Key Laboratory of Software Development Environment Institute of Artificial Intelligence Beihang University Beijing China

Low earth orbit (LEO) constellation has the potential to provide low-latency Internet services with the global coverage. However, LEO satellite networks with open channels and dynamic topology are faced with crucial security issue on authenticating terrestrial users. In this paper, we propose an access authentication method for LEO satellite networks based on consensus protocol PBFT, aiming to achieve small communication overhead and high authentication accuracy. The PBFT consensus protocol tolerates Byzantine nodes with the ratio 1/3, but incurs a tremendous communication overhead, therefore, it is not deployable in mega-constellation. To address this challenge, we propose to divide the LEO constellation into multiple consensus groups and present the design of hierarchical PBFT (H-PBFT). We analytically derive the communication overhead incurred by PBFT and our proposed H - PBFT and analyze the authentication accuracy achieved by these two protocols given the faulty satellite probability. The analytical results allow us to properly optimize the scale of the consensus groups and attain the desired tradeoff between small communication overhead and high authentication accuracy. Simulation results show that our proposed access authentication method based on H - PBFT outperforms state-of-the-art consensus protocols (i.e., PBFT and double-layer PBFT) in terms of communication overhead and authentication accuracy.

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Joint Optimization of Active and Passive Beamforming in Multi-IRS Aided mmWave Communications

Joint Optimization of Active and Passive Beamforming in Mult...

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2022 IEEE GLOBECOM Workshops, GC Wkshps 2022

作者： Wei, Renlong Xue, Qing Ma, Shaodan Xu, Yongjun Yan, Li Fang, Xuming Chongqing University of Posts and Telecommunications School of Communication and Information Engineering Chongqing400065 China University of Macau State Key Laboratory of Internet of Things for Smart City China University of Macau Department of Electrical and Computer Engineering China Southwest Jiaotong University Key Lab of Information Coding & Transmission Chengdu610031 China

ISBN: (纸本)9781665459754

Intelligent reflecting surface (IRS) has been considered as a promising technology to alleviate the blockage effect and enhance coverage in millimeter wave (mmWave) communication. To explore the impact of IRS on the performance of mmWave communication, we investigate a multi-IRS assisted mmWave communication network and formulate a sum rate maximization problem by jointly optimizing the active and passive beamforming and the set of IRSs for assistance. The optimization problem is intractable due to the lack of convexity of the objective function and the binary nature of the IRS selection variables. To tackle the complex non-convex problem, an alternating iterative approach is proposed. In particular, utilizing the fractional programming method to optimize the active and passive beamforming and the optimization of IRS selection is solved by enumerating. Simulation results demonstrate the performance gain of our proposed approach. © 2022 IEEE.

关键词： Beamforming

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Student-Teacher Feature Pyramid Matching for Anomaly Detection 32

Student-Teacher Feature Pyramid Matching for Anomaly Detecti...

引用

32nd British Machine Vision Conference, BMVC 2021

作者： Wang, Guodong Han, Shumin Ding, Errui Huang, Di State Key Laboratory of Software Development Environment Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China Department of Computer Vision Technology Baidu Inc. Beijing China

Anomaly detection is a challenging task and usually formulated as an one-class learning problem for the unexpectedness of anomalies. This paper proposes a simple yet powerful approach to this issue, which is implemented in the student-teacher framework for its advantages but substantially extends it in terms of both accuracy and efficiency. Given a strong model pre-trained on image classification as the teacher, we distill the knowledge into a single student network with the identical architecture to learn the distribution of anomaly-free images and this one-step transfer preserves the crucial clues as much as possible. Moreover, we integrate the multi-scale feature matching strategy into the framework, and this hierarchical feature matching enables the student network to receive a mixture of multi-level knowledge from the feature pyramid under better supervision, thus allowing to detect anomalies of various sizes. The difference between feature pyramids generated by the two networks serves as a scoring function indicating the probability of anomaly occurring. Due to such operations, our approach achieves accurate and fast pixel-level anomaly detection. Very competitive results are delivered on the MVTec anomaly detection dataset, superior to the state of the art ones. © 2021. The copyright of this document resides with its authors.

关键词： Anomaly detection

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Routing in LEO Satellite Networks: How Many Link-state Updates Do We Need?

Routing in LEO Satellite Networks: How Many Link-State Updat...

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Satellite Computing (Satellite), IEEE International Conference on

作者： Qian Shan Zhiyuan Wang Shan Zhang Qingkai Meng Hongbin Luo School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China State Key Laboratory of Virtual Reality Technology and Systems State Key Laboratory of Software Development Environment Institute of Artificial Intelligence Beihang University Beijing China

Low earth orbit (LEO) satellite networks equipped with inter-satellite links (ISLs) will be an indispensable component of the future Internet. Routing protocol installed on LEO satellite networks plays a significant role on achieving efficient data delivery. LEO satellite networks exhibit two typical topology features, i.e., deterministic neighbor relationship and intermittent ISLs, which are different from terrestrial Internet. Hence the classic link-state routing protocols (e.g., OSPF) designed for terrestrial Internet are naturally unable to accommodate these topology features. In this paper, we present the design of localized fine-grained (LoFi) link-state routing protocol for LEO satellite networks. The key idea of LoFi is to reduce the dissemination range of coarse-grained link connectivity and incorporate fine-grained link-load awareness. Specifically, LoFi has two key parameters, i.e., state dissemination range and load variation threshold, which determine how far to disseminate link state update and how precise to detect the link load change, respectively. The two parameters allow the LEO satellite networks to flexibly achieve a desired tradeoff between good packet delivery performance and small control overhead. We evaluate the performance of LoFi via extensive packet-level experiments on OMNeT++ under Iridium constellation. Results show that LoFi improves the packet delivery performance (up to 94.4 %) and reduces the control overhead (up to 85.7 %) compared to state-of-the-art link-state protocol ELB.

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An exploratory study on automatic identification of assumptions in the development of deep learning frameworks

arXiv

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arXiv 2024年

Context: Stakeholders constantly make assumptions in the development of deep learning (DL) frameworks. These assumptions are related to various types of software artifacts (e.g., requirements, design decisions, and technical debt) and can turn out to be invalid, leading to system failures. Existing approaches and tools for assumption management usually depend on manual identification of assumptions. However, assumptions are scattered in various sources (e.g., code comments, commits, pull requests, and issues) of DL framework development, and manually identifying assumptions has high costs (e.g., time and resources). Objective: The objective of the study is to evaluate different classification models for the purpose of identification with respect to assumptions from the point of view of developers and users in the context of DL framework projects (i.e., issues, pull requests, and commits) on GitHub. Method: First, we constructed a new and largest dataset (i.e., the AssuEval dataset) of assumptions collected from the TensorFlow and Keras repositories on GitHub. Then we explored the performance of seven non-transformers based models (e.g., Support Vector Machine, Classification and Regression Trees), the ALBERT model, and three decoder-only models (i.e., ChatGPT, Claude, and Gemini) for identifying assumptions on the AssuEval dataset. Results: The study results show that ALBERT achieves the best performance (f1-score: 0.9584) for identifying assumptions on the AssuEval dataset, which is much better than the other models (the 2nd best f1-score is 0.8858, achieved by the Claude 3.5 Sonnet model). Though ChatGPT, Claude, and Gemini are popular models, we do not recommend using them to identify assumptions in DL framework development because of their low performance. Fine-tuning ChatGPT, Claude, Gemini, or other language models (e.g., Llama3, Falcon, and BLOOM) specifically for assumptions might improve their performance for assumption identification. Conclusions: This stu

关键词： Chatbots

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Transformer doctor: diagnosing and treating vision transformers 24

Transformer doctor: diagnosing and treating vision transform...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Jiacong Hu Hao Chen Kejia Chen Yang Gao Jingwen Ye Xingen Wang Mingli Song Zunlei Feng College of Computer Science and Technology Zhejiang University and State Key Laboratory of Blockchain and Data Security Zhejiang University College of Computer Science and Technology Zhejiang University School of Software Technology Zhejiang University Bangsheng Technology Co. Ltd. Electrical and Computer Engineering National University of Singapore College of Computer Science and Technology Zhejiang University and Bangsheng Technology Co. Ltd. College of Computer Science and Technology Zhejiang University and State Key Laboratory of Blockchain and Data Security Zhejiang University and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security School of Software Technology Zhejiang University and State Key Laboratory of Blockchain and Data Security Zhejiang University and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security

ISBN: (纸本)9798331314385

Due to its powerful representational capabilities, Transformers have gradually become the mainstream model in the field of machine vision. However, the vast and complex parameters of Transformers impede researchers from gaining a deep understanding of their internal mechanisms, especially error mechanisms. Existing methods for interpreting Transformers mainly focus on understanding them from the perspectives of the importance of input tokens or internal modules, as well as the formation and meaning of features. In contrast, inspired by research on information integration mechanisms and conjunctive errors in the biological visual system, this paper conducts an in-depth exploration of the internal error mechanisms of Transformers. We first propose an information integration hypothesis for Transformers in the machine vision domain and provide substantial experimental evidence to support this hypothesis. This includes the dynamic integration of information among tokens and the static integration of information within tokens in Transformers, as well as the presence of conjunctive errors therein. Addressing these errors, we further propose heuristic dynamic integration constraint methods and rule-based static integration constraint methods to rectify errors and ultimately improve model performance. The entire methodology framework is termed as Transformer Doctor, designed for diagnosing and treating internal errors within transformers. Through a plethora of quantitative and qualitative experiments, it has been demonstrated that Transformer Doctor can effectively address internal errors in transformers, thereby enhancing model performance. For more information, please visit https://***/.

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