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FlexSATE: Flexible and Distributed Traffic Engineering with Supervised Learning in Ultra-Dense Low-Earth-Orbit Satellite Networks

FlexSATE: Flexible and Distributed Traffic Engineering with ...

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2024 IEEE Global Communications conference, GLOBECOM 2024

作者： Liu, Xiaoyu Zhang, Zitian Qin, Xiaohan Zhou, Haibo Zhao, Lian Nanjing University School of Electronic Science and Engineering Nanjing210023 China Zhejiang Gongshang University School of Information and Electronic Engineering Hangzhou China Toronto Metropolitan University Department of Electrical Computer Biomedical Engineering TorontoONM5B 2K3 Canada

ISBN: (纸本)9798350351255

The ultra-dense low earth orbit (UD-LEO) satellite network is being vigorously developed due to its great potential in providing global coverage and services. For the sake of improved network performance in resource-constrained satellite networks, multipath schemes are being explored. However, state-of-the-art multipath routing algorithms face the challenge when dealing with highly dynamic satellite network features (i.e., frequent traffic variation, link failures) and fail to exploit the simple grid topology to design fast yet efficient traffic engineering (TE) approaches. In this paper, we propose a novel distributed TE scheme called Flexible Satellite Traffic Engineering (FlexSATE), which leverages global path computation coupled with distributed local routing decisions to improve the overall load balancing performance for ultra-dense LEO satellite networks. By constructing a minimum-hop binary tree (MHBT), we propose an MHBT-based k-segment Routing algorithm, which is capable of promptly discovering routing paths with low latency, high diversity, and good load balancing. To further enhance network transmission performance, we employ supervised learning into dynamic rate adaption, where FlexSATE employs centralized offline learning to derive insights from the globally optimal routing strategy and utilizes distributed deployment to predict the optimal distribution of traffic in real time. Our simulation results on a real-world typical Walker-delta type LEO constellation with 720 satellites show that FlexSATE outperforms some existing approaches with superior robustness and flexibility. © 2024 IEEE.

关键词： Binary trees

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Efficient ILT via Multigrid-Schwartz Method 24

Efficient ILT via Multigrid-Schwartz Method

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61st ACM/IEEE Design Automation conference, DAC 2024

作者： Sun, Shuyuan Yang, Fan Yu, Bei Shang, Li Zhou, Dian Zeng, Xuan State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai China School of Microelectronics Fudan University Shanghai China China and Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China Chinese University of Hong Kong Hong Kong University of Texas DallasTX United States

ISBN: (纸本)9798400706011

Inverse Lithography Technology (ILT) is an important Resolution Enhancement Technology (RET) in chip manufacturing. Due to the high computational demands of ILT, large-scale layouts are typically partitioned into smaller tiles for independent processing. In this paper, we propose a multigrid-Schwarz method to overcome challenges in tile assembly. Experimental results show that our approach achieves comparable performance to the full-chip ILT, offering increased parallelizability and speedup in parallel mode. Unlike the traditional divide-and-conquer algorithm, it effectively alleviates discontinuities of tile stitching, preventing manufacturing failures. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Inverse problems

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Optimizing Blocked-Davidson Method based on Adaptive Algorithm using the New Sunway Supercomputer 2

Optimizing Blocked-Davidson Method based on Adaptive Algorit...

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2022 2nd conference on high performance Computing and Communication Engineering, HPCCE 2022

作者： Yang, Chaochao Liu, Anjun Liu, Weifeng Hou, Jie Guo, Meng Shandong Computer Science Center Qilu University of Technology China Jinan Key Laboratory of High Performance Industrial Software China Institute of Applied Physics and Computational Mathematics China Jinan Institute of Supercomputing Technology China

ISBN: (纸本)9781510663374

The Davidson method is a traditional large-scale iterative diagonalization method in computational quantum chemistry. The Blocked-Davidson method is a classical version of the Davidson method, which is an important part of VASP software, occupying 22.9% of the calculation time. When VASP is transplanted to the new Sunway supercomputer, it is incompatible with the many-core heterogeneous environment of Sunway processor. The Blocked-Davidson method does not use slave-core accelerators. This paper designs collaborative computing, double buffer, store variables in slave-core LDM, LDM Level 2 Access, and segmented transmission methods of Blocked-Davidson method based on Sunway many-core architecture. The test results showed that the function in the Blocked-Davidson method accelerated 30.65 times with a single core group in the silica test case. However, negative optimization of the Blocked-Davidson method happens in some test cases such as ZrTiAlVSiN5 and Hg50. Therefore, an adaptive algorithm of automatic selection of less time-consuming optimization paths is designed to solve the negative optimization. The maximum acceleration is 23.6 times compared with not using the adaptive algorithm. © 2023 SPIE.

关键词： Supercomputers

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Comparison of self-supervised learning methods for optical coherence tomography image classification 24

Comparison of self-supervised learning methods for optical c...

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8th International conference on Medical and Health Informatics, ICMHI 2024

作者： Shim, Sungho Yang, Hyun-Lim Kim, Min-Soo Electrical Engineering and Computer Science Daegu Gyeongbuk Institute of Science and Technology Daegu Korea Republic of Office of Hospital Information Seoul National University Hospital Seoul Korea Republic of School of Computing Korea Advanced Institute of Science and Technology Daejeon Korea Republic of

ISBN: (纸本)9798400716874

Self-supervised learning enables deep learning algorithms to achieve high performance even with limited labeled data. Numerous self-supervised learning methods have been proposed, and several medical artificial intelligence studies have confirmed the effectiveness of this technique in enhancing model performance. Nonetheless, only a few studies have investigated which self-supervised learning methods are appropriate for medical images. In this study, a comparative analysis to determine the most suitable self-supervised learning methods for developing medical artificial intelligence models using optical coherence tomography images. Four self-supervised learning methods were tested, and models were developed and validated using two publicly available optical coherence tomography datasets. The reliability of the model output was assessed through Gradient-weighted Class Activation Mapping analysis. As a result, using contrastive learning-based self-supervised learning methods, better performance was observed compared to other models, resulting in 99.90% and 99.05% accuracy for the two public optical coherence tomography datasets, which represents a 0.8% and 2.86% improvement over conventional supervised learning training. In addition, we confirmed that the contrastive learning-based model can more reliably localize disease lesions in the Gradient-weighted Class Activation Mapping analysis. © 2024 Copyright held by the owner/author(s)

关键词： Self-supervised learning

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Offloading Algorithm for Edge Computing Tasks Based on Energy Optimization 8

Offloading Algorithm for Edge Computing Tasks Based on Energ...

引用

8th IEEE International conference on Smart Cloud, SmartCloud 2023

作者： Gao, Xing Gu, Haonan Liu, Fang Hu, Wei Wuhan City College Wuhan China College of Computer Science and Technology Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China

ISBN: (纸本)9798350313505

Current research in computation offloading encompasses various aspects, including task partitioning and allocation, resource management, communication, and data management. This paper focuses on developing new algorithms and techniques to enhance the efficiency and effectiveness of the offloading process while addressing challenges arising from dynamic and heterogeneous mobile environments. Researchers are also examining the trade-off between offloading benefits like improved performance and energy efficiency, and the associated overhead costs such as communication and data management. This paper proposes an optimization strategy for computational offloading to enhance the Grey Wolf algorithm. The solution aims to quickly optimize system energy consumption while considering the arithmetic power constraint of the mobile edge computing server and time delay constraints. Simulation experiments demonstrate that the algorithm in this paper significantly outperforms the traditional Grey Wolf and PSO algorithms in terms of both performance and computational speed. ©2023 IEEE.

关键词： Information management

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Representing the Intangible Cultural Heritage Knowledge Graph with Vector Embedding 25

Representing the Intangible Cultural Heritage Knowledge Grap...

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25th IEEE International conferences on high performance Computing and Communications, 9th International conference on Data Science and Systems, 21st IEEE International conference on Smart City and 9th IEEE International conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Han, Mao Wang, Qing Chen, Hong Chen, Wei Zhang, Junchang Wang, Guang College of Information and Electrical Engineering China Agricultural University Beijing China School of Computer Science Beijing University of Posts and Telecommunications Beijing China Century College Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798350330014

The construction and effective application of an intangible cultural heritage knowledge graph (ICH KG) can realize the knowledge integration, and optimize the ICH knowledge management. However, high complexity and low computational efficiency of ICH KG make its application face challenges. We propose a multi-source knowledge graph embedding (KGE) model named ICHMKGE to convert the ICH KG into the vector representations to improve the computational efficiency of ICH KG and promote the digital sustainable development of ICH. Firstly, we take the Chinese ICH project 24 solar terms as an example and combine multiple official data sources to construct the Chinese ICH KG as a basis for this study. Secondly, as the ICH project is being further explored with the limited coverage of ICH knowledge, entity sparsity poses a serious challenge for ICH KGE. This paper employs the BERT model to encode the complete description information of entities, and establishes connection between triples entities and ontology concepts via cross-view modeling. Finally, the proposed ICHMKGE model is compared with the baseline models, and the experimental results demonstrate that the model exhibits superiority. © 2023 IEEE.

关键词： Vector spaces

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Image-Adaptive Hint Generation via Vision Transformer for Outpainting 22

Image-Adaptive Hint Generation via Vision Transformer for Ou...

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22nd IEEE/CVF Winter conference on Applications of computer Vision (WACV)

作者： Kong, Daehyeon Kong, Kyeongbo Kim, Kyunghun Min, Sung-Jun Kang, Suk-Ju Sogang Univ Seoul South Korea Pukyong Natl Univ Busan South Korea

ISBN: (纸本)9781665409155

Image outpainting has recently received considerable attention because it can be useful in tasks such as image retargeting and panorama image generation. In general, the problem of extending an image beyond its given boundaries is still ill-posed. Conventional methods predominantly attempt image outpainting by using complex network structures. Some recent studies have tried to decrease the problem complexity through the conversion techniques from outpainting to inpainting. Although these methodologies work well in simple cases, their performance reduces considerably for asymmetrical images. This paper proposes a novel hint-based outpainting methodology that can adaptively select the most plausible patches as hints from a given image to reduce the difficulty of outpainting. To estimate high-quality hints, inspired by patch-based image inpainting methods, we utilize Vision Transformer that considers self-attention for each patch. The estimated hints are attached on both boundaries of the input image and the inside missing regions are predicted by using an inpainting network. After finishing the prediction, the output image is obtained by removing the hints. Experiments show that our image-adaptive hint framework, when employed in representative inpainting networks, can consistently improve its performance compared to the other conversion techniques from outpainting to inpainting on SUN and Beach benchmark datasets.

关键词： computer vision Image synthesis Neural networks Complex networks Benchmark testing Transformers Complexity theory

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Learning Intrinsic Dimension via Information Bottleneck for Explainable Aspect-based Sentiment Analysis 30

Learning Intrinsic Dimension via Information Bottleneck for ...

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Joint 30th International conference on Computational Linguistics and 14th International conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Cheng, Zhenxiao Zhou, Jie Wu, Wen Chen, Qin He, Liang School of Computer Science and Technology East China Normal University Shanghai China

ISBN: (纸本)9782493814104

Gradient-based explanation methods are increasingly used to interpret neural models in natural language processing (NLP) due to their high fidelity. Such methods determine word-level importance using dimension-level gradient values through a norm function, often presuming equal significance for all gradient dimensions. However, in the context of Aspect-based Sentiment Analysis (ABSA), our preliminary research suggests that only specific dimensions are pertinent. To address this, we propose the Information Bottleneck-based Gradient (IBG) explanation framework for ABSA. This framework leverages an information bottleneck to refine word embeddings into a concise intrinsic dimension, maintaining essential features and omitting unrelated information. Comprehensive tests show that our IBG approach considerably improves both the models' performance and interpretability by identifying sentiment-aware features. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Sentiment analysis

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VeCAF: Vision-language Collaborative Active Finetuning with Training Objective Awareness 24

VeCAF: Vision-language Collaborative Active Finetuning with ...

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32nd ACM International conference on Multimedia, MM 2024

作者： Zhang, Rongyu Cai, Zefan Yang, Huanrui Liu, Zidong Gudovskiy, Denis Okuno, Tomoyuki Nakata, Yohei Keutzer, Kurt Chang, Baobao Du, Yuan Du, Li Zhang, Shanghang Nanjing University Nanjing China Peking University Beijing China University of Wisconsin - Madison MadisonWI United States University of California Berkeley BerkeleyCA United States University of Arizona Tucson United States Tsinghua University Beijing China Panasonic Corporation Mountainview United States Panasonic Corporation Osaka Japan State Key Laboratory of Multimedia Information Processing School of Computer Science Peking University China

ISBN: (纸本)9798400706868

Finetuning a pretrained vision model (PVM) is a common technique for learning downstream vision tasks. The conventional finetuning process with the randomly sampled data points results in diminished training efficiency. To address this drawback, we propose a novel approach, Vision- languag e C ollaborative A ctive F inetuning (VeCAF). VeCAF optimizes a parametric data selection model by incorporating the training objective of the model being tuned. Effectively, this guides the PVM towards the performance goal with improved data and computational *** the ever-growing feasibility of acquiring labels and natural language annotations of image data through web-scale crawling, we exploit the inherent semantic richness of the text embedding space and utilize text embeddings of image annotations to augment PVM image features for better data selection and finetuning. Furthermore, the flexibility of text-domain augmentation gives VeCAF the unique ability to handle out-of-distribution scenarios without external augmented data. Extensive experiments show the leading performance and high efficiency of VeCAF that is superior to baselines in both in-distribution and out-of-distribution image classification tasks. On ImageNet, VeCAF needs up to 3.3× less training batches to reach the target performance compared to full fine-tuning and achieves an accuracy improvement of 2.8% over active SOTA fine-tuning methods with the same number of batches. Our code is now available at https://***/RoyZry98/VeCAF-Pytorch. © 2024 ACM.

关键词： Semantics

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medIKAL: Integrating Knowledge Graphs as Assistants of LLMs for Enhanced Clinical Diagnosis on EMRs 31

medIKAL: Integrating Knowledge Graphs as Assistants of LLMs ...

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31st International conference on Computational Linguistics, COLING 2025

作者： Jia, Mingyi Duan, Junwen Song, Yan Wang, Jianxin Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University China University of Science and Technology of China China

ISBN: (纸本)9798891761964

Electronic Medical Records (EMRs), while integral to modern healthcare, present challenges for clinical reasoning and diagnosis due to their complexity and information redundancy. To address this, we proposed medIKAL (Integrating Knowledge Graphs as Assistants of LLMs), a framework that combines Large Language Models (LLMs) with knowledge graphs (KGs) to enhance diagnostic capabilities. medIKAL assigns weighted importance to entities in medical records based on their type, enabling precise localization of candidate diseases within KGs. It innovatively employs a residual network-like approach, allowing initial diagnosis by LLMs to be merged into KG search results. Through a path-based reranking algorithm and a fill-in-the-blank style prompt template, it further refined the diagnostic process. We validated medIKAL's effectiveness through extensive experiments on a newly introduced open-sourced Chinese EMR dataset, demonstrating its potential to improve clinical diagnosis in real-world settings. The code and dataset are publicly available at https://***/CSU-NLP-Group/mediKAL. © 2025 Association for Computational Linguistics.

关键词： Knowledge graph

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