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Optimizing Link-Identified Forwarding Framework in LEO Satellite Networks 21

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Optimizing Link-Identified Forwarding Framework in LEO Satel...

21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2023

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

ISBN: (纸本)9783903176553

Low earth orbit (LEO) satellite networks have the potential to provide low-latency communication with global coverage. To unleash this potential, it is crucial to achieve efficient data delivery. In this paper, we analyze the topology characteristics of LEO satellite networks, and propose a source-route-style forwarding framework. Specifically, we leverage the deterministic neighbor relationship and identify all the unidirectional inter-satellite links (ISLs). Moreover, our framework utilizes the in-packet bloom filter (BF) to store the source-route-style forwarding information. This way, the source satellite could encode multiple ISL identifiers into the BF, which actually specifies the forwarding path. The intermediate satellites only need to check whether the outgoing ISLs are encoded and forward packets accordingly. Due to false positives caused by BF, the more ISLs are encoded at a time, the more redundant forwardings emerge. To reduce forwarding overhead, we take into account segment encoding, allowing the source and intermediate satellites to encode part of ISLs towards the destination. Overall, segment encoding seeks the right balance between forwarding overhead and encoding delay. We characterize a wide range of segment encoding policy in a unified framework, and derive the expected forwarding overhead in a closed-form. The segment encoding design is formulated as a binary non-linear programming, which is NP-hard. To overcome the challenge, we leverage its decomposable structure, and propose an efficient algorithm to solve it optimally. Finally, we validate our analytical results via packet-level experiments. Results also show that our proposed segment encoding policy significantly reduces the queuing delay compared to source encoding. © 2023 IFIP.

关键词： Satellites

Generalizability of memorization neural networks 24

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Generalizability of memorization neural networks

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

作者： Lijia Yu Xiao-Shan Gao Lijun Zhang Yibo Miao Key Laboratory of System Software (Chinese Academy of Sciences) and State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China and University of Chinese Academy of Sciences Beijing China Key Laboratory of System Software (Chinese Academy of Sciences) and State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences and University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798331314385

The neural network memorization problem is to study the expressive power of neural networks to interpolate a finite dataset. Although memorization is widely believed to have a close relationship with the strong generalizability of deep learning when using over-parameterized models, to the best of our knowledge, there exists no theoretical study on the generalizability of memorization neural networks. In this paper, we give the first theoretical analysis of this topic. Since using i.i.d. training data is a necessary condition for a learning algorithm to be generalizable, memorization and its generalization theory for i.i.d. datasets are developed under mild conditions on the data distribution. First, algorithms are given to construct memorization networks for an i.i.d. dataset, which have the smallest number of parameters and even a constant number of parameters. Second, we show that, in order for the memorization networks to be generalizable, the width of the network must be at least equal to the dimension of the data, which implies that the existing memorization networks with an optimal number of parameters are not generalizable. Third, a lower bound for the sample complexity of general memorization algorithms and the exact sample complexity for memorization algorithms with constant number of parameters are given. It is also shown that there exist data distributions such that, to be generalizable for them, the memorization network must have an exponential number of parameters in the data dimension. Finally, an efficient and generalizable memorization algorithm is given when the number of training samples is greater than the efficient memorization sample complexity of the data distribution.

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Space-time Reinforcement Network for Video Object Segmentation

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arXiv 2024年

作者： Chen, Yadang Zhu, Wentao Yang, Zhi-Xin Wu, Enhua School of Computer Science Nanjing University of Information Science and Technology Nanjing China State Key Laboratory of Internet of Things for Smart City University of Macau China Key Laboratory of System Software State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China

Recently, video object segmentation (VOS) networks typically use memory-based methods: for each query frame, the mask is predicted by space-time matching to memory frames. Despite these methods having superior performance, they suffer from two issues: 1) Challenging data can destroy the space-time coherence between adjacent video frames. 2) Pixel-level matching will lead to undesired mismatching caused by the noises or distractors. To address the aforementioned issues, we first propose to generate an auxiliary frame between adjacent frames, serving as an implicit short-temporal reference for the query one. Next, we learn a prototype for each video object and prototype-level matching can be implemented between the query and memory. The experiment demonstrated that our network outperforms the state-of-the-art method on the DAVIS 2017, achieving a J&F score of 86.4%, and attains a competitive result 85.0% on YouTube VOS 2018. In addition, our network exhibits a high inference speed of 32+ FPS. Copyright © 2024, The Authors. All rights reserved.

关键词： Motion compensation

Revisiting Restarts of CDCL: Should the Search Information be Preserved?

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arXiv 2024年

作者： Zhang, Xindi Chen, Zhihan Cai, Shaowei Key Laboratory of System Software Chinese Academy of Sciences State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences China

SAT solvers are indispensable in formal verification for hardware and software with many important applications. CDCL is the most widely used framework for modern SAT solvers, and restart is an essential technique of CDCL. When restarting, CDCL solvers cancel the current variable assignment while maintaining the branching order, variable phases, and learnt clauses. This type of restart is referred to as warm restart in this paper. Although different restart policies have been studied, there is no study on whether such information should be kept after restarts. This work addresses this question and finds some interesting observations. This paper indicates that under this popular warm restart scheme, there is a substantial variation in run-time with different randomized initial orders and phases, which motivates us to forget some learned information periodically to prevent being stuck in a disadvantageous search space. We propose a new type of restart called cold restart, which differs from previous restarts by forgetting some of the learned information. Experiments show that modern CDCL solvers can benefit from periodically conducting cold restarts. Based on the analysis of the cold-restart strategies, we develop a parallel SAT solver. Both the sequential and parallel versions of cold restart are more suitable for satisfiable instances, which suggests that existing CDCL heuristics for information management should be revised if one hopes to construct a satisfiable-oriented SAT solver. Copyright © 2024, The Authors. All rights reserved.

关键词： Information management

TMFIF:Transformer-based multi-focus image fusion 5

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TMFIF:Transformer-based multi-focus image fusion

5th International Conference on computer Vision, Image and Deep Learning, CVIDL 2024

作者： Li, Rui Geng, Shengling Zhang, Dan Zhou, Mingquan Qinghai Normal University School of Computer Science Xining China Nanyang Institute of Technology School of Computer and Software Nanyang China People's Government of Qinghai Province & Beijing Normal University Academy of Plateau Science and Sustainability Xining China Qinghai Normal University The State Key Laboratory of Tibetan Intelligent Information Processing and Application China

ISBN: (纸本)9798350373820

Multi-focus image fusion is a hot topic in the field of image processing, and it is a fundamental problem in the fields of image editing, image synthesis, and target retrieval. In previous fusion methods, although feature-rich datasets, models, and algorithms have been provided, there are still many problems with the effective fusion of distant and near views in complex backgrounds. To solve the challenging multi-focus image fusion problem more accurately, we introduce the Transformer Network based on Encoder Decoder (TMFIF), which can extract more generalized features based on the features of multi-focused images to achieve image fusion in complex backgrounds for better visual effects. In this work, we achieve fusion by inputting two images, near and far view. We compare the performance with other multifocal image fusion algorithms by conducting experiments on publicly available datasets and illustrated using existing evaluation methods and evaluation metrics;the results of the experiments show that our method visualizes the fusion effect better through the encoder and the decoder and the evaluation metrics are also relatively good. © 2024 IEEE.

关键词： Image fusion

DNLSAT: A Dynamic Variable Ordering MCSAT Framework for Nonlinear Real Arithmetic

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arXiv 2024年

作者： Wang, Zhonghan State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

Satisfiability modulo nonlinear real arithmetic theory (SMT(NRA)) solving is essential to multiple applications, including program verification, program synthesis and software testing. In this context, recently model constructing satisfiability calculus (MCSAT) has been invented to directly search for models in the theory space. Although following papers discussed practical directions and updates on MCSAT, less attention has been paid to the detailed implementation. In this paper, we present an efficient implementation of dynamic variable orderings of MCSAT, called dnlsat. We show carefully designed data structures and promising mechanisms, such as branching heuristic, restart, and lemma management. Besides, we also give a theoretical study of potential influences brought by the dynamic variablr ordering. The experimental evaluation shows that dnlsat accelerates the solving speed and solves more satisfiable instances than other state-of-the-art SMT solvers. Demonstration Video: https://***/T2Z0gZQjnPw. Code: https://***/yogurt-shadow/dnlsat/tree/master/code Benchmark: https://***/records/10607722/files/QF_***?download=1 Copyright © 2024, The Authors. All rights reserved.

关键词： software testing

Covert Communication in Underlay Multi-Antenna Cognitive Radio Networks

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IEEE Transactions on Cognitive Communications and Networking 2024年第3期11卷 1493-1507页

作者： Fan, Hongbin Yang, Weiwei Shi, Hui Pan, Gaofeng Si, Jiangbo Wang, Yutao Rong, Chuanzhen Army Engineering University of PLA College of Communications Engineering Nanjing210007 China Xiangnan University College of Computer and Artificial Intelligence Chenzhou423000 China Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software Guilin541004 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Xidian University State Key Laboratory of Integrated Services Networks Xi'an710100 China

In this paper, we investigated a multi-antenna covert communication in underlay cognitive radio networks (UCRNs), by considering both cases that Willie has the perfect and statistical channel state information (CSI) of the link from the secondary transmitter (ST) to the primary receiver (PR), respectively. Specifically, the optimal antenna of the secondary receiver (SR) is selected to receive the private information from ST, and then one of the remaining antennas is randomly selected to transmit jamming signals. The closed-form expressions of Willie's detection error probability, the average minimum detection error probability, the transmission outage probability and the maximum effective covert rate under both cases are derived. The maximum effective covert rate is obtained by optimising the maximum jamming power of SR. The numerical results show that the combined effect of the maximum interference tolerance of PR and the maximum interference power of SR provides the positive effective covert rate between ST and SR. It is further found that the maximum effective covert rate between ST and SR when Willie knows the perfect CSI of the ST-SR link is greater than that when Willie only knows the statistical CSI of the ST-SR link. © 2015 IEEE.

关键词： Jamming

Denoising Sequence Embeddings via Contrastive Learning for Micro-video Recommendation 19

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Denoising Sequence Embeddings via Contrastive Learning for M...

2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Zhao, Rui Jin, Beihong Yu, Yisong Li, Beibei Institute of Software Chinese Academy of Sciences State Key Laboratory of Computer Science Beijing China University of Chinese Academy of Sciences Beijing China Chongqing University College of Computer Science Chongqing China

ISBN: (纸本)9798350346558

In recent years, micro-video apps such as TikTok, Kwai, etc. have become widely popular, but recommendation models dedicated for micro-videos are relatively few in number. The paper analyses the characteristics of micro-video apps and sequences of interactions between users and micro-videos, and then presents a model CLASRec for micro-video recommendation. CLASRec follows the typical method of sequential recommendation, i.e., learns embeddings of user interests from historical interaction sequences. On this basis, for an interaction sequence of the same user, CLASRec adopts contrastive learning to reduce the noise implicit in interaction embeddings. In particular, two contrastive encoders are presented to align the embeddings of a pair of semantically-equivalent sequences. The extensive experiments are conducted on two real-world datasets. The experimental results show that CLASRec outperforms existing multiple models and achieves significant performance improvements in terms of Recall and NDCG. © 2022 IEEE.

关键词： Embeddings

clauseSMT: A NLSAT-Based Clause-Level Framework for Satisfiability Modulo Nonlinear Real Arithmetic Theory

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arXiv 2024年

Model-constructing satisfiability calculus (MCSAT) framework has been applied to SMT problems on different arithmetic theories. NLSAT, an implementation using cylindrical algebraic decomposition for explanation, is especially competitive among nonlinear real arithmetic constraints. However, current Conflict-Driven Clause Learning (CDCL)-style algorithms only consider literal information for decision, and thus ignore clause-level influence on arithmetic variables. As a consequence, NLSAT encounters unnecessary conflicts caused by improper literal decisions. In this work, we analyze the literal decision caused conflicts, and introduce clause-level information with a direct effect on arithmetic variables. Two main algorithm improvements are presented: clause-level feasible set based look-ahead mechanism and arithmetic propagation based branching heuristic. We implement our solver named clauseSMT on our dynamic variable ordering framework. Experiments show that clauseSMT is competitive on nonlinear real arithmetic theory against existing SMT solvers (CVC5, Z3, YICES2), and outperforms all these solvers on satisfiable instances of SMT(QF_NRA) in SMT-LIB. The effectiveness of our proposed methods are also studied. Copyright © 2024, The Authors. All rights reserved.

关键词： Formal logic