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Introduction to Space Archaeology 1

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1000年

作者： Huadong Guo Xinyuan Wang Fulong Chen Cheng Wang

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RELIEF: Reinforcement Learning Empowered Graph Feature Prompt Tuning

arXiv

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arXiv 2024年

作者： Zhu, Jiapeng Ding, Zichen Yu, Jianxiang Tan, Jiaqi Li, Xiang Qian, Weining East China Normal University Shanghai China Engineering Research Center of Blockchain Data Management East China Normal University Ministry of Education Shanghai Engineering Research Center of Big Data Management Shanghai China

The advent of the "pre-train, prompt" paradigm has recently extended its generalization ability and data efficiency to graph representation learning, following its achievements in Natural Language Processing (NLP). Initial graph prompt tuning approaches tailored specialized prompting functions for Graph Neural Network (GNN) models pre-trained with specific strategies, such as edge prediction, thus limiting their applicability. In contrast, another pioneering line of research has explored universal prompting via adding prompts to the input graph’s feature space, thereby removing the reliance on specific pre-training strategies. However, the necessity to add feature prompts to all nodes remains an open question. Motivated by findings from prompt tuning research in the NLP domain, which suggest that highly capable pre-trained models need less conditioning signal to achieve desired behaviors, we advocate for strategically incorporating necessary and lightweight feature prompts to certain graph nodes to enhance downstream task performance. This introduces a combinatorial optimization problem, requiring a policy to decide 1) which nodes to prompt and 2) what specific feature prompts to attach. We then address the problem by framing the prompt incorporation process as a sequential decision-making problem and propose our method, RELIEF, which employs Reinforcement Learning (RL) to optimize it. At each step, the RL agent selects a node (discrete action) and determines the prompt content (continuous action), aiming to maximize cumulative performance Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Known-class aware self-ensemble for open set domain adaptation

arXiv

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arXiv 2019年

作者： Lian, Qing Li, Wen Chen, Lin Duan, Lixin Big Data Research Center University of Electronic Science and Technology of China EHT Zurich Huawei Technologies

Existing domain adaptation methods generally assume different domains have the identical label space, which is quite restrict for real world applications. In this paper, we focus on a more realistic and challenging case of open set domain adaptation. Particularly, in open set domain adaptation, we allow the classes from the source and target domains to be partially overlapped. In this case, the assumption of conventional distribution alignment does not hold anymore, due to the different label spaces in two domains. To tackle this challenge, we propose a new approach coined as Known-class Aware Self-Ensemble (KASE), which is built upon the recently developed self-ensemble model. In KASE, we first introduce a Known-class Aware Recognition (KAR) module to identify the known and unknown classes from the target domain, which is achieved by encouraging a low cross-entropy for known classes and a high entropy based on the source data from the unknown class. Then, we develop a Known-class Aware Adaptation (KAA) module to better adapt from the source domain to the target by reweighing the adaptation loss based on the likeliness to belong to known classes of unlabeled target samples as predicted by KAR. Extensive experiments on multiple benchmark datasets demonstrate the effectiveness of our approach. Copyright © 2019, The Authors. All rights reserved.

关键词： Entropy

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Model Predictive Control for Carbon-Neutral data centers

Model Predictive Control for Carbon-Neutral Data Centers

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Ren, Guanyu Wan, Jianxiong Li, Leixiao Liu, Chuyi Wang, Xiaolei Inner Mongolia University of Technology Hohhot China Inner Mongolia Autonomous Reg. Eng. Technology Research Center of Big Data Based Software Service Hohhot China Research Center of Large-Scale Energy Storage Technologies Ministry of Education of the People's Republic of China China

ISBN: (纸本)9798350337020

As one of the major carbon producers, data centers produce around 1% of global carbon emissions per year. researchers are making significant effort to reduce the data center carbon emissions. However, the current carbon-neutral data center solutions seldom take the carbon quotas into account, nor do they integrate new emission reduction technologies like Carbon Capture (CC) and Power-to-Gas (P2G), etc. To bridge this gap, in this paper a novel carbon-neutral data center architecture is proposed based on which a holistic cost minimizing problem is formulated. We use the Variational Mode Decomposition (VMD) and the Long Short-Term Memory (LSTM) neural network to construct highly accurate prediction models, and develop a Model Predictive Control (MPC) algorithm for energy and carbon management. Finally, simulations on real-world data demonstrate that our approach reduces up to 19.19% overall cost compared with traditional solutions. © 2023 IEEE.

关键词： carbon neutrality data center model predictive control

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Remote sensing archaeology: The next century

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The Innovation 2022年第6期3卷 5-6页

作者： Lei Luo Xinyuan Wang Huadong Guo International Research Center of Big Data for Sustainable Development Goals(CBAS) Beijing 100094China Key Laboratory of Digital Earth Science Aerospace Information Research Institute(AIR)Chinese Academy of Sciences(CAS)Beijing 100094China International Centre on Space Technologies for Natural and Cultural Heritage(HIST)Under the Auspices of UNESCO Beijing 100094China

THE SCIENCE OF REMOTE SENSING ARCHAEOLOGY The science of remote sensing archaeology(RSA)is a traditional yet innovative sub-discipline of archaeology that studies how to use general remote sensing technology to capture and interpret the characteristics of data taken from physical remains related to human activities,then using these findings to understand human history and *** theoretical and practical basis for aerial photography archaeology,the principle behind RSA,was first described by Crawford in 1923.

关键词： Remote arch innovative

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Optimal Sharding for Dynamic Throughput Optimization in blockchain Systems with Deep Reinforcement Learning

Optimal Sharding for Dynamic Throughput Optimization in Bloc...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Yao, Bingbing Wan, Jianxiong Jaffry, Shan Li, Leixiao Ma, Zhiqiang Liu, Chuyi Inner Mongolia University of Technology Hohhot China Inner Mongolia Autonomous Region Engineering & Technology Research Center of Big Data Based Software Service Hohhot China Research Center of Large-Scale Energy Storage Technologies Ministry of Education of the People's Republic of China China Syntronic Research and Development Canada Inc. Canada

ISBN: (纸本)9798350337020

The rapid advancement in blockchain technology has enabled its applications across wide spectrum of fields. The blockchain throughput, which is usually measured by Transactions Per Second (TPS), is one of the key metrics to reflect the performance of the blockchain systems. However, current blockchain systems have low TPS rates that makes them unsuitable for latency critical applications like Vehicle-to-vehicle (V2V) communication. To address the above issue, the sharding technology, which divides the network into multiple disjoint groups so that transactions can be processed in parallel, is applied to the blockchain systems as a promising solution to improve TPS. This paper considers the Optimal blockchain Sharding (OBCS) problem which is formulated as a Markov Decision Process (MDP) where the decision variables are the number of shards, block size and block interval. Previous works solved the OBCS problem via Deep Reinforcement Learning (DRL) based methods where the action space has to be discretized such that it is not too large for tractability. However, the discretization degrades the solution quality since the optimal solution usually lies between discrete values. In this paper, we treat the block size and block interval as continuous decision variables and propose a sharding control algorithm based on Parametrized Deep Q-Networks (P-DQN) to efficiently handle the discrete-continuous hybrid action space without the scalability issue. Experimental results show that our Parametrized Deep Q-Networks blockchain Sharding (P-DQNBS) method can effectively improve the TPS by up to 20%. © 2023 IEEE.

关键词： blockchain deep reinforcement learning hybrid action space sharding

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POSTER: Will Sentiment of Forex News Effect Forecast of the RMB Exchange Rate? 16

POSTER: Will Sentiment of Forex News Effect Forecast of the ...

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16th ACM International Conference on Computing Frontiers, CF 2019

作者： Cheng, Zhou Wang, Jixiang Qi, Tianmei Zhao, Junfeng Wang, Zhihong Guo, Yi Zhou, Yu Co. Ltd Shanghai China Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Business Intelligence and Visualization Research Center National Engineering Laboratory for Big Data Distribution and Exchange Technologies Shanghai China

ISBN: (纸本)9781450366854

The forecast and analysis of the trend of the RMB exchange rate have been deeply explored by many researchers in the financial field, but the combination of public opinion sentiment data and historical market data to forecast the RMB exchange rate in the short term has been studied little. With the rapid development of the Internet, the influence of public opinion sentiment on the economy and society is increasing. Online public opinion sentiment data not only have an impact on stock prices [1] and commodity prices, but also have a significant impact on foreign exchange (Forex) rates. However, the public opinion data are not applied for the RMB exchange rate forecast, because the impact of public events on the exchange rate is ignored. Besides, the lack of exact temporal sliding window of public opinion ignores its timeliness and sensibility. © 2019 Association for Computing Machinery.

关键词： Forecasting

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Analyzing Reward Dynamics and Decentralization in Ethereum 2.0: An Advanced data Engineering Workflow and Comprehensive datasets for Proof-of-Stake Incentives

arXiv

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arXiv 2024年

作者： Yan, Tao Li, Shengnan Kraner, Benjamin Zhang, Luyao Tessone, Claudio J. Blockchain áDistributed Ledger Technologies Group Department of Informatics UZH Blockchain Center University of Zurich Zurich8057 Switzerland Data Science Research Center and Social Science Division Duke Kunshan University Suzhou215316 China

Ethereum 2.0, as the preeminent smart contract blockchain platform, guarantees the precise execution of applications without third-party intervention. At its core, this system leverages the Proof-of-Stake (PoS) consensus mechanism, which utilizes a stochastic process to select validators for block proposal and validation, consequently rewarding them for their contributions. However, the implementation of blockchain technology often diverges from its central tenet of decentralized consensus, presenting significant analytical challenges. Our study collects consensus reward data from the Ethereum Beacon chain and conducts a comprehensive analysis of reward distribution and evolution, categorizing them into attestation, proposer and sync committee rewards. To evaluate the degree of decentralization in PoS Ethereum, we apply several inequality indices, including the Shannon entropy, the Gini Index, the Nakamoto Coefficient, and the Herfindahl-Hirschman Index (HHI). Our comprehensive dataset is publicly available on Harvard dataverse, and our analytical methodologies are accessible via GitHub, promoting open-access research. Additionally, we provide insights on utilizing our data for future investigations focused on assessing, augmenting, and refining the decentralization, security, and efficiency of blockchain systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Ethereum

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Demystifying Ethereum account diversity:observations,models and analysis

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Frontiers of Computer Science 2022年第4期16卷 119-130页

作者： Chaofan Wang Xiaohai Dai Jiang Xiao Chenchen Li Ming Wen Bingbing Zhou Hai Jin Clusters and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabWuhan430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan430074China School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan430074China Centre for Distributed and High Performance Computing School of Information TechnologiesThe University of SydneyCamperdownNew South Wales2006Australia

blockchain platform Ethereum has involved millions of accounts due to its strong potential for providing numerous services based on smart *** massive accounts can be divided into diverse categories,such as miners,tokens,and exchanges,which is termed as account diversity in this *** benefit of investigating diversity are multi-fold,including understanding the Ethereum ecosystem deeper and opening the possibility of tracking certain abnormal ***,the exploration of blockchain account diversity remains *** the most relevant studies,which focus on the deanonymization of the accounts on Bitcoin,can hardly be applied on Ethereum since their underlying protocols and user idioms are *** this end,we present the first attempt to demystify the account diversity on *** key observation is that different accounts exhibit diverse behavior patterns,leading us to propose the heuristics for classification as the *** then raise the coverage rate of classification by the statistical learning model Maximum Likelihood Estimation(MLE).We collect real-world data through extensive efforts to evaluate our proposed method and show its ***,we make an in-depth analysis of the dynamic evolution of the Ethereum ecosystem and uncover the abnormal arbitrage *** for the former,we validate two sweeping statements reliably:(1)standalone miners are gradually replaced by the mining pools and cooperative miners;(2)transactions related to the mining pool and exchanges take up a large share of the total *** latter analysis shows that there are a large number of arbitrage transactions transferring the coins from one exchange to another to make a price difference.

关键词： blockchain Ethereum classification account diversity network analysis

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Advanced Wallis: A domain adaption method based on remote sensing image homogenization technique

Advanced Wallis: A domain adaption method based on remote se...

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Machine Vision, Image Processing and Imaging Technology (MVIPIT), International Conference on

作者： Hui Wang Mengdie Zhou Panpan Tang Bo Zhao Research Center of Big Data Technology Nanhu Laboratory & Advanced Institute of Big Data Jiaxing & Beijing China Remote Sensing Platform R&D Center KQ GEO Technologies Co. Ltd Beijing China

ISBN: (数字)9798350306545

ISBN: (纸本)9798350306552

Domain adaption has been a hot topic in artificial intelligence (AI) -driven understanding tasks. Simultaneously, as a critical technology in image preprocessing, the homogenization method plays an irreplaceable role in remote sensing interpretation. This paper proposes a domain adaptation method based on the Wallis homogenization algorithm to improve the reasoning performance of deep models, which is named advanced Wallis. Its core idea is to reduce the variability of intra-class features by reducing the hue difference between the source and target domains. The method was tested on three publicly available datasets and yielded an average improvement of 2.3% in Fl scores. A convenient implementation of the method is provided to make sure it does not consume additional computational resources and storage space.

关键词： Training Adaptation models Computational modeling Machine vision Imaging Stability analysis Inference algorithms

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