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An in-depth Exploration of LAMOST Unknown Spectra Based on Density Clustering

Research in Astronomy and Astrophysics 2023年第5期23卷 52-65页

作者： Hai-Feng Yang Xiao-Na Yin Jiang-Hui Cai Yu-Qing Yang A-Li Luo Zhong-Rui Bai Li-Chan Zhou Xu-Jun Zhao Ya-Ling Xun Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and TechnologyTaiyuan 030024China School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030024China School of Computer Science and Technology North University of ChinaTaiyuan 030051China National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China

Large sky Area Multi-Object fiber Spectroscopic Telescope(LAMOST) has completed the observation of nearly 20 million celestial objects,including a class of spectra labeled “Unknown.” Besides low signal-to-noise ratio,these spectra often show some anomalous features that do not work well with current *** this paper,a total of 637,889 “Unknown” spectra from LAMOST DR5 are selected,and an unsupervised-based analytical framework of “Unknown” spectra named SA-Frame(Spectra Analysis-Frame) is provided to explore their origins from different *** SA-Frame is composed of three parts:NAPC-Spec clustering,characterization and origin ***,NAPC-Spec(Nonparametric density clustering algorithm for spectra) characterizes different features in the “unknown” spectrum by adjusting the influence space and divergence distance to minimize the effects of noise and high dimensionality,resulting in 13 ***,characteristic extraction and representation of clustering results are carried out based on spectral lines and continuum,where these 13 types are characterized as regular spectra with low S/Ns,splicing problems,suspected galactic emission signals,contamination from city light and un-gregarious type ***,a preliminary analysis of their origins is made from the characteristics of the observational targets,contamination from the sky,and the working status of the *** results would be valuable for improving the overall data quality of large-scale spectral surveys.

关键词： methods data analysis-surveys-techniques spectroscopic-site testing-methods analytical

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An SDN Traffic Engineering Approach Based on Traffic Unsupervised Contrastive Representation and Reinforcement Learning 6

An SDN Traffic Engineering Approach Based on Traffic Unsuper...

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6th International Conference on Next Generation data-Driven Networks, NGDN 2024

作者： Yang, Ruiyu Tang, Qi Guo, Yingya College of Computer and Data Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (纸本)9798350388374

With the rapid development of Internet technology and the continuous explosive growth of network traffic, Traffic Engineering (TE), as a viable method for optimizing network traffic distribution and improving network performance, attracts widespread attention from both industry and academia. Software Defined Networks (SDN), which decouples the data plane and the control plane, realizes a flexible routing and improves the TE performance. Existing TE approaches in SDN mainly utilize Reinforcement Learning (RL) methods to learn the mapping relationship between network traffic and routing policies. However, due to the continuous expansion of network size and dynamic changes in traffic, the enlargement of traffic state space hinders RL from converging to the optimal routing policy, leading to a decline in network performance. To address these issues, this paper presents a TE method based on unsupervised contrastive representation and RL. This method first shrinks the original traffic state space by efficiently extracting traffic features through Contrastive Learning (CL), aiding quick convergence of RL. It then uses RL to directly learn the mapping from traffic features to traffic splitting policies. Finally, through numerous experiments on real network traffic and topology, it demonstrates that the proposed TE method can effectively achieve load balancing of network traffic under complex and volatile dynamic traffic demands, thereby enhancing network performance. © 2024 IEEE.

关键词： Reinforcement learning

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Test-Time Model Adaptation with Only Forward Passes 41

Test-Time Model Adaptation with Only Forward Passes

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41st International Conference on Machine Learning, ICML 2024

作者： Niu, Shuaicheng Miao, Chunyan Chen, Guohao Wu, Pengcheng Zhao, Peilin College of Computing and Data Science Nanyang Technological University Singapore Joint NTU-WeBank Research Centre on Fintech Singapore Singapore Tencent AI Lab Shenzhen China

Test-time adaptation has proven effective in adapting a given trained model to unseen test samples with potential distribution shifts. However, in real-world scenarios, models are usually deployed on resource-limited devices, e.g., FPGAs, and are often quantized and hard-coded with non-modifiable parameters for acceleration. In light of this, existing methods are often infeasible since they heavily depend on computation-intensive backpropagation for model updating that may be not supported. To address this, we propose a test-time Forward-Optimization Adaptation (FOA) method. In FOA, we seek to solely learn a newly added prompt (as model's input) via a derivative-free covariance matrix adaptation evolution strategy. To make this strategy work stably under our online unsupervised setting, we devise a novel fitness function by measuring test-training statistic discrepancy and model prediction entropy. Moreover, we design an activation shifting scheme that directly tunes the model activations for shifted test samples, making them align with the source training domain, thereby further enhancing adaptation performance. Without using any backpropagation and altering model weights, FOA runs on quantized 8-bit ViT outperforms gradient-based TENT on full-precision 32-bit ViT, while achieving an up to 24-fold memory reduction on ImageNet-C. Code: https://***/mr-eggplant/FOA. Copyright 2024 by the author(s)

关键词： Image coding

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Towards the New XAI: A Hypothesis-Driven Approach to Decision Support Using Evidence 27

Towards the New XAI: A Hypothesis-Driven Approach to Decisio...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Le, Thao Miller, Tim Sonenberg, Liz Singh, Ronal School of Computing and Information Systems The University of Melbourne Australia School of Electrical Engineering and Computer Science The University of Queensland Australia CSIRO's Data61 Australia

ISBN: (纸本)9781643685489

Prior research on AI-assisted human decision-making has explored several different explainable AI (XAI) approaches. A recent paper has proposed a paradigm shift calling for hypothesis-driven XAI through a conceptual framework called evaluative AI that gives people evidence that supports or refutes hypotheses without necessarily giving a decision-aid recommendation. In this paper, we describe and evaluate an approach for hypothesis-driven XAI based on the Weight of Evidence (WoE) framework, which generates both positive and negative evidence for a given hypothesis. Through human behavioural experiments, we show that our hypothesis-driven approach increases decision accuracy and reduces reliance compared to a recommendation-driven approach and an AI-explanation-only baseline, but with a small increase in under-reliance compared to the recommendation-driven approach. Further, we show that participants used our hypothesis-driven approach in a materially different way to the two baselines. © 2024 The Authors.

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IF-Font: Ideographic Description Sequence-Following Font Generation 38

IF-Font: Ideographic Description Sequence-Following Font Gen...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Xinping Ke, Xiao Guo, Wenzhong Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350116 China

Few-shot font generation (FFG) aims to learn the target style from a limited number of reference glyphs and generate the remaining glyphs in the target font. Previous works focus on disentangling the content and style features of glyphs, combining the content features of the source glyph with the style features of the reference glyph to generate new glyphs. However, the disentanglement is challenging due to the complexity of glyphs, often resulting in glyphs that are influenced by the style of the source glyph and prone to artifacts. We propose IF-Font, a novel paradigm which incorporates Ideographic Description Sequence (IDS) instead of the source glyph to control the semantics of generated glyphs. To achieve this, we quantize the reference glyphs into tokens, and model the token distribution of target glyphs using corresponding IDS and reference tokens. The proposed method excels in synthesizing glyphs with neat and correct strokes, and enables the creation of new glyphs based on provided IDS. Extensive experiments demonstrate that our method greatly outperforms state-of-the-art methods in both one-shot and few-shot settings, particularly when the target styles differ significantly from the training font styles. The code is available at https://***/Stareven233/IF-Font. © 2024 Neural information processing systems foundation. All rights reserved.

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Influence-Aware Group Recommendation for Social Media Propagation 24

Influence-Aware Group Recommendation for Social Media Propag...

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24th IEEE International Conference on data Mining, ICDM 2024

作者： He, Chengkun Zhou, Xiangmin Wang, Chen Cao, Longbing Shao, Jie Tari, Zahir School of Computing Technologies RMIT University Melbourne Australia Data61 CSIRO Australia Macquarie University Australia University of Electronic Science and Technology of China China

ISBN: (纸本)9798331506681

Group recommendation over social media streams has attracted attention due to its wide applications such as e-commerce, entertainment and online news broadcasting. However, existing stream group recommendation techniques ignore the influence of user groups, which are not effective for item propagation over social networks. To address this problem, we propose a framework for Influence-aware Group Recommendation (IGR) over high-speed social streams. Specifically, we first propose a novel GroupGCN model to capture the dynamics of user attributes and interactions which maps groups and items to their embeddings. A Temporal GroupGCN-RNN-Autoencoder (TGGCN-RA) model is designed to extend GroupGCN for sequence-based tasks, enabling the prediction of group interests over time. Then, we adopt an Independent Cascade (IC) model to predict the influence propagation of social items over user groups. Extensive experiments prove the high effectiveness and efficiency of IGR. © 2024 IEEE.

关键词： Social networking (online)

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Time Matters: An End-to-End Solution for Temporal Claim Verification

Time Matters: An End-to-End Solution for Temporal Claim Veri...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Barik, Anab Maulana Hsu, Wynne Lee, Mong Li School of Computing National University of Singapore Singapore Institute of Data Science National University of Singapore Singapore Centre for Trusted Internet & Community National University of Singapore Singapore

ISBN: (纸本)9798891761667

Automated claim verification plays an essential role in fostering trust in the digital space. Temporal claim verification brings new challenges where cues of the temporal information need to be extracted, and temporal reasoning involving various temporal aspects of the text must be applied. In this work, we describe an end-to-end solution for temporal claim verification that considers the temporal information in claims to obtain relevant evidence sentences and harnesses the power of a large language model for temporal reasoning. We curate two datasets comprising a diverse range of temporal claims to learn time-sensitive representations that encapsulate not only the semantic relationships among the events, but also their chronological proximity. Experiment results demonstrate that the proposed approach significantly enhances the accuracy of temporal claim verification, thereby advancing current state-of-the-art in automated claim verification. © 2024 Association for Computational Linguistics.

关键词： Spatio-temporal data

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A Comparative Analysis of Cloud Load Balancing Algorithms Using CloudSim Simulations 1

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International Conference on Advanced Engineering, Technology and Applications, ICAETA 2024

作者： Abdullah, Fasih Razi, Muhammad Faraz Ud Din Aleem, Muhammad Jamil, Akhtar Hameed, Alaa Ali National University of Computer and Emerging Sciences Islamabad Pakistan Istinye University Istanbul Turkey School of Computing Mathematics and Data Science Coventry University Coventry United Kingdom

ISBN: (数字)9783031709241

ISBN: (纸本)9783031709234

Cloud computing provides a set of services that allows its users to lease digital infrastructure as software. This abstraction allows users to focus on development without managing low-level aspects and associated challenges. The infrastructure comprises heterogeneous VMs with varying performance parameters such as MIPS and bandwidth. Thus, user cloud jobs must be executed with great care and consideration of load balancing. If scheduling is not done efficiently, the latency that users experience increases, resulting in lower customer satisfaction and decreased revenue. Since scheduling problems are NP-Hard, there is a need for continuous research into improving existing algorithms. Ideally, the scheduling of cloud jobs should be fair and well-balanced, with minimal waiting time and minimal overhead. This study focuses on the comparison of nine such state-of-the-art, batch-based, non-preemptive scheduling schemes. All algorithms have been implemented in Java with the help of the CloudSim simulator and tested across seven performance metrics. We provide an in-depth performance analysis of Random Selection, Round Robin, Minimum Completion Time, Suffrage, Min-Min, Max-Min, Weighted Mean Time-Min, Resource Aware Load Balancing Algorithm, and QoS Guided Weighted Mean Time Min Heuristic across asymptotic time and space complexity, make-span, throughput, average resource utilization rate (ARUR), load balancing level, and batch scheduling time. Comparison of the mechanisms and their performance shall aid future work in comparing and contrasting the concepts deployed in existing schemes. It was observed that Suffrage and QWMTM performed the best across most of the performance metrics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Customer satisfaction

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Mixed-Based Approach for an Intelligent E-Commerce Recommender System 16

Mixed-Based Approach for an Intelligent E-Commerce Recommend...

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16th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2024

作者： Jusoh, Shaidah Ng, Ger Qin Xiamen University Malaysia School of Eletrical Engineering and Artificial Intelligence Selagor Sepang Malaysia Xiamen University Malaysia School of Computing and Data Science Selangor Sepang Malaysia

ISBN: (纸本)9798350371154

Content-based recommender systems and collaborative filtering recommender systems might be useful in recommending items that might be preferred by users based on their preferences from the past, these systems rely on users' preferences, items' ratings, and items' descriptions. Thus, these systems might be less useful for items for which users' preferences and ratings cannot be easily collected. On the other hand, a constraint-based recommender system can suggest items that fulfil users' requirements (or constraints) by relying on users' requirements and knowledge about the domain of the items for giving a recommendation. In the case where, no item can be found, the system will proceed with the constraints of a relaxation phase to search for possible modifications in the constraints, give advice to the user, and enable them to edit their requirements to obtain more results. In this study, we propose a new approach called a mixed-based approach. The approach is based on an integration of soft and hard relaxation-based approaches. The proposed approach is implemented as a notebook recommender system. We conducted experiments on the proposed approach using collected data from e-commerce websites. The results also show that the proposed mixed-based approach is able to control constraints, and gives better performance in giving recommendations to users. © 2024 IEEE.

关键词： Recommender systems

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A Graph with Adaptive AdjacencyMatrix for Relation Extraction

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Computers, Materials & Continua 2024年第9期80卷 4129-4147页

作者： Run Yang YanpingChen Jiaxin Yan Yongbin Qin Engineering Research Center of Text Computing&Cognitive Intelligence Ministry of EducationGuizhou UniversityGuiyang550025China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025China College of Computer Science and Technology Guizhou UniversityGuiyang550025China

The relation is a semantic expression relevant to two named entities in a *** a sentence usually contains several named entities,it is essential to learn a structured sentence representation that encodes dependency information specific to the two named *** related work,graph convolutional neural networks are widely adopted to learn semantic dependencies,where a dependency tree initializes the adjacency ***,this approach has two main ***,parsing a sentence heavily relies on external toolkits,which can be ***,the dependency tree only encodes the syntactical structure of a sentence,which may not align with the relational semantic *** this paper,we propose an automatic graph learningmethod to autonomously learn a sentence’s structural *** of using a fixed adjacency matrix initialized by a dependency tree,we introduce an Adaptive Adjacency Matrix to encode the semantic dependency between *** elements of thismatrix are dynamically learned during the training process and optimized by task-relevant learning objectives,enabling the construction of task-relevant semantic dependencies within a *** model demonstrates superior performance on the TACRED and SemEval 2010 datasets,surpassing previous works by 1.3%and 0.8%,*** experimental results show that our model excels in the relation extraction task,outperforming prior models.

关键词： Relation extraction graph convolutional neural network adaptive adjacency matrix

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