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Performance of OTFS-NOMA Scheme for Coordinated Direct and Relay Transmission Networks in High-Mobility Scenarios

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arXiv 2023年

作者： Xu, Yao Du, Zhen Yuan, Weijie Jia, Shaobo Leung, Victor C.M. School of Electronic and Information Engineering Nanjing University of Information Science and Technology Nanjing210044 China The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen518055 China The School of Information Engineering Zhengzhou University Zhengzhou450001 China The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada

In this paper, an orthogonal time frequency space (OTFS) based non-orthogonal multiple access (NOMA) scheme is investigated for the coordinated direct and relay transmission system, where a source directly communicates with a near user with high mobile speed, and it needs the relaying assistance to serve the far user also having high mobility. Due to the coexistence of signal superposition coding and multi-domain transformation, the performance of OTFS-based NOMA is usually challenging to be measured from a theoretical perspective. To accurately evaluate the system performance of the proposed scheme, we derive the closed-form expressions for the outage probability and the outage sum rate by using the Inversion formula and characteristic function. Numerical results verify the performance superiority and the effectiveness of the proposed scheme. Copyright © 2023, The Authors. All rights reserved.

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Human Factors in Model-Driven engineering: Future Research Goals and Initiatives for MDE

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arXiv 2024年

作者： Liebel, Grischa Klünder, Jil Hebig, Regina Lazik, Christopher Nunes, Inês Graßl, Isabella Steghöfer, Jan-Philipp Exelmans, Joeri Oertel, Julian Marquardt, Kai Juhnke, Katharina Schneider, Kurt Gren, Lucas Happe, Lucia Herrmann, Marc Wyrich, Marvin Tichy, Matthias Goulão, Miguel Wohlrab, Rebekka Kalantari, Reyhaneh Heinrich, Robert Greiner, Sandra Rukmono, Satrio Adi Chakraborty, Shalini Abrahão, Silvia Amaral, Vasco Department of Computer Science Reykjavik University Menntavegur 1 Reykjavik102 Iceland Software Engineering Group Leibniz University Hannover Welfengarten 1 Hannover30167 Germany Department of Software Engineering University of Rostock Albert-Einstein-Straße Rostock18057 Germany Department of Software Engineering Humboldt-Universität zu Berlin Unter den Linden 6 Berlin10099 Germany Frankfurt Germany Department of Software Engineering II University of Passau Innstraße 33 Passau94032 Germany XITASO GmbH Austraße 35 Augsburg86153 Germany Department of Computer Science University of Antwerp Middelheimlaan 1 Antwerp2020 Belgium Department of Informatics Karlsruhe Institute of Technology Am Fasanengarten 5 Karlsruhe75131 Germany SMT-EMI6 Carl Zeiss SMT GmbH Rudolf-Eber-Straße 2 Oberkochen73447 Germany Department of Computer Science and Engineering Chalmers | University of Gothenburg Gothenburg41127 Sweden Department of Interdisciplinary Didactics Karlsruhe Institute of Technology Engesserstr. 2 Karlsruhe75131 Germany Department of Computer Science Saarland University Saarland Informatics Campus Saarbrücken66123 Germany Institute of Software Engineering and Languages Ulm University James-Franck-Ring Ulm89081 Germany Department of Computer Science NOVA School of Science & Technology Campus de Caparica Caparica2829-516 Portugal Department of Engineering Design and Teaching Innovation University of Ottawa 800 King Edward Avenue OttawaK1N6N5 Canada KASTEL - Institute of Information Security and Dependability Karlsruhe Institute of Technology Am Fasanengarten 5 Karlsruhe75131 Germany Department of Computer Science University of Bern Bern3012 Switzerland Department of Mathematics and Computer Science Eindhoven University of Technology De Zaale Eindhoven5600 MB Netherlands Instituto Universitario Mixto de Tecnología Informática Universitat Politècnica de València Camino de Vera s/n Valencia46022 Spain

Purpose: software modelling and Model-Driven engineering (MDE) is traditionally studied from a technical perspective. However, one of the core motivations behind the use of software models is inherently human-centred. Models aim to enable practitioners to communicate about software designs, make software understandable, or make software easier to write through domain-specific modelling languages. Several recent studies challenge the idea that these aims can always be reached and indicate that human factors play a role in the success of MDE. However, there is an under-representation of research focusing on human factors in modelling. Methods: During a GI-Dagstuhl seminar, topics related to human factors in modelling were discussed by 26 expert participants from research and industry. Results: In breakout groups, five topics were covered in depth, namely modelling human aspects, factors of modeller experience, diversity and inclusion in MDE, collaboration and MDE, and teaching human-aware MDE. Conclusion: We summarise our insights gained during the discussions on the five topics. We formulate research goals, questions, and propositions that support directing future initiatives towards an MDE community that is aware of and supportive of human factors and values. © 2024, CC BY.

关键词： software design

Ensemble Distillation for Out-of-distribution Detection

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Ensemble Distillation for Out-of-distribution Detection

International Conference on Parallel and Distributed Systems (ICPADS)

作者： Dongdong Wang Jingyao Xu Siyang Lu Xiang Wei Liqiang Wang Department of Civil Environmental and Construction Engineering University of Central Florida Orlando FL US School of Computer and Information Technolog Beijing Jiaotong University Beijing China School of Software Engineering Beijing Jiaotong University Beijing China Department of Computer Science University of Central Florida Orlando FL US

Out-of-distribution detection is critical to a reliable application of deep neural networks. To reduce model uncertainty, we propose a simple yet effective approach of ensemble knowledge distillation. We blend ensemble model and knowledge distillation to improve model generalization on indomain recognition, thereby yielding accurate and robust out-of-distribution detection. The former effectively expands data recognition feature space, while the latter further regularizes the model through knowledge distillation, enhancing in-domain feature recognition. This effective integration successfully yields lower model uncertainty on in-domain feature recognition and improves anomaly detection in a more scalable manner. We justify our approach through extensive experiments on various benchmarks, demonstrating its significant improvement in out- of-distribution detection. We validate our approach with a variety of up-to-date DNNs, like Vision Transformer.

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A Dimensional Structure based Knowledge Distillation Method for Cross-Modal Learning

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arXiv 2023年

作者： Si, Lingyu Dong, Hongwei Qiang, Wenwen Yu, Junzhi Zhai, Wenlong Zheng, Changwen Xu, Fanjiang Sun, Fuchun Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences China College of Engineering Peking University China Department of Computer Science and Technology Tsinghua University China

Due to limitations in data quality, some essential visual tasks are difficult to perform independently. Introducing previously unavailable information to transfer informative dark knowledge has been a common way to solve such hard tasks. However, research on why transferred knowledge works has not been extensively explored. To address this issue, in this paper, we discover the correlation between feature discriminability and dimensional structure (DS) by analyzing and observing features extracted from simple and hard tasks. On this basis, we express DS using deep channel-wise correlation and intermediate spatial distribution, and propose a novel cross-modal knowledge distillation (CMKD) method for better supervised cross-modal learning (CML) performance. The proposed method enforces output features to be channel-wise independent and intermediate ones to be uniformly distributed, thereby learning semantically irrelevant features from the hard task to boost its accuracy. This is especially useful in specific applications where the performance gap between dual modalities is relatively large. Furthermore, we collect a real-world CML dataset to promote community development. The dataset contains more than 10, 000 paired optical and radar images and is continuously being updated. Experimental results on real-world and benchmark datasets validate the effectiveness of the proposed method. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

CMIVGSD: CircRNA-miRNA Interaction Prediction Based on Variational Graph Auto-Encoder and Singular Value Decomposition

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CMIVGSD: CircRNA-miRNA Interaction Prediction Based on Varia...

2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Qian, Yurong Zheng, Jingjing Zhang, Zhe Jiang, Ying Zhang, Jiaxuan Deng, Lei Xinjiang University School of Software Urumqi China Central South University School of Computer Science and Engineering Changsha China University of California San Diego Department of Cognitive Science La Jolla United States

ISBN: (纸本)9781665401265

A large amount of evidence shows that circular RNAs(circRNAs) participate in transcription and translation regulation and function as 'micro RNA(miRNA)-sponges'. Recognizing circRNA-miRNA interaction is helpful to understand the function of circRNAs, especially its role in complex diseases. Obtaining interactive information based on traditional biological experiments is usually small-scale, time-consuming, and laborious. Considering that there are few calculation methods, it is urgent to develop efficient and accurate methods to extract the interaction between circRNA and miRNA. In this work, we proposed a computational framework called CMIVGSD, which uses singular value decomposition and graph variational auto-encoders to predict circRNA-miRNA interaction. To our best knowledge, CMIVGSD is the first calculation framework to predict circRNA-miRNA interaction. CMIVGSD uses the singular value decomposition (SVD) algorithm to obtain linear features from the circRNA-miRNA interaction matrix. We have constructed the similarity networks of circRNA and miRNA, respectively. The graph variational auto-encoder (VGAE) is employed to mine the non-linear features of circRNA-miRNA in similarity networks. Finally, we combine linear and non-linear features and use LightGBM to predict interaction scores. We performed five-fold cross-validation experiments. Experimental results show that our proposed method is better than other methods. The case study further proves the effectiveness of CMIVGSD in predicting circRNA-miRNA interaction. © 2021 IEEE.

关键词： Singular value decomposition

Seed Feature Maps-based CNN Models for LEO Satellite Remote Sensing Services

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arXiv 2023年

作者： Lu, Zhichao Ding, Chuntao Wang, Shangguang Cheng, Ran Juefei-Xu, Felix Boddeti, Vishnu Naresh School of Software Engineering Sun Yat-Sen University Zhuhai China School of Computer and Information Technology Beijing Jiaotong University Beijing China Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China New York University New YorkNY United States Department of Computer Science and Engineering Michigan State University East LansingMI United States

Deploying high-performance convolutional neural network (CNN) models on low-earth orbit (LEO) satellites for rapid remote sensing image processing has attracted significant interest from industry and academia. However, the limited resources available on LEO satellites contrast with the demands of resource-intensive CNN models, necessitating the adoption of ground-station server assistance for training and updating these models. Existing approaches often require large floating-point operations (FLOPs) and substantial model parameter transmissions, presenting considerable challenges. To address these issues, this paper introduces a ground-station server-assisted framework. With the proposed framework, each layer of the CNN model contains only one learnable feature map (called the seed feature map) from which other feature maps are generated based on specific rules. The hyperparameters of these rules are randomly generated instead of being trained, thus enabling the generation of multiple feature maps from the seed feature map and significantly reducing FLOPs. Furthermore, since the random hyperparameters can be saved using a few random seeds, the ground station server assistance can be facilitated in updating the CNN model deployed on the LEO satellite. Experimental results on the ISPRS Vaihingen, ISPRS Potsdam, UAVid, and LoveDA datasets for semantic segmentation services demonstrate that the proposed framework outperforms existing state-of-the-art approaches. In particular, the SineFM-based model achieves a higher mIoU than the UNetFormer on the UAVid dataset, with 3.3× fewer parameters and 2.2× fewer FLOPs. Copyright © 2023, The Authors. All rights reserved.

关键词： Neural network models

GCNSDA: Predicting snoRNA-disease associations via graph convolutional network

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GCNSDA: Predicting snoRNA-disease associations via graph con...

2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Liu, Dayun Luo, Yi Zheng, Jingjing Xu, Hanlin Zhang, Jiaxuan Deng, Lei Central South University School of Computer Science and Engineering Changsha China Xinjiang University School of Software Urumqi China University of California San Diego Department of Cognitive Science La Jolla United States

ISBN: (纸本)9781665401265

Small nucleolar RNAs(snoRNAs) represent an abundant group of noncoding RNAs in the nucleolus of eukaryotes. Recent studies revealed that snoRNAs play a significant role in a wide range of diseases. Identifying snoRNA-disease associations can provide great insights into understanding the disease and treatment and boosting drug discovery development. Traditional methods of using biological experiments are often usually small-scale and time-consuming. Therefore, it is urgent to develop a computational framework to predict snoRNA-disease associations. In this work, we proposed a novel Graph Convolutional Network(GCN) based framework GCNSDA for predicting snoRNA-disease associations. To our best knowledge, GCNSDA is the first framework that uses a graph convolutional network to predict snoRNA-disease associations. GCNSDA is built on the bipartite graph of snoRNAs and diseases;it uses a graph neural network to discover latent factors that cause the association between snoRNAs and diseases and then generate embedded representations of snoRNAs and diseases. Experimental results showed that GCNSDA achieved better performance than the other six state-of-the-art methods. Case study further confirmed the effectiveness of GCNSDA in predicting snoRNA-disease associations. © 2021 IEEE.

关键词： Graph neural networks

The CLaC System at the TREC 2020 News Track 29

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The CLaC System at the TREC 2020 News Track

29th Text REtrieval Conference, TREC 2020

作者： Khloponin, Pavel Kosseim, Leila ClaC Lab Department of Computer Science and Software Engineering Gina Cody School of Engineering and Computer Science Concordia University MontrealQC Canada

This paper describes our approach to the TREC 2020 News Track. The goal of the News Track is to provide background links and entity linking to target news articles within a collection of articles. We submitted 4 runs, 2 of which achieved scores higher than the median. The first approach is based on the classic Okapi BM25 using the entire article as a query. This run obtained an nDCG@5 of 0.5924. The second approach is based on a combination of BM25 with GPT-2 embeddings which lead to an nDCG@5 of 0.5873. These top models were chosen after exploring a variety of representations of documents as embedding vectors and proximity measures. Combining document embeddings and Okapi BM25 is shown to diversify the results without much impact on quality, and can help to overcome the limitations of a single model system implementation. © 2020 29th Text REtrieval Conference, TREC 2020 - Proceedings. All Rights Reserved.

关键词： Embeddings

WMMSE Beamforming for User-Centric Cell-Free Networks with Non-Coherent Joint Transmission

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WMMSE Beamforming for User-Centric Cell-Free Networks with N...

IEEE Conference on Global Communications (GLOBECOM)

作者： Xi Wang Xiaotong Zhao Juncheng Wang Qingjiang Shi School of Software Engineering Tongji University Shanghai China Department of Computer Science Hong Kong Baptist University Hong Kong China Shenzhen Research Institute of Big Data Shenzhen China

We consider downlink beamforming design to maximize the weighted sum rate (WSR) in a user-centric cell-free network, where distributed access points (APs) are organized in a cluster to jointly serve each user equipment (UE). This architecture ensures uniform service quality even at the cell edge, but synchronization between APs can be problematic, necessitating the utilization of non-coherent joint transmission (NCJT) that eliminates the need for strict synchronization. Most existing works on beamforming design for NCJT assume that the classic weighted minimum mean square error (WMMSE) approach is not applicable, and design their beamforming algorithms based on the successive convex approximation (SCA) method with high computational complexity. In this work, we for the first time demonstrate the applicability of the WMMSE approach for NCJT in cell-free networks. Based on the unique observations on the structures of the WSR maximization problem for NCJT, we propose an efficient WMMSE based beamforming algorithm. Our proposed algorithm is guaranteed to converge to a stationary point of the WSR maximization problem. Furthermore, our beamforming updates are in closed form with low computational complexity. Simulation results demonstrate substantial performance gain of our proposed algorithm over the current best SCA based alternatives in both computational complexity and convergence time.

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