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arXiv 2024年

作者： Hu, Xiaoyin Xiao, Nachuan Liu, Xin Toh, Kim-Chuan School of Computer and Computing Science Hangzhou City University Hangzhou310015 China Institute of Operational Research and Analytics National University of Singapore Singapore State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences University of Chinese Academy of Sciences China Department of Mathematics Institute of Operations Research and Analytics National University of Singapore Singapore119076 Singapore

In this paper, we propose a generalized framework for developing learning-rate-free momentum stochastic gradient descent (SGD) methods in the minimization of nonsmooth nonconvex functions, especially in training nonsmooth neural networks. Our framework adaptively generates learning rates based on the historical data of stochastic subgradients and iterates. Under mild conditions, we prove that our proposed framework enjoys global convergence to the stationary points of the objective function in the sense of the conservative field, hence providing convergence guarantees for training nonsmooth neural networks. Based on our proposed framework, we propose a novel learning-rate-free momentum SGD method (LFM). Preliminary numerical experiments reveal that LFM performs comparably to the state-of-the-art learning-rate-free methods (which have not been shown theoretically to be convergence) across well-known neural network training benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Momentum

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Modelling the Dependence of Chlorophyll Leaf-Clip Measures on Vegetation Indices Derived from Multispectral UAS Images in Vineyards Parcels

Modelling the Dependence of Chlorophyll Leaf-Clip Measures o...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Gil Gonçalves Umberto Andriolo Lúcio Paiva Cristiano Premebida António Ferraz Department of Mathematics University of Coimbra Coimbra Portugal Department of Electrical and Computer Engineering INESC Coimbra Coimbra Portugal Research Centre for Natural Resources Environment and Society Polytechnic Institute of Coimbra Coimbra Agrarian Technical School Coimbra Portugal Department of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Coimbra Portugal NASA Jet Propulsion Laboratory Pasadena United States

Multispectral images and leaf-clip measurements were used for evaluating the Chlorophyll (Chl) content on grapevine leaves through the use of vegetation indices. The multispectral leaf images were taken by a sensor onboard an UAS, which was placed over a table at a height of 70 cm. Images were radiometrically and geometrically processed to obtain accurate coregistered five band image stacks. Using a Kmeans segmentation, each leaf imaged in a multispectral image was automatically detected and the mean leaf reflectance values were used to compute three vegetation indices: NDVI, NDRE, and GLI. When compared to leaf-clip measurements, the results indicated that the NDRE index was the best predictor of Chl leaf content (R2=0.81). The obtained NDRE regression model can be used in UAS-based multispectral orthomosaics to generate canopy Chl maps at vine-row scale, which can assist vine growers in monitoring the spatial and temporal variability of grapevine vigor.

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Adaptive Multi-Channel Contrastive Graph Convolutional Network with Graph and Feature Fusion

SSRN

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SSRN 2023年

作者： Zhong, Luying Lu, Jielong Chen, Zhaoliang Song, Na Wang, Shiping College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China School of Mechanical Electrical and Information Engineering Putian University Putian351100 China

Multi-view semi-supervised classification is an attractive topic in real-world applications. Due to the powerful capability of gathering information from neighbors, Graph Convolutional Network (GCN) has become a hotspot in the classification task. However, most multi-view classification works based on GCN only assign weights for feature fusion, and directly consider the weighted sum of the adjacency matrices, ignoring the interaction and correlation among features. These may be problematic because aggregating the matrices from less relevant views may destroy the original topology space, leading to undesired performance. To address these issues, this paper proposes an Adaptive Multi-Channel Graph Convolutional Network (AMC-GCN). To extract the interactive information, AMC-GCN designs a deep interactive feature integration network to incorporate consensus and complementary information. To fuse the graph structures, AMC-GCN exploits the relevance between views and imposes an adjacency matrix fusion network to construct multiple GCN channels, thereby delivering valuable information from relevant graphs. To enhance the homogeneity of the framework, AMC-GCN applies a contrastive loss to joint learning during the optimization for classification. With these techniques, AMC-GCN exploits relevant and interactive information between views to promote graph and feature fusion. Extensive experimental results on real-world datasets demonstrate the superiority of the proposed algorithm. © 2023, The Authors. All rights reserved.

关键词： Classification (of information)

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Recognition of Greek Orthodox Hymns Using Audio Fingerprint Techniques

Recognition of Greek Orthodox Hymns Using Audio Fingerprint ...

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South Eastern European Design Automation, computer engineering, computer Networks and Social Media Conference (SEEDA-CECNSM)

作者： Konstantinos Karasavvidis Dimitris Kampelopoulos Lazaros Moysis Achilles D. Boursianis Spiridon Nikolaidis Panagiotis Sarigiannidis Sotirios K. Goudos ELEDIA@AUTH School of Physics Aristotle University of Thessaloniki Thessaloniki Greece Laboratory of Nonlinear Systems - Circuits & Complexity School of Physics Aristotle University of Thessaloniki Thessaloniki Greece Department of Mechanical Engineering University of Western Macedonia Kozani Greece Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece

Audio fingerprinting was originally developed for music song identification, and over the years has been used for many more cases. With fingerprinting, an equivalent signature of the audio signal is saved in a simple form, so that it can be easily compared with another fingerprint. In this work, an audio fingerprinting algorithm is developed for the recognition of Greek Orthodox hymns. The main difference between music songs and Greek Orthodox hymns is the absence of music instruments, as there is only the chanter's voice. The test dataset consists of 10 hymns, and for each hymn there are 5 different performances, creating a dataset of 50 performances. Several tunable parameters of the fingerprinting technique are tested, like the parameters of the Hamming window, the frequency spectrum, the sample duration, and the sample noise, to find the differences in Greek Orthodox hymns. The recognition results for all cases are positive. The algorithm, in most cases, had a high recognition rate. even with noisy samples.

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NTIRE 2023 Challenge on Light Field Image Super-Resolution: Dataset, Methods and Results

NTIRE 2023 Challenge on Light Field Image Super-Resolution: ...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Wang, Yingqian Wang, Longguang Liang, Zhengyu Yang, Jungang Timofte, Radu Guo, Yulan Jin, Kai Wei, Zeqiang Yang, Angulia Guo, Sha Gao, Mingzhi Zhou, Xiuzhuang Van Duong, Vinh Huu, Thuc Nguyen Yim, Jonghoon Jeon, Byeungwoo Liu, Yutong Cheng, Zhen Xiao, Zeyu Xu, Ruikang Xiong, Zhiwei Liu, Gaosheng Jin, Manchang Yue, Huanjing Yang, Jingyu Gao, Chen Zhang, Shuo Chang, Song Lin, Youfang Chao, Wentao Wang, Xuechun Wang, Guanghui Duan, Fuqing Xia, Wang Wang, Yan Xia, Peiqi Wang, Shunzhou Lu, Yao Cong, Ruixuan Sheng, Hao Yang, Da Chen, Rongshan Wang, Sizhe Cui, Zhenglong Chen, Yilei Lu, Yongjie Cai, Dongjun An, Ping Salem, Ahmed Ibrahem, Hatem Yagoub, Bilel Kang, Hyun-Soo Zeng, Zekai Wu, Heng National University of Defense Technology China Aviation University of Air Force China University of Würzburg Germany Eth Zürich Switzerland Sun Yat-sen University The Shenzhen Campus of Sun Yat-sen University China Bigo Technology Pte. Ltd. Singapore Smart Medical Innovation Lab Beijing University of Posts and Telecommunications China Global Explorer Ltd. Suzhou China National Engineering Research Center of Visual Technology School of Computer Science Peking University China School of Artificial Intelligence Beijing University of Posts and Telecommunications China Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of University of Science and Technology of China China School of Electrical and Information Engineering Tianjin University China Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University China Beijing Normal University China Toronto Metropolitan University Canada Beijing Institute of Technology China Shenzhen MSU-BIT University China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beihang Hangzhou Innovation Institute Yuhang China Faculty of Applied Sciences Macao Polytechnic University China School of Communication and Information Engineering Shanghai University China School of Information and Communication Engineering Chungbuk National University Korea Republic of Guangdong University of Technology China

ISBN: (纸本)9798350302493

In this report, we summarize the first NTIRE challenge on light field (LF) image super-resolution (SR), which aims at super-resolving LF images under the standard bicubic degradation with a magnification factor of 4. This challenge develops a new LF dataset called NTIRE-2023 for validation and test, and provides a toolbox called BasicLFSR to facilitate model development. Compared with single image SR, the major challenge of LF image SR lies in how to exploit complementary angular information from plenty of views with varying disparities. In total, 148 participants have registered the challenge, and 11 teams have successfully submitted results with PSNR scores higher than the baseline method LF-InterNet [1]. These newly developed methods have set new state-of-the-art in LF image SR, e.g., the winning method achieves around 1 dB PSNR improvement over the existing state-of-the-art method DistgSSR [2]. We report the solutions proposed by the participants, and summarize their common trends and useful tricks. We hope this challenge can stimulate future research and inspire new ideas in LF image SR. © 2023 IEEE.

关键词： Image enhancement

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A Computational Model to Analyse E/I (Excitation/Inhibition) Dynamics for Neural Network Integrated with Astrocyte

A Computational Model to Analyse E/I (Excitation/Inhibition)...

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2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2020

作者： Bandyopadhyay, Anirban Sharma, Gaurav Roy Chowdhury, Shubhajit Biomedical Systems Laboratory Multimedia Analytics Networks and Systems Group School of Basic Science United States Biomedical Systems Laboratory Multimedia Analytics Networks and Systems Group School of Computing and Electrical Engineering Indian Institute of Technology Mandi India

ISBN: (纸本)9781728194684

Excitatory glutamate and inhibitory GABA dynamics homeostasis, which is often designated as E/I ratio, is regulated by different types of neural inhibitory circuit system. Gliotransmitter released from astrocyte coupled with two main types of the neural network system, feedback and feedforward inhibitory network can render alteration in E/I dynamics. The recent study provides a new model to study the E/I dynamics by integrating the neural circuit with astrocyte, aiming to understand the effect of input synaptic current ranging 20 -40μA/cm2, the effect of two different types of neural inhibition networks on the E/I dynamics and the effect of information transmission from astrocyte to the neural network with different coupling parameters on the E/I dynamics. The simulated result reveals that the frequency of E/I peak and the maximum peak height is increased with increasing the input current. The maximum E/I peak is diminished in the case of feedback inhibition system than the feedforward system with the constant input current. Another important outcome of the model is that the E/I ratio is dependent on the astrocytic glutamate with a limiting coupling parameter value. © 2020 IEEE.

关键词： Dynamics

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Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services

arXiv

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arXiv 2023年

作者： Xu, Minrui Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Mao, Shiwen Han, Zhu Jamalipour, Abbas Kim, Dong In Shen, Xuemin Leung, Victor C.M. Poor, H. Vincent The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore The Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States The Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of The School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada The College of Computer Science and Software Engineering Shenzhen University Shenzhen518061 China The Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Artificial Intelligence-Generated Content (AIGC) is an automated method for generating, manipulating, and modifying valuable and diverse data using AI algorithms creatively. This survey paper focuses on the deployment of AIGC applications, e.g., ChatGPT and Dall-E, at mobile edge networks, namely mobile AIGC networks, that provide personalized and customized AIGC services in real time while maintaining user privacy. We begin by introducing the background and fundamentals of generative models and the lifecycle of AIGC services at mobile AIGC networks, which includes data collection, training, fine-tuning, inference, and product management. We then discuss the collaborative cloud-edge-mobile infrastructure and technologies required to support AIGC services and enable users to access AIGC at mobile edge networks. Furthermore, we explore AIGC-driven creative applications and use cases for mobile AIGC networks. Additionally, we discuss the implementation, security, and privacy challenges of deploying mobile AIGC networks. Finally, we highlight some future research directions and open issues for the full realization of mobile AIGC networks. © 2023, CC BY.

关键词： Life cycle

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Distributed Task-Oriented Communication Networks with Multimodal Semantic Relay and Edge Intelligence

arXiv

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arXiv 2024年

作者： Guo, Jie Chen, Hao Song, Bin Chi, Yuhao Yuen, Chau Yu, Fei Richard Li, Geoffrey Ye Niyato, Dusit The State Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China The Hangzhou Institute of Technology Xidian University Hangzhou311231 China The School of Electrical and Electronics Engineering The School of Computer Science and Engineering Respectively Nanyang Technological University 639798 Singapore The Department of Systems and Computer Engineering Carleton University OttawaK1S 5B6 Canada The Faculty of Engineering Imperial College London LondonSW7 2AZ United Kingdom

In this article, we present a novel framework, named distributed task-oriented communication networks (DTCN), based on recent advances in multimodal semantic transmission and edge intelligence. In DTCN, the multimodal knowledge of semantic relays and the adaptive adjustment capability of edge intelligence can be integrated to improve task performance. Specifically, we propose the key techniques in the framework, such as semantic alignment and complement, a semantic relay scheme for deep joint source-channel relay coding, and collaborative device-server optimization and inference. Furthermore, a multimodal classification task is used as an example to demonstrate the benefits of the proposed DTCN over existing methods. Numerical results validate that DTCN can significantly improve the accuracy of classification tasks, even in harsh communication scenarios (e.g., low signal-to-noise regime), thanks to multimodal semantic relay and edge intelligence. © 2024, CC BY-NC-SA.

关键词： Signal to noise ratio

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Analyzing the decoding rate of circular coding in a noisy transmission channel

Analyzing the decoding rate of circular coding in a noisy tr...

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2020 Media Watermarking, Security, and Forensics Conference, MWSF 2020

作者： Sun, Yufang Allebach, Jan P. Electronic Imaging Systems Laboratory School of Electrical and Computer Engineering Purdue University West LafayetteIN47906 United States

Embedding information into a printed image is useful in many aspects, in which reliable channel encoding/decoding systems are crucial, since there is information loss and error propagation during transmission. Circular coding is a general two-dimensional channel coding method that allows data recovery with only a cropped portion of the code, and without the knowledge of the carrier image. While some traditional methods add redundancy bits to extend the length of the original massage length, this method embeds message into image rows in a repeated and shifted manner with redundancy, then uses the majority votes of the redundancy bits for recovery. In this paper, we developed a closed-form formula to predict its decoding success rate in a noisy channel under various transmission noise levels, using probabilistic modeling. The theoretical result is validated with simulations. This result enables the optimal parameter selection in the encoder and decoder system design, and decoding rate prediction with different levels of transmission error. © 2020, Society for Imaging Science and Technology.

关键词： Redundancy

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LORA-BERT: A NATURAL LANGUAGE PROCESSING MODEL FOR ROBUST AND ACCURATE PREDICTION OF LONG NON-CODING RNAS

arXiv

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arXiv 2024年

作者： Jeon, Nicholas Qian, Xiaoning SaidyKhan, Lamin de Figueiredo, Paul Yoon, Byung-Jun Department of Electrical and Computer Engineering Texas A&M University United States Computing and Data Sciences Brookhaven National Laboratory Christopher S. Bond Life Sciences Center Department of Molecular Microbiology and Immunology The University of Missouri United States Department of Veterinary Pathobiology Department of Chemical and Biomedical Engineering University of Missouri School of Medicine United States

Long non-coding RNAs (lncRNAs) serve as crucial regulators in numerous biological processes. Although they share sequence similarities with messenger RNAs (mRNAs), lncRNAs perform entirely different roles, providing new avenues for biological research. The emergence of next-generation sequencing technologies has greatly advanced the detection and identification of lncRNA transcripts and deep learning-based approaches have been introduced to classify long non-coding RNAs (lncRNAs). These advanced methods have significantly enhanced the efficiency of identifying lncRNAs. However, many of these methods are devoid of robustness and accuracy due to the extended length of the sequences involved. To tackle this issue, we have introduced a novel pre-trained bidirectional encoder representation called LoRA-BERT. LoRA-BERT is designed to capture the importance of nucleotide-level information during sequence classification, leading to more robust and satisfactory outcomes. In a comprehensive comparison with commonly used sequence prediction tools, we have demonstrated that LoRA-BERT outperforms them in terms of accuracy and efficiency. Our results indicate that, when utilizing the transformer model, LoRA-BERT achieves state-of-the-art performance in predicting both lncRNAs and mRNAs for human and mouse species. Through the utilization of LoRA-BERT, we acquire valuable insights into the traits of lncRNAs and mRNAs, offering the potential to aid in the comprehension and detection of diseases linked to lncRNAs in humans. © 2024, CC BY-NC-ND.

关键词： Deep learning

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