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Journal articles on the topic 'LSGAN'

Author: Grafiati

Published: 10 March 2023

Last updated: 31 July 2025

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1

Nie, Jianghua, Yongsheng Xiao, Lizhen Huang, and Feng Lv. "Time-Frequency Analysis and Target Recognition of HRRP Based on CN-LSGAN, STFT, and CNN." Complexity 2021 (April 12, 2021): 1–10. http://dx.doi.org/10.1155/2021/6664530.

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Aiming at the problem of radar target recognition of High-Resolution Range Profile (HRRP) under low signal-to-noise ratio conditions, a recognition method based on the Constrained Naive Least-Squares Generative Adversarial Network (CN-LSGAN), Short-time Fourier Transform (STFT), and Convolutional Neural Network (CNN) is proposed. Combining the Least-Squares Generative Adversarial Network (LSGAN) with the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP), the CN-LSGAN is presented and applied to the HRRP denoise. The frequency domain and phase features of HRRP are gaine

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Yue, Yunpeng, Hai Liu, Xu Meng, Yinguang Li, and Yanliang Du. "Generation of High-Precision Ground Penetrating Radar Images Using Improved Least Square Generative Adversarial Networks." Remote Sensing 13, no. 22 (2021): 4590. http://dx.doi.org/10.3390/rs13224590.

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Deep learning models have achieved success in image recognition and have shown great potential for interpretation of ground penetrating radar (GPR) data. However, training reliable deep learning models requires massive labeled data, which are usually not easy to obtain due to the high costs of data acquisition and field validation. This paper proposes an improved least square generative adversarial networks (LSGAN) model which employs the loss functions of LSGAN and convolutional neural networks (CNN) to generate GPR images. This model can generate high-precision GPR data to address the scarci

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Fang, Weidong, Yihan Guo, and Ji Zhang. "Generative Adversarial Network-Based Voltage Fault Diagnosis for Electric Vehicles under Unbalanced Data." Electronics 13, no. 16 (2024): 3131. http://dx.doi.org/10.3390/electronics13163131.

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The research of electric vehicle power battery fault diagnosis technology is turning to machine learning methods. However, during operation, the time of occurrence of faults is much smaller than the normal driving time, resulting in too small a proportion of fault data as well as a single fault characteristic in the collected data. This has hindered the research progress in this field. To address this problem, this paper proposes a data enhancement method using Least Squares Generative Adversarial Networks (LSGAN). The method consists of training the original power battery fault dataset using

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Aguirre, Nicolas, Leandro J. Cymberknop, Edith Grall-Maës, Eugenia Ipar, and Ricardo L. Armentano. "Central Arterial Dynamic Evaluation from Peripheral Blood Pressure Waveforms Using CycleGAN: An In Silico Approach." Sensors 23, no. 3 (2023): 1559. http://dx.doi.org/10.3390/s23031559.

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Arterial stiffness is a major condition related to many cardiovascular diseases. Traditional approaches in the assessment of arterial stiffness supported by machine learning techniques are limited to the pulse wave velocity (PWV) estimation based on pressure signals from the peripheral arteries. Nevertheless, arterial stiffness can be assessed based on the pressure–strain relationship by analyzing its hysteresis loop. In this work, the capacity of deep learning models based on generative adversarial networks (GANs) to transfer pressure signals from the peripheral arterial region to pressure an

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Kumar Yadalam, Pradeep. "Leveraging LSGAN for synthetic gingival keratinization genomic data: Insights from drug interaction and gene ontology in an early fusion framework." Gaceta Médica de Caracas 133, no. 2 (2025): 399–408. https://doi.org/10.47307/gmc.2025.133.2.11.

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Introduction: Gingival keratinization, a vital process in oral health, involves the formation of a keratin-rich protective epithelial layer, providing resilience against mechanical stress, pathogens, and environmental factors. Objective: This study employs an early fusion omics approach with Least-Squares Generative Adversarial Networks (LSGAN) to generate synthetic genomic data, incorporating insights from drug interactions and geneontology annotations. Methods: Gene expression data from the NCBI GEO dataset (GSE182196) were analyzed to identify differentially expressed genes (DEGs) across di

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Huo, Yongqing, Jing Gan, and Wenke Jiang. "Multi-exposure high dynamic range imaging based on LSGAN." Displays 83 (July 2024): 102707. http://dx.doi.org/10.1016/j.displa.2024.102707.

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Dewi, Christine, Rung-Ching Chen, Yan-Ting Liu, and Hui Yu. "Various Generative Adversarial Networks Model for Synthetic Prohibitory Sign Image Generation." Applied Sciences 11, no. 7 (2021): 2913. http://dx.doi.org/10.3390/app11072913.

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A synthetic image is a critical issue for computer vision. Traffic sign images synthesized from standard models are commonly used to build computer recognition algorithms for acquiring more knowledge on various and low-cost research issues. Convolutional Neural Network (CNN) achieves excellent detection and recognition of traffic signs with sufficient annotated training data. The consistency of the entire vision system is dependent on neural networks. However, locating traffic sign datasets from most countries in the world is complicated. This work uses various generative adversarial networks

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Gao, Dan, Xiaofang Wu, Zhijin Wen, Yue Xu, and Zhengchao Chen. "Few-shot SAR vehicle target augmentation based on generative adversarial networks." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-1-2024 (May 9, 2024): 83–90. http://dx.doi.org/10.5194/isprs-annals-x-1-2024-83-2024.

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Abstract. The study of few-shot SAR image generation is an effective way to expand the SAR dataset, which not only provides diversified data support for SAR target classification, but also provides a high-fidelity false image template for SAR deceptive jamming. In this paper, we have constructed a multi-frequency and multi-target type SAR vehicle imagery dataset that encompasses frequencies such as X, Ka, P, and S bands. The vehicle types are coaster, suv and cabin. Subsequently, we utilized various Generative Adversarial Networks for image generation from the SAR vehicle dataset. The experime

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Man, Zhenlong, Jinqing Li, Xiaoqiang Di, et al. "A novel image encryption algorithm based on least squares generative adversarial network random number generator." Multimedia Tools and Applications 80, no. 18 (2021): 27445–69. http://dx.doi.org/10.1007/s11042-021-10979-w.

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AbstractIn cryptosystems, the generation of random keys is crucial. The random number generator is required to have a sufficiently fast generation speed to ensure the size of the keyspace. At the same time, the randomness of the key is an important indicator to ensure the security of the encryption system. The chaotic random number generator has been widely used in cryptosystems due to the uncertainty, non-repeatability, and unpredictability of chaotic systems. However, chaotic systems, especially high-dimensional chaotic systems, have slow calculation speed and long iteration time. This cause

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Pardede, Jasman, and Anisa Putri Setyaningrum. "Implementation of Generative Adversarial Network to Generate Fake Face Image." Jurnal Online Informatika 8, no. 1 (2023): 44–51. http://dx.doi.org/10.15575/join.v8i1.790.

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In recent years, many crimes use technology to generate someone's face which has a bad effect on that person. Generative adversarial network is a method to generate fake images using discriminators and generators. Conventional GAN involved binary cross entropy loss for discriminator training to classify original image from dataset and fake image that generated from generator. However, use of binary cross entropy loss cannot provided gradient information to generator in creating a good fake image. When generator creates a fake image, discriminator only gives a little feedback (gradient informat

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Liu, Jie, Wenfeng Deng, Chunhua Yang, Aina Qin, and Keke Huang. "SI-LSGAN: Complex network structure inference based on least square generative adversarial network." Chaos, Solitons & Fractals 173 (August 2023): 113739. http://dx.doi.org/10.1016/j.chaos.2023.113739.

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Bhanbhro, Hina, Yew Kwang Hooi, Worapan Kusakunniran, and Zaira Hassan Amur. "A Symbol Recognition System for Single-Line Diagrams Developed Using a Deep-Learning Approach." Applied Sciences 13, no. 15 (2023): 8816. http://dx.doi.org/10.3390/app13158816.

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In numerous electrical power distribution systems and other engineering contexts, single-line diagrams (SLDs) are frequently used. The importance of digitizing these images is growing. This is primarily because better engineering practices are required in areas such as equipment maintenance, asset management, safety, and others. Processing and analyzing these drawings, however, is a difficult job. With enough annotated training data, deep neural networks perform better in many object detection applications. Based on deep-learning techniques, a dataset can be used to assess the overall quality

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Bhatia, Himesh, William Paul, Fady Alajaji, Bahman Gharesifard, and Philippe Burlina. "Least kth-Order and Rényi Generative Adversarial Networks." Neural Computation 33, no. 9 (2021): 2473–510. http://dx.doi.org/10.1162/neco_a_01416.

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Abstract We investigate the use of parameterized families of information-theoretic measures to generalize the loss functions of generative adversarial networks (GANs) with the objective of improving performance. A new generator loss function, least kth-order GAN (LkGAN), is introduced, generalizing the least squares GANs (LSGANs) by using a kth-order absolute error distortion measure with k≥1 (which recovers the LSGAN loss function when k=2). It is shown that minimizing this generalized loss function under an (unconstrained) optimal discriminator is equivalent to minimizing the kth-order Pears

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Rayavarapu, Swarajya Madhuri, Tammineni Shanmukha Prasanthi, Gottapu Santosh Kumar, Gottapu Sasibhushana Rao, and Gottapu Prashanti. "A GENERATIVE MODEL FOR DEEP FAKE AUGMENTATION OF PHONOCARDIOGRAM AND ELECTROCARDIOGRAM SIGNALS USING LSGAN AND CYCLE GAN." Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 13, no. 4 (2023): 34–38. http://dx.doi.org/10.35784/iapgos.3783.

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In order to diagnose a range of cardiac conditions, it is important to conduct an accurate evaluation of either phonocardiogram (PCG) and electrocardiogram (ECG) data. Artificial intelligence and machine learning-based computer-assisted diagnostics are becoming increasingly commonplace in modern medicine, assisting clinicians in making life-or-death decisions. The requirement for an enormous amount of information for training to establish the framework for a deep learning-based technique is an empirical challenge in the field of medicine. This increases the risk of personal information being m

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Zhao, Yunze. "GAN-based image generation." Applied and Computational Engineering 92, no. 1 (2024): 128–35. http://dx.doi.org/10.54254/2755-2721/92/20241743.

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Generative Adversarial Networks (GANs), as a deep learning model, have made significant progress in the field of image generation and style migration. This study aims to methodically investigate GAN-based image generation methods. First, this paper outlines the basic principles of GAN and its application in image generation, focusing on analyzing the structure and performance of representative models such as DCGAN, ProGAN and StyleGAN. This paper summarizes the improvement methods such as WGAN and LSGAN, and evaluates their efficacy in improving the stability of the model and the quality of th

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Veiner, Justin, Fady Alajaji, and Bahman Gharesifard. "A Unifying Generator Loss Function for Generative Adversarial Networks." Entropy 26, no. 4 (2024): 290. http://dx.doi.org/10.3390/e26040290.

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A unifying α-parametrized generator loss function is introduced for a dual-objective generative adversarial network (GAN) that uses a canonical (or classical) discriminator loss function such as the one in the original GAN (VanillaGAN) system. The generator loss function is based on a symmetric class probability estimation type function, Lα, and the resulting GAN system is termed Lα-GAN. Under an optimal discriminator, it is shown that the generator’s optimization problem consists of minimizing a Jensen-fα-divergence, a natural generalization of the Jensen-Shannon divergence, where fα is a con

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Jia-Li Li, Jia-Li Li, Xing-Guo Jiang Jia-Li Li, Li He Xing-Guo Jiang, and De-Cai Li Li He. "Face Age Feature Analysis Based on Improved Conditional Adversarial Auto-encoder (I-CAAE)." 電腦學刊 34, no. 1 (2023): 063–73. http://dx.doi.org/10.53106/199115992023023401005.

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<p>In recent years, the research of face age features has achieved rapid development driven by deep learning. The faces generated by the Conditional Adversarial Auto-encoder (CAAE) model are not only highly credible, but also closer to the target age. However, there are many problems, such as low resolution of human face image generation and poor local feature retention effect of human face features. To this end, this paper improves on the CAAE network. Firstly, referring to the LSGAN network structure, the 4 convolution layers of the encoder are added to 5 layers and the 4 convolution l

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Wu, Mengfei, Taiji Lan, Xucheng Xue, and Xinwei Xu. "Unsupervised Image Enhancement Method Based on Attention Map Network Guidance and Attention Mechanism." Electronics 12, no. 8 (2023): 1887. http://dx.doi.org/10.3390/electronics12081887.

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Low-light image enhancement is a crucial preprocessing task in complex vision tasks. It directly impacts object detection, image segmentation, and image recognition outcomes. In recent years, with the continuous development of deep learning techniques, an increasing number of image enhancement methods based on deep learning have emerged. However, due to the high cost of data collection and the limited content of supervised learning datasets, more and more scholars have shifted their focus to the field of unsupervised image enhancement. Unsupervised image enhancement methods do not require pair

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Tataryn, T., D. Savytskii, L. Vasylechko, C. Paulmann, and U. Bismayer. "Crystal and twin structure in LSGMn crystals." Acta Crystallographica Section A Foundations of Crystallography 68, a1 (2012): s181. http://dx.doi.org/10.1107/s0108767312096511.

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Liu, Jia-Bao, and Ali Zafari. "Computing Minimal Doubly Resolving Sets and the Strong Metric Dimension of the Layer Sun Graph and the Line Graph of the Layer Sun Graph." Complexity 2020 (September 24, 2020): 1–8. http://dx.doi.org/10.1155/2020/6267072.

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Let G be a finite, connected graph of order of, at least, 2 with vertex set VG and edge set EG. A set S of vertices of the graph G is a doubly resolving set for G if every two distinct vertices of G are doubly resolved by some two vertices of S. The minimal doubly resolving set of vertices of graph G is a doubly resolving set with minimum cardinality and is denoted by ψG. In this paper, first, we construct a class of graphs of order 2n+Σr=1k−2nmr, denoted by LSGn,m,k, and call these graphs as the layer Sun graphs with parameters n, m, and k. Moreover, we compute minimal doubly resolving sets a

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Chen, Bin, Kai Yang, Wenxin Tai, et al. "Interpreting Temporal Knowledge Graph Reasoning (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (2024): 23451–53. http://dx.doi.org/10.1609/aaai.v38i21.30425.

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Temporal knowledge graph reasoning is an essential task that holds immense value in diverse real-world applications. Existing studies mainly focus on leveraging structural and sequential dependencies, excelling in tasks like entity and link prediction. However, they confront a notable interpretability gap in their predictions, a pivotal facet for comprehending model behavior. In this study, we propose an innovative method, LSGAT, which not only exhibits remarkable precision in entity predictions but also enhances interpretability by identifying pivotal historical events influencing event predi

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Yang, Cheng-Hong, Sin-Hua Moi, Yu-Da Lin, and Li-Yeh Chuang. "Genetic Algorithm Combined with a Local Search Method for Identifying Susceptibility Genes." Journal of Artificial Intelligence and Soft Computing Research 6, no. 3 (2016): 203–12. http://dx.doi.org/10.1515/jaiscr-2016-0015.

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Abstract Detecting genetic association models between single nucleotide polymorphisms (SNPs) in various disease-related genes can help to understand susceptibility to disease. Statistical tools have been widely used to detect significant genetic association models, according to their related statistical values, including odds ratio (OR), chi-square test (χ2), p-value, etc. However, the high number of computations entailed in such operations may limit the capacity of such statistical tools to detect high-order genetic associations. In this study, we propose lsGA algorithm, a genetic algorithm b

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Kabir, Homayun, Jeevan Kanesan, Ahmed Wasif Reza, and Harikrishnan Ramiah. "A Mathematical Algorithm of Locomotive Source Localization Based on Hyperbolic Technique." International Journal of Distributed Sensor Networks 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/384180.

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Recent trend shows that sensors situated on an axis in two-dimensional scenario measuring the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) of the emitting signal from a moving source can estimate the emitting signal’s position and velocity from the intersection point of hyperbola, which derives from TDOA and FDOA. However, estimating the location of an emitter based on hyperbolic measurements is a highly nonlinear problem with inconsistent data, which are created due to the measurement noise, the deviation between assumption model and actual field of the velocit

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Stratulat, Lacramioara, Maria Geba, and Daniela Salajan. "Village from Muscel by Ion Marinescu Valsan State of Conservation and the Chromatic Palette." Revista de Chimie 69, no. 12 (2019): 3464–68. http://dx.doi.org/10.37358/rc.18.12.6770.

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Ion Marinescu V�lsan (1865 - 1936) was a Romanian painter belonging to the modern Romanian art from the beginning of the 20th century. Many of the paintings painted by him have picturesque landscapes in his native town, Malureni, Arges County. He was influencedby Nicolae Grigorescu. Village from Muscel painting, by Ion Marinescu V�lsan, was examined by several non-invasive techniques (Vis, UV &grazing light examination, IR reflectography, optical microscopy and X-rays fluorescence spectrometry) to obtain information on its chromatic palette and state of conservation. Zinc white, Lead white

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Wang, Zhixing, Hui Luo, Dongxu Liu, et al. "Macaron Attention: The Local Squeezing Global Attention Mechanism in Tracking Tasks." Remote Sensing 16, no. 16 (2024): 2896. http://dx.doi.org/10.3390/rs16162896.

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The Unmanned Aerial Vehicle (UAV) tracking tasks find extensive utility across various applications. However, current Transformer-based trackers are generally tailored for diverse scenarios and lack specific designs for UAV applications. Moreover, due to the complexity of training in tracking tasks, existing models strive to improve tracking performance within limited scales, making it challenging to directly apply lightweight designs. To address these challenges, we introduce an efficient attention mechanism known as Macaron Attention, which we integrate into the existing UAV tracking framewo

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Curr, Kenneth, Snehlata Tripathi, Johan Lennerstrand, Brendan A. Larder, and Vinayaka R. Prasad. "Influence of naturally occurring insertions in the fingers subdomain of human immunodeficiency virus type 1 reverse transcriptase on polymerase fidelity and mutation frequencies in vitro." Journal of General Virology 87, no. 2 (2006): 419–28. http://dx.doi.org/10.1099/vir.0.81458-0.

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The fingers subdomain of human immunodeficiency virus type 1 (HIV-1) reverse transcriptase (RT) is a hotspot for nucleoside analogue resistance mutations. Some multi-nucleoside analogue-resistant variants contain a T69S substitution along with dipeptide insertions between residues 69 and 70. This set of mutations usually co-exists with classic zidovudine-resistance mutations (e.g. M41L and T215Y) or an A62V mutation and confers resistance to multiple nucleoside analogue inhibitors. As insertions lie in the vicinity of the dNTP-binding pocket, their influence on RT fidelity was investigated. Co

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Jiang, Nana, Wenbo Zhao, Hui Wang, Huiqi Luo, Zezhou Chen, and Jubo Zhu. "Lightweight Super-Resolution Generative Adversarial Network for SAR Images." Remote Sensing 16, no. 10 (2024): 1788. http://dx.doi.org/10.3390/rs16101788.

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Due to a unique imaging mechanism, Synthetic Aperture Radar (SAR) images typically exhibit degradation phenomena. To enhance image quality and support real-time on-board processing capabilities, we propose a lightweight deep generative network framework, namely, the Lightweight Super-Resolution Generative Adversarial Network (LSRGAN). This method introduces Depthwise Separable Convolution (DSConv) in residual blocks to compress the original Generative Adversarial Network (GAN) and uses the SeLU activation function to construct a lightweight residual module (LRM) suitable for SAR image characte

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Ghosh, Payel, Melanie Mitchell, and Judith Gold. "LSGA: combining level-sets and genetic algorithms for segmentation." Evolutionary Intelligence 3, no. 1 (2010): 1–11. http://dx.doi.org/10.1007/s12065-010-0036-x.

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Sturesson, Lennart, and Charlotte Froese Fischer. "LSGEN - a program to generate configuration-state lists of LS-coupled basis functions." Computer Physics Communications 74, no. 3 (1993): 432–40. http://dx.doi.org/10.1016/0010-4655(93)90024-7.

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Yang, Yanhong, Zhixuan Jing, Fei Wang, Hao Luan, Hongtao Wang, and Guodao Zhang. "3D-LSKAN: 3D large separable kernel attention network for hyperspectral image super-resolution." Remote Sensing Letters 16, no. 7 (2025): 758–72. https://doi.org/10.1080/2150704x.2025.2501100.

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Chen, Bingzhi, Yishu Liu, Zheng Zhang, et al. "Deep Active Context Estimation for Automated COVID-19 Diagnosis." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 3s (2021): 1–22. http://dx.doi.org/10.1145/3457124.

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Many studies on automated COVID-19 diagnosis have advanced rapidly with the increasing availability of large-scale CT annotated datasets. Inevitably, there are still a large number of unlabeled CT slices in the existing data sources since it requires considerable consuming labor efforts. Notably, cinical experience indicates that the neighboring CT slices may present similar symptoms and signs. Inspired by such wisdom, we propose DACE, a novel CNN-based deep active context estimation framework, which leverages the unlabeled neighbors to progressively learn more robust feature representations a

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Tataryn, T., L. Vasylechko, D. Savytskii, et al. "Thermal behaviour of crystal and domain structure of LSGMn-05 anode material for SOFC." Solid State Ionics 240 (June 2013): 29–33. http://dx.doi.org/10.1016/j.ssi.2013.03.026.

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Fade, Geraldine, Fabienne Gobel, Eric Pele, et al. "Anatomical basis of the lateral superior gluteal artery perforator (LSGAP) flap and role in bilateral breast reconstruction." Journal of Plastic, Reconstructive & Aesthetic Surgery 66, no. 6 (2013): 756–62. http://dx.doi.org/10.1016/j.bjps.2013.02.017.

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Monfared, Mohammad E. Dehghan, and Fazlollah Lak. "Comparing Two MLEs of The Change Point When an lspan style=qtext-decoration:overline;qgligXl/igl/spang Control Chart Is Used." Journal of Statistical Theory and Applications 16, no. 2 (2017): 209. http://dx.doi.org/10.2991/jsta.2017.16.2.6.

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Lurin, I. A., I. P. Khomenko, E. M. Khoroshun, et al. "A clinical case of using the concept of monitoring in the treatment of a gunshot defect of the soft tissues of the knee joint." Medicni perspektivi 28, no. 2 (2023): 197–207. http://dx.doi.org/10.26641/2307-0404.2023.2.283427.

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Rapid rehabilitation and restoration of the function of damaged anatomical areas in the military is the main goal of military medicine. In the case of gunshot wounds of the knee joint with defects of soft tissues, one of the optional methods of reconstructive and plastic "closure" is the usage of a propeller flap. The clinical case represents a mine-explosive wound of the lower extremities with a defect of the soft tissues of the lateral aspect of the right knee joint in a serviceman of the Ukrainian Armed Forces as a result of artillery shelling in the east of Ukraine in November 2022. There

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Lurin, I.A., I.P. Khomenko, E.M. Khoroshun, et al. "A clinical case of using the concept of monitoring in the treatment of a gunshot defect of the soft tissues of the knee joint." Medicni perspektivi 28, no. 2 (2023): 197–207. https://doi.org/10.26641/2307-0404.2023.2.283427.

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Rapid rehabilitation and restoration of the function of damaged anatomical areas in the military is the main goal of military medicine. In the case of gunshot wounds of the knee joint with defects of soft tissues, one of the optional methods of reconstructive and plastic "closure" is the usage of a propeller flap. The clinical case represents a mine-explosive wound of the lower extremities with a defect of the soft tissues of the lateral aspect of the right knee joint in a serviceman of the Ukrainian Armed Forces as a result of artillery shelling in the east of Ukraine in November 20

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Liu, Tao, Xiang Gao, Bei-Gang He, and Jing-Kun Yu. "A Limiting Current Oxygen Sensor Based on LSGM as a Solid Electrolyte and LSGMN (N = Fe, Co) as a Dense Diffusion Barrier." Journal of Materials Engineering and Performance 25, no. 7 (2016): 2943–50. http://dx.doi.org/10.1007/s11665-016-2171-8.

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Sharma, Devilal. "Role of Tax Revenue to Strengthen of Pokhara Sub-Metropolitan Corporation." Janapriya Journal of Interdisciplinary Studies 2 (August 17, 2017): 51–60. http://dx.doi.org/10.3126/jjis.v2i1.18066.

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After the restoration of the democracy in Nepal, the demand for spending up decentralization process has been gained ground. As a result of increasingly assertive role of stakeholders, the enactment of local self-governance Act (LSGA), 1999 in line with 9th plan, objective had been achieved providing the base for further promotion of decentralization framework in the country. Municipal financing is new concept in Nepal. Municipal financing indicates the study of various sources of revenue (both internal and external) and their collection, allocation, mobilization and utilization of those resou

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Li, Liangyu, Su Tiexiong, Fukang Ma, and Yu Pu. "Research on a small sample fault diagnosis method for a high-pressure common rail system." Advances in Mechanical Engineering 13, no. 9 (2021): 168781402110461. http://dx.doi.org/10.1177/16878140211046103.

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In the fault diagnosis of high-pressure common rail diesel engines, it is often necessary to face the problem of insufficient diagnostic training samples due to the high cost of obtaining fault samples or the difficulty of obtaining fault samples, resulting in the inability to diagnose the fault state. To solve the above problem, this paper proposes a small-sample fault diagnosis method for a high-pressure common rail system using a small-sample learning method based on data augmentation and a fault diagnosis method based on a GA_BP neural network. The data synthesis of the training set using

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Newitt, David C., Ying Lu, Brian MacDonald, Sharmila Majumdar, and Laurent Pothuaud. "In vivo application of 3D-line skeleton graph analysis (LSGA) technique with high-resolution magnetic resonance imaging of trabecular bone structure." Osteoporosis International 15, no. 5 (2004): 411–19. http://dx.doi.org/10.1007/s00198-003-1563-4.

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Kwon, Moonhyuk, Connor L. Hodgins, Tegan M. Haslam, et al. "Germacrene A Synthases for Sesquiterpene Lactone Biosynthesis Are Expressed in Vascular Parenchyma Cells Neighboring Laticifers in Lettuce." Plants 11, no. 9 (2022): 1192. http://dx.doi.org/10.3390/plants11091192.

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Sesquiterpene lactone (STL) and natural rubber (NR) are characteristic isoprenoids in lettuce (Lactuca sativa). Both STL and NR co-accumulate in laticifers, pipe-like structures located along the vasculature. NR-biosynthetic genes are exclusively expressed in laticifers, but cell-type specific expression of STL-biosynthetic genes has not been studied. Here, we examined the expression pattern of germacrene A synthase (LsGAS), which catalyzes the first step in STL biosynthesis in lettuce. Quantitative PCR and Illumina read mapping revealed that the transcripts of two GAS isoforms (LsGAS1/LsGAS2)

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Bhattarai, Naba Raj. "Strengths and Challenges of Federal Governance in Nepal: Re Thinking with Rural Development Approach." Research Nepal Journal of Development Studies 3, no. 2 (2020): 38–48. http://dx.doi.org/10.3126/rnjds.v3i2.34457.

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Rural development in Nepal is a complex phenomenon. It is an interaction among economic, social, political and cultural factors. The concept of rural development is a process of wholistic development and change to improve rural livelihood. Rural development is linked with infrastructural development, commercialization of agriculture, proper utilization and of resources, food security, creating opportunities, inclusive development in the rural area and positive change in overall society. The main objective of the study is to assess the strength and challenges of federal governance in Nepal. Fur

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Septrianto, Wahyu, and Ussisa ‘alat Taqwa. "Tafakur Menurut Imam Al-Ghozali dan Implikasinya Terhadap Terapi Psikospiritual Mahasantri Santri Universitas Darussalam Gontor." Educatia : Jurnal Pendidikan dan Agama Islam 14, no. 1 (2024): 59–75. http://dx.doi.org/10.69879/p58dk037.

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Tafakur merupakan sebuah terapi psikospiritual yang memiliki akar dalam tradisi Islam, namun mencakup elemen universal yang relevan bagi berbagai latar belakang keagamaan. Terapi psikospiritual dengan tafakur ini menekankan pada penggabungan dimensi psikologis dan rohaniah dalam rangka mencapai pemahaman yang lebih mendalam tentang diri dan hubungan dengan Tuhan atau dimensi spiritual lainnya. Dengan pemahaman yang demikian dapat mengembangkan dimensi spiritualitas dan kesejahteraan mahasantri di lingkungan universitas Darussalam Gontor. Penelitian ini bertujuan untuk mendalami pemahaman konse

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Wang, Jianhua, Xiaolin Chang, Yixiang Wang, Ricardo J. Rodríguez, and Jianan Zhang. "LSGAN-AT: enhancing malware detector robustness against adversarial examples." Cybersecurity 4, no. 1 (2021). http://dx.doi.org/10.1186/s42400-021-00102-9.

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AbstractAdversarial Malware Example (AME)-based adversarial training can effectively enhance the robustness of Machine Learning (ML)-based malware detectors against AME. AME quality is a key factor to the robustness enhancement. Generative Adversarial Network (GAN) is a kind of AME generation method, but the existing GAN-based AME generation methods have the issues of inadequate optimization, mode collapse and training instability. In this paper, we propose a novel approach (denote as LSGAN-AT) to enhance ML-based malware detector robustness against Adversarial Examples, which includes LSGAN m

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Xue, Tao, Jin Yan, Deshuai Zheng, and Yong Liu. "Semantic prior guided fine-grained facial expression manipulation." Complex & Intelligent Systems, March 27, 2024. http://dx.doi.org/10.1007/s40747-024-01401-7.

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AbstractFacial expression manipulation has gained wide attention and has been applied in various fields, such as film production, electronic games, and short videos. However, existing facial expression manipulation methods often overlook the details of local regions in images, resulting in the failure to preserve local structures and textures of images. To solve this problem, this paper proposes a local semantic segmentation mask-based GAN (LSGAN) to generate fine-grained facial expression images. LSGAN is composed of a semantic mask generator, an adversarial autoencoder, a transformative gene

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Yıldız, Eyyüp, Erkan Yüksel, and Selçuk Sevgen. "Investigating the effect of loss functions on single-image GAN performance." Journal of Innovative Science and Engineering (JISE), August 9, 2024. https://doi.org/10.38088/jise.1497968.

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Loss functions are crucial in training generative adversarial networks (GANs) and shaping the resulting outputs. These functions, specifically designed for GANs, optimize generator and discriminator networks together but in opposite directions. GAN models, which typically handle large datasets, have been successful in the field of deep learning. However, exploring the factors that influence the success of GAN models developed for limited data problems is an important area of research. In this study, we conducted a comprehensive investigation into the loss functions commonly used in GAN literat

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He, Hang, and Manman Yuan. "A novel few-sample wind power prediction model based on generative adversarial network and quadratic mode decomposition." Frontiers in Energy Research 11 (July 6, 2023). http://dx.doi.org/10.3389/fenrg.2023.1211360.

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With the emergence of various new power systems, accurate wind power prediction plays a critical role in their safety and stability. However, due to the historical wind power data with few samples, it is difficult to ensure the accuracy of power system prediction for new wind farms. At the same time, wind power data show significant uncertainty and fluctuation. To address this issue, it is proposed in this research to build a novel few-sample wind power prediction model based on the least-square generative adversarial network (LSGAN) and quadratic mode decomposition (QMD). Firstly, a small amo

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Christine, Dewi, Chen Rung-Ching, Liu Yan-Ting, Jiang Xiaoyi, and Hartomo Kristoko. "Yolo V4 for advanced traffic sign recognition with synthetic training data generated by various GAN." November 2, 2021. https://doi.org/10.5281/zenodo.5639836.

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Convolutional Neural Networks (CNN) achieves perfection in traffic sign identification with enough annotated training data. The dataset determines the quality of the complete visual system based on CNN. Unfortunately, databases for traffic signs from the majority of the world’s nations are few. In this scenario, Generative Adversarial Networks (GAN) may be employed to produce more realistic and varied training pictures to supplement the actual arrangement of images. The purpose of this research is to describe how the quality of synthetic pictures created by DCGAN, LSGAN, and WGAN is dete

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Wang, Changgang, Yu Cao, Shi Zhang, and Tong Ling. "A Reconstruction Method for Missing Data in Power System Measurement Based on LSGAN." Frontiers in Energy Research 9 (March 29, 2021). http://dx.doi.org/10.3389/fenrg.2021.651807.

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The integrity of data is an essential basis for analyzing power system operating status based on data. Improper handling of measurement sampling, information transmission, and data storage can lead to data loss, thus destroying the data integrity and hindering data mining. Traditional data imputation methods are suitable for low-latitude, low-missing-rate scenarios. In high-latitude, high-missing-rate scenarios, the applicability of traditional methods is in doubt. This paper proposes a reconstruction method for missing data in power system measurement based on LSGAN (Least Squares Generative

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Dong, Chuangchuang, Huaming Liu, Xiuyou Wang, and Xuehui Bi. "Image inpainting method based on AU-GAN." Multimedia Systems 30, no. 2 (2024). http://dx.doi.org/10.1007/s00530-024-01290-3.

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AbstractImage inpainting refers to the process of filling in missing regions or removing objects, and has broad application prospects. The rapid development of deep learning has led to new technological breakthroughs in image repair technology, continuously improving the quality of image inpainting. However, when we inpaint large missing regions, the texture and structural features of the image cannot be comprehensively utilized. This leads to blurry images. To solve this problem, we propose an improved dual-stream U-Net algorithm that adds an attention mechanism to the two U-Net networks know

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