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Journal articles on the topic 'Spectral networks'
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Wu, Tingzeng, and Huazhong Lü. "Per-Spectral Characterizations of Bicyclic Networks." Journal of Applied Mathematics 2017 (2017): 1–5. http://dx.doi.org/10.1155/2017/7541312.
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Spectral techniques are used for the study of several network properties: community detection, bipartition, clustering, design of highly synchronizable networks, and so forth. In this paper, we investigate which kinds of bicyclic networks are determined by their per-spectra. We find that the permanental spectra cannot determine sandglass graphs in general. When we restrict our consideration to connected graphs or quadrangle-free graphs, sandglass graphs are determined by their permanental spectra. Furthermore, we construct countless pairs of per-cospectra bicyclic networks.
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Gaiotto, Davide, Gregory W. Moore, and Andrew Neitzke. "Spectral Networks." Annales Henri Poincaré 14, no. 7 (March 8, 2013): 1643–731. http://dx.doi.org/10.1007/s00023-013-0239-7.
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Anastasiadis, Johannes, and Michael Heizmann. "GAN-regularized augmentation strategy for spectral datasets." tm - Technisches Messen 89, no. 4 (February 5, 2022): 278–88. http://dx.doi.org/10.1515/teme-2021-0109.
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Abstract Artificial neural networks are used in various fields including spectral unmixing, which is used to determine the proportions of substances involved in a mixture, and achieve promising results. This is especially true if there is a non-linear relationship between the spectra of mixtures and the spectra of the substances involved (pure spectra). To achieve sufficient results, neural networks need lots of representative training data. We present a method that extends existing training data for spectral unmixing consisting of spectra of mixtures by learning the mixing characteristic using an artificial neural network. Spectral variability is considered by random inputs. The network structure used is a generative adversarial net that takes the dependence on the abundances of pure substances into account by an additional term in its objective function, which is minimized during training. After training further data for abundance vectors for which there is no real measurement data in the original training dataset can be generated. A neural network trained with the augmented training dataset shows better performance in spectral unmixing compared to being trained with the original dataset. The presented network structure improves already existing results obtained with a generative convolutional neural network, which is superior to model-based approaches.
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Penttilä, A., H. Hietala, and K. Muinonen. "Asteroid spectral taxonomy using neural networks." Astronomy & Astrophysics 649 (May 2021): A46. http://dx.doi.org/10.1051/0004-6361/202038545.
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Aims. We explore the performance of neural networks in automatically classifying asteroids into their taxonomic spectral classes. We particularly focus on what the methodology could offer the ESA Gaia mission. Methods. We constructed an asteroid dataset that can be limited to simulating Gaia samples. The samples were fed into a custom-designed neural network that learns how to predict the samples’ spectral classes and produces the success rate of the predictions. The performance of the neural network is also evaluated using three real preliminary Gaia asteroid spectra. Results. The overall results show that the neural network can identify taxonomic classes of asteroids in a robust manner. The success in classification is evaluated for spectra from the nominal 0.45–2.45 μm wavelength range used in the Bus-DeMeo taxonomy, and from a limited range of 0.45–1.05 μm following the joint wavelength range of Gaia observations and the Bus-DeMeo taxonomic system. Conclusions. The obtained results indicate that using neural networks to execute automated classification is an appealing solution for maintaining asteroid taxonomies, especially as the size of the available datasets grows larger with missions like Gaia.
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Avdic, Senada, Roumiana Chakarova, and Imre Pazsit. "Analysis of the experimental positron lifetime spectra by neural networks." Nuclear Technology and Radiation Protection 18, no. 1 (2003): 16–21. http://dx.doi.org/10.2298/ntrp0301016a.
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This paper deals with the analysis of experimental positron lifetime spectra in polymer materials by using various algorithms of neural networks. A method based on the use of artificial neural networks for unfolding the mean lifetime and intensity of the spectral components of simulated positron lifetime spectra was previously suggested and tested on simulated data [Pzzsitetal, Applied Surface Science, 149 (1998), 97]. In this work, the applicability of the method to the analysis of experimental positron spectra has been verified in the case of spectra from polymer materials with three components. It has been demonstrated that the backpropagation neural network can determine the spectral parameters with a high accuracy and perform the decomposi-tion of lifetimes which differ by 10% or more. The backpropagation network has not been suitable for the identification of both the parameters and the number of spectral components. Therefore, a separate artificial neural network module has been designed to solve the classification problem. Module types based on self-organizing map and learning vector quantization algorithms have been tested. The learning vector quantization algorithm was found to have better performance and reliability. A complete artificial neural network analysis tool of positron lifetime spectra has been constructed to include a spectra classification module and parameter evaluation modules for spectra with a different number of components. In this way, both flexibility and high resolution can be achieved.
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Tanabe, Kazutoshi, Takatoshi Matsumoto, Tadao Tamura, Jiro Hiraishi, Shinnosuke Saeki, Miwako Arima, Chisato Ono, et al. "Identification of Chemical Structures from Infrared Spectra by Using Neural Networks." Applied Spectroscopy 55, no. 10 (October 2001): 1394–403. http://dx.doi.org/10.1366/0003702011953531.
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Structure identification of chemical substances from infrared spectra can be done with various approaches: a theoretical method using quantum chemistry calculations, an inductive method using standard spectral databases of known chemical substances, and an empirical method using rules between spectra and structures. For various reasons, it is difficult to definitively identify structures with these methods. The relationship between structures and infrared spectra is complicated and nonlinear, and for problems with such nonlinear relationships, neural networks are the most powerful tools. In this study, we have evaluated the performance of a neural network system that mimics the methods used by specialists to identify chemical structures from infrared spectra. Neural networks for identifying over 100 functional groups have been trained by using over 10 000 infrared spectral data compiled in the integrated spectral database system (SDBS) constructed in our laboratory. Network structures and training methods have been optimized for a wide range of conditions. It has been demonstrated that with neural networks, various types of functional groups can be identified, but only with an average accuracy of about 80%. The reason that 100% identification accuracy has not been achieved is discussed.
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Cabrol-Bass, D., C. Cachet, C. Cleva, A. Eghbaldar, and T. P. Forrest. "Application pratique des réseaux neuro mimétiques aux données spectroscopiques (infrarouge et masse) en vue de l'élucidation structurale." Canadian Journal of Chemistry 73, no. 9 (September 1, 1995): 1412–26. http://dx.doi.org/10.1139/v95-176.
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In the last few years, intensive research by several groups has shown that neural networks can be used to analyse spectral data for structural elucidation, and that their performance approaches that of an expert in the field. The construction of such networks, their training and evaluation, requires large structural and spectral databases and significant computational resources and time. However, once the network has been completed it can be used very effectively for practical applications on an ordinary desktop computer. In this article we describe the methodology for creating such a network for infrared and mass spectra, and present a program for use on a personal computer, either connected to a spectrometer or independently. The program accepts data in ASCII format, both for the network description and for the spectral information. This approach permits the use of neural networks in an analytical laboratory with limited computational resources. Keywords: neural networks, infrared spectroscopy, mass spectroscopy, structure determination.
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Pancoska, Petr, Vit Janota, and Timothy A. Keiderling. "Interconvertibility of Electronic and Vibrational Circular Dichroism Spectra of Proteins: A Test of Principle Using Neural Network Mapping." Applied Spectroscopy 50, no. 5 (May 1996): 658–68. http://dx.doi.org/10.1366/0003702963905916.
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Electronic circular dichroism (ECD) and vibrational circular dichroism (VCD) are compared with respect to their interconvertibility for protein structural studies. ECD and amide I' VCD spectra of 28 proteins were used with a backpropagation projection neural network with one hidden layer to develop a mapping between the two spectral types. After the network converged, the number of neurons in the hidden layer was optimized by principal component analysis of the synaptic weights of the pilot network topology with redundant hidden neurons. Actual prediction of one spectrum from the other for individual proteins was tested by retraining these networks with 28 reduced training sets having one protein systematically left out. Comparison of network-predicted spectra with experimental ones is used to identify those spectral features which are unique in each method. Similarly, the VCD spectra of 23 proteins measured in both D2O and H2O in the amide I region were mapped onto each other with the use of the same type of neural network calculation. The results show that the effects of partial deuteration on the VCD spectra band shape are predictable from the H2O spectra. An analysis of the synaptic weights of the optimized networks was performed which allowed identification of the linear and nonlinear parts of the obtained mappings. Insight into the details of how the neural networks encode and process the spectroscopic information is derived from a spectral representation of these weight matrices.
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Humphries, Mark D., Javier A. Caballero, Mat Evans, Silvia Maggi, and Abhinav Singh. "Spectral estimation for detecting low-dimensional structure in networks using arbitrary null models." PLOS ONE 16, no. 7 (July 2, 2021): e0254057. http://dx.doi.org/10.1371/journal.pone.0254057.
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Discovering low-dimensional structure in real-world networks requires a suitable null model that defines the absence of meaningful structure. Here we introduce a spectral approach for detecting a network’s low-dimensional structure, and the nodes that participate in it, using any null model. We use generative models to estimate the expected eigenvalue distribution under a specified null model, and then detect where the data network’s eigenspectra exceed the estimated bounds. On synthetic networks, this spectral estimation approach cleanly detects transitions between random and community structure, recovers the number and membership of communities, and removes noise nodes. On real networks spectral estimation finds either a significant fraction of noise nodes or no departure from a null model, in stark contrast to traditional community detection methods. Across all analyses, we find the choice of null model can strongly alter conclusions about the presence of network structure. Our spectral estimation approach is therefore a promising basis for detecting low-dimensional structure in real-world networks, or lack thereof.
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Bunimovich, Leonid, D. J. Passey, Dallas Smith, and Benjamin Webb. "Spectral and Dynamic Consequences of Network Specialization." International Journal of Bifurcation and Chaos 30, no. 06 (May 2020): 2050091. http://dx.doi.org/10.1142/s0218127420500911.
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One of the hallmarks of real networks is the ability to perform increasingly complex tasks as their topology evolves. To explain this, it has been observed that as a network grows certain subsets of the network begin to specialize the function(s) they perform. A recent model of network growth based on this notion of specialization has been able to reproduce some of the most well-known topological features found in real-world networks including right-skewed degree distributions, the small world property, modular as well as hierarchical topology, etc. Here we describe how specialization under this model also effects the spectral properties of a network. This allows us to give the conditions under which a network is able to maintain its dynamics as its topology evolves. Specifically, we show that if a network is intrinsically stable, which is a stronger version of the standard notion of global stability, then the network maintains this type of dynamics as the network evolves. This is one of the first steps toward unifying the rigorous study of the two types of dynamics exhibited by networks. These are the dynamics of a network, which is the topological evolution of the network’s structure, modeled here by the process of network specialization, and the dynamics on a network, which is the changing state of the network elements, where the type of dynamics we consider is global stability. The main examples we apply our results to are recurrent neural networks, which are the basis of certain types of machine learning algorithms.
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Schulze, H. Georg, Michael W. Blades, Alan V. Bree, Boris B. Gorzalka, L. Shane Greek, and Robin F. B. Turner. "Characteristics of Backpropagation Neural Networks Employed in the Identification of Neurotransmitter Raman Spectra." Applied Spectroscopy 48, no. 1 (January 1994): 50–57. http://dx.doi.org/10.1366/0003702944027688.
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We have shown that neural networks are capable of accurately identifying the Raman spectra of aqueous solutions of small-molecule neurotransmitters. It was found that the networks performed optimally when the ratio of the number of hidden nodes to the number of input nodes was 0.16, that network accuracy increased with the number of input layer nodes, and that input features influenced the abilities of networks to discriminate or generalize between spectra. Furthermore, networks employing sine transfer functions for their hidden layers trained faster and were better at discriminating between closely related spectra, but they were less tolerant of spectral distortions than the networks using sigmoid transfer functions. The latter type of network produced superior results where generalization between spectra was required.
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MALETIĆ, SLOBODAN, DANIJELA HORAK, and MILAN RAJKOVIĆ. "COOPERATION, CONFLICT AND HIGHER-ORDER STRUCTURES OF SOCIAL NETWORKS." Advances in Complex Systems 15, supp01 (June 2012): 1250055. http://dx.doi.org/10.1142/s0219525912500555.
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Simplicial complexes represent powerful models of complex networks and complex systems in general. We explore the properties of spectra of combinatorial Laplacian operator of simplicial complexes in the context of connectivity of cliques in the simplicial clique complex associated with social networks. The necessity of higher order spectral analysis is discussed and compared with results for ordinary graph spectra. Methods and results are applied using social network of the Zachary karate club and the network of characters from Victor Hugo's novel Les Miserables.
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Ma, Chen, Junjun Jiang, Huayi Li, Xiaoguang Mei, and Chengchao Bai. "Hyperspectral Image Classification via Spectral Pooling and Hybrid Transformer." Remote Sensing 14, no. 19 (September 21, 2022): 4732. http://dx.doi.org/10.3390/rs14194732.
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Hyperspectral images (HSIs) contain spatially structured information and pixel-level sequential spectral attributes. The continuous spectral features contain hundreds of wavelength bands and the differences between spectra are essential for achieving fine-grained classification. Due to the limited receptive field of backbone networks, convolutional neural networks (CNNs)-based HSI classification methods show limitations in modeling spectral-wise long-range dependencies with fixed kernel size and a limited number of layers. Recently, the self-attention mechanism of transformer framework is introduced to compensate for the limitations of CNNs and to mine the long-term dependencies of spectral signatures. Therefore, many joint CNN and Transformer architectures for HSI classification have been proposed to obtain the merits of both networks. However, these architectures make it difficult to capture spatial–spectral correlation and CNNs distort the continuous nature of the spectral signature because of the over-focus on spatial information, which means that the transformer can easily encounter bottlenecks in modeling spectral-wise similarity and long-range dependencies. To address this problem, we propose a neighborhood enhancement hybrid transformer (NEHT) network. In particular, a simple 2D convolution module is adopted to achieve dimensionality reduction while minimizing the distortion of the original spectral distribution by stacked CNNs. Then, we extract group-wise spatial–spectral features in a parallel design to enhance the representation capability of each token. Furthermore, a feature fusion strategy is introduced to increase subtle discrepancies of spectra. Finally, the self-attention of transformer is employed to mine the long-term dependencies between the enhanced feature sequences. Extensive experiments are performed on three well-known datasets and the proposed NEHT network shows superiority over state-of-the-art (SOTA) methods. Specifically, our proposed method outperforms the SOTA method by 0.46%, 1.05% and 0.75% on average in overall accuracy, average accuracy and kappa coefficient metrics.
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Sharma, Kaushal, Ajit Kembhavi, Aniruddha Kembhavi, T. Sivarani, Sheelu Abraham, and Kaustubh Vaghmare. "Application of convolutional neural networks for stellar spectral classification." Monthly Notices of the Royal Astronomical Society 491, no. 2 (November 6, 2019): 2280–300. http://dx.doi.org/10.1093/mnras/stz3100.
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ABSTRACT Due to the ever-expanding volume of observed spectroscopic data from surveys such as SDSS and LAMOST, it has become important to apply artificial intelligence (AI) techniques for analysing stellar spectra to solve spectral classification and regression problems like the determination of stellar atmospheric parameters Teff, $\rm {\log g}$, and [Fe/H]. We propose an automated approach for the classification of stellar spectra in the optical region using convolutional neural networks (CNNs). Traditional machine learning (ML) methods with ‘shallow’ architecture (usually up to two hidden layers) have been trained for these purposes in the past. However, deep learning methods with a larger number of hidden layers allow the use of finer details in the spectrum which results in improved accuracy and better generalization. Studying finer spectral signatures also enables us to determine accurate differential stellar parameters and find rare objects. We examine various machine and deep learning algorithms like artificial neural networks, Random Forest, and CNN to classify stellar spectra using the Jacoby Atlas, ELODIE, and MILES spectral libraries as training samples. We test the performance of the trained networks on the Indo-U.S. Library of Coudé Feed Stellar Spectra (CFLIB). We show that using CNNs, we are able to lower the error up to 1.23 spectral subclasses as compared to that of two subclasses achieved in the past studies with ML approach. We further apply the trained model to classify stellar spectra retrieved from the SDSS data base with SNR > 20.
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Centeno, Rebecca, Natasha Flyer, Lipi Mukherjee, Ricky Egeland, Roberto Casini, Tanausú del Pino Alemán, and Matthias Rempel. "Convolutional Neural Networks and Stokes Response Functions." Astrophysical Journal 925, no. 2 (February 1, 2022): 176. http://dx.doi.org/10.3847/1538-4357/ac402f.
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Abstract In this work, we study the information content learned by a convolutional neural network (CNN) when trained to carry out the inverse mapping between a database of synthetic Ca ii intensity spectra and the vertical stratification of the temperature of the atmospheres used to generate such spectra. In particular, we evaluate the ability of the neural network to extract information about the sensitivity of the spectral line to temperature as a function of height. By training the CNN on sufficiently narrow wavelength intervals across the Ca ii spectral profiles, we find that the error in the temperature prediction shows an inverse relationship to the response function of the spectral line to temperature, that is, different regions of the spectrum yield a better temperature prediction at their expected regions of formation. This work shows that the function that the CNN learns during the training process contains a physically meaningful mapping between wavelength and atmospheric height.
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Frainay, Clément, Emma Schymanski, Steffen Neumann, Benjamin Merlet, Reza Salek, Fabien Jourdan, and Oscar Yanes. "Mind the Gap: Mapping Mass Spectral Databases in Genome-Scale Metabolic Networks Reveals Poorly Covered Areas." Metabolites 8, no. 3 (September 15, 2018): 51. http://dx.doi.org/10.3390/metabo8030051.
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The use of mass spectrometry-based metabolomics to study human, plant and microbial biochemistry and their interactions with the environment largely depends on the ability to annotate metabolite structures by matching mass spectral features of the measured metabolites to curated spectra of reference standards. While reference databases for metabolomics now provide information for hundreds of thousands of compounds, barely 5% of these known small molecules have experimental data from pure standards. Remarkably, it is still unknown how well existing mass spectral libraries cover the biochemical landscape of prokaryotic and eukaryotic organisms. To address this issue, we have investigated the coverage of 38 genome-scale metabolic networks by public and commercial mass spectral databases, and found that on average only 40% of nodes in metabolic networks could be mapped by mass spectral information from standards. Next, we deciphered computationally which parts of the human metabolic network are poorly covered by mass spectral libraries, revealing gaps in the eicosanoids, vitamins and bile acid metabolism. Finally, our network topology analysis based on the betweenness centrality of metabolites revealed the top 20 most important metabolites that, if added to MS databases, may facilitate human metabolome characterization in the future.
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Zhu, Kaiqiang, Yushi Chen, Pedram Ghamisi, Xiuping Jia, and Jón Atli Benediktsson. "Deep Convolutional Capsule Network for Hyperspectral Image Spectral and Spectral-Spatial Classification." Remote Sensing 11, no. 3 (January 22, 2019): 223. http://dx.doi.org/10.3390/rs11030223.
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Capsule networks can be considered to be the next era of deep learning and have recently shown their advantages in supervised classification. Instead of using scalar values to represent features, the capsule networks use vectors to represent features, which enriches the feature presentation capability. This paper introduces a deep capsule network for hyperspectral image (HSI) classification to improve the performance of the conventional convolutional neural networks (CNNs). Furthermore, a modification of the capsule network named Conv-Capsule is proposed. Instead of using full connections, local connections and shared transform matrices, which are the core ideas of CNNs, are used in the Conv-Capsule network architecture. In Conv-Capsule, the number of trainable parameters is reduced compared to the original capsule, which potentially mitigates the overfitting issue when the number of available training samples is limited. Specifically, we propose two schemes: (1) A 1D deep capsule network is designed for spectral classification, as a combination of principal component analysis, CNN, and the Conv-Capsule network, and (2) a 3D deep capsule network is designed for spectral-spatial classification, as a combination of extended multi-attribute profiles, CNN, and the Conv-Capsule network. The proposed classifiers are tested on three widely-used hyperspectral data sets. The obtained results reveal that the proposed models provide competitive results compared to the state-of-the-art methods, including kernel support vector machines, CNNs, and recurrent neural network.
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Guzman, Grover E. C., Peter F. Stadler, and André Fujita. "Efficient Laplacian spectral density computations for networks with arbitrary degree distributions." Network Science 9, no. 3 (September 2021): 312–27. http://dx.doi.org/10.1017/nws.2021.10.
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AbstractThe network Laplacian spectral density calculation is critical in many fields, including physics, chemistry, statistics, and mathematics. It is highly computationally intensive, limiting the analysis to small networks. Therefore, we present two efficient alternatives: one based on the network’s edges and another on the degrees. The former gives the exact spectral density of locally tree-like networks but requires iterative edge-based message-passing equations. In contrast, the latter obtains an approximation of the spectral density using only the degree distribution. The computational complexities are 𝒪(|E|log(n)) and 𝒪(n), respectively, in contrast to 𝒪(n3) of the diagonalization method, where n is the number of vertices and |E| is the number of edges.
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Liu, Ying, Belle R. Upadhyaya, and Masoud Naghedolfeizi. "Chemometric Data Analysis Using Artificial Neural Networks." Applied Spectroscopy 47, no. 1 (January 1993): 12–23. http://dx.doi.org/10.1366/0003702934048406.
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The on-line measurement of chemical composition under different operating conditions is an important problem in many industries. An approach based on hybrid signal preprocessing and artificial neural network paradigms for estimating composition from chemometric data has been developed. The performance of this methodology was tested with the use of near-infrared (NIR) and Raman spectra from both laboratory and industrial samples. The sensitivity-of-composition estimation as a function of spectral errors, spectral preprocessing, and choice of parameter vector was studied. The optimal architecture of multilayer neural networks and the guidelines for achieving them were also studied. The results of applications to FT-Raman data and NIR data demonstrate that this methodology is highly effective in establishing a generalized mapping between spectral information and sample composition, and that the parameters can be estimated with high accuracy.
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Zhang, Jing, Renjie Zheng, Zekang Wan, Ruijing Geng, Yi Wang, Yu Yang, Xuepeng Zhang, and Yunsong Li. "Hyperspectral Image Super-Resolution Based on Feature Diversity Extraction." Remote Sensing 16, no. 3 (January 23, 2024): 436. http://dx.doi.org/10.3390/rs16030436.
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Deep learning is an important research topic in the field of image super-resolution. Problematically, the performance of existing hyperspectral image super-resolution networks is limited by feature learning for hyperspectral images. Nevertheless, the current algorithms exhibit some limitations in extracting diverse features. In this paper, we address limitations to existing hyperspectral image super-resolution networks, focusing on feature learning challenges. We introduce the Channel-Attention-Based Spatial–Spectral Feature Extraction network (CSSFENet) to enhance hyperspectral image feature diversity and optimize network loss functions. Our contributions include: (a) a convolutional neural network super-resolution algorithm incorporating diverse feature extraction to enhance the network’s diversity feature learning by elevating the matrix rank, (b) a three-dimensional (3D) feature extraction convolution module, the Channel-Attention-Based Spatial–Spectral Feature Extraction Module (CSSFEM), to boost the network’s performance in both the spatial and spectral domains, (c) a feature diversity loss function designed based on the image matrix’s singular value to maximize element independence, and (d) a spatial–spectral gradient loss function introduced based on space and spectrum gradient values to enhance the reconstructed image’s spatial–spectral smoothness. In contrast to existing hyperspectral super-resolution algorithms, we used four evaluation indexes, PSNR, mPSNR, SSIM, and SAM, and our method showed superiority during testing with three common hyperspectral datasets.
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Palla, Gergely, and Gábor Vattay. "Spectral transitions in networks." New Journal of Physics 8, no. 12 (December 6, 2006): 307. http://dx.doi.org/10.1088/1367-2630/8/12/307.
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Galakhov, Dmitry, Pietro Longhi, and Gregory W. Moore. "Spectral Networks with Spin." Communications in Mathematical Physics 340, no. 1 (August 29, 2015): 171–232. http://dx.doi.org/10.1007/s00220-015-2455-0.
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Gaiotto, Davide, Gregory W. Moore, and Andrew Neitzke. "Spectral Networks and Snakes." Annales Henri Poincaré 15, no. 1 (March 13, 2013): 61–141. http://dx.doi.org/10.1007/s00023-013-0238-8.
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Zhu, He, Jing Luo, and Sailing He. "Detecting Multiple Mixed Bacteria Using Dual-Mode Hyperspectral Imaging and Deep Neural Networks." Applied Sciences 14, no. 4 (February 14, 2024): 1525. http://dx.doi.org/10.3390/app14041525.
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Identifying and analyzing mixed pathogenic bacteria is important for clinical diagnosis and antibiotic therapy of multiple bacterial infection. In this paper, a dual-mode hyperspectral microscopic detection technology with hybrid deep neural networks (DNNs) was proposed for simultaneous quantitative analysis of four kinds of pathogenic bacteria in mixed samples. To acquire both transmission and fluorescence spectra regarding the mixed pathogens, we developed a dual-mode hyperspectral detection system with fine spectral resolution and wide wavelength range, which can also generate spatial images that can be used to calculate the total amount of mixed bacteria. The dual-mode spectra were regarded as mixed proportion characteristics and the input of the neural network for predicting the proportion of each bacterium present in the mixture. To better analyze the dual-mode spectral data, we customized a mixed bacteria measurement network (MB-Net) with hybrid DNNs architectures based on spectral feature fusion. Using the fusion strategy, two DNNs frameworks applied for transmission/fluorescence spectral feature processing were stacked to form the MB-Net that processes these features simultaneously, and the achieved average coefficient of determination (R2) and RMSE of validation set are 0.96 and 0.03, respectively. To the best of our knowledge, it is the first time of simultaneously detecting four types of mixed pathogenic bacteria using spectral detection technology, showing excellent potential in clinical practice.
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Zhao, Yiji, Youfang Lin, Zhihao Wu, Yang Wang, and Haomin Wen. "Context-aware Distance Measures for Dynamic Networks." ACM Transactions on the Web 16, no. 1 (February 28, 2022): 1–34. http://dx.doi.org/10.1145/3476228.
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Dynamic networks are widely used in the social, physical, and biological sciences as a concise mathematical representation of the evolving interactions in dynamic complex systems. Measuring distances between network snapshots is important for analyzing and understanding evolution processes of dynamic systems. To the best of our knowledge, however, existing network distance measures are designed for static networks. Therefore, when measuring the distance between any two snapshots in dynamic networks, valuable context structure information existing in other snapshots is ignored. To guide the construction of context-aware distance measures, we propose a context-aware distance paradigm, which introduces context information to enrich the connotation of the general definition of network distance measures. A Context-aware Spectral Distance (CSD) is then given as an instance of the paradigm by constructing a context-aware spectral representation to replace the core component of traditional Spectral Distance (SD). In a node-aligned dynamic network, the context effectively helps CSD gain mainly advantages over SD as follows: (1) CSD is not affected by isospectral problems; (2) CSD satisfies all the requirements of a metric, while SD cannot; and (3) CSD is computationally efficient. In order to process large-scale networks, we develop a kCSD that computes top- k eigenvalues to further reduce the computational complexity of CSD. Although kCSD is a pseudo-metric, it retains most of the advantages of CSD. Experimental results in two practical applications, i.e., event detection and network clustering in dynamic networks, show that our context-aware spectral distance performs better than traditional spectral distance in terms of accuracy, stability, and computational efficiency. In addition, context-aware spectral distance outperforms other baseline methods.
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Hu, Lei, Xingzhuo Chen, and Lifan Wang. "Spectroscopic Studies of Type Ia Supernovae Using LSTM Neural Networks." Astrophysical Journal 930, no. 1 (May 1, 2022): 70. http://dx.doi.org/10.3847/1538-4357/ac5c48.
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Abstract We present a data-driven method based on long short-term memory (LSTM) neural networks to analyze spectral time series of Type Ia supernovae (SNe Ia). The data set includes 3091 spectra from 361 individual SNe Ia. The method allows for accurate reconstruction of the spectral sequence of an SN Ia based on a single observed spectrum around maximum light. The precision of the spectral reconstruction increases with more spectral time coverages, but the significant benefit of multiple epoch data at around optical maximum is only evident for observations separated by more than a week. The method shows great power in extracting the spectral information of SNe Ia and suggests that the most critical information of an SN Ia can be derived from a single spectrum around the optical maximum. The algorithm we have developed is important for the planning of spectroscopic follow-up observations of future SN surveys with the LSST/Rubin and WFIRST/Roman telescopes.
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Raza, Ali, Mobeen Munir, Tasawar Abbas, Sayed M. Eldin, and Ilyas Khan. "Spectrum of prism graph and relation with network related quantities." AIMS Mathematics 8, no. 2 (2022): 2634–47. http://dx.doi.org/10.3934/math.2023137.
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<abstract><p>Spectra of network related graphs have numerous applications in computer sciences, electrical networks and complex networks to explore structural characterization like stability and strength of these different real-world networks. In present article, our consideration is to compute spectrum based results of generalized prism graph which is well-known planar and polyhedral graph family belongs to the generalized Petersen graphs. Then obtained results are applied to compute some network related quantities like global mean-first passage time, average path length, number of spanning trees, graph energies and spectral radius.</p></abstract>
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Berec, Vesna. "Quantum networks: topology and spectral characterization." EPJ Web of Conferences 182 (2018): 02014. http://dx.doi.org/10.1051/epjconf/201818202014.
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To utilize a scalable quantum network and perform a quantum state transfer within distant arbitrary nodes, coherence and control of the dynamics of couplings between the information units must be achieved as a prerequisite ingredient for quantum information processing within a hierarchical structure. Graph theoretic approach provides a powerful tool for the characterization of quantum networks with non-trivial clustering properties. By encoding the topological features of the underlying quantum graphs, relations between the quantum complexity measures are presented revealing the intricate links between a quantum and a classical networks dynamics.
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Vilavicencio-Arcadia, Edgar, Silvana G. Navarro, Luis J. Corral, Cynthia A. Martínez, Alberto Nigoche, Simon N. Kemp, and Gerardo Ramos-Larios. "Application of Artificial Neural Networks for the Automatic Spectral Classification." Mathematical Problems in Engineering 2020 (April 14, 2020): 1–15. http://dx.doi.org/10.1155/2020/1751932.
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Classification in astrophysics is a fundamental process, especially when it is necessary to understand several aspects of the evolution and distribution of the objects. Over an astronomical image, we need to discern between stars and galaxies and to determine the morphological type for each galaxy. The spectral classification of stars provides important information about stellar physical parameters like temperature and allows us to determine their distance; with this information, it is possible to evaluate other parameters like their physical size and the real 3D distribution of each type of objects. In this work, we present the application of two Artificial Intelligence (AI) techniques for the automatic spectral classification of stellar spectra obtained from the first data release of LAMOST and also to the more recent release (DR5). Two types of Artificial Neural Networks were selected: a feedforward neural network trained according to the Levenberg–Marquardt Optimization Algorithm (LMA) and a Generalized Regression Neural Network (GRNN). During the study, we used four datasets: the first was obtained from the LAMOST first data release and consisted of 50731 spectra with signal-to-noise ratio above 20, the second dataset was obtained from the Indo-US spectral database (1273 spectra), the third one (the STELIB spectral database) was used as an independent test dataset, and the fourth dataset was obtained from LAMOST DR5 and consisted of 17990 stellar spectra with signal-to-noise ratio above 20 also. The results in the first part of the work, when the autoconsistency of the DR1 data was probed, showed some problems in the spectral classification available in LAMOST DR1. In order to accomplish a better classification, we made a two-step process: first the LAMOST and STELIB datasets were classified by the two IA techniques trained with the entire Indo-US dataset. The resulted classification allows us to discriminate at least three groups: the first group contained O and B type stars, whereas the second contained A, F, and G type stars, and finally, the third group contained K and M type stars. The second step consisted of a refinement of the classification, but this time for every group, the most relevant indices were selected. We compared the accuracy reached by the two techniques when they are trained and tested using LAMOST spectra and their published classification and the resultant classifications obtained with the ANNs trained with the Indo-US dataset and applied over the STELIB and LAMOST spectra. Finally, in the first part, we compared the LAMOST DR1 classification with the classification obtained by the application of the NNs GRNNs and LMA trained with the Indo-US dataset. In the second part of the paper, we analyze a set of 17990 stellar spectra from LAMOST DR5 and the very significant improvement in the spectral classification available in DR5 database was verified. For this, we trained ANNs using the k-fold cross-validation technique with k = 5.
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Tanabe, Kazutoshi, Tadao Tamura, and Hiroyuki Uesaka. "Neural Network System for the Identification of Infrared Spectra." Applied Spectroscopy 46, no. 5 (May 1992): 807–10. http://dx.doi.org/10.1366/0003702924124619.
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A neural network system has been developed on a personal computer to identify 1129 infrared spectra. The system is composed of two steps of networks. The first step classifies 1129 spectra into 40 categories, and each unit of the output layer is connected to one of the 40 networks in the second step, which identify each spectrum. Each network is composed of three layers. The input, intermediate, and output layers are composed of 250, 40, and 40 units, respectively. Intensity data at 250 wavenumber points between 1800 and 550 cm−1 of the infrared spectra are entered into the input layer of each network. The training of the networks was carried out with the spectral data of 1129 compounds stored in the SDBS system, and thus the networks were successfully constructed. On the basis of the results, the system has been developed by preparing pre- and post-processing programs. The system can identify each unknown spectrum within 0.1 s, and is quite efficient for identifying infrared spectra on a personal computer.
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Сафина, О. С., А. В. Воронов, А. Р. Сафин, М. Ф. Булатов, Д. В. Чуриков, and Е. Д. Суровяткина. "Спектры нормальных мод иерархических ансамблей взаимосвязанных осцилляторов." Письма в журнал технической физики 45, no. 17 (2019): 24. http://dx.doi.org/10.21883/pjtf.2019.17.48219.17651.
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The family of spectra of normal modes of hierarchical networks of the identical mutually coupled oscillators with different topology of the organization is constructed. It is shown that treelike networks possess a fractal range of normal modes of type " devil's staircase", and with growth of quantity of branching lines and at introduction of additional inter-element couplings of network the quantity of degenerate modes increases. The analysis of influence of topology of networks and forces of communications between its elements on spectral characteristics is provided
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González-Ambriz, Sergio Jesús, Rolando Menchaca-Méndez, Sergio Alejandro Pinacho-Castellanos, and Mario Eduardo Rivero-Ángeles . "A Spectral Gap-Based Topology Control Algorithm for Wireless Backhaul Networks." Future Internet 16, no. 2 (January 26, 2024): 43. http://dx.doi.org/10.3390/fi16020043.
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This paper presents the spectral gap-based topology control algorithm (SGTC) for wireless backhaul networks, a novel approach that employs the Laplacian Spectral Gap (LSG) to find expander-like graphs that optimize the topology of the network in terms of robustness, diameter, energy cost, and network entropy. The latter measures the network’s ability to promote seamless traffic offloading from the Macro Base Stations to smaller cells by providing a high diversity of shortest paths connecting all the stations. Given the practical constraints imposed by cellular technologies, the proposed algorithm uses simulated annealing to search for feasible network topologies with a large LSG. Then, it computes the Pareto front of the set of feasible solutions found during the annealing process when considering robustness, diameter, and entropy as objective functions. The algorithm’s result is the Pareto efficient solution that minimizes energy cost. A set of experimental results shows that by optimizing the LSG, the proposed algorithm simultaneously optimizes the set of desirable topological properties mentioned above. The results also revealed that generating networks with good spectral expansion is possible even under the restrictions imposed by current wireless technologies. This is a desirable feature because these networks have strong connectivity properties even if they do not have a large number of links.
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Han, Xiaolin, Jing Yu, Jiqiang Luo, and Weidong Sun. "Hyperspectral and Multispectral Image Fusion Using Cluster-Based Multi-Branch BP Neural Networks." Remote Sensing 11, no. 10 (May 16, 2019): 1173. http://dx.doi.org/10.3390/rs11101173.
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Fusion of the high-spatial-resolution hyperspectral (HHS) image using low-spatial- resolution hyperspectral (LHS) and high-spatial-resolution multispectral (HMS) image is usually formulated as a spatial super-resolution problem of LHS image with the help of an HMS image, and that may result in the loss of detailed structural information. Facing the above problem, the fusion of HMS with LHS image is formulated as a nonlinear spectral mapping from an HMS to HHS image with the help of an LHS image, and a novel cluster-based fusion method using multi-branch BP neural networks (named CF-BPNNs) is proposed, to ensure a more reasonable spectral mapping for each cluster. In the training stage, considering the intrinsic characteristics that the spectra are more similar within each cluster than that between clusters and so do the corresponding spectral mapping, an unsupervised clustering is used to divide the spectra of the down-sampled HMS image (marked as LMS) into several clusters according to spectral correlation. Then, the spectrum-pairs from the clustered LMS image and the corresponding LHS image are used to train multi-branch BP neural networks (BPNNs), to establish the nonlinear spectral mapping for each cluster. In the fusion stage, a supervised clustering is used to group the spectra of HMS image into the clusters determined during the training stage, and the final HHS image is reconstructed from the clustered HMS image using the trained multi-branch BPNNs accordingly. Comparison results with the related state-of-the-art methods demonstrate that our proposed method achieves a better fusion quality both in spatial and spectral domains.
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Yu, Guihai, and Hui Qu. "More on Spectral Analysis of Signed Networks." Complexity 2018 (October 16, 2018): 1–6. http://dx.doi.org/10.1155/2018/3467158.
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Spectral graph theory plays a key role in analyzing the structure of social (signed) networks. In this paper we continue to study some properties of (normalized) Laplacian matrix of signed networks. Sufficient and necessary conditions for the singularity of Laplacian matrix are given. We determine the correspondence between the balance of signed network and the singularity of its Laplacian matrix. An expression of the determinant of Laplacian matrix is present. The symmetry about 1 of eigenvalues of normalized Laplacian matrix is discussed. We determine that the integer 2 is an eigenvalue of normalized Laplacian matrix if and only if the signed network is balanced and bipartite. Finally an expression of the coefficient of normalized Laplacian characteristic polynomial is present.
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Al-Dulaimi, Al-Waled, Todd K. Moon, and Jacob H. Gunther. "Voice Transformation Using Two-Level Dynamic Warping and Neural Networks." Signals 2, no. 3 (July 14, 2021): 456–74. http://dx.doi.org/10.3390/signals2030028.
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Voice transformation, for example, from a male speaker to a female speaker, is achieved here using a two-level dynamic warping algorithm in conjunction with an artificial neural network. An outer warping process which temporally aligns blocks of speech (dynamic time warp, DTW) invokes an inner warping process, which spectrally aligns based on magnitude spectra (dynamic frequency warp, DFW). The mapping function produced by inner dynamic frequency warp is used to move spectral information from a source speaker to a target speaker. Artifacts arising from this amplitude spectral mapping are reduced by reconstructing phase information. Information obtained by this process is used to train an artificial neural network to produce spectral warping information based on spectral input data. The performance of the speech mapping compared using Mel-Cepstral Distortion (MCD) with previous voice transformation research, and it is shown to perform better than other methods, based on their reported MCD scores.
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Wang, Yike, Gaoxia Wang, Ximei Hou, and Fan Yang. "Motif adjacency matrix and spectral clustering of directed weighted networks." AIMS Mathematics 8, no. 6 (2023): 13797–814. http://dx.doi.org/10.3934/math.2023706.
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<abstract> <p>In the spectral clustering methods, different from the network division based on edges, some research has begun to divide the network based on network motifs; the corresponding objective function of partition also becomes related to the motif information. But, the related research on the directed weighted network needs to be further deepened. The weight of the network has a great influence on the structural attributes of the network, so it is necessary to extend the motif-based clustering to the weighted network. In this paper, a motif-based spectral clustering method for directed weighted networks is proposed. At the same time, this paper supplements the method of obtaining matrix expressions of the motif adjacency matrix in directed unweighted networks and provides a method to deal with the weight of networks, which will be helpful for the application research of motifs. This clustering method takes into account the higher-order connectivity patterns in networks and broadens the applicable range of spectral clustering to directed weighted networks. In this method, the motif-based clustering of directed weighted networks can be transformed into the clustering of the undirected weighted network corresponding to the motif-based adjacency matrix. The results show that the clustering method can correctly identify the partition structure of the benchmark network, and experiments on some real networks show that this method performs better than the method that does not consider the weight of networks.</p> </abstract>
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Cvetkovic, Dragos. "Spectral recognition of graphs." Yugoslav Journal of Operations Research 22, no. 2 (2012): 145–61. http://dx.doi.org/10.2298/yjor120925025c.
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At some time, in the childhood of spectral graph theory, it was conjectured that non-isomorphic graphs have different spectra, i.e. that graphs are characterized by their spectra. Very quickly this conjecture was refuted and numerous examples and families of non-isomorphic graphs with the same spectrum (cospectral graphs) were found. Still some graphs are characterized by their spectra and several mathematical papers are devoted to this topic. In applications to computer sciences, spectral graph theory is considered as very strong. The benefit of using graph spectra in treating graphs is that eigenvalues and eigenvectors of several graph matrices can be quickly computed. Spectral graph parameters contain a lot of information on the graph structure (both global and local) including some information on graph parameters that, in general, are computed by exponential algorithms. Moreover, in some applications in data mining, graph spectra are used to encode graphs themselves. The Euclidean distance between the eigenvalue sequences of two graphs on the same number of vertices is called the spectral distance of graphs. Some other spectral distances (also based on various graph matrices) have been considered as well. Two graphs are considered as similar if their spectral distance is small. If two graphs are at zero distance, they are cospectral. In this sense, cospectral graphs are similar. Other spectrally based measures of similarity between networks (not necessarily having the same number of vertices) have been used in Internet topology analysis, and in other areas. The notion of spectral distance enables the design of various meta-heuristic (e.g., tabu search, variable neighbourhood search) algorithms for constructing graphs with a given spectrum (spectral graph reconstruction). Several spectrally based pattern recognition problems appear in many areas (e.g., image segmentation in computer vision, alignment of protein-protein interaction networks in bio-informatics, recognizing hard instances for combinatorial optimization problems such as the travelling salesman problem). We give a survey of such and other graph spectral recognition techniques used in computer sciences.
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Li, Baoxia, Wenzhuo Chen, Shaohuang Bian, Lusi A, Xiaojiang Tang, Yang Liu, Junwei Guo, Dan Zhang, Cheng Yang, and Feng Huang. "Recognition of Ethylene Plasma Spectra 1D Data Based on Deep Convolutional Neural Networks." Electronics 13, no. 5 (March 4, 2024): 983. http://dx.doi.org/10.3390/electronics13050983.
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As a commonly used plasma diagnostic method, the spectral analysis methodology generates a large amount of data and has a complex quantitative relationship with discharge parameters, which result in low accuracy and time-consuming operation of traditional manual spectral recognition methods. To quickly and efficiently recognize the discharge parameters based on the collected spectral data, a one-dimensional (1D) deep convolutional neural network was constructed, which can learn the data features of different classes of ethylene plasma spectra to obtain the corresponding discharge parameters. The results show that this method has a higher recognition accuracy of higher than 98%. This model provides a new idea for plasma spectral diagnosis and its related application.
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Despagne, Frédéric, Beata Walczak, and Desire-Luc Massart. "Transfer of Calibrations of Near-Infrared Spectra Using Neural Networks." Applied Spectroscopy 52, no. 5 (May 1998): 732–45. http://dx.doi.org/10.1366/0003702981944157.
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A new approach for multivariate instrument standardization is presented. This approach is based on the use of neural networks (NNs) for modeling spectral differences between two instruments. In contrast to the piecewise direct standardization (PDS) method to which it is compared, the proposed method builds a single transfer model for all spectral windows. The apparently incompatible requirements for a high number of training objects and a low number of standardization samples are addressed by truncating spectra in finite-size windows and assessing a position index to each window. Each spectral window with the corresponding position index constitutes a training object. No prior background correction is required with this method. Both the proposed method and PDS were applied to some real and simulated data sets, and results were evaluated for reconstruction and subsequent calibration. On the studied data sets, the neural network approach was found to perform at least as well as PDS for both reconstruction and calibration.
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Wang, Lingxiao, Shuzhe Shi, and Kai Zhou. "Unsupervised learning spectral functions with neural networks." Journal of Physics: Conference Series 2586, no. 1 (September 1, 2023): 012158. http://dx.doi.org/10.1088/1742-6596/2586/1/012158.
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Abstract Reconstructing spectral functions from Euclidean Green’s functions is an ill-posed inverse problem that is crucial for understanding the properties of many-body systems. In this proceeding, we propose an automatic differentiation (AD) framework utilizing neural network representations for spectral reconstruction from propagator observables. We construct spectral functions using neural networks and optimize the network parameters unsupervisedly based on the reconstruction error of the propagator. Compared to the maximum entropy method, the AD framework demonstrates better performance in situations with high noise levels. It is noteworthy that neural network representations provide non-local regularization, which has the potential to significantly improve the solution of inverse problems.
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Voevodski, Konstantin, Shang-Hua Teng, and Yu Xia. "Spectral affinity in protein networks." BMC Systems Biology 3, no. 1 (2009): 112. http://dx.doi.org/10.1186/1752-0509-3-112.
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Sarkar, Camellia, and Sarika Jalan. "Spectral properties of complex networks." Chaos: An Interdisciplinary Journal of Nonlinear Science 28, no. 10 (October 2018): 102101. http://dx.doi.org/10.1063/1.5040897.
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Pinto, Pedro, and Moe Win. "Spectral characterization of wireless networks." IEEE Wireless Communications 14, no. 6 (December 2007): 27–31. http://dx.doi.org/10.1109/mwc.2007.4407224.
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Comellas, Francesc, and Jordi Diaz-Lopez. "Spectral reconstruction of complex networks." Physica A: Statistical Mechanics and its Applications 387, no. 25 (November 2008): 6436–42. http://dx.doi.org/10.1016/j.physa.2008.07.032.
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Kunegis, Jérôme, Damien Fay, and Christian Bauckhage. "Spectral evolution in dynamic networks." Knowledge and Information Systems 37, no. 1 (October 20, 2012): 1–36. http://dx.doi.org/10.1007/s10115-012-0575-9.
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Ibrahimov, Bayram. "Research and analysis of the efficiency fiber-optic communication lines using DWDM technologies." International Robotics & Automation Journal 9, no. 1 (March 28, 2023): 35–38. http://dx.doi.org/10.15406/iratj.2023.09.00260.
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The performance indicators fiber-optic communication lines using spectral technology with separation communication channels are analyzed. The effectiveness of the use network resources optical telecommunication systems using spectral technologies based on the architectural concept of the next NGN (NGN, Next Generation Network) and future FN (FN, Future Network) networks has been studied. This work is devoted to the construction methods for calculating the indicators optical networks and the study methods and tools for improving the efficiency using network and channel resources fiber-optic communication lines using dense spectral multiplexing optical signals with separation communication channels. The problem ensuring effective management channel and network resources in optical communication networks are considered. As a result of the study technology spectral multiplexing, a new approach to the construction of a calculation method is proposed that describes the efficiency managing network and channel resources in fiber-optic communication lines, taking into account the numerous requirements their parameters and transfer characteristics. On the basis of the calculation method, analytical expressions are obtained that allow estimating the resources of the system, indicators informational and spectral efficiency of the functioning fiber-optic communication lines. The results of the research can be applied by cellular operators when designing an optical telecommunications network, in particular, to determine the optimal value of the capacity optical systems based on wavelength multiplexing technology and modulation spectral efficiencies.
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Hocˇevar, Marko, Brane Sˇirok, and Igor Grabec. "Experimental Turbulent Field Modeling by Visualization and Neural Networks." Journal of Fluids Engineering 126, no. 3 (May 1, 2004): 316–22. http://dx.doi.org/10.1115/1.1760534.
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Turbulent flow field was modeled based on experimental flow visualization and radial-basis neural networks. Turbulent fluctuations were modeled based on the recorded concentration at various locations in the Karman vortex street, which were used as inputs and outputs of the neural network. From the measured and the modeled concentration the power spectra and spatial correlation functions were calculated. The measured and the modeled concentration power spectra correspond well to the −5/3 turbulence decay law, and exhibit the basic spectral peak of fluctuation power at the same frequency. The predicted and measured correlation functions of concentration exhibit similar behavior.
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HȦKANSSON, N., A. JONSSON, J. LENNARTSSON, T. LINDSTRÖM, and U. WENNERGREN. "GENERATING STRUCTURE SPECIFIC NETWORKS." Advances in Complex Systems 13, no. 02 (April 2010): 239–50. http://dx.doi.org/10.1142/s0219525910002517.
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Theoretical exploration of network structure significance requires a range of different networks for comparison. Here, we present a new method to construct networks in a spatial setting that uses spectral methods in combination with a probability distribution function. Nearly all previous algorithms for network construction have assumed randomized distribution of links or a distribution dependent on the degree of the nodes. We relax those assumptions. Our algorithm is capable of creating spectral networks along a gradient from random to highly clustered or diverse networks. Number of nodes and link density are specified from start and the structure is tuned by three parameters (γ, σ, κ). The structure is measured by fragmentation, degree assortativity, clustering and group betweenness of the networks. The parameter γ regulates the aggregation in the spatial node pattern and σ and κ regulates the probability of link forming.
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Qing, Huan. "Estimating Mixed Memberships in Directed Networks by Spectral Clustering." Entropy 25, no. 2 (February 13, 2023): 345. http://dx.doi.org/10.3390/e25020345.
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Community detection is an important and powerful way to understand the latent structure of complex networks in social network analysis. This paper considers the problem of estimating community memberships of nodes in a directed network, where a node may belong to multiple communities. For such a directed network, existing models either assume that each node belongs solely to one community or ignore variation in node degree. Here, a directed degree corrected mixed membership (DiDCMM) model is proposed by considering degree heterogeneity. An efficient spectral clustering algorithm with a theoretical guarantee of consistent estimation is designed to fit DiDCMM. We apply our algorithm to a small scale of computer-generated directed networks and several real-world directed networks.
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Gulati, R. K., A. Bravo, G. Padilla, and R. L. Altamirano. "The Application of Artificial Neural Networks: A Catalog of Spectral Indices." Symposium - International Astronomical Union 192 (1999): 369–72. http://dx.doi.org/10.1017/s0074180900204373.
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Artificial neural networks have been applied to a catalog of spectral indices based on low-resolution spectra in the wavelength regions 3820–4500 Å and 4780–5450 Å. We classify the spectral indices using supervised neural networks. This project is in continuation of the previous efforts by one of us, applying new tools to determine basic properties of stars. We envisage the use of such methods for stellar contents studies of local stellar systems for which spectroscopic surveys are underway.