Relevant bibliographies by topics / Deep MCCA

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Deep MCCA'

Author: Grafiati
Published: 6 September 2023

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Journal articles on the topic "Deep MCCA"

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Yang, Hai, Rui Chen, Dongdong Li, and Zhe Wang. "Subtype-GAN: a deep learning approach for integrative cancer subtyping of multi-omics data." Bioinformatics 37, no. 16 (February 18, 2021): 2231–37. http://dx.doi.org/10.1093/bioinformatics/btab109.

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Abstract Motivation The discovery of cancer subtyping can help explore cancer pathogenesis, determine clinical actionability in treatment, and improve patients' survival rates. However, due to the diversity and complexity of multi-omics data, it is still challenging to develop integrated clustering algorithms for tumor molecular subtyping. Results We propose Subtype-GAN, a deep adversarial learning approach based on the multiple-input multiple-output neural network to model the complex omics data accurately. With the latent variables extracted from the neural network, Subtype-GAN uses consensus clustering and the Gaussian Mixture model to identify tumor samples' molecular subtypes. Compared with other state-of-the-art subtyping approaches, Subtype-GAN achieved outstanding performance on the benchmark datasets consisting of ∼4000 TCGA tumors from 10 types of cancer. We found that on the comparison dataset, the clustering scheme of Subtype-GAN is not always similar to that of the deep learning method AE but is identical to that of NEMO, MCCA, VAE and other excellent approaches. Finally, we applied Subtype-GAN to the BRCA dataset and automatically obtained the number of subtypes and the subtype labels of 1031 BRCA tumors. Through the detailed analysis, we found that the identified subtypes are clinically meaningful and show distinct patterns in the feature space, demonstrating the practicality of Subtype-GAN. Availabilityand implementation The source codes, the clustering results of Subtype-GAN across the benchmark datasets are available at https://github.com/haiyang1986/Subtype-GAN. Supplementary information Supplementary data are available at Bioinformatics online.
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Huang, Dian, Chen Yu, Zongze Shao, Minmin Cai, Guangyu Li, Longyu Zheng, Ziniu Yu, and Jibin Zhang. "Identification and Characterization of Nnematicidal Volatile Organic Compounds from Deep-Sea Virgibacillus dokdonensis MCCC 1A00493." Molecules 25, no. 3 (February 9, 2020): 744. http://dx.doi.org/10.3390/molecules25030744.

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Root-knot nematode diseases cause severe yield and economic losses each year in global agricultural production. Virgibacillus dokdonensis MCCC 1A00493, a deep-sea bacterium, shows a significant nematicidal activity against Meloidogyne incognita in vitro. However, information about the active substances of V. dokdonensis MCCC 1A00493 is limited. In this study, volatile organic compounds (VOCs) from V. dokdonensis MCCC 1A00493 were isolated and analyzed through solid-phase microextraction and gas chromatography–mass spectrometry. Four VOCs, namely, acetaldehyde, dimethyl disulfide, ethylbenzene, and 2-butanone, were identified, and their nematicidal activities were evaluated. The four VOCs had a variety of active modes on M. incognita juveniles. Acetaldehyde had direct contact killing, fumigation, and attraction activities; dimethyl disulfide had direct contact killing and attraction activities; ethylbenzene had an attraction activity; and 2-butanone had a repellent activity. Only acetaldehyde had a fumigant activity to inhibit egg hatching. Combining this fumigant activity against eggs and juveniles could be an effective strategy to control the different developmental stages of M. incognita. The combination of direct contact and attraction activities could also establish trapping and killing strategies against root-knot nematodes. Considering all nematicidal modes or strategies, we could use V. dokdonensis MCCC 1A00493 to set up an integrated strategy to control root-knot nematodes.
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Niu, Siwen, Dong Liu, Zongze Shao, Jiang Huang, Aili Fan, and Wenhan Lin. "Chlorinated metabolites with antibacterial activities from a deep-sea-derived Spiromastix fungus." RSC Advances 11, no. 47 (2021): 29661–67. http://dx.doi.org/10.1039/d1ra05736g.

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Morshed, Ahsan, Prem Prakash Jayaraman, Timos Sellis, Dimitrios Georgakopoulos, Massimo Villari, and Rajiv Ranjan. "Deep Osmosis: Holistic Distributed Deep Learning in Osmotic Computing." IEEE Cloud Computing 4, no. 6 (November 2017): 22–32. http://dx.doi.org/10.1109/mcc.2018.1081070.

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Geng, Huantong, and Liangchao Geng. "MCCS-LSTM: Extracting Full-Image Contextual Information and Multi-Scale Spatiotemporal Feature for Radar Echo Extrapolation." Atmosphere 13, no. 2 (January 25, 2022): 192. http://dx.doi.org/10.3390/atmos13020192.

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Precipitation nowcasting has been gaining importance in the operational weather forecast, being essential for economic and social development. Conventional methods of precipitation nowcasting are mainly focused on the task of radar echo extrapolation. In recent years, deep learning methods have been used in this task. Nevertheless, raising the accuracy and extending the lead time of prediction remains as a challenging problem. To address the problem, we proposed a Multi-Scale Criss-Cross Attention Context Sensing Long Short-Term Memory (MCCS-LSTM). In this model, Context Sensing framework (CS framework) focuses on contextual correlations, and Multi-Scale Spatiotemporal block (MS block) with criss-cross attention is designed to extract multi-scale spatiotemporal feature and full-image dependency. To validate the effectiveness of our model, we conduct experiments on CIKM AnalytiCup 2017 data sets and Guangdong Province of China radar data sets. By comparing with existing deep learning models, the results demonstrate that the MCCS-LSTM has the best prediction performance, especially for predicting accuracy with longer lead times.
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Xiao, Jing, Yingxue Luo, Shujie Xie, and Jun Xu. "Serinicoccus profundi sp. nov., an actinomycete isolated from deep-sea sediment, and emended description of the genus Serinicoccus." International Journal of Systematic and Evolutionary Microbiology 61, no. 1 (January 1, 2011): 16–19. http://dx.doi.org/10.1099/ijs.0.019976-0.

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A Gram-reaction-positive bacterial strain of the genus Serinicoccus, designated MCCC 1A05965T, was isolated from a deep-sea (5368 m) sediment of the Indian Ocean. Comparison of 16S rRNA gene sequences revealed that the isolate shared 97.6 % sequence similarity with Serinicoccus marinus JC1078T, the type strain of the only described species of the genus Serinicoccus. The DNA–DNA relatedness between these two strains was 46.2 % (standard deviation 1.86 %). The cell wall contained alanine, glycine, serine, l-ornithine and glutamic acid, which corresponds to the description of the genus Serinicoccus. The acyl type of the glycan chain of the peptidoglycan was glycolyl. Other characteristics of strain MCCC 1A05965T were consistent with those of the genus Serinicoccus. Cells were coccoid, moderately halophilic, oxidase-negative, catalase-positive and non-spore-forming. The major menaquinone was MK-8(H4). The predominant cellular fatty acids were iso-C15 : 0 (34.7 %) and iso-C16 : 0 (17.0 %). The polar lipids were phosphatidylglycerol, diphosphatidylglycerol, phosphatidylcholine, phosphatidylinositol and an unknown glycolipid. The DNA G+C content was 72 mol%. Strain MCCC 1A05965T (=0714S6-1T =DSM 21363T =CGMCC 4.5582T) is assigned as the type strain of a novel species, for which the name Serinicoccus profundi sp. nov. is proposed.
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Niu, Siwen, Chun-Lan Xie, Tianhua Zhong, Wei Xu, Zhu-Hua Luo, Zongze Shao, and Xian-Wen Yang. "Sesquiterpenes from a deep-sea-derived fungus Graphostroma sp. MCCC 3A00421." Tetrahedron 73, no. 52 (December 2017): 7267–73. http://dx.doi.org/10.1016/j.tet.2017.11.013.

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Mehta, Sanket, Nicholas C. Danford, Venkat Boddapati, Bonnie Y. Chien, and Justin K. Greisberg. "Discriminative Ability for Adverse Outcomes in Traumatic Ankle Fracture: A Comparison of the Modified Charlson Comorbidity Index, Elixhauser Comorbidity Measure, and Modified Frailty Index." Foot & Ankle Orthopaedics 7, no. 4 (October 2022): 2473011421S0080. http://dx.doi.org/10.1177/2473011421s00803.

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Category: Trauma; Ankle Introduction/Purpose: The modified Charlson Comorbidity Index (mCCI), Elixhauser comorbidity measure (ECM), and 5- factor modified Frailty Index (mFI-5) have been validated for the purpose of outcome prediction in foot and ankle orthopedic care. However, from the perspective of clinical utility, no study has sought to compare the predictive performance of these measures specifically following traumatic ankle fracture. The present study compares the discriminative ability of the mCCI, ECM, and mFI-5, as well as various demographic characteristics, such as age, gender, and race, to predict in-hospital mortality and complications after the surgical management of traumatic ankle fracture. Methods: We performed a retrospective cohort study of adult patients registered in the National Trauma Data Bank (NTDB) 2011-2016 experiencing ankle trauma as malleolar fracture and undergoing surgical management. Patients missing baseline or comorbidity information, dead on arrival, or with a pilon fracture or stress fracture were excluded. Enhanced ICD-9 algorithms were used to calculate mCCI, ECM, and mFI-5 as has been done in prior orthopedic literature. The discriminative ability of the indices for adverse outcomes was assessed using area under the curve analysis from receiver operating characteristic curves. Outcomes included death, severe adverse events (death, deep surgical site infection (SSI), myocardial infarction (MI), cardiac arrest, deep vein thrombosis (DVT), pulmonary embolism (PE), sepsis, stroke, compartment syndrome), minor adverse events (acute kidney injury (AKI), pneumonia, superficial SSI, urinary tract infection (UTI)), infectious events (deep SSI, organ/space SSI, superficial SSI, pneumonia, UTI, catheter-related bloodstream infection, osteomyelitis, sepsis), and any adverse event. Results: In total, 171,097 patients met inclusion criteria. The median age was 50 years and 49% of patients were male. Compared to ECM and mFI-5, mCCI had the largest discriminative ability for the outcome of in-hospital mortality (P=0.02 versus ECM, P<0.001 versus mFI-5, Table I). ECM, however, had a larger discriminative ability for major adverse event, minor adverse event, infectious event, and any complication during the hospitalization (P<0.001, all comparisons). In an analysis of demographic factors, age demonstrated higher discriminative ability for in-hospital mortality compared to gender (P<0.001) and race (P<0.001). Race had sole or shared inferior discriminative ability for all outcomes. The most discriminative comorbidity index (ECM) outperformed the most discriminative demographic factor (age, gender) for any complication, minor adverse event, and infectious events. A combination analysis of the most predictive comorbidity index and the most predictive demographic factor resulted in discriminative improvements in all five outcome variables. Conclusion: Among comorbidity indices, the mCCI demonstrated significantly greater discriminative ability for mortality and the ECM demonstrated significantly greater discriminative ability for multiple adverse events during hospitalization. The use of these indices in conjunction with easily accessible demographic factors, such as age, resulted in further improvements in discrimination ability. These findings suggest that increased use of the mCCI and ECM may assist in the identification and management of patients at risk of death and postoperative complications, respectively, and thereby help optimize risk stratification, inform patient expectations, and guide outcomes-based reimbursements in the management of traumatic ankle fracture.
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Chen, Wen, Jinping Wang, Dian Huang, Wanli Cheng, Zongze Shao, Minmin Cai, Longyu Zheng, Ziniu Yu, and Jibin Zhang. "Volatile Organic Compounds from Bacillus aryabhattai MCCC 1K02966 with Multiple Modes against Meloidogyne incognita." Molecules 27, no. 1 (December 24, 2021): 103. http://dx.doi.org/10.3390/molecules27010103.

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Plant-parasitic nematodes cause severe losses to crop production and economies all over the world. Bacillus aryabhattai MCCC 1K02966, a deep-sea bacterium, was obtained from the Southwest Indian Ocean and showed nematicidal and fumigant activities against Meloidogyne incognita in vitro. The nematicidal volatile organic compounds (VOCs) from the fermentation broth of B. aryabhattai MCCC 1K02966 were investigated further using solid-phase microextraction gas chromatography-mass spectrometry. Four VOCs, namely, pentane, 1-butanol, methyl thioacetate, and dimethyl disulfide, were identified in the fermentation broth. Among these VOCs, methyl thioacetate exhibited multiple nematicidal activities, including contact nematicidal, fumigant, and repellent activities against M. incognita. Methyl thioacetate showed a significant contact nematicidal activity with 87.90% mortality at 0.01 mg/mL by 72 h, fumigant activity in mortality 91.10% at 1 mg/mL by 48 h, and repellent activity at 0.01–10 mg/mL. In addition, methyl thioacetate exhibited 80–100% egg-hatching inhibition on the 7th day over the range of 0.5 mg/mL to 5 mg/mL. These results showed that methyl thioacetate from MCCC 1K02966 control M. incognita with multiple nematicidal modes and can be used as a potential biological control agent.
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He, Zhi-Hui, Jia Wu, Lin Xu, Man-Yi Hu, Ming-Ming Xie, You-Jia Hao, Shu-Jin Li, Zong-Ze Shao, and Xian-Wen Yang. "Chemical Constituents of the Deep-Sea-Derived Penicillium solitum." Marine Drugs 19, no. 10 (October 17, 2021): 580. http://dx.doi.org/10.3390/md19100580.

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A systematic chemical investigation of the deep-sea-derived fungus Penicillium solitum MCCC 3A00215 resulted in the isolation of one novel polyketide (1), two new alkaloids (2 and 3), and 22 known (4–25) compounds. The structures of the new compounds were established mainly on the basis of exhaustive analysis of 1D and 2D NMR data. Viridicatol (13) displayed moderate anti-tumor activities against PANC-1, Hela, and A549 cells with IC50 values of around 20 μM. Moreover, 13 displayed potent in vitro anti-food allergic activity with an IC50 value of 13 μM, compared to that of 92 μM for the positive control, loratadine, while indole-3-acetic acid methyl ester (9) and penicopeptide A (10) showed moderate effects (IC50 = 50 and 58 μM, respectively).
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Dissertations / Theses on the topic "Deep MCCA"

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Katthi, Jaswanth Reddy. "Deep Learning Methods For Audio EEG Analysis." Thesis, 2021. https://etd.iisc.ac.in/handle/2005/5734.

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The perception of speech and audio is one of the defining features of humans. Much of the brain’s underlying processes as we listen to acoustic signals are unknown, and significant research efforts are needed to unravel them. The non-invasive recordings capturing the brain activations like electroencephalogram (EEG) and magnetoencephalogram (MEG) are commonly deployed to capture the brain responses to auditory stimuli. But these non-invasive techniques capture artifacts and signals not related to the stimuli, which distort the stimulus-response analysis. The effect of the artifacts be- comes more evident for naturalistic stimuli. To reduce the inter-subject redundancies and amplify the components related to the stimuli, the EEG responses from multiple subjects listening to a common naturalistic stimulus need to be normalized. The currently used normalization and pre-processing methods are the canonical correlation analysis (CCA) models and the temporal response function based forward/backward models. However, these methods assume a simplistic linear relationship between the audio features and the EEG responses and therefore, may not alleviate the recording artifacts and interfering signals in EEG. This thesis proposes novel methods using machine learning advances to improve the audio-EEG analysis. We propose a deep learning framework for audio-EEG analysis in intra-subject and inter-subject settings. The deep learning based intra-subject analysis methods are trained with a Pearson correlation-based cost function between the stimuli and EEG responses. This model allows the transformation of the audio and EEG features that are maximally correlated. The correlation-based cost function can be optimized with the learnable parameters of the model trained using standard gradient descent- based methods. This model is referred to as the deep CCA (DCCA) model. Several experiments are performed on the EEG data recorded when the subjects are listening to naturalistic speech and music stimuli. We show that the deep methods obtain better representations than the linear methods and results in statistically significant improvements in correlation values. Further, we propose a neural network model with shared encoders that align the EEG responses from multiple subjects listening to the same audio stimuli. This inter-subject model boosts the signals common across the subjects and suppresses the subject-specific artifacts. The impact of improving stimulus-response correlations are highlighted based on multi-subject EEG data from speech and music tasks. This model is referred to as the deep multi-way canonical correlation analysis (DMCCA). The combination of inter-subject analysis using DMCCA and intra-subject analysis using DCCA is shown to provide the best stimulus-response in audio-EEG experiments. We highlight how much of the audio signal can be recovered purely from the non- invasive EEG recordings with modern machine learning methods, and conclude with a discussion on future challenges in audio-EEG analysis.
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Reed, Andrew Jay. "Molecular analysis of microbial 16S rRNA, mcrA, dsrAB and pmoA genes from deep-sea hydrothermal vent and cold seep sites." 2008. http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.17554.

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Dubey, Abhishek. "Multimodal Deep Learning for Multi-Label Classification and Ranking Problems." Thesis, 2015. http://etd.iisc.ac.in/handle/2005/3681.

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In recent years, deep neural network models have shown to outperform many state of the art algorithms. The reason for this is, unsupervised pretraining with multi-layered deep neural networks have shown to learn better features, which further improves many supervised tasks. These models not only automate the feature extraction process but also provide with robust features for various machine learning tasks. But the unsupervised pretraining and feature extraction using multi-layered networks are restricted only to the input features and not to the output. The performance of many supervised learning algorithms (or models) depends on how well the output dependencies are handled by these algorithms [Dembczy´nski et al., 2012]. Adapting the standard neural networks to handle these output dependencies for any specific type of problem has been an active area of research [Zhang and Zhou, 2006, Ribeiro et al., 2012]. On the other hand, inference into multimodal data is considered as a difficult problem in machine learning and recently ‘deep multimodal neural networks’ have shown significant results [Ngiam et al., 2011, Srivastava and Salakhutdinov, 2012]. Several problems like classification with complete or missing modality data, generating the missing modality etc., are shown to perform very well with these models. In this work, we consider three nontrivial supervised learning tasks (i) multi-class classification (MCC), (ii) multi-label classification (MLC) and (iii) label ranking (LR), mentioned in the order of increasing complexity of the output. While multi-class classification deals with predicting one class for every instance, multi-label classification deals with predicting more than one classes for every instance and label ranking deals with assigning a rank to each label for every instance. All the work in this field is associated around formulating new error functions that can force network to identify the output dependencies. Aim of our work is to adapt neural network to implicitly handle the feature extraction (dependencies) for output in the network structure, removing the need of hand crafted error functions. We show that the multimodal deep architectures can be adapted for these type of problems (or data) by considering labels as one of the modalities. This also brings unsupervised pretraining to the output along with the input. We show that these models can not only outperform standard deep neural networks, but also outperform standard adaptations of neural networks for individual domains under various metrics over several data sets considered by us. We can observe that the performance of our models over other models improves even more as the complexity of the output/ problem increases.
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Dubey, Abhishek. "Multimodal Deep Learning for Multi-Label Classification and Ranking Problems." Thesis, 2015. http://etd.iisc.ernet.in/2005/3681.

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In recent years, deep neural network models have shown to outperform many state of the art algorithms. The reason for this is, unsupervised pretraining with multi-layered deep neural networks have shown to learn better features, which further improves many supervised tasks. These models not only automate the feature extraction process but also provide with robust features for various machine learning tasks. But the unsupervised pretraining and feature extraction using multi-layered networks are restricted only to the input features and not to the output. The performance of many supervised learning algorithms (or models) depends on how well the output dependencies are handled by these algorithms [Dembczy´nski et al., 2012]. Adapting the standard neural networks to handle these output dependencies for any specific type of problem has been an active area of research [Zhang and Zhou, 2006, Ribeiro et al., 2012]. On the other hand, inference into multimodal data is considered as a difficult problem in machine learning and recently ‘deep multimodal neural networks’ have shown significant results [Ngiam et al., 2011, Srivastava and Salakhutdinov, 2012]. Several problems like classification with complete or missing modality data, generating the missing modality etc., are shown to perform very well with these models. In this work, we consider three nontrivial supervised learning tasks (i) multi-class classification (MCC), (ii) multi-label classification (MLC) and (iii) label ranking (LR), mentioned in the order of increasing complexity of the output. While multi-class classification deals with predicting one class for every instance, multi-label classification deals with predicting more than one classes for every instance and label ranking deals with assigning a rank to each label for every instance. All the work in this field is associated around formulating new error functions that can force network to identify the output dependencies. Aim of our work is to adapt neural network to implicitly handle the feature extraction (dependencies) for output in the network structure, removing the need of hand crafted error functions. We show that the multimodal deep architectures can be adapted for these type of problems (or data) by considering labels as one of the modalities. This also brings unsupervised pretraining to the output along with the input. We show that these models can not only outperform standard deep neural networks, but also outperform standard adaptations of neural networks for individual domains under various metrics over several data sets considered by us. We can observe that the performance of our models over other models improves even more as the complexity of the output/ problem increases.
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Book chapters on the topic "Deep MCCA"

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Deng, Qiuwei, Di Wang, Tianxiang Luan, and Bin Hao. "Tiny Deep Convolution Recurrent Network for Online Speech Enhancement with Various Noise Types." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2022. http://dx.doi.org/10.3233/faia220555.

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Nowadays, voice interaction is increasingly applied to smart home appliances. There are various types of noises in our real lives, which requires speech enhancement technology to deal with multiple noisy speech scenarios and to process them in real-time. Traditional technologies of speech noise reduction require estimating the noise power spectrum first, then estimating the spectrogram gain value of noise reduction, such as minima controlled recursive averaging (MCRA), which can only deal with stationary environmental noises but cannot estimate noises with serious fluctuations of the power spectrum within quite limited durations. A highly complicated deep-learning model can estimate the power spectrum of various types of noise, but it cannot meet the requirement of real-time processing due to the large number of parameters of these general models. In this paper, we proposed a method combining deep-learning technologies with traditional signal processing techniques to estimate the power spectrum of various types of noises by designing a new model with fewer parameters, tiny deep convolutional recurrent network (TDCRN), and computing the speech gain value with the power spectrum. The result of our experiment indicates that, compared with the traditional technology and complicated deep-learning model, the proposed method, with only 0.29M parameters, increases the PESQ by more than 0.6, the STOI by more than 0.2 and the wake-up rate by more than 6%.
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Conference papers on the topic "Deep MCCA"

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Sharrab, Yousef, Dimah Al-Fraihat, Monther Tarawneh, and Ahmad Sharieh. "Medicinal Plants Recognition Using Deep Learning." In 2023 International Conference on Multimedia Computing, Networking and Applications (MCNA). IEEE, 2023. http://dx.doi.org/10.1109/mcna59361.2023.10185880.

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Elbes, Mohammed, Shadi AlZu'bi, and Tarek Kanan. "Deep Learning-Based Earthquake Prediction Technique Using Seismic Data." In 2023 International Conference on Multimedia Computing, Networking and Applications (MCNA). IEEE, 2023. http://dx.doi.org/10.1109/mcna59361.2023.10185869.

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Attard, Leanne, Carl James Debono, Gianluca Valentino, and Mario di Castro. "Specular Highlights Detection Using a U-Net Based Deep Learning Architecture." In 2020 Fourth International Conference on Multimedia Computing, Networking and Applications (MCNA). IEEE, 2020. http://dx.doi.org/10.1109/mcna50957.2020.9264278.

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Victor, Uboho, Xishuang Dong, Xiangfang Li, Pamela Obiomon, and Lijun Qian. "Effective COVID-19 Screening using Chest Radiography Images via Deep Learning." In 2020 Fourth International Conference on Multimedia Computing, Networking and Applications (MCNA). IEEE, 2020. http://dx.doi.org/10.1109/mcna50957.2020.9264294.

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Gracic, Emil, Fredrik Svensson, Jesko Ehrich, Oliver Beck, and Maximilian Jansen. "Concept for Safety-Related Development of Deep Neural Networks in the Automotive." In 2020 Fourth International Conference on Multimedia Computing, Networking and Applications (MCNA). IEEE, 2020. http://dx.doi.org/10.1109/mcna50957.2020.9264279.

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Caillon, Didier, Benjamin Groschaus, Wilfried Matsiona, Theben Boumba, Manfred Bledou, Nicolas Dupouy, Robert Ilyasov, et al. "Successful Implementation of a Modified Carbonate Emulsion Acid System Combined with an Engineered Diversion Approach Delivers Outstanding Results – A Case Study from the Moho Nord Deep Offshore Field in Congo." In IADC/SPE Asia Pacific Drilling Technology Conference. SPE, 2021. http://dx.doi.org/10.2118/201054-ms.

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Abstract Moho Nord deep offshore field is located 80 kilometers offshore Pointe-Noire in the Republic of the Congo. The wells produce crude from the Albian age reservoir and lithology consists of alternating sequences of carbonates and sandstone layers with high heterogeneity and permeability contrast, including the presence vacuolar layers called "hyperdrains". This paper describes the application of a novel acid system and the methodology successfully applied to effectively acid stimulate the Albian drain. The combination of long perforation intervals with lithology and permeability contrasts, natural fractures, and the potential for asphaltene deposition resulted in adoption of a Modified Carbonate Emulsion Acid (MCEA) fluid system containing a solvent to provide asphaltene deposition prevention. The MCEA stimulation treatments were bullheaded from a stimulation vessel and an engineered diversion process was implemented for effective acid diversion using a combination of mechanical ball sealers and a degradable particle system (DPS). The selection of number of ball sealers and the DPS diverter design depended upon the interpretation of zone permeability profile from the logs, and the final distribution of perforations selected along the drain. A fluid placement simulator indicated low sealing efficiency of the ball sealers would lead to an overstimulation of the highest permeability areas. Subsequent simulations indicated that the DPS would provide better acid coverage with lower skin (S). Results and observations presented indicate that the decision to improve the acid diversion design and combine ball sealers with a DPS diversion technique to improve zonal coverage was validated. During the stimulation treatment execution, the high stimulation treatment efficiency was clearly apparent from the pressure responses to the acid and the diverter system which sealed off perforations and diverted the treatment to other layers with lower permeability. The MCEA also has proven to have self-diverting properties due to its high viscosity and low reaction rate which creates a better coverage of the drain, even with limited pumping rate, allowing live acid penetrating deeper into the formation. The production results reported from the 15 wells stimulation campaign (10 producers, 5 injectors) indicated that the productivity indexes (PI) exceeded expectations and resultant post-stimulation skin values ranged from −2.5 to −4.1. The Moho Nord deep offshore stimulation campaign yielded outstanding production results and showed significant validation for use of the MCEA system and the diversion methodology applied. On the producer wells the use of both chemical and mechanical diversion was valuable, as the DPS proved to complement the Ball Sealers for layers with lower injectivity and also at the high injection rates. High injectivity gain coupled with effective diversion was crucial for enhanced wormholing and good drain coverage.
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Eguchi, Shin, and Fumio Yamagishi. "Surface-relief hologram reproduction." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/oam.1992.mcc4.

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One of the factors determining the diffraction efficiency of the grating of a surface-relief hologram is the refractive index of the material. The refractive index of the photopoly­mer constituting a reproduced grating is smaller than that of the photoresist constituting the master grating. To reproduce the grating with a high diffraction efficiency, we studied how to optimize the grating shape. We found that a grating 0.5 μm wide required a master grating 0.9 μm wide to get the highest diffraction efficiency and that the reproduced grating had to be at least 1 μm deep. We fabricated a master and stamp and replicated gratings with a diffraction efficiency of 78 ±5%.
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Luo, Zhaojie, Tetsuya Takiguchi, and Yasuo Ariki. "Emotional voice conversion using deep neural networks with MCC and F0 features." In 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS). IEEE, 2016. http://dx.doi.org/10.1109/icis.2016.7550889.

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Guan, Sheng, Min Chen, Hsin-Yu Ha, Shu-Ching Chen, Mei-Ling Shyu, and Chengde Zhang. "Deep Learning with MCA-based Instance Selection and Bootstrapping for Imbalanced Data Classification." In 2015 IEEE Conference on Collaboration and Internet Computing (CIC). IEEE, 2015. http://dx.doi.org/10.1109/cic.2015.40.

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Lian, Hailun, Yuting Hu, Jian Zhou, Huabin Wang, and Liang Tao. "Whisper to Normal Speech Based on Deep Neural Networks with MCC and F0 Features." In 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP). IEEE, 2018. http://dx.doi.org/10.1109/icdsp.2018.8631888.

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You might also be interested in the extended bibliographies on the topic 'Deep MCCA' for particular source types:
Journal articles
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