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Su S, Li X, An Q, Liang T, Wang Y, Deng H, Xiong X, Wong WL, Zhang H, Li C. A smart cysteine-activated and heavy-atom-free nano-photosensitizer for photodynamic therapy to treat cancers. Chem Commun (Camb) 2024; 60:3910-3913. [PMID: 38333927 DOI: 10.1039/d3cc06019e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2024]
Abstract
A smart and heavy-atom-free photoinactive nano-photosensitizer capable of being activated by cysteine at the tumor site to generate highly photoactive nano-photosensitizers that show strong NIR absorption and fluorescence with a good singlet oxygen quantum yield (16.8%) for photodynamic therapy is reported.
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Affiliation(s)
- Shengze Su
- Key Laboratory of Catalysis and Energy Materials Chemistry of Ministry of Education & Hubei Key Laboratory of Catalysis and Materials Science & Key Laboratory of Analytical Chemistry of the State Ethnic Affairs Commission, South-Central Minzu University, Wuhan 430074, China
| | - Xingcan Li
- Key Laboratory of Catalysis and Energy Materials Chemistry of Ministry of Education & Hubei Key Laboratory of Catalysis and Materials Science & Key Laboratory of Analytical Chemistry of the State Ethnic Affairs Commission, South-Central Minzu University, Wuhan 430074, China
| | - Qian An
- Key Laboratory of Catalysis and Energy Materials Chemistry of Ministry of Education & Hubei Key Laboratory of Catalysis and Materials Science & Key Laboratory of Analytical Chemistry of the State Ethnic Affairs Commission, South-Central Minzu University, Wuhan 430074, China
| | - Tao Liang
- Ministry of Education Key Laboratory for the Synthesis and Application of Organic Functional Molecules, Hubei University, Wuhan 430062, China
| | - Yanying Wang
- Key Laboratory of Catalysis and Energy Materials Chemistry of Ministry of Education & Hubei Key Laboratory of Catalysis and Materials Science & Key Laboratory of Analytical Chemistry of the State Ethnic Affairs Commission, South-Central Minzu University, Wuhan 430074, China
| | - Hongping Deng
- Renmin Hospital of Wuhan University, Wuhan University, Wuhan 430060, China
| | - Xiaoxing Xiong
- Renmin Hospital of Wuhan University, Wuhan University, Wuhan 430060, China
| | - Wing-Leung Wong
- State Key Laboratory of Chemical Biology and Drug Discovery, Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR 999077, China
| | - Huijuan Zhang
- Key Laboratory of Catalysis and Energy Materials Chemistry of Ministry of Education & Hubei Key Laboratory of Catalysis and Materials Science & Key Laboratory of Analytical Chemistry of the State Ethnic Affairs Commission, South-Central Minzu University, Wuhan 430074, China
| | - Chunya Li
- Key Laboratory of Catalysis and Energy Materials Chemistry of Ministry of Education & Hubei Key Laboratory of Catalysis and Materials Science & Key Laboratory of Analytical Chemistry of the State Ethnic Affairs Commission, South-Central Minzu University, Wuhan 430074, China
- Renmin Hospital of Wuhan University, Wuhan University, Wuhan 430060, China
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2
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Hung SH, Ye ZR, Cheng CF, Chen B, Tsai MK. Enhanced Predictions for the Experimental Photophysical Data Using the Featurized Schnet-Bondstep Approach. J Chem Theory Comput 2023. [PMID: 37126224 DOI: 10.1021/acs.jctc.3c00054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
An assessment of modifying the SchNET model for the predictions of experimental molecular photophysical properties, including absorption energy (ΔEabs), emission energy (ΔEemi), and photoluminescence quantum yield (PLQY), was reported. The solution environment was properly introduced outside the interaction layers of SchNET for not overly amplifying the solute-solvent interactions, particularly being supported by the changes of prediction errors between the presence and absence of the solvent effect. Two featurization schemes under the framework of the Schnet-bondstep approach, with featuring the concepts of reduced-atomic-number and reduced-atomic-neighbor, were demonstrated. These featurized models can consequently provide fine predictions for ΔEabs and ΔEemi with errors less than 0.1 eV. The corresponding predictions of PLQY were shown to be comparable to the previous graph convolution network model.
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Affiliation(s)
- Sheng-Hsuan Hung
- Department of Chemistry, National Taiwan Normal University, Taipei 11677, Taiwan
| | - Zong-Rong Ye
- Department of Chemistry, National Taiwan Normal University, Taipei 11677, Taiwan
| | - Chi-Feng Cheng
- Department of Chemistry, National Taiwan Normal University, Taipei 11677, Taiwan
| | - Berlin Chen
- Department of Computer Science and Information Engineering, National Taiwan Normal University, Taipei 11677, Taiwan
| | - Ming-Kang Tsai
- Department of Chemistry, National Taiwan Normal University, Taipei 11677, Taiwan
- Department of Chemistry, Fu-Jen Catholic University, New Taipei City 24205, Taiwan
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3
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Wang J, Lu S, Wang SH, Zhang YD. A review on extreme learning machine. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:41611-41660. [DOI: 10.1007/s11042-021-11007-7] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 02/26/2021] [Accepted: 05/05/2021] [Indexed: 08/30/2023]
Abstract
AbstractExtreme learning machine (ELM) is a training algorithm for single hidden layer feedforward neural network (SLFN), which converges much faster than traditional methods and yields promising performance. In this paper, we hope to present a comprehensive review on ELM. Firstly, we will focus on the theoretical analysis including universal approximation theory and generalization. Then, the various improvements are listed, which help ELM works better in terms of stability, efficiency, and accuracy. Because of its outstanding performance, ELM has been successfully applied in many real-time learning tasks for classification, clustering, and regression. Besides, we report the applications of ELM in medical imaging: MRI, CT, and mammogram. The controversies of ELM were also discussed in this paper. We aim to report these advances and find some future perspectives.
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Ksenofontov AA, Lukanov MM, Bocharov PS. Can machine learning methods accurately predict the molar absorption coefficient of different classes of dyes? SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 279:121442. [PMID: 35660154 DOI: 10.1016/j.saa.2022.121442] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 05/25/2022] [Accepted: 05/26/2022] [Indexed: 06/15/2023]
Abstract
In this article, we provide a convenient tool for all researchers to predict the value of the molar absorption coefficient for a wide number of dyes without any computer costs. The new model is based on RFR method (ALogPS, OEstate + Fragmentor + QNPR) and is able to predict the molar absorption coefficient with an accuracy (5-fold cross-validation RMSE) of 0.26 log unit. This accuracy was achieved due to the fact that the model was trained on data for more than 20,000 unique dye molecules. To our knowledge, this is the first model for predicting the molar absorption coefficient trained on such a large and diverse set of dyes. The model is available at https://ochem.eu/article/145413. We hope that the new model will allow researchers to predict dyes with practically significant spectral characteristics and verify existing experimental data.
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Affiliation(s)
- Alexander A Ksenofontov
- G.A. Krestov Institute of Solution Chemistry of the Russian Academy of Sciences, Akademicheskaya Street, 153045 Ivanovo, Russia.
| | - Michail M Lukanov
- G.A. Krestov Institute of Solution Chemistry of the Russian Academy of Sciences, Akademicheskaya Street, 153045 Ivanovo, Russia; Ivanovo State University of Chemistry and Technology, 7, Sheremetevskiy Avenue, Ivanovo 153000, Russia
| | - Pavel S Bocharov
- G.A. Krestov Institute of Solution Chemistry of the Russian Academy of Sciences, Akademicheskaya Street, 153045 Ivanovo, Russia
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5
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Gupta A, Chakraborty S, Ghosh D, Ramakrishnan R. Data-driven modeling of S 0 → S 1 excitation energy in the BODIPY chemical space: High-throughput computation, quantum machine learning, and inverse design. J Chem Phys 2021; 155:244102. [PMID: 34972385 DOI: 10.1063/5.0076787] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Derivatives of BODIPY are popular fluorophores due to their synthetic feasibility, structural rigidity, high quantum yield, and tunable spectroscopic properties. While the characteristic absorption maximum of BODIPY is at 2.5 eV, combinations of functional groups and substitution sites can shift the peak position by ±1 eV. Time-dependent long-range corrected hybrid density functional methods can model the lowest excitation energies offering a semi-quantitative precision of ±0.3 eV. Alas, the chemical space of BODIPYs stemming from combinatorial introduction of-even a few dozen-substituents is too large for brute-force high-throughput modeling. To navigate this vast space, we select 77 412 molecules and train a kernel-based quantum machine learning model providing <2% hold-out error. Further reuse of the results presented here to navigate the entire BODIPY universe comprising over 253 giga (253 × 109) molecules is demonstrated by inverse-designing candidates with desired target excitation energies.
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Affiliation(s)
- Amit Gupta
- Centre for Interdisciplinary Sciences, Tata Institute of Fundamental Research, Hyderabad 500107, India
| | - Sabyasachi Chakraborty
- Centre for Interdisciplinary Sciences, Tata Institute of Fundamental Research, Hyderabad 500107, India
| | - Debashree Ghosh
- Indian Association for the Cultivation of Science, Kolkata 700032, India
| | - Raghunathan Ramakrishnan
- Centre for Interdisciplinary Sciences, Tata Institute of Fundamental Research, Hyderabad 500107, India
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Abstract
Theoretical simulations of electronic excitations and associated processes in molecules are indispensable for fundamental research and technological innovations. However, such simulations are notoriously challenging to perform with quantum mechanical methods. Advances in machine learning open many new avenues for assisting molecular excited-state simulations. In this Review, we track such progress, assess the current state of the art and highlight the critical issues to solve in the future. We overview a broad range of machine learning applications in excited-state research, which include the prediction of molecular properties, improvements of quantum mechanical methods for the calculations of excited-state properties and the search for new materials. Machine learning approaches can help us understand hidden factors that influence photo-processes, leading to a better control of such processes and new rules for the design of materials for optoelectronic applications.
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Yıldırım H, Revan Özkale M. LL-ELM: A regularized extreme learning machine based on $$L_{1}$$-norm and Liu estimator. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-05806-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Abstract
We present a machine learning (ML) method to accelerate the nuclear ensemble approach (NEA) for computing absorption cross sections. ML-NEA is used to calculate cross sections on vast ensembles of nuclear geometries to reduce the error due to insufficient statistical sampling. The electronic properties-excitation energies and oscillator strengths-are calculated with a reference electronic structure method only for a relatively few points in the ensemble. The KREG model (kernel-ridge-regression-based ML combined with the RE descriptor) as implemented in MLatom is used to predict these properties for the remaining tens of thousands of points in the ensemble without incurring much of additional computational cost. We demonstrate for two examples, benzene and a 9-dicyanomethylene derivative of acridine, that ML-NEA can produce statistically converged cross sections even for very challenging cases and even with as few as several hundreds of training points.
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Affiliation(s)
- Bao-Xin Xue
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Department of Chemistry, and College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
| | | | - Pavlo O Dral
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Department of Chemistry, and College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
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9
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Saucedo LI, Roacho RI, Tu P, Metta‐Magaña AJ, Belmonte‐Vázquez JL, Peña‐Cabrera E, Pannell KH. 8‐Amido‐BODIPYs: Synthesis, Structure and Optical Properties Illustrating Amine to Amide, Blue to Green Emission. ChemistrySelect 2020. [DOI: 10.1002/slct.201904583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Laura I. Saucedo
- Department of Chemistry University of Texas at El Paso El Paso, TX. 79968-0513 USA
| | - Robinson I. Roacho
- Department of Chemistry University of Texas at El Paso El Paso, TX. 79968-0513 USA
| | - Peiyu Tu
- Department of Chemistry University of Texas at El Paso El Paso, TX. 79968-0513 USA
| | | | - José L. Belmonte‐Vázquez
- Departamento de Química Universidad de Guanajuato. Col. Noria Alta S/N. Guanajuato, Gto. 36050 MX
| | - Eduardo Peña‐Cabrera
- Departamento de Química Universidad de Guanajuato. Col. Noria Alta S/N. Guanajuato, Gto. 36050 MX
| | - Keith H. Pannell
- Department of Chemistry University of Texas at El Paso El Paso, TX. 79968-0513 USA
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Ye ZR, Huang IS, Chan YT, Li ZJ, Liao CC, Tsai HR, Hsieh MC, Chang CC, Tsai MK. Predicting the emission wavelength of organic molecules using a combinatorial QSAR and machine learning approach. RSC Adv 2020; 10:23834-23841. [PMID: 35517310 PMCID: PMC9054811 DOI: 10.1039/d0ra05014h] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Accepted: 06/16/2020] [Indexed: 12/16/2022] Open
Abstract
Organic fluorescent molecules play critical roles in fluorescence inspection, biological probes, and labeling indicators. More than ten thousand organic fluorescent molecules were imported in this study, followed by a machine learning based approach for extracting the intrinsic structural characteristics that were found to correlate with the fluorescence emission. A systematic informatics procedure was introduced, starting from descriptor cleaning, descriptor space reduction, and statistical-meaningful regression to build a broad and valid model for estimating the fluorescence emission wavelength. The least absolute shrinkage and selection operator (Lasso) regression coupling with the random forest model was finally reported as the numerical predictor as well as being fulfilled with the statistical criteria. Such an informatics model appeared to bring comparable predictive ability, being complementary to the conventional time-dependent density functional theory method in emission wavelength prediction, however, with a fractional computational expense. The combinatorial QSAR and machine learning approach provides the qualitative and computationally efficient prediction for fluorescence emission wavelength of organic molecules.![]()
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Affiliation(s)
- Zong-Rong Ye
- Department of Chemistry
- National Taiwan Normal University
- Taipei
- Taiwan
| | - I.-Shou Huang
- Department of Chemistry
- National Taiwan Normal University
- Taipei
- Taiwan
- Department of Chemistry
| | - Yu-Te Chan
- Department of Chemistry
- National Taiwan Normal University
- Taipei
- Taiwan
- Theoretical Chemistry and Catalysis Research Center
| | - Zhong-Ji Li
- Department of Chemistry
- National Taiwan Normal University
- Taipei
- Taiwan
| | - Chen-Cheng Liao
- Department of Chemistry
- National Taiwan Normal University
- Taipei
- Taiwan
| | - Hao-Rong Tsai
- Department of Chemistry
- National Taiwan Normal University
- Taipei
- Taiwan
| | - Meng-Chi Hsieh
- Department of Chemistry
- National Taiwan Normal University
- Taipei
- Taiwan
| | - Chun-Chih Chang
- Department of Chemistry
- National Taiwan Normal University
- Taipei
- Taiwan
- Department of Chemical and Materials Engineering
| | - Ming-Kang Tsai
- Department of Chemistry
- National Taiwan Normal University
- Taipei
- Taiwan
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11
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Gawale Y, Rhyman L, Elzagheid MI, Ramasami P, Sekar N. Excited State and Non-linear Optical Properties of NIR Absorbing β-Thiophene-Fused BF2-Azadipyrromethene Dyes—Computational Investigation. J Fluoresc 2017; 28:243-250. [DOI: 10.1007/s10895-017-2186-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2017] [Accepted: 10/27/2017] [Indexed: 12/31/2022]
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12
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Prlj A, Vannay L, Corminboeuf C. Fluorescence Quenching in BODIPY Dyes: The Role of Intramolecular Interactions and Charge Transfer. Helv Chim Acta 2017. [DOI: 10.1002/hlca.201700093] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Antonio Prlj
- Institut des Sciences et Ingénierie Chimiques; École polytechnique fédérale de Lausanne; CH-1015 Lausanne
| | - Laurent Vannay
- Institut des Sciences et Ingénierie Chimiques; École polytechnique fédérale de Lausanne; CH-1015 Lausanne
| | - Clemence Corminboeuf
- Institut des Sciences et Ingénierie Chimiques; École polytechnique fédérale de Lausanne; CH-1015 Lausanne
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13
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Abstract
BODIPY laser dyes constitute a fascinating topic of research in modern photochemistry due to the large variety of options its chromophore offers, which is ready available for a multitude of synthetic routes. Indeed, in the literature one can find a huge battery of compounds based on the indacene core. The possibility of modulating the spectroscopic properties or inducing new photophysical processes by the substitution pattern of the BODIPY dyes has boosted the number of scientific and technological applications for these fluorophores. Along the following lines, I will overview the main results achieved in our laboratory with BODIPYs oriented to optoelectronic as well to biophotonic applications, stressing the more relevant photophysical issues to be considered in the design of a tailor-made BODIPY for a certain application and pointing out some of the remaining challenges.
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Affiliation(s)
- Jorge Bañuelos
- Dpto. Química Física, Universidad del País Vasco (UPV/EHU), Aptdo. 644, 48080, Bilbao, Spain
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14
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Prlj A, Fabrizio A, Corminboeuf C. Rationalizing fluorescence quenching in meso-BODIPY dyes. Phys Chem Chem Phys 2016; 18:32668-32672. [DOI: 10.1039/c6cp06799a] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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15
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Li H, Zhong Z, Li L, Gao R, Cui J, Gao T, Hu LH, Lu Y, Su ZM, Li H. A cascaded QSAR model for efficient prediction of overall power conversion efficiency of all-organic dye-sensitized solar cells. J Comput Chem 2015; 36:1036-46. [DOI: 10.1002/jcc.23886] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2014] [Revised: 11/25/2014] [Accepted: 02/08/2015] [Indexed: 01/19/2023]
Affiliation(s)
- Hongzhi Li
- School of Computer Science and Information Technology; Northeast Normal University; Changchun 130117 China
| | - Ziyan Zhong
- School of Computer Science and Information Technology; Northeast Normal University; Changchun 130117 China
| | - Lin Li
- School of Computer Science and Information Technology; Northeast Normal University; Changchun 130117 China
| | - Rui Gao
- Institute of Functional Material Chemistry, Faculty of Chemistry, Northeast Normal University; Changchun 130024 China
| | - Jingxia Cui
- Institute of Functional Material Chemistry, Faculty of Chemistry, Northeast Normal University; Changchun 130024 China
| | - Ting Gao
- School of Computer Science and Information Technology; Northeast Normal University; Changchun 130117 China
| | - Li Hong Hu
- School of Computer Science and Information Technology; Northeast Normal University; Changchun 130117 China
| | - Yinghua Lu
- School of Computer Science and Information Technology; Northeast Normal University; Changchun 130117 China
- Institute of Functional Material Chemistry, Faculty of Chemistry, Northeast Normal University; Changchun 130024 China
| | - Zhong-Min Su
- Institute of Functional Material Chemistry, Faculty of Chemistry, Northeast Normal University; Changchun 130024 China
| | - Hui Li
- School of Computer Science and Information Technology; Northeast Normal University; Changchun 130117 China
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16
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Huang G, Huang GB, Song S, You K. Trends in extreme learning machines: a review. Neural Netw 2014; 61:32-48. [PMID: 25462632 DOI: 10.1016/j.neunet.2014.10.001] [Citation(s) in RCA: 467] [Impact Index Per Article: 46.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2014] [Revised: 08/25/2014] [Accepted: 10/02/2014] [Indexed: 01/29/2023]
Abstract
Extreme learning machine (ELM) has gained increasing interest from various research fields recently. In this review, we aim to report the current state of the theoretical research and practical advances on this subject. We first give an overview of ELM from the theoretical perspective, including the interpolation theory, universal approximation capability, and generalization ability. Then we focus on the various improvements made to ELM which further improve its stability, sparsity and accuracy under general or specific conditions. Apart from classification and regression, ELM has recently been extended for clustering, feature selection, representational learning and many other learning tasks. These newly emerging algorithms greatly expand the applications of ELM. From implementation aspect, hardware implementation and parallel computation techniques have substantially sped up the training of ELM, making it feasible for big data processing and real-time reasoning. Due to its remarkable efficiency, simplicity, and impressive generalization performance, ELM have been applied in a variety of domains, such as biomedical engineering, computer vision, system identification, and control and robotics. In this review, we try to provide a comprehensive view of these advances in ELM together with its future perspectives.
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Affiliation(s)
- Gao Huang
- Department of Automation, Tsinghua University, Beijing 100084, China.
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17
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Frenette M, Hatamimoslehabadi M, Bellinger-Buckley S, Laoui S, Bag S, Dantiste O, Rochford J, Yelleswarapu C. Nonlinear optical properties of multipyrrole dyes. Chem Phys Lett 2014; 608:303-307. [PMID: 25242819 DOI: 10.1016/j.cplett.2014.06.002] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
The nonlinear optical properties of a series of pyrrolic compounds consisting of BODIPY and aza-BODIPY systems are investigated using 532 nm nanosecond laser and the Z-scan technique. Results show that 3,5-distyryl extension of BODIPY to the red shifted MeO2BODIPY dye has a dramatic impact on its nonlinear absorption properties changing it from a saturable absorber to an efficient reverse saturable absorbing material with a nonlinear absorption coefficient of 4.64 × 10-10 m/W. When plotted on a concentration scale per mole of dye in solution MeO2BODIPY far outperforms the recognized zinc(II) phthalocyanine dye and is comparable to that of zinc(II) tetraphenylporphyrin.
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Affiliation(s)
- Mathieu Frenette
- Department of Chemistry, University of Massachusetts Boston, Boston, MA 02125
| | | | | | - Samir Laoui
- Department of Physics, University of Massachusetts Boston, Boston, MA 02125
| | - Seema Bag
- Department of Chemistry, University of Massachusetts Boston, Boston, MA 02125
| | - Olivier Dantiste
- Department of Physics, University of Massachusetts Boston, Boston, MA 02125
| | - Jonathan Rochford
- Department of Chemistry, University of Massachusetts Boston, Boston, MA 02125
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