Sparse Bayesian learning approach for baseline correction
Spectral techniques in analytical chemistry are often affected by baselines in practical implementation. Without baseline correction, the accuracy of the qualitative/quantitative analytical results may degrade substantially. Sparse representation has been applied to baseline correction recently, whi...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Li, Haoran [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Migration and characterisation of nanosilver from food containers by AF4-ICP-MS - Artiaga, G. ELSEVIER, 2015, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:204 ; year:2020 ; day:15 ; month:09 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.chemolab.2020.104088 |
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| 520 | |a Spectral techniques in analytical chemistry are often affected by baselines in practical implementation. Without baseline correction, the accuracy of the qualitative/quantitative analytical results may degrade substantially. Sparse representation has been applied to baseline correction recently, which can provide state-of-the-art performance. However, it suffers from possible performance degradation when realized using l 1 -norm approximation. To significantly improve the performance for baseline correction, a sparse Bayesian learning (SBL) framework for joint pure spectrum fitting and baseline correction is presented in this work. Since the SBL framework provides high flexibility to tackle the minimum l 0 -norm problem instead of the l 1 -norm approximation, it is possible to yield higher baseline correction accuracy. Moreover, the proposed method has an inherent learning capability, so no additional regularization terms are required. Note that the sparse representation performance would degrade if the grid points used in dictionary matrix are not sufficiently dense. Therefore, we further consider grid points as adjustable parameters and then adopt a grid refinement technique to handle the off-grid gap. Results on both simulated and real datasets reveal substantial performance improvement of the proposed SBL-based method over the existing schemes on baseline correction. | ||
| 520 | |a Spectral techniques in analytical chemistry are often affected by baselines in practical implementation. Without baseline correction, the accuracy of the qualitative/quantitative analytical results may degrade substantially. Sparse representation has been applied to baseline correction recently, which can provide state-of-the-art performance. However, it suffers from possible performance degradation when realized using l 1 -norm approximation. To significantly improve the performance for baseline correction, a sparse Bayesian learning (SBL) framework for joint pure spectrum fitting and baseline correction is presented in this work. Since the SBL framework provides high flexibility to tackle the minimum l 0 -norm problem instead of the l 1 -norm approximation, it is possible to yield higher baseline correction accuracy. Moreover, the proposed method has an inherent learning capability, so no additional regularization terms are required. Note that the sparse representation performance would degrade if the grid points used in dictionary matrix are not sufficiently dense. Therefore, we further consider grid points as adjustable parameters and then adopt a grid refinement technique to handle the off-grid gap. Results on both simulated and real datasets reveal substantial performance improvement of the proposed SBL-based method over the existing schemes on baseline correction. | ||
| 650 | 7 | |a Sparse Bayesian learning (SBL) |2 Elsevier | |
| 650 | 7 | |a Sparse representation |2 Elsevier | |
| 650 | 7 | |a Baseline correction |2 Elsevier | |
| 650 | 7 | |a Raman spectroscopy |2 Elsevier | |
| 700 | 1 | |a Dai, Jisheng |4 oth | |
| 700 | 1 | |a Pan, Tianhong |4 oth | |
| 700 | 1 | |a Chang, Chunqi |4 oth | |
| 700 | 1 | |a So, Hing Cheung |4 oth | |
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10.1016/j.chemolab.2020.104088 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001146.pica (DE-627)ELV051143607 (ELSEVIER)S0169-7439(19)30773-7 DE-627 ger DE-627 rakwb eng 540 VZ 35.00 bkl Li, Haoran verfasserin aut Sparse Bayesian learning approach for baseline correction 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Spectral techniques in analytical chemistry are often affected by baselines in practical implementation. Without baseline correction, the accuracy of the qualitative/quantitative analytical results may degrade substantially. Sparse representation has been applied to baseline correction recently, which can provide state-of-the-art performance. However, it suffers from possible performance degradation when realized using l 1 -norm approximation. To significantly improve the performance for baseline correction, a sparse Bayesian learning (SBL) framework for joint pure spectrum fitting and baseline correction is presented in this work. Since the SBL framework provides high flexibility to tackle the minimum l 0 -norm problem instead of the l 1 -norm approximation, it is possible to yield higher baseline correction accuracy. Moreover, the proposed method has an inherent learning capability, so no additional regularization terms are required. Note that the sparse representation performance would degrade if the grid points used in dictionary matrix are not sufficiently dense. Therefore, we further consider grid points as adjustable parameters and then adopt a grid refinement technique to handle the off-grid gap. Results on both simulated and real datasets reveal substantial performance improvement of the proposed SBL-based method over the existing schemes on baseline correction. Spectral techniques in analytical chemistry are often affected by baselines in practical implementation. Without baseline correction, the accuracy of the qualitative/quantitative analytical results may degrade substantially. Sparse representation has been applied to baseline correction recently, which can provide state-of-the-art performance. However, it suffers from possible performance degradation when realized using l 1 -norm approximation. To significantly improve the performance for baseline correction, a sparse Bayesian learning (SBL) framework for joint pure spectrum fitting and baseline correction is presented in this work. Since the SBL framework provides high flexibility to tackle the minimum l 0 -norm problem instead of the l 1 -norm approximation, it is possible to yield higher baseline correction accuracy. Moreover, the proposed method has an inherent learning capability, so no additional regularization terms are required. Note that the sparse representation performance would degrade if the grid points used in dictionary matrix are not sufficiently dense. Therefore, we further consider grid points as adjustable parameters and then adopt a grid refinement technique to handle the off-grid gap. Results on both simulated and real datasets reveal substantial performance improvement of the proposed SBL-based method over the existing schemes on baseline correction. Sparse Bayesian learning (SBL) Elsevier Sparse representation Elsevier Baseline correction Elsevier Raman spectroscopy Elsevier Dai, Jisheng oth Pan, Tianhong oth Chang, Chunqi oth So, Hing Cheung oth Enthalten in Elsevier Science Artiaga, G. ELSEVIER Migration and characterisation of nanosilver from food containers by AF4-ICP-MS 2015 Amsterdam [u.a.] (DE-627)ELV012980455 volume:204 year:2020 day:15 month:09 pages:0 https://doi.org/10.1016/j.chemolab.2020.104088 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_40 GBV_ILN_62 35.00 Chemie: Allgemeines VZ AR 204 2020 15 0915 0 |
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10.1016/j.chemolab.2020.104088 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001146.pica (DE-627)ELV051143607 (ELSEVIER)S0169-7439(19)30773-7 DE-627 ger DE-627 rakwb eng 540 VZ 35.00 bkl Li, Haoran verfasserin aut Sparse Bayesian learning approach for baseline correction 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Spectral techniques in analytical chemistry are often affected by baselines in practical implementation. Without baseline correction, the accuracy of the qualitative/quantitative analytical results may degrade substantially. Sparse representation has been applied to baseline correction recently, which can provide state-of-the-art performance. However, it suffers from possible performance degradation when realized using l 1 -norm approximation. To significantly improve the performance for baseline correction, a sparse Bayesian learning (SBL) framework for joint pure spectrum fitting and baseline correction is presented in this work. Since the SBL framework provides high flexibility to tackle the minimum l 0 -norm problem instead of the l 1 -norm approximation, it is possible to yield higher baseline correction accuracy. Moreover, the proposed method has an inherent learning capability, so no additional regularization terms are required. Note that the sparse representation performance would degrade if the grid points used in dictionary matrix are not sufficiently dense. Therefore, we further consider grid points as adjustable parameters and then adopt a grid refinement technique to handle the off-grid gap. Results on both simulated and real datasets reveal substantial performance improvement of the proposed SBL-based method over the existing schemes on baseline correction. Spectral techniques in analytical chemistry are often affected by baselines in practical implementation. Without baseline correction, the accuracy of the qualitative/quantitative analytical results may degrade substantially. Sparse representation has been applied to baseline correction recently, which can provide state-of-the-art performance. However, it suffers from possible performance degradation when realized using l 1 -norm approximation. To significantly improve the performance for baseline correction, a sparse Bayesian learning (SBL) framework for joint pure spectrum fitting and baseline correction is presented in this work. Since the SBL framework provides high flexibility to tackle the minimum l 0 -norm problem instead of the l 1 -norm approximation, it is possible to yield higher baseline correction accuracy. Moreover, the proposed method has an inherent learning capability, so no additional regularization terms are required. Note that the sparse representation performance would degrade if the grid points used in dictionary matrix are not sufficiently dense. Therefore, we further consider grid points as adjustable parameters and then adopt a grid refinement technique to handle the off-grid gap. Results on both simulated and real datasets reveal substantial performance improvement of the proposed SBL-based method over the existing schemes on baseline correction. Sparse Bayesian learning (SBL) Elsevier Sparse representation Elsevier Baseline correction Elsevier Raman spectroscopy Elsevier Dai, Jisheng oth Pan, Tianhong oth Chang, Chunqi oth So, Hing Cheung oth Enthalten in Elsevier Science Artiaga, G. ELSEVIER Migration and characterisation of nanosilver from food containers by AF4-ICP-MS 2015 Amsterdam [u.a.] (DE-627)ELV012980455 volume:204 year:2020 day:15 month:09 pages:0 https://doi.org/10.1016/j.chemolab.2020.104088 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_40 GBV_ILN_62 35.00 Chemie: Allgemeines VZ AR 204 2020 15 0915 0 |
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10.1016/j.chemolab.2020.104088 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001146.pica (DE-627)ELV051143607 (ELSEVIER)S0169-7439(19)30773-7 DE-627 ger DE-627 rakwb eng 540 VZ 35.00 bkl Li, Haoran verfasserin aut Sparse Bayesian learning approach for baseline correction 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Spectral techniques in analytical chemistry are often affected by baselines in practical implementation. Without baseline correction, the accuracy of the qualitative/quantitative analytical results may degrade substantially. Sparse representation has been applied to baseline correction recently, which can provide state-of-the-art performance. However, it suffers from possible performance degradation when realized using l 1 -norm approximation. To significantly improve the performance for baseline correction, a sparse Bayesian learning (SBL) framework for joint pure spectrum fitting and baseline correction is presented in this work. Since the SBL framework provides high flexibility to tackle the minimum l 0 -norm problem instead of the l 1 -norm approximation, it is possible to yield higher baseline correction accuracy. Moreover, the proposed method has an inherent learning capability, so no additional regularization terms are required. Note that the sparse representation performance would degrade if the grid points used in dictionary matrix are not sufficiently dense. Therefore, we further consider grid points as adjustable parameters and then adopt a grid refinement technique to handle the off-grid gap. Results on both simulated and real datasets reveal substantial performance improvement of the proposed SBL-based method over the existing schemes on baseline correction. Spectral techniques in analytical chemistry are often affected by baselines in practical implementation. Without baseline correction, the accuracy of the qualitative/quantitative analytical results may degrade substantially. Sparse representation has been applied to baseline correction recently, which can provide state-of-the-art performance. However, it suffers from possible performance degradation when realized using l 1 -norm approximation. To significantly improve the performance for baseline correction, a sparse Bayesian learning (SBL) framework for joint pure spectrum fitting and baseline correction is presented in this work. Since the SBL framework provides high flexibility to tackle the minimum l 0 -norm problem instead of the l 1 -norm approximation, it is possible to yield higher baseline correction accuracy. Moreover, the proposed method has an inherent learning capability, so no additional regularization terms are required. Note that the sparse representation performance would degrade if the grid points used in dictionary matrix are not sufficiently dense. Therefore, we further consider grid points as adjustable parameters and then adopt a grid refinement technique to handle the off-grid gap. Results on both simulated and real datasets reveal substantial performance improvement of the proposed SBL-based method over the existing schemes on baseline correction. Sparse Bayesian learning (SBL) Elsevier Sparse representation Elsevier Baseline correction Elsevier Raman spectroscopy Elsevier Dai, Jisheng oth Pan, Tianhong oth Chang, Chunqi oth So, Hing Cheung oth Enthalten in Elsevier Science Artiaga, G. ELSEVIER Migration and characterisation of nanosilver from food containers by AF4-ICP-MS 2015 Amsterdam [u.a.] (DE-627)ELV012980455 volume:204 year:2020 day:15 month:09 pages:0 https://doi.org/10.1016/j.chemolab.2020.104088 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_40 GBV_ILN_62 35.00 Chemie: Allgemeines VZ AR 204 2020 15 0915 0 |
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10.1016/j.chemolab.2020.104088 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001146.pica (DE-627)ELV051143607 (ELSEVIER)S0169-7439(19)30773-7 DE-627 ger DE-627 rakwb eng 540 VZ 35.00 bkl Li, Haoran verfasserin aut Sparse Bayesian learning approach for baseline correction 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Spectral techniques in analytical chemistry are often affected by baselines in practical implementation. Without baseline correction, the accuracy of the qualitative/quantitative analytical results may degrade substantially. Sparse representation has been applied to baseline correction recently, which can provide state-of-the-art performance. However, it suffers from possible performance degradation when realized using l 1 -norm approximation. To significantly improve the performance for baseline correction, a sparse Bayesian learning (SBL) framework for joint pure spectrum fitting and baseline correction is presented in this work. Since the SBL framework provides high flexibility to tackle the minimum l 0 -norm problem instead of the l 1 -norm approximation, it is possible to yield higher baseline correction accuracy. Moreover, the proposed method has an inherent learning capability, so no additional regularization terms are required. Note that the sparse representation performance would degrade if the grid points used in dictionary matrix are not sufficiently dense. Therefore, we further consider grid points as adjustable parameters and then adopt a grid refinement technique to handle the off-grid gap. Results on both simulated and real datasets reveal substantial performance improvement of the proposed SBL-based method over the existing schemes on baseline correction. Spectral techniques in analytical chemistry are often affected by baselines in practical implementation. Without baseline correction, the accuracy of the qualitative/quantitative analytical results may degrade substantially. Sparse representation has been applied to baseline correction recently, which can provide state-of-the-art performance. However, it suffers from possible performance degradation when realized using l 1 -norm approximation. To significantly improve the performance for baseline correction, a sparse Bayesian learning (SBL) framework for joint pure spectrum fitting and baseline correction is presented in this work. Since the SBL framework provides high flexibility to tackle the minimum l 0 -norm problem instead of the l 1 -norm approximation, it is possible to yield higher baseline correction accuracy. Moreover, the proposed method has an inherent learning capability, so no additional regularization terms are required. Note that the sparse representation performance would degrade if the grid points used in dictionary matrix are not sufficiently dense. Therefore, we further consider grid points as adjustable parameters and then adopt a grid refinement technique to handle the off-grid gap. Results on both simulated and real datasets reveal substantial performance improvement of the proposed SBL-based method over the existing schemes on baseline correction. Sparse Bayesian learning (SBL) Elsevier Sparse representation Elsevier Baseline correction Elsevier Raman spectroscopy Elsevier Dai, Jisheng oth Pan, Tianhong oth Chang, Chunqi oth So, Hing Cheung oth Enthalten in Elsevier Science Artiaga, G. ELSEVIER Migration and characterisation of nanosilver from food containers by AF4-ICP-MS 2015 Amsterdam [u.a.] (DE-627)ELV012980455 volume:204 year:2020 day:15 month:09 pages:0 https://doi.org/10.1016/j.chemolab.2020.104088 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_40 GBV_ILN_62 35.00 Chemie: Allgemeines VZ AR 204 2020 15 0915 0 |
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10.1016/j.chemolab.2020.104088 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001146.pica (DE-627)ELV051143607 (ELSEVIER)S0169-7439(19)30773-7 DE-627 ger DE-627 rakwb eng 540 VZ 35.00 bkl Li, Haoran verfasserin aut Sparse Bayesian learning approach for baseline correction 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Spectral techniques in analytical chemistry are often affected by baselines in practical implementation. Without baseline correction, the accuracy of the qualitative/quantitative analytical results may degrade substantially. Sparse representation has been applied to baseline correction recently, which can provide state-of-the-art performance. However, it suffers from possible performance degradation when realized using l 1 -norm approximation. To significantly improve the performance for baseline correction, a sparse Bayesian learning (SBL) framework for joint pure spectrum fitting and baseline correction is presented in this work. Since the SBL framework provides high flexibility to tackle the minimum l 0 -norm problem instead of the l 1 -norm approximation, it is possible to yield higher baseline correction accuracy. Moreover, the proposed method has an inherent learning capability, so no additional regularization terms are required. Note that the sparse representation performance would degrade if the grid points used in dictionary matrix are not sufficiently dense. Therefore, we further consider grid points as adjustable parameters and then adopt a grid refinement technique to handle the off-grid gap. Results on both simulated and real datasets reveal substantial performance improvement of the proposed SBL-based method over the existing schemes on baseline correction. Spectral techniques in analytical chemistry are often affected by baselines in practical implementation. Without baseline correction, the accuracy of the qualitative/quantitative analytical results may degrade substantially. Sparse representation has been applied to baseline correction recently, which can provide state-of-the-art performance. However, it suffers from possible performance degradation when realized using l 1 -norm approximation. To significantly improve the performance for baseline correction, a sparse Bayesian learning (SBL) framework for joint pure spectrum fitting and baseline correction is presented in this work. Since the SBL framework provides high flexibility to tackle the minimum l 0 -norm problem instead of the l 1 -norm approximation, it is possible to yield higher baseline correction accuracy. Moreover, the proposed method has an inherent learning capability, so no additional regularization terms are required. Note that the sparse representation performance would degrade if the grid points used in dictionary matrix are not sufficiently dense. Therefore, we further consider grid points as adjustable parameters and then adopt a grid refinement technique to handle the off-grid gap. Results on both simulated and real datasets reveal substantial performance improvement of the proposed SBL-based method over the existing schemes on baseline correction. Sparse Bayesian learning (SBL) Elsevier Sparse representation Elsevier Baseline correction Elsevier Raman spectroscopy Elsevier Dai, Jisheng oth Pan, Tianhong oth Chang, Chunqi oth So, Hing Cheung oth Enthalten in Elsevier Science Artiaga, G. ELSEVIER Migration and characterisation of nanosilver from food containers by AF4-ICP-MS 2015 Amsterdam [u.a.] (DE-627)ELV012980455 volume:204 year:2020 day:15 month:09 pages:0 https://doi.org/10.1016/j.chemolab.2020.104088 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_40 GBV_ILN_62 35.00 Chemie: Allgemeines VZ AR 204 2020 15 0915 0 |
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Spectral techniques in analytical chemistry are often affected by baselines in practical implementation. Without baseline correction, the accuracy of the qualitative/quantitative analytical results may degrade substantially. Sparse representation has been applied to baseline correction recently, which can provide state-of-the-art performance. However, it suffers from possible performance degradation when realized using l 1 -norm approximation. To significantly improve the performance for baseline correction, a sparse Bayesian learning (SBL) framework for joint pure spectrum fitting and baseline correction is presented in this work. Since the SBL framework provides high flexibility to tackle the minimum l 0 -norm problem instead of the l 1 -norm approximation, it is possible to yield higher baseline correction accuracy. Moreover, the proposed method has an inherent learning capability, so no additional regularization terms are required. Note that the sparse representation performance would degrade if the grid points used in dictionary matrix are not sufficiently dense. Therefore, we further consider grid points as adjustable parameters and then adopt a grid refinement technique to handle the off-grid gap. Results on both simulated and real datasets reveal substantial performance improvement of the proposed SBL-based method over the existing schemes on baseline correction. |
| abstractGer |
Spectral techniques in analytical chemistry are often affected by baselines in practical implementation. Without baseline correction, the accuracy of the qualitative/quantitative analytical results may degrade substantially. Sparse representation has been applied to baseline correction recently, which can provide state-of-the-art performance. However, it suffers from possible performance degradation when realized using l 1 -norm approximation. To significantly improve the performance for baseline correction, a sparse Bayesian learning (SBL) framework for joint pure spectrum fitting and baseline correction is presented in this work. Since the SBL framework provides high flexibility to tackle the minimum l 0 -norm problem instead of the l 1 -norm approximation, it is possible to yield higher baseline correction accuracy. Moreover, the proposed method has an inherent learning capability, so no additional regularization terms are required. Note that the sparse representation performance would degrade if the grid points used in dictionary matrix are not sufficiently dense. Therefore, we further consider grid points as adjustable parameters and then adopt a grid refinement technique to handle the off-grid gap. Results on both simulated and real datasets reveal substantial performance improvement of the proposed SBL-based method over the existing schemes on baseline correction. |
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Spectral techniques in analytical chemistry are often affected by baselines in practical implementation. Without baseline correction, the accuracy of the qualitative/quantitative analytical results may degrade substantially. Sparse representation has been applied to baseline correction recently, which can provide state-of-the-art performance. However, it suffers from possible performance degradation when realized using l 1 -norm approximation. To significantly improve the performance for baseline correction, a sparse Bayesian learning (SBL) framework for joint pure spectrum fitting and baseline correction is presented in this work. Since the SBL framework provides high flexibility to tackle the minimum l 0 -norm problem instead of the l 1 -norm approximation, it is possible to yield higher baseline correction accuracy. Moreover, the proposed method has an inherent learning capability, so no additional regularization terms are required. Note that the sparse representation performance would degrade if the grid points used in dictionary matrix are not sufficiently dense. Therefore, we further consider grid points as adjustable parameters and then adopt a grid refinement technique to handle the off-grid gap. Results on both simulated and real datasets reveal substantial performance improvement of the proposed SBL-based method over the existing schemes on baseline correction. |
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