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Analysis of compatibility of dyes in mixtures by means of non-negative matrix factorization

COLOR RESEARCH AND APPLICATION 2021年第5期46卷 1057-1065页

作者： Charmi, Fatemeh Amirshahi, Seyed Hossein Amirkabir Univ Technol Dept Text Engn Tehran Polytech Tehran 15914 Iran

The Non-negative matrix factorization is used for analyzing the compatibility of dyes in mixtures. Samples have been collected from simulated and actual dip-dyeing tests. First, different sets of binary mixtures of dyes with different dyeing rates have been virtually created by changing the rates of absorption of employed primaries. Hence, an exponential function has been found to be appropriate to model different exhaustion rates of dyes. Besides, data of 17 different binary mixtures of nine cationic dyes, which were obtained using the dip-test method, have been also collected from actual dyeing process. The reflectance spectra of each dip-test dyed samples have been converted to scalable Kubelka-Munk function and the NNMF and Principal Component Analysis methods are employed to analyze the data. It is shown that the mixtures of fully compatible dyes could be completely represented by only one basic function that may either be extracted by Non-negative matrix factorization or Principal Component Analysis factorization methods. Oppositely, dyes with great degree of incompatibility could not be characterized by one principal vector. For the later sets of samples, the extracted Non-negative matrix factorization basis could successfully resemble the spectra of primaries. The spectral analysis of both simulated and real dyed samples show that the spectra of compatible primaries could be presented by a non-negative basic function with the average of root mean square errors value smaller than 0.1. This finding is accordance with the known practical fact that dyes are allowed to be mixed with maximum 0.5-unit differences in their K values.

关键词： cationic dye dye compatibility non‐ negative matrix factorization principal component analysis simulation

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Dimensional Stacking for Machine Learning in ToF-SIMS Analysis of Heterostructures

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ADVANCED MATERIALS INTERFACES 2021年第3期8卷

作者： Abbasi, Kevin Smith, Hugh Hoffman, Matthew Farghadany, Elahe Bruckman, Laura S. Sehirlioglu, Alp Case Western Reserve Univ Swagelok Ctr Surface Anal Mat Case Sch Engn Cleveland OH 44106 USA Case Western Reserve Univ Case Sch Engn Dept Mat Sci & Engn Cleveland OH 44106 USA Case Western Reserve Univ SDLE Res Ctr Cleveland OH 44106 USA

Output from multidimensional datasets obtained from spectroscopic imaging techniques provides large data suitable for machine learning techniques to elucidate physical and chemical attributes that define the maximum variance in the specimens. Here, a recently proposed technique of dimensional stacking is applied to obtain a cumulative depth over several LaAlO3/SrTiO3 heterostructures with varying thicknesses. Through dimensional reduction techniques via non-negative matrix factorization (NMF) and principal component analysis (PCA), it is shown that dimensional stacking provides much more robust statistics and consensus while still being able to separate different specimens of varying parameters. The results of stacked and unstacked samples as well as the dimensional reduction techniques are compared. Applied to four LaAlO3/SrTiO3 heterostructures with varying thicknesses, NMF is able to separate 1) surface and film termination;2) film;3) interface position;and 4) substrate attributes from each other with near perfect consensus. However, PCA results in the loss of data related to the substrate.

关键词： dimensional stacking heterostructures non‐ negative matrix factorization principal component analysis time‐ of‐ flight secondary ion mass spectroscopy

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Subgrouping by gene expression profiles to improve relapse risk prediction in paediatric B-precursor acute lymphoblastic leukaemia

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CANCER MEDICINE 2021年第11期10卷 3782-3793页

作者： Huang, Qingsheng Zhong, Jiayong Gao, Huan Li, Kuanrong Liang, Huiying Hanshan Normal Univ Sch Math & Stat Chaozhou Peoples R China Guangzhou Med Univ Inst Paediat Guangzhou Women & Childrens Med Ctr Guangzhou Peoples R China Sun Yat Sen Univ Zhongshan Ophthalm Ctr State Key Lab Ophthalmol Guangzhou Peoples R China Guangdong Acad Med Sci Guangdong Prov Peoples Hosp Clin Data Ctr Guangzhou Peoples R China

Relapsed acute lymphoblastic leukaemia (ALL) remains a prevalent paediatric cancer and one of the most common causes of mortality from malignancy in children. Tailoring the intensity of therapy according to early stratification is a promising strategy but remains a major challenge due to heterogeneity and subtyping difficulty. In this study, we subgroup B-precursor ALL patients by gene expression profiles, using non-negative matrix factorization and minimum description length which unsupervisedly determines the number of subgroups. Within each of the four subgroups, logistic and Cox regression with elastic net regularization are used to build models predicting minimal residual disease (MRD) and relapse-free survival (RFS) respectively. Measured by area under the receiver operating characteristic curve (AUC), subgrouping improves prediction of MRD in one subgroup which mostly overlaps with subtype TCF3-PBX1 (AUC = 0 center dot 986 in the training set and 1 center dot 0 in the test set), compared to a global model published previously. The models predicting RFS displayed acceptable concordance in training set and discriminate high-relapse-risk patients in three subgroups of the test set (Wilcoxon test p = 0 center dot 048, 0 center dot 036, and 0 center dot 016). Genes playing roles in the models are specific to different subgroups. The improvement of subgrouped MRD prediction and the differences of genes in prediction models of subgroups suggest that the heterogeneity of B-precursor ALL can be handled by subgrouping according to gene expression profiles to improve the prediction accuracy.

关键词： B‐ precursor acute lymphoblastic leukaemia gene expression profiles minimal residual disease non‐ negative matrix factorization relapse

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Immune response drives outcomes in prostate cancer: implications for immunotherapy

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MOLECULAR ONCOLOGY 2021年第5期15卷 1358-1375页

作者： Meng, Jialin Zhou, Yujie Lu, Xiaofan Bian, Zichen Chen, Yiding Zhou, Jun Zhang, Li Hao, Zongyao Zhang, Meng Liang, Chaozhao Anhui Med Univ Dept Urol Affiliated Hosp 1 218th Jixi Rd Hefei 230022 Peoples R China Anhui Med Univ Inst Urol 218th Jixi Rd Hefei 230022 Peoples R China Anhui Med Univ Anhui Prov Key Lab Genitourinary Dis 218th Jixi Rd Hefei 230022 Peoples R China Shanghai Jiao Tong Univ Renji Hosp Sch MedMinist HlthKey Lab Gastroenterol & Hepat Shanghai Inst Digest DisDiv Gastroenterol & Hepa Shanghai Peoples R China China Pharmaceut Univ Res Ctr Biostat & Computat Pharm State Key Lab Nat Med Nanjing Peoples R China Shenzhen Univ Affiliated Hosp 3 Urol Inst Shenzhen Guangdong Peoples R China

The heterogeneity of the immune microenvironment leads to different responses in immune checkpoint blockade therapy. We aimed to propose a robust molecular classification system to investigate the relevance of the immune microenvironment subtype and prognosis of prostate cancer patients, as well as the therapeutic response to immune checkpoint blockade therapy. A total of 1,557 prostate cancer patients were enrolled, including 69 real-world samples from our institute (titled the AHMU-PC cohort). The non-negative matrix factorization algorithm was employed to virtually microdissect patients. The immune enrichment was characterized by a high enrichment of T cell-, B cell-, NK cell-, and macrophage-associated signatures, by which patients were subclassified into nonimmune and immune classes. Subsequently, the immune class was dichotomized into immune-activated and immune-suppressed subtypes based on the stromal signature, represented by the activation of WNT/TGF-beta, TGF-beta 1, and C-ECM signatures. Approximately 14.9% to 24.3% of patients belonged to the immune-activated subtype, which was associated with favorable recurrence-free survival outcomes. In addition, patients in the immune-activated subtype were predicted to benefit more from anti-PD-1/PD-L1 therapy. In conclusion, our study identifies a novel immune molecular classifier that is closely related to clinical prognosis and provides novel insights into immunotherapeutic strategies for prostate cancer patients.

关键词： immune checkpoint blockade therapy immune molecular subclassification system immunotherapy non‐ negative matrix factorization prostate cancer

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Exhaust and non-exhaust contributions from road transport to PM10 at a Southern European traffic site*

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ENVIRONMENTAL POLLUTION 2023年 316卷

作者： Scerri, Mark M. Weinbruch, Stephan Delmaire, Gilles Mercieca, Nadine Nolle, Michael Prati, Paolo Massabo, Dario Univ Malta Inst Earth Syst Msida 2080 Msd Malta Tech Univ Darmstadt Inst Appl Geosci Schnittspahnstr 9 D-64287 Darmstadt Germany Univ Littoral Cote dOpale Lab Informat Signal & Image Cote dOpale LISIC F-62228 Calais France Environm & Resources Author Hexagon HouseSpencer Hill Marsa 1441 MRS Malta Univ Genoa Phys Dept Via Dodecaneso 33 I-16146 Genoa Italy Univ Genoa Ist Nazl Fis Nucl Via Dodecaneso 33 I-16146 Genoa Italy

It is a well - established fact that road traffic is one of the main contributors to ambient levels of airborne particulate matter (APM). This study was carried out at a traffic site in which the PM10 levels are monitored all year round. A trend analysis of these levels revealed that over a decade there was no discernible trend, with the PM10 concentrations normally hovering around the EU limit values. In 2018, one of these limit values was exceeded. The contribution of traffic at the site was therefore investigated through a chemical speciation of 209 PM10 samples collected throughout this year. The speciation data were used in a source apportionment exercise in which the output of the PMF model was further refined using the lesser-known, constraint weighted non - negative matrix factorization (CW - NMF) model. This technique enabled the isolation of two factors clearly related to traffic, which were labelled as "exhaust contribution" (responsible for 3.4% of the PM10), "tire/brake wear contribution" (contributing 17% of the PM10). Additionally, a factor including both traffic resuspended dust and crustal material was also isolated and labelled "road dust/crustal" factor. The two contributors to the factor jointly contribute 18% to the PM10 and the contribution of the traffic resuspended dust was estimated at 7.3%. The traffic resuspended component of this factor together with the "tire/brake wear contribution" jointly make up the non-exhaust contribution of traffic - derived dust. Consonant with what has been known for quite some time, the exhaust fraction is the minor component of traffic PM10. It is therefore, clear that policies aimed at controlling traffic derived PM10 pollution at the receptor will have a minimal effect unless the non - exhaust emissions are adequately controlled.

关键词： Positive matrix factorization Constraint weighted non negative matrix factorization Source apportionment PM10 Traffic aerosol Exhaust contribution Non-exhaust contribution Road dust

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Nonnegative matrix factorization with Hessian regularizer

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PATTERN ANALYSIS AND APPLICATIONS 2018年第2期21卷 501-513页

作者： Min, Xiaoping Chen, Youbing Ge, Shengxiang Xiamen Univ Sch Informat Sci & Engn Dept Comp Sci Xiamen 361005 Peoples R China Xiamen Univ Natl Inst Diagnost & Vaccine Dev Infect Dis Xiamen 361005 Peoples R China

Because of the underlying data structure preserved by the manifold regularization term, the Nonnegative matrix factorization (NMF) with manifold regularizer demonstrates an advantage over the variants of NMF for many data analysis tasks. Currently, the Laplacian regularizer is commonly used as the smooth operator to preserve the locality of data space. However, with the Laplacian regularizer, coding vectors are biased to a constant, which leads to a lack of extrapolating power. Thus, the locality of data space cannot be preserved, as would be expected. To address this drawback, a novel variant of NMF, namely HsNMF, is proposed, where the Hessian regularization term is incorporated into the traditional NMF framework. Because Hessian Energy favors the functions whose values vary linearly with respect to the geodesics of the data manifold, the local structure of data space is more effectively preserved. Clustering and classification experimental results on real-world image datasets demonstrate that our proposed NMF is superior to the variants of NMF based on Laplacian Embedding.

关键词： Hessian negative matrix factorization Manifold learning

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Onomatopoeia Search System Focused on Attributes Based on Sensibility and Various Sounds

Onomatopoeia Search System Focused on Attributes Based on Se...

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IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

作者： Yamada, R. Ito, T. Ishizu, S. Aoyama Gakuin Univ Grad Sch Sci & Engn Sagamihara Kanagawa Japan Aoyama Gakuin Univ Dept Ind & Syst Engn Sagamihara Kanagawa Japan

ISBN: (纸本)9781665437714

In recent years, several onomatopoeias have been used in food advertisements as words to express texture. When consumers observe onomatopoeias in advertisements, they link them to food sales by associating them with the willingness to purchase a particular product. It is important to select onomatopoeias that appropriately express the product image. If the most suitable onomatopoeias that represent the advertised product or review text can be searched, it will be easier for consumers to understand the product better, and the advertisement, as a result, will be successful. The purpose of this study was to identify the attributes that characterize onomatopoeias from user reviews and to analyze the relationship between the sounds of onomatopoeias and each of their attributes to construct a system that can search for onomatopoeias by matching attribute images. In the proposed system, onomatopoeia words were extracted by arranging the onomatopoeias on a plane using principal component analysis and then clicking on this plane using a mouse. In addition, we confirmed whether the extracted onomatopoeias matched the images of the attributes through verification experiments.

关键词： Search system user review negative matrix factorization onomatopoeia principal component analysis

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Supervised and Semi-supervised Speech Enhancement Using Weighted Nonnegative matrix factorization 9

Supervised and Semi-supervised Speech Enhancement Using Weig...

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9th International Conference on Wireless Communication and Signal Processing (WCSP)

作者： Zou, Xia Hu, Yonggang Zhang, Xiongwei Army Engn Univ PLA Lab Intelligent Informat Proc Nanjing 210007 Jiangsu Peoples R China Australian Natl Univ Res Sch Engn Canberra ACT 2601 Australia

ISBN: (纸本)9781538620625

According to the prior knowledge used in the algorithms, NMF-based speech enhancement can be categorized into supervised (NMF) one and semi-supervised (SNMF) one. In the supervised version, speech is estimated with the prior knowledge of both speech and noise. For the semi-supervised version, speech bases are learned on clean speech data in advance and adapted to different noise conditions. With the probabilistic estimation of whether the speech is present or not in a certain frame, this paper presents an extension to both NMF- and SNMF-based speech enhancement techniques by incorporating the speech presence probability (SPP) into their multiplicative update process. The performance of the algorithms is evaluated on three metrics (PESQ, LSD and SDR) by making experiments on TIMIT with 20 noise types at various signal-to-noise ratio (SNR) levels. All the proposed algorithms show the superiority over the conventional NMF, SNMF-based speech enhancement techniques, and also obtain better performance than some unsupervised state-of-the-art algorithms.

关键词： negative matrix factorization supervised NMF semi-supervised NMF speech presence probability

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Quantitative assessment in thermal image segmentation for artistic objects 6

Quantitative assessment in thermal image segmentation for ar...

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Conference on Optics for Arts, Architecture, and Archaeology VI

作者： Yousefi, Bardia Sfarra, Stefano Maldague, Xavier P. V. Laval Univ Dept Elect & Comp Engn Comp Vis & Syst Lab 1065 Av Med Quebec City PQ G1V 0A6 Canada Univ Aquila Dept Ind & Informat Engn & Econ DIIIE LasER Lab Piazzale E Pontieri 1 I-67100 Rolo Poggio Laquila AQ Italy

ISBN: (数字)9781510611085

ISBN: (纸本)9781510611085;9781510611078

The application of the thermal and infrared technology in different areas of research is considerably increasing. These applications involve Non-destructive Testing (NDT), Medical analysis (Computer Aid Diagnosis/DetectionCAD), Arts and Archaeology among many others. In the arts and archaeology field, infrared technology provides significant contributions in term of finding defects of possible impaired regions. This has been done through a wide range of different thermographic experiments and infrared methods. The proposed approach here focuses on application of some known factor analysis methods such as standard Non-negative matrix factorization (NMF) optimized by gradient-descent-based multiplicative rules (SNMF1) and standard NMF optimized by Non-negative least squares (NNLS) active-set algorithm (SNMF2) and eigen decomposition approaches such as Principal Component Thermography (PCT), Candid Covariance-Free Incremental Principal Component Thermography (CCIPCT) to obtain the thermal features. On one hand, these methods are usually applied as preprocessing before clustering for the purpose of segmentation of possible defects. On the other hand, a wavelet based data fusion combines the data of each method with PCT to increase the accuracy of the algorithm. The quantitative assessment of these approaches indicates considerable segmentation along with the reasonable computational complexity. It shows the promising performance and demonstrated a confirmation for the outlined properties. In particular, a polychromatic wooden statue and a fresco were analyzed using the above mentioned methods and interesting results were obtained.

关键词： Thermal image segmentation Factor analysis in infrared imagery negative matrix factorization Gradient-descent-based multiplicative rules Non-negative least squares (NNLS) active-set algorithm wavelet data fusion Clustering

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