The Study of Maximizing Customer Equity by Segmentation: A Modified K-Means Approach

As segmentation has been one of the central marketing tasks for decades and customer profitability valuation has seen wide study during the past few years, surprisingly, up to this date, there is a gap in marketing research that await a bridge to link up of these two important and closely related dimensions. In this paper, we introduce a decision support system with the goal of maximizing customer equity by segmentation. The decision support system introduced here is unique in that it accommodates the essence of customer profitability valuation into a segmentation scheme in a sensible and flexible manner, that it suggests the number of segments to be determined by the goal of profit maximization instead of some arbitrary numerical criterion, and that central to its technical core the outlier problem which is pervasive in cluster analysis has been addressed by a modified K-Means algorithm so that clustering can reflect the pattern of the majority of ordinary observations in a data set instead of being influenced by a handful of outliers. It followed by a number of test datasets from a public data source and a conclusion remark was made at the end

Learning with Noisily-labeled Class-imbalanced Data

Real-world large-scale datasets are both noisily labeled and class-imbalanced. The issues seriously hurt the generalization of trained models. It is hence significant to address the simultaneous incorrect labeling and class-imbalance, i.e., the problem of learning with noisy labels on long-tailed data. Previous works develop several methods for the problem. However, they always rely on strong assumptions that are invalid or hard to be checked in practice. In this paper, to handle the problem and address the limitations of prior works, we propose a representation calibration method RCAL. Specifically, RCAL works with the representations extracted by unsupervised contrastive learning. We assume that without incorrect labeling and class imbalance, the representations of instances in each class conform to a multivariate Gaussian distribution, which is much milder and easier to be checked. Based on the assumption, we recover underlying representation distributions from polluted ones resulting from mislabeled and class-imbalanced data. Additional data points are then sampled from the recovered distributions to help generalization. Moreover, during classifier training, representation learning takes advantage of representation robustness brought by contrastive learning, which further improves the classifier performance. Experiments on multiple benchmarks justify our claims and confirm the superiority of the proposed method

A Global Method for a Two-Dimensional Cutting Stock Problem in the Manufacturing Industry

A two-dimensional cutting stock problem (2DCSP) needs to cut a set of given rectangular items from standard-sized rectangular materials with the objective of minimizing the number of materials used. This problem frequently arises in different manufacturing industries such as glass, wood, paper, plastic, etc. However, the current literatures lack a deterministic method for solving the 2DCSP. However, this study proposes a global method to solve the 2DCSP. It aims to reduce the number of binary variables for the proposed model to speed up the solving time and obtain the optimal solution. Our experiments demonstrate that the proposed method is superior to current reference methods for solving the 2DCSP

Identification, cloning and characterization of sis7 and sis10 sugar-insensitive mutants of Arabidopsis

<p>Abstract</p> <p>Background</p> <p>The levels of soluble sugars, such as glucose and sucrose, help regulate many plant metabolic, physiological and developmental processes. Genetic screens are helping identify some of the loci involved in plant sugar response and reveal extensive cross-talk between sugar and phytohormone response pathways.</p> <p>Results</p> <p>A forward genetic screen was performed to identify mutants with increased resistance to the inhibitory effects of high levels of exogenous sugars on early <it>Arabidopsis </it>seedling development. The positional cloning and characterization of two of these <it>sugar insensitive </it>(<it>sis</it>) mutants, both of which are also involved in abscisic acid (ABA) biosynthesis or response, are reported. Plants carrying mutations in <it>SIS7/NCED3/STO1 </it>or <it>SIS10/ABI3 </it>are resistant to the inhibitory effects of high levels of exogenous Glc and Suc. Quantitative RT-PCR analyses indicate transcriptional upregulation of ABA biosynthesis genes by high concentrations of Glc in wild-type germinating seeds. Gene expression profiling revealed that a significant number of genes that are expressed at lower levels in germinating <it>sis7-1/nced3-4/sto1-4 </it>seeds than in wild-type seeds are implicated in auxin biosynthesis or transport, suggesting cross-talk between ABA and auxin response pathways. The degree of sugar insensitivity of different <it>sis10/abi3 </it>mutant seedlings shows a strong positive correlation with their level of ABA insensitivity during seed germination.</p> <p>Conclusion</p> <p>Mutations in the <it>SIS7/NCED3/STO1 </it>gene, which is primarily required for ABA biosynthesis under drought conditions, confer a sugar-insensitive phenotype, indicating that a constitutive role in ABA biosynthesis is not necessary to confer sugar insensitivity. Findings presented here clearly demonstrate that mutations in <it>ABI3 </it>can confer a sugar-insensitive phenotype and help explain previous, mixed reports on this topic by showing that ABA and sugar insensitivity exhibit a strong positive correlation in different <it>abi3 </it>mutants. Expression profiling revealed a potentially novel regulation of auxin metabolism and transport in an ABA deficient mutant, <it>sis7-1/nced3-4/sto1-4</it>.</p

SIS 8, a putative mitogen‐activated protein kinase kinase kinase, regulates sugar‐resistant seedling development in Arabidopsis

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106112/1/tpj12404-sup-0001-FigS1-S2.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/106112/2/tpj12404.pd

Modeling of polyethylene, poly(l-lactide), and CNT composites: a dissipative particle dynamics study

Dissipative particle dynamics (DPD), a mesoscopic simulation approach, is used to investigate the effect of volume fraction of polyethylene (PE) and poly(l-lactide) (PLLA) on the structural property of the immiscible PE/PLLA/carbon nanotube in a system. In this work, the interaction parameter in DPD simulation, related to the Flory-Huggins interaction parameter χ, is estimated by the calculation of mixing energy for each pair of components in molecular dynamics simulation. Volume fraction and mixing methods clearly affect the equilibrated structure. Even if the volume fraction is different, micro-structures are similar when the equilibrated structures are different. Unlike the blend system, where no relationship exists between the micro-structure and the equilibrated structure, in the di-block copolymer system, the micro-structure and equilibrated structure have specific relationships

catena-Poly[[bis­(pyridine-κN)nickel(II)]-μ-oxalato-κ4 O 1,O 2:O 1′,O 2′]

The title compound, [Ni(C2O4)(C5H5N)2]n, was synthesized under hydro­(solvo)thermal conditions. The NiII atom, lying on a twofold rotation axis, has an octa­hedral coordination geometry involving two N atoms from two pyridine ligands and four O atoms from two oxalate ligands. The Ni atoms are connected by the tetra­dentate bridging oxalate ligands into a one-dimensional zigzag chain