Auto Insurance Business Analytics Approach for Customer Segmentation Using Multiple Mixed-Type Data Clustering Algorithms

Customer segmentation is critical for auto insurance companies to gain competitive advantage by mining useful customer related information. While some efforts have been made for customer segmentation to support auto insurance decision making, their customer segmentation results tend to be affected by the characteristics of the algorithm used and lack multiple validation from multiple algorithms. To this end, we propose an auto insurance business analytics approach that segments customers by using three mixed-type data clustering algorithms including k-prototypes, improved k-prototypes and similarity-based agglomerative clustering. The customer segmentation results of these algorithms can complement and reinforce each other and demonstrate as much information as possible to support decision-making. To confirm its practical value, the proposed approach extracts seven rules for an auto insurance company that may support the company to make customer related decisions and develop insurance products

Duality between the deconfined quantum-critical point and the bosonic topological transition

Recently significant progress has been made in $(2+1)$-dimensional conformal field theories without supersymmetry. In particular, it was realized that different Lagrangians may be related by hidden dualities, i.e., seemingly different field theories may actually be identical in the infrared limit. Among all the proposed dualities, one has attracted particular interest in the field of strongly-correlated quantum-matter systems: the one relating the easy-plane noncompact CP$^1$ model (NCCP$^1$) and noncompact quantum electrodynamics (QED) with two flavors ($N = 2$) of massless two-component Dirac fermions. The easy-plane NCCP$^1$ model is the field theory of the putative deconfined quantum-critical point separating a planar (XY) antiferromagnet and a dimerized (valence-bond solid) ground state, while $N=2$ noncompact QED is the theory for the transition between a bosonic symmetry-protected topological phase and a trivial Mott insulator. In this work we present strong numerical support for the proposed duality. We realize the $N=2$ noncompact QED at a critical point of an interacting fermion model on the bilayer honeycomb lattice and study it using determinant quantum Monte Carlo (QMC) simulations. Using stochastic series expansion QMC, we study a planar version of the $S=1/2$ $J$-$Q$ spin Hamiltonian (a quantum XY-model with additional multi-spin couplings) and show that it hosts a continuous transition between the XY magnet and the valence-bond solid. The duality between the two systems, following from a mapping of their phase diagrams extending from their respective critical points, is supported by the good agreement between the critical exponents according to the proposed duality relationships.Comment: 14 pages, 9 figure

Multi-Objective Personalized Product Retrieval in Taobao Search

In large-scale e-commerce platforms like Taobao, it is a big challenge to retrieve products that satisfy users from billions of candidates. This has been a common concern of academia and industry. Recently, plenty of works in this domain have achieved significant improvements by enhancing embedding-based retrieval (EBR) methods, including the Multi-Grained Deep Semantic Product Retrieval (MGDSPR) model [16] in Taobao search engine. However, we find that MGDSPR still has problems of poor relevance and weak personalization compared to other retrieval methods in our online system, such as lexical matching and collaborative filtering. These problems promote us to further strengthen the capabilities of our EBR model in both relevance estimation and personalized retrieval. In this paper, we propose a novel Multi-Objective Personalized Product Retrieval (MOPPR) model with four hierarchical optimization objectives: relevance, exposure, click and purchase. We construct entire-space multi-positive samples to train MOPPR, rather than the single-positive samples for existing EBR models.We adopt a modified softmax loss for optimizing multiple objectives. Results of extensive offline and online experiments show that MOPPR outperforms the baseline MGDSPR on evaluation metrics of relevance estimation and personalized retrieval. MOPPR achieves 0.96% transaction and 1.29% GMV improvements in a 28-day online A/B test. Since the Double-11 shopping festival of 2021, MOPPR has been fully deployed in mobile Taobao search, replacing the previous MGDSPR. Finally, we discuss several advanced topics of our deeper explorations on multi-objective retrieval and ranking to contribute to the community.Comment: 9 pages, 4 figures, submitted to the 28th ACM SIGKDD Conference on Knowledge Discovery & Data Minin

Identification of Colitis and Cancer in Colon Biopsies by Fourier Transform Infrared Spectroscopy and Chemometrics

Cancer is a disease that does great harms to the health of human beings. FT-IR spectroscopy could identify variability at the molecular level in biological specimens. It is a rapid and noninvasive method, which could be used intraoperatively to modify surgical procedures. The aim of this paper is to identify and separate cancer from colitis in endoscopic colon biopsies through the use of FT-IR spectroscopy. A total of 88 endoscopic colon samples, including 41 cases of colitis and 47 cases of colon cancer, were obtained. Specimens were placed on an ATR accessory linked to FT-IR spectrometer with a MCT detector for greater stability and sensitivity. Later, specimens were sent for the histological examination as the reference in the spectral analysis. 41 colitis and 47 cancer specimens were compared. Spectra preprocessed with smoothing and normalization were used for discrimination analysis. PCA was processed to simplify the spectrum data set. Naive Bayes classifier model was constructed for diagnostic classification. Leave-one-out cross-validation method was utilized to assess the discrimination results. The sensitivity of FT-IR detection for cancer achieves 97.6%. The results showed that colon cancer could be distinguished from colitis with high accuracy using FT-IR spectroscopy and chemometrics

Predicting binding sites of hydrolase-inhibitor complexes by combining several methods

Background Protein-protein interactions play a critical role in protein function. Completion of many genomes is being followed rapidly by major efforts to identify interacting protein pairs experimentally in order to decipher the networks of interacting, coordinated-in-action proteins. Identification of protein-protein interaction sites and detection of specific amino acids that contribute to the specificity and the strength of protein interactions is an important problem with broad applications ranging from rational drug design to the analysis of metabolic and signal transduction networks. Results In order to increase the power of predictive methods for protein-protein interaction sites, we have developed a consensus methodology for combining four different methods. These approaches include: data mining using Support Vector Machines, threading through protein structures, prediction of conserved residues on the protein surface by analysis of phylogenetic trees, and the Conservatism of Conservatism method of Mirny and Shakhnovich. Results obtained on a dataset of hydrolase-inhibitor complexes demonstrate that the combination of all four methods yield improved predictions over the individual methods. Conclusions We developed a consensus method for predicting protein-protein interface residues by combining sequence and structure-based methods. The success of our consensus approach suggests that similar methodologies can be developed to improve prediction accuracies for other bioinformatic problems

The Influence of Cu\u3csub\u3e3\u3c/sub\u3e(BTC)\u3csub\u3e2\u3c/sub\u3e metal organic framework on the permeability and perm-selectivity of PLLA-MOF mixed matrix membranes

Poly(l-lactic acid) (PLLA) - 20% (w/w) and Cu3(BTC)2 metal organic framework (MOF) based mixed matrix membranes (MMMs) were fabricated by a vertical corotating twin screw microcompounder followed by an injection molding process. Water vapor, CO2, O2, and selected aroma mass transfer properties of PLLA and PLLA MMMs were evaluated. The CO2/O2 perm-selectivity of PLLA (αCO2/O2) MMMs increased from 7.6 to 10.3 with the incorporation of 20% Cu3(BTC)2 MOF. Gravimetric permeability studies of trans-2-hexenal performed at 23°C and 50% RH indicated that permeability coefficient of PLLA MMMs increased by around 60% as compared to regular PLLA film. However, no changes in mass transfer rates were observed for acetaldehyde. Furthermore, the thermal processing parameters as well as the presence of MOF did not show any significant effect on the molecular weight of the PLLA matrix nor on the crystalline structure of the Cu3(BTC)2 MOF, which was confirmed by both gel permeation chromatography and X-ray diffraction studies

Somatic SF3B1 hotspot mutation in prolactinomas.

The genetic basis and corresponding clinical relevance of prolactinomas remain poorly understood. Here, we perform whole genome sequencing (WGS) on 21 patients with prolactinomas to detect somatic mutations and then validate the mutations with digital polymerase chain reaction (PCR) analysis of tissue samples from 227 prolactinomas. We identify the same hotspot somatic mutation in splicing factor 3 subunit B1 (SF3B1R625H) in 19.8% of prolactinomas. These patients with mutant prolactinomas display higher prolactin (PRL) levels (p = 0.02) and shorter progression-free survival (PFS) (p = 0.02) compared to patients without the mutation. Moreover, we identify that the SF3B1R625H mutation causes aberrant splicing of estrogen related receptor gamma (ESRRG), which results in stronger binding of pituitary-specific positive transcription factor 1 (Pit-1), leading to excessive PRL secretion. Thus our study validates an important mutation and elucidates a potential mechanism underlying the pathogenesis of prolactinomas that may lead to the development of targeted therapeutics