Observation of tW production in the single-lepton channel in pp collisions at root s=13 TeV

A measurement of the cross section of the associated production of a single top quark and a W boson in final states with a muon or electron and jets in proton-proton collisions at root s = 13 TeV is presented. The data correspond to an integrated luminosity of 36 fb(-1) collected with the CMS detector at the CERN LHC in 2016. A boosted decision tree is used to separate the tW signal from the dominant t (t) over bar background, whilst the subleading W+jets and multijet backgrounds are constrained using data-based estimates. This result is the first observation of the tW process in final states containing a muon or electron and jets, with a significance exceeding 5 standard deviations. The cross section is determined to be 89 +/- 4 (stat) +/- 12 (syst) pb, consistent with the standard model.Peer reviewe

Detecting Corresponding Vertex Pairs between Planar Tessellation Datasets with Agglomerative Hierarchical Cell-Set Matching.

This paper proposes a method to detect corresponding vertex pairs between planar tessellation datasets. Applying an agglomerative hierarchical co-clustering, the method finds geometrically corresponding cell-set pairs from which corresponding vertex pairs are detected. Then, the map transformation is performed with the vertex pairs. Since these pairs are independently detected for each corresponding cell-set pairs, the method presents improved matching performance regardless of locally uneven positional discrepancies between dataset. The proposed method was applied to complicated synthetic cell datasets assumed as a cadastral map and a topographical map, and showed an improved result with the F-measures of 0.84 comparing to a previous matching method with the F-measure of 0.48

Samsung card lending model

Samsung Card Lending Model (SCLM) analyzes cash flow in individual accounts and measures the level of company-wide risk. Serving as a risk and portfolio management model in the consumer lending business, the main features of SCLM are as follows. Default ratios such as intrinsic balance default probability and annual default ratio are computed using the past, present, and future cash flows of accounts. The provision is shown as the total sum of write-offs. The size of capital required is determined by default probability distribution. The price for new accounts is quoted based on cash flow simulations reflecting future business environments. SCLM has shown good performance in Samsung card consumer lending business since the Korean credit card crisis of 2003. (C) 2010 Elsevier B.V. All rights reserved.*INT ACC STAND BOA, 2008, INT ACC STAND, V39MCNAB H, 2008, CONSUMER CREDIT RISKde Andrade FWM, 2007, EUR J OPER RES, V183, P1569, DOI 10.1016/j.ejor.2006.07.049KIM J, 2005, THESIS A M UAllen L, 2004, J BANK FINANC, V28, P727, DOI 10.1016/j.jbankfin.2003.10.004Schmit M, 2004, J BANK FINANC, V28, P811, DOI 10.1016/j.jbankfin.2003.10.008Calem PS, 2004, J BANK FINANC, V28, P647, DOI 10.1016/S0378-4266(03)00039-6NEFTCI S, 2000, J DERIVATIVES SPR, P12

Model order reduction by proper orthogonal decomposition for a 500 MWe tangentially fired pulverized coal boiler

Reduced order models (ROMs) are constructed by proper orthogonal decomposition (POD) and regression by Kriging and Radial Basis Neural Network (RBFN) for a 500 MWe tangentially fired pulverized coal boiler. POD is performed to extract low-dimensional basis vectors to reproduce 3-D distribution of reacting scalars with respect to the operation parameters of total secondary air (TSA) and burner zone stoichiometric ratio (BSR). The ROMs by Kriging and RBFN both reproduce the scalar fields within 6% averaged relative L2 norm error at three validation points in the parameter space. It is possible to reproduce a 3-D scalar field at any unexplored operation condition within a few seconds through parallel computation of the ROM. It allows fast evaluation of the effects of varying operation parameters in the design stage and real time response of a digital twin based on the ROM for smart operation and maintenance of industrial combustion facilities. ? 202111Nsciescopu

Samsung card lending model

Samsung Card Lending Model (SCLM) analyzes cash flow in individual accounts and measures the level of company-wide risk. Serving as a risk and portfolio management model in the consumer lending business, the main features of SCLM are as follows. Default ratios such as intrinsic balance default probability and annual default ratio are computed using the past, present, and future cash flows of accounts. The provision is shown as the total sum of write-offs. The size of capital required is determined by default probability distribution. The price for new accounts is quoted based on cash flow simulations reflecting future business environments. SCLM has shown good performance in Samsung card consumer lending business since the Korean credit card crisis of 2003.Consumer lending business Credit risk Default probability Provision Capital required

The thresholds of the proposed method.

<p>The thresholds of the proposed method.</p

The pseudo-code of clustering method.

<p>The pseudo-code of clustering method.</p

Vertex matching criteria.

<p>Vertices (<i>v</i>) and their interior angles (<i>θ</i>) of boundaries of super-cells in dataset A and B with distance and angle difference threshold.</p

Comparison of the detection results.

<p>Results by applying the ICP algorithm(a, d, g) and those by the proposed method (b, e, h); (c), (f) and (i) show the transformed dataset 1 at the final iteration and the dataset 2 (Printed under a CC BY license 4.0, with permission from Spatial Informatics & Systems Lab., Seoul National Univ.).</p

The statistical evaluation of the proposed method and the ICP algorithm for datasets in Fig 4.

<p><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0157913#pone.0157913.s013" target="_blank">S13 Dataset</a> is the detected CVPs by the ICP algorithm and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0157913#pone.0157913.s014" target="_blank">S14 Dataset</a> is the transformed <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0157913#pone.0157913.s002" target="_blank">S2 Dataset</a> with <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0157913#pone.0157913.s013" target="_blank">S13 Dataset</a>. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0157913#pone.0157913.s015" target="_blank">S15 Dataset</a> is manually detected corresponding point pairs between <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0157913#pone.0157913.s010" target="_blank">S10 Dataset</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0157913#pone.0157913.s001" target="_blank">S1 Dataset</a> for statistical evaluation.</p