13,439 research outputs found
Convolutional Autoencoders and Clustering for Low-dimensional Parametrization of Incompressible Flows
No full textWe provide the complete code base of the presented approaches in the paper.
Jan Heiland and Yongho Kim (2022), Convolutional Auto Encoders and Clustering for Low-dimensional Parametrization of Incompressible Flows, 25th International Symposium on Mathematical Theory of Networks and Systems (MTNS
sj-docx-1-cqx-10.1177_19389655231223359 – Supplemental material for When Does (Mis)Fit Between Customer Service Orientation and Internal Service Qualities Matter for Frontline Employees’ Pride in Membership and Their Behaviors?
No full textSupplemental material, sj-docx-1-cqx-10.1177_19389655231223359 for When Does (Mis)Fit Between Customer Service Orientation and Internal Service Qualities Matter for Frontline Employees’ Pride in Membership and Their Behaviors? by Martin Yongho Hyun, Lisa Gao, Seoki Lee and Hyeon-Cheol Kim in Cornell Hospitality Quarterly</p
Evaluation on Property and Reliability of Micro-bump Joint between Si Chip and Flexible Substrate
No full textConstant-Factor Approximation Algorithms for the Parity-Constrained Facility Location Problem
No full textFacility location is a prominent optimization problem that has inspired a large quantity of both theoretical and practical studies in combinatorial optimization. Although the problem has been investigated under various settings reflecting typical structures within the optimization problems of practical interest, little is known on how the problem behaves in conjunction with parity constraints. This shortfall of understanding was rather discouraging when we consider the central role of parity in the field of combinatorics.
In this paper, we present the first constant-factor approximation algorithm for the facility location problem with parity constraints. We are given as the input a metric on a set of facilities and clients, the opening cost of each facility, and the parity requirement - odd, even, or unconstrained - of every facility in this problem. The objective is to open a subset of facilities and assign every client to an open facility so as to minimize the sum of the total opening costs and the assignment distances, but subject to the condition that the number of clients assigned to each open facility must have the same parity as its requirement.
Although the unconstrained facility location problem as a relaxation for this parity-constrained generalization has unbounded gap, we demonstrate that it yields a structured solution whose parity violation can be corrected at small cost. This correction is prescribed by a T-join on an auxiliary graph constructed by the algorithm. This auxiliary graph does not satisfy the triangle inequality, but we show that a carefully chosen set of shortcutting operations leads to a cheap and sparse T-join. Finally, we bound the correction cost by exhibiting a combinatorial multi-step construction of an upper bound
Author Correction: Evaluation of skin cancer resection guide using hyper‑realistic in‑vitro phantom fabricated by 3D printing
No full textThe original version of this Article contained an error in the spelling of the author Taehun Kim which was incorrectly given as Teahun Kim. The original Article has been corrected
A mobility management scheme using SCTP-SIP for real-time services across heterogeneous networks
No full textDBLP-derived labeled data for author name disambiguation
No full textThis is a DBLP-derived labeled data originally created by Dr. C. Lee Giles at Penn State University and filtered for duplicate removal and error correction by Dr. Jinseok Kim at University of Michigan. For more details, see references below.1. Kim, Jinseok (2018). Evaluating author name disambiguation for digital libraries: a case of DBLP. Scientometrics. doi:10.1007/s11192-018-2824-5 2. Kim, Jinseok & Kim, Jenna (2018). The impact of imbalanced training data on machine learning for author name disambiguation. Scientometrics. doi: 10.1007/s11192-018-2865-9Each row refers to an author name instance with following feature information separated by tab.author name: full name string extracted from DBLPunique author id: labels assigned manually by Dr. C. Lee Giles's teampaper id: assigned by Dr. Jinseok Kimauthor list: names of authors in the byline of the paperyear: publication yearvenue: conference or journal namestitle: stopwords removed and stemmed by the Porter's stemmerIf you want to use this dataset, please consider to cite papers below.For the original dataset: Han, H., Giles, L., Zha, H., Li, C., & Tsioutsiouliklis, K. (2004). Two Supervised Learning Approaches for Name Disambiguation in Author Citations. JCDL 2004: Proceedings of the Fourth ACM/IEEE Joint Conference on Digital Libraries, 296-305. doi:10.1145/996350.996419For the filtered dataset: 1. Kim, Jinseok (2018). Evaluating author name disambiguation for digital libraries: a case of DBLP. Scientometrics. doi:10.1007/s11192-018-2824-5 or2. Kim, Jinseok & Kim, Jenna (2018). The impact of imbalanced training data on machine learning for author name disambiguation. Scientometrics. doi: 10.1007/s11192-018-2865-9</div
Polytopic Autoencoders with Smooth Clustering for Reduced-order Modelling of Flows
No full text<p>This repository contains simulation code files for results introduced in the paper "Polytopic Autoencoders with Smooth Clustering for Reduced-order Modelling of Flows". (J. Heiland and Y. Kim)</p>
Khoo Kay Kim, professor of Malaysian history : a biobibliometric study
No full textPresents an analysis of the publication productivity, authorship pattern, channels of communication, journal preference and language preference of Professor Dato' Khoo Kay Kim, Professor of Malaysian History in the University of Malaya, Kuala Lumpur. The results of this biobibliometric study indicate that he can be a role model for future Malaysian historians to emulate his various achievements especially in the field of history education
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