RPC Gap Production and Performance for CMS RE4 Upgrade

CMS experiment constructed the fourth Resistive Plate Chamber (RPC) trigger station composed of 144 RPCs to enhance the high momentum muon trigger efficiency at both endcap regions. All new CMS endcap RPC gaps are produced in accordance with QA and QC at the Korea Detector Laboratory (KODEL) in Korea. All qualified gaps have been delivered to three assembly sites: CERN in Switzerland, BARC in India, and Ghent University in Belgium for the RPC detector assembly. In this paper, we present the detailed procedures used in the production of RPC gaps adopted for the CMS upgrade.Comment: RPC2014 conference contribution, 7 pages, 8 figure

Multiple Sluicing in English

PACLIC 21 / Seoul National University, Seoul, Korea / November 1-3, 200

On the Analysis of Cross-Lingual Prompt Tuning for Decoder-based Multilingual Model

An exciting advancement in the field of multilingual models is the emergence of autoregressive models with zero- and few-shot capabilities, a phenomenon widely reported in large-scale language models. To further improve model adaptation to cross-lingual tasks, another trend is to further fine-tune the language models with either full fine-tuning or parameter-efficient tuning. However, the interaction between parameter-efficient fine-tuning (PEFT) and cross-lingual tasks in multilingual autoregressive models has yet to be studied. Specifically, we lack an understanding of the role of linguistic distributions in multilingual models in the effectiveness of token-based prompt tuning. To address this question, we conduct experiments comparing prompt tuning and fine-tuning on the decoder-based multilingual model, XGLM, with four cross-lingual tasks (XNLI, PAWS-X, POS, NER). According to our study, prompt tuning achieves on par or better performance over fine-tuning across all languages while updating at most 0.13\% of the model parameters. Moreover, we empirically show that prompt tuning is more effective in enhancing the performance of low-resource languages than fine-tuning. Our further analysis shows that the phenomenon is related to the tokenization scheme of the multilingual model

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Graduate School of Technology and Innovation Management Technology and Innovation ManagementThe existing studies on mergers and acquisitions (M&A) have mainly focused on firm???s technological capabilities based on patent analysis. Therefore, they have not considered the evolutionary and industry-level aspects of M&A to support M&A decision making. To counter this, we propose a systematic approach to identifying patterns of M&A at the industry level on the basis of the change of historical M&A transactions. For this, first, historical M&A transaction data providing industry information to which firms belonged on the transaction date from Securities Data Company (SDC) platinum database is collected at regularly spaced interval of time. Second, Association Rule Mining (ARM) is modified to take into account a direction of M&A transaction to extract significant M&A transaction rules of which indices are greater than the cut-off value. Third, network analysis is conducted to construct an M&A transaction relationship network and to measure six quantitative indicators for confirmation of characteristics of industry and significant M&A transaction rules via the concept of degree centrality. Finally, significant M&A transaction rules are categorized into dynamic and structural patterns of M&A using indicator analysis and cluster analysis respectively to identify the evolution of trend in M&A transactions. Our empirical analysis employs a total of 71,264 M&A transactions data from 1995 to 2016, and enables practitioner to obtain not only the specific industry information but various in M&A patterns for establishing M&A implementation strategies at the industry level. We expect that the proposed approach will be effective as complementary tool for M&A decision making where corporations determine a principal screening or selection criteria to shorten monetary and time cost for searching candidates as target industry.ope

Case Study on the Enterprise Microblog Usage: Focusing on Knowledge Learning

Knowledge Management Strategy can be classified by codification strategy and personalization strategy (Hansen et. al., 1999), and how to manage the two strategies were always studied. Also, current studies regarding the knowledge management strategy were targeted mostly for major companies, resulting in lack of studies in how it can be applied on SMEs. This research, with the knowledge management strategy suited for SMEs, sets an Enterprise Microblog (EMB), and with the EMB applied on SMEs’ Knowledge Management Strategy, it is reviewed on the perspective of SMEs’ Codification and Personalization Strategies. Through the advanced research regarding Knowledge Management Strategy and EMB, the hypothesis is set that “Depending on the development of the company, the main application of EMB alters from Codification Strategy to Personalization Strategy.” To check the hypothesis, SME that have used the EMB called ‘Yammer’ was analyzed from the data of their foundation until today. The case study has implemented longitudinal analysis which divides the period when the EMBs were used into three stages and analyzes the contents. As the result of the study, this suggests a substantial implication regarding the application of Knowledge Management Strategy and its Knowledge Management System that is suitable for SME

Evaluation of a specific diagnostic marker for rheumatoid arthritis based on cyclic citrullinated peptide

AbstractA specific peptide marker for diagnosing rheumatoid arthritis (RA) was found based on cyclic citrullinated peptide (CCP) using the following three steps: (1) analysis of the binding epitope of autoimmune antibodies using ϵ-aminocaproic acid-modified peptides; (2) RA diagnosis using sequence-modified peptides; and (3) evaluation of the peptides’ diagnostic performance for RA diagnosis. Ninety-five serum samples were analyzed by ELISA and compared using MedCalc (version 15.2.1). Microplate binding ϵ-aminocaproic acid was added to the N- or C-terminal of the CCP sequence. The N-terminal anchoring peptide assay showed 15% higher specificity compared with the C-terminal anchoring peptide assay. Based on this result, the hydrophilic C-terminal sequence of CCP was substituted with a hydrophobic amino acid. Among the sequence-modified peptides, CCP11A (in which alanine was substituted for the 11th amino acid of CCP) assay showed the highest sensitivity (87%) and specificity (100%) for RA diagnosis. Thus, CCP11A was selected as a possible specific marker peptide for RA diagnosis and further analyzed. The results of this analysis indicated that CCP11A showed better specificity than the CCP assay in both healthy individuals (11% better) and OA cohort (20% better). From these results, CCP11A was evaluated as a specific marker for diagnosing RA with higher diagnostic performance