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How does electronic word of mouth on Instagram affect travel behaviour in Indonesia: A perspective of the information adoption model

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Cogent Social sciences 2023年第1期9卷

作者： Pantas H. Silaban Wen-Kuo Chen Surmida Sormin Yehezkiel Natamatua B. P. Panjaitan Andri Dayarana K. Silalahi 1 Graduate School Master of Management Program University of HKBP Nommensen Medan Indonesia 2 Department of Money and Banking National Kaohsiung University of Science and Technology Kaohsiung Taiwan 3 Faculty of Economics and Business Management Program University of HKBP Nommensen Medan Indonesia 4 Department of Business Administration Chaoyang University of Technology Taichung Taiwan 3 Faculty of Economics and Business Management Program University of HKBP Nommensen Medan Indonesia4 Department of Business Administration Chaoyang University of Technology Taichung Taiwan

Today, electronic word-of-mouth (eWOM) is considered one of tourism businesses’ most effective forms of communication. Using eWOM on social media as an alternative decision aid can benefit consumers. This study examines the information adoption model to determine how consumers adopt eWOM on Instagram, and the subsequent influence on their travel behaviour. This study identifies the need for information as a predictor of information usefulness, converted to adoption and visit intentions. To test the research hypotheses, 263 respondents who had visited destinations based on eWOM recommendations on Instagram participated in the experiment. The data were analysed using a structural equation modelling approach using Smart-PLS 3.0. The findings indicate that the need for information is the most significant factor affecting the usefulness of the information. Furthermore, source credibility and argument quality are critical factors in enhancing the information’s usefulness. Thus, this study has significantly contributed to the theory by adding the need of information to predict visit intentions in the IAM model. Consumers are more likely to adopt high-quality information and convert it into a visit intention. This study provides practical guidelines for tourism managers who wish to utilise social media platforms (e.g., Instagram) as marketing communication tools to encourage eWOM about their destinations.

关键词： information adoption model electronic word of mouth travel behaviour visit intention need for information Instagram marketing communications consumer behaviour internet/digital marketing/e-marketing

Commissioning of the camera of the first Large Size Telescope of the Cherenkov Telescope Array 37

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Commissioning of the camera of the first Large Size Telescop...

37th International Cosmic Ray Conference, ICRC 2021

作者： Saito, T. Delgado, C. Blanch, O. Artero, M. Barrio, J.A. Cassol, F. Diaz, C. Hadasch, D. Hoffmann, D. Houles, J. Inome, Y. Iori, M. Jouvin, L. Kerszberg, D. Kobayashi, Y. Kubo, H. Martinez, G. Mazin, D. Moretti, E. Nakamori, T. Nozaki, S. Oka, T. Okumura, A. Palatiello, M. Polo, M. Prast, J. Sakurai, S. Sunada, Y. Takahashi, M. Takeishi, R. Tejedor, L.A. Yamamoto, T. Yoshida, T. Abe, H. Aguasca, A. Agudo, I. Antonelli, L.A. Aramo, C. Armstrong, T. Asano, K. Ashkar, H. Aubert, P. Baktash, A. Bamba, A. Baquero Larriva, A. Baroncelli, L. Barres de Almeida, U. Batkovic, I. Becerra González, J. Bernardos, M.I. Berti, A. Biederbeck, N. Bigongiari, C. Bonnoli, G. Bordas, P. Bose, D. Bulgarelli, A. Burelli, I. Buscemi, M. Cardillo, M. Caroff, S. Carosi, A. Cerruti, M. Chai, Y. Cheng, K. Chikawa, M. Chytka, L. Contreras, J.L. Cortina, J. Costantini, H. Dalchenko, M. De Angelis, A. de Bony de Lavergne, M. Deleglise, G. Delgado Mengual, J. della Volpe, D. Depaoli, D. Di Pierro, F. Di Venere, L. Dominik, R.M. Dominis Prester, D. Donini, A. Dorner, D. Doro, M. Elsässer, D. Emery, G. Escudero, J. Fiasson, A. Foffano, L. Fonseca, M.V. Freixas Coromina, L. Fukami, S. Fukazawa, Y. Garcia, E. Garcia López, R. Giglietto, N. Giordano, F. Gliwny, P. Godinovic, N. Green, D. Grespan, P. Gunji, S. Hackfeld, J. Hahn, A. Hassan, T. Hayashi, K. Heckmann, L. Heller, M. Herrera Llorente, J. Hirotani, K. Horns, D. Hrabovsky, M. Hrupec, D. Hui, D. Hütten, M. Inada, T. Ishio, K. Iwamura, Y. Jacquemont, M. Jimenez Martinez, I. Jurysek, J. Kagaya, M. Karas, V. Katagiri, H. Kataoka, J. Kong, A. Kushida, J. Lamanna, G. Lamastra, A. Le Flour, T. Longo, F. López-Coto, R. López-Moya, M. López-Oramas, A. Luque-Escamilla, P.L. Majumdar, P. Makariev, M. Mandat, D. Manganaro, M. Mannheim, K. Mariotti, M. Marquez, P. Marsella, G. Martí, J. Martinez, O. Martínez, M. Marusevec, P. Mas, A. Maurin, G. Mestre Guillen, E. Micanovic, S. Miceli, D. Miener, T. Miranda, J.M. ICRR Institute for Cosmic Ray Research University of Tokyo 5-1-5 Kashiwa-no-ha Chiba Kashiwa277-8582 Japan CIEMAT Avda. Complutense 40 Madrid28040 Spain The Barcelona Institute of Science and Technology Bellaterra Campus UAB Barcelona08193 Spain EMFTEL Department IPARCOS Universidad Complutense de Madrid Madrid28040 Spain Aix-Marseille Université CNRS IN2P3 CPPM 163 Avenue de Luminy MarseilleCedex 09 13288 France INFN Sezione di Roma La Sapienza P.le Aldo Moro 2 Roma00185 Italy Division of Physics and Astronomy Graduate School of Science Kyoto University Sakyo-ku Kyoto606-8502 Japan Department of Physics Yamagata University Yamagata990-8560 Japan ISEE Nagoya University Chikusa-ku Nagoya464-8602 Japan Graduate School of Science and Engineering Saitama University 255 Simo-Ohkubo Sakura-ku Saitama338-8570 Japan CNRS Laboratoire d'Annecy de Physique des Particules IN2P3 Univ. Grenoble Alpes Univ. Savoie Mont Blanc Annecy74000 France Department of Physics Konan University Hyogo Kobe658-8501 Japan Faculty of Science Ibaraki University Ibaraki Mito310-8512 Japan Departament de Física Quàntica i Astrofísica Institut de Ciències del Cosmos Universitat de Barcelona IEEC-UB Spain Instituto de Astrofísica de Andalucía CSIC Spain INAF - Osservatorio Astronomico di Roma Italy INFN Sezione di Napoli Italy IRFU CEA Université Paris-Saclay France Universität Hamburg Institut für Experimentalphysik Germany Graduate School of Science University of Tokyo Japan INAF - Osservatorio di Astrofisica e Scienza dello Spazio di Bologna Italy Centro Brasileiro de Pesquisas Físicas Brazil INFN Sezione di Padova Università degli Studi di Padova Italy Instituto de Astrofísica de Canarias Departamento de Astrofísica Universidad de La Laguna Spain Max-Planck-Institut für Physik Germany Department of Physics TU Dortmund University Germany Saha Institute of Nuclear Physics India INFN Sezione di Trieste Università degli Studi di Udine Italy INFN Sezio

The first Large Size Telescope (LST-1) of the Cherenkov Telescope Array has been operational since October 2018 at La Palma, Spain. We report on the results obtained during the camera commissioning. The noise level of the readout is determined as a 0.2 p.e. level. The gain of PMTs are well equalized within 2% variation, using the calibration flash system. The effect of the night sky background on the signal readout noise as well as the PMT gain estimation are also well evaluated. Trigger thresholds are optimized for the lowest possible gamma-ray energy threshold and the trigger distribution synchronization has been achieved within 1 ns precision. Automatic rate control realizes the stable observation with 1.5% rate variation over 3 hours. The performance of the novel DAQ system demonstrates a less than 10% dead time for 15 kHz trigger rate even with sophisticated online data correction. © 2022 Sissa Medialab Srl. All rights reserved.

关键词： Cameras

Deep-learning-driven event reconstruction applied to simulated data from a single Large-Sized Telescope of CTA 37

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Deep-learning-driven event reconstruction applied to simulat...

37th International Cosmic Ray Conference, ICRC 2021

作者： Abe, H. Aguasca, A. Agudo, I. Antonelli, L.A. Aramo, C. Armstrong, T. Artero, M. Asano, K. Ashkar, H. Aubert, P. Baktash, A. Bamba, A. Baquero Larriva, A. Baroncelli, L. Barres de Almeida, U. Barrio, J.A. Batkovic, I. Becerra González, J. Bernardos, M.I. Berti, A. Biederbeck, N. Bigongiari, C. Blanch, O. Bonnoli, G. Bordas, P. Bose, D. Bulgarelli, A. Burelli, I. Buscemi, M. Cardillo, M. Caroff, S. Carosi, A. Cassol, F. Cerruti, M. Chai, Y. Cheng, K. Chikawa, M. Chytka, L. Contreras, J.L. Cortina, J. Costantini, H. Dalchenko, M. De Angelis, A. de Bony de Lavergne, M. Deleglise, G. Delgado, C. Delgado Mengual, J. della Volpe, D. Depaoli, D. Di Pierro, F. Di Venere, L. Díaz, C. Dominik, R.M. Dominis Prester, D. Donini, A. Dorner, D. Doro, M. Elsässer, D. Emery, G. Escudero, J. Fiasson, A. Foffano, L. Fonseca, M.V. Freixas Coromina, L. Fukami, S. Fukazawa, Y. Garcia, E. Garcia López, R. Giglietto, N. Giordano, F. Gliwny, P. Godinovic, N. Green, D. Grespan, P. Gunji, S. Hackfeld, J. Hadasch, D. Hahn, A. Hassan, T. Hayashi, K. Heckmann, L. Heller, M. Herrera Llorente, J. Hirotani, K. Hoffmann, D. Horns, D. Houles, J. Hrabovsky, M. Hrupec, D. Hui, D. Hütten, M. Inada, T. Inome, Y. Iori, M. Ishio, K. Iwamura, Y. Jacquemont, M. Jimenez Martinez, I. Jouvin, L. Jurysek, J. Kagaya, M. Karas, V. Katagiri, H. Kataoka, J. Kerszberg, D. Kobayashi, Y. Kong, A. Kubo, H. Kushida, J. Lamanna, G. Lamastra, A. Le Flour, T. Longo, F. López-Coto, R. López-Moya, M. López-Oramas, A. Luque-Escamilla, P.L. Majumdar, P. Makariev, M. Mandat, D. Manganaro, M. Mannheim, K. Mariotti, M. Marquez, P. Marsella, G. Martí, J. Martinez, O. Martínez, G. Martínez, M. Marusevec, P. Mas, A. Maurin, G. Mazin, D. Mestre Guillen, E. Micanovic, S. Miceli, D. Miener, T. Miranda, J.M. Miranda, L.D.M. Mirzoyan, R. Mizuno, T. Molina, E. Montaruli, T. Monteiro, I. Moralejo, A. Morcuende, D. Moretti, E. Morselli, A. Mrakovcic, K. Murase, K. Nagai, A. Nakamori, T. Nickel, L. LISTIC Université Savoie Mont-Blanc Polytech Annecy-Chambéry 5 chemin de bellevue Annecy74940 France Institute for Cosmic Ray Research University of Tokyo Japan Departament de Física Quàntica i Astrofísica Institut de Ciències del Cosmos Universitat de Barcelona IEEC-UB Spain Instituto de Astrofísica de Andalucía-CSIC Italy INAF Osservatorio Astronomico di Roma Italy INFN Sezione di Napoli Italy Aix Marseille Univ CNRS IN2P3 CPPM France The Barcelona Institute of Science and Technology Spain IRFU CEA Université Paris-Saclay France Laboratoire d'Annecy de Physique des Particules Univ. Grenoble Alpes Univ. Savoie Mont Blanc CNRS LAPP 9 chemin de bellevue Annecy74940 France Universität Hamburg Institut für Experimentalphysik Germany Graduate School of Science University of Tokyo Japan Instituto de Física de Partículas y del Cosmos Departamento de EMFTEL Universidad Complutense de Madrid Madrid Spain INAF Osservatorio di Astrofisica e Scienza Spazio di Bologna Italy Centro Brasileiro de Pesquisas Físicas Brazil INFN Istituto Nazionale di Fisica Nucleare Sezione di Padova PadovaI-35131 Italy Università degli Studi di Padova Italy Instituto de Astrofísica de Canarias Departamento de Astrofísica Universidad de La Laguna Spain Max-Planck-Institut für Physik Germany Department of Physics TU Dortmund University Germany Saha Institute of Nuclear Physics INFN Sezione di Trieste Università degli Studi di Udine Italy INFN Sezione di Catania Italy Italy University of Geneva Département de Physique Nucléaire et Corpusculaire Switzerland Palacky University Olomouc Faculty of Science Czech Republic CIEMAT Port d'Informació Científica INFN Sezione di Torino Italy Dipartimento di Fisica Universitá degli Studi di Torino Italy INFN Sezione di Bari Università di Bari Italy University of Rijeka Department of Physics Croatia Institute for Theoretical Physics and Astrophysics Universität Würzburg Germany Physics Program Graduate School of Advanced Science and Engin

When very-high-energy gamma rays interact high in the Earth's atmosphere, they produce cascades of particles that induce flashes of Cherenkov light. Imaging Atmospheric Cherenkov Telescopes (IACTs) detect these flashes and convert them into shower images that can be analyzed to extract the properties of the primary gamma ray. The dominant background for IACTs is comprised of air shower images produced by cosmic hadrons, with typical noise-to-signal ratios of several orders of magnitude. The standard technique adopted to differentiate between images initiated by gamma rays and those initiated by hadrons is based on classical machine learning algorithms, such as Random Forests, that operate on a set of handcrafted parameters extracted from the images. Likewise, the inference of the energy and the arrival direction of the primary gamma ray is performed using those parameters. State-of-the-art deep learning techniques based on convolutional neural networks (CNNs) have the potential to enhance the event reconstruction performance, since they are able to autonomously extract features from raw images, exploiting the pixel-wise information washed out during the parametrization process. Here we present the results obtained by applying deep learning techniques to the reconstruction of Monte Carlo simulated events from a single, next-generation IACT, the Large-Sized Telescope (LST) of the Cherenkov Telescope Array (CTA). We use CNNs to separate the gamma-ray-induced events from hadronic events and to reconstruct the properties of the former, comparing their performance to the standard reconstruction technique. Three independent implementations of CNN-based event reconstruction models have been utilized in this work, producing consistent results. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)

关键词： Hadrons

Simultaneous multicolour transit photometry of hot Jupiters HAT-P-19b, HAT-P-51b, HAT-P-55b, and HAT-P-65b

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arXiv 2024年

作者： Kang, Huiyi Chen, Guo Pallé, Enric Murgas, Felipe García, Nestor Abreu de Leon, Jerome Enoc, Gareb Esparza-Borges, Emma Fukuda, Izuru Fukui, Akihiko Galán, Daniel Hayashi, Yuya Isogai, Keisuke Kagetani, Taiki Kawauchi, Kiyoe Korth, Judith Livingston, John H. Luque, Rafael Ma, Yuehua Madrigal-Aguado, Alberto Meni, Pedro Rodriguez, Pilar Montañes Mori, Mori Torres, Sara Muñoz Narita, Norio Orell-Miquel, Jaume Parviainen, Hannu Peláez-Torres, Alberto Stangret, Monika Tamura, Motohide Watanabe, Noriharu CAS Key Laboratory of planetary sciences Purple Mountain Observatory Chinese Academy of Sciences Nanjing210023 China School of Astronomy and Space Science University of Science and Technology of China Hefei230026 China CAS Center for Excellence in Comparative Planetology Hefei230026 China Instituto de Astrofísica de Canarias Vía Láctea s/n Tenerife La Laguna38205 Spain Departamento de Astrofísica Universidad de La Laguna C/Padre Herrera Tenerife La Laguna38206 Spain Department of Multi-Disciplinary Sciences Graduate School of Arts and Sciences The University of Tokyo 3-8-1 Komaba Meguro Tokyo153-8902 Japan Komaba Institute for Science The University of Tokyo 3-8-1 Komaba Meguro Tokyo153-8902 Japan Okayama Observatory Kyoto University 3037-5 Honjo Kamogatacho Okayama Asakuchi719-0232 Japan Department of Physical Sciences Ritsumeikan University Shiga Kusatsu525-8577 Japan Lund Observatory Division of Astrophysics Department of Physics Lund University Box 43 Lund22100 Sweden Astrobiology Center 2-21-1 Osawa Mitaka Tokyo181-8588 Japan National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Astronomical Science Program Graduate University for Advanced Studies SOKENDAI 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Department of Astronomy & Astrophysics University of Chicago ChicagoIL60637 United States School of Architecture Universidad Europea de Canarias C/Inocencio Garcia 1 Tenerife La Orotava38300 Spain INAF - Osservatorio Astronomico di Padova Vicolo dell'Osservatorio 5 Padova35122 Italy Department of Astronomy University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan

Accurate physical parameters of exoplanet systems are essential for further exploration of planetary internal structure, atmospheres, and formation history. We aim to use simultaneous multicolour transit photometry to improve the estimation of transit parameters, to search for transit timing variations (TTVs), and to establish which of our targets should be prioritised for follow-up transmission spectroscopy. We performed time series photometric observations of 12 transits for the hot Jupiters HAT-P-19b, HAT-P-51b, HAT-P-55b, and HAT-P-65b using the simultaneous four-colour camera MuSCAT2 on the Telescopio Carlos Sánchez. We collected 56 additional transit light curves from TESS photometry. To derive transit parameters, we modelled the MuSCAT2 light curves with Gaussian processes to account for correlated noise. To derive physical parameters, we performed EXOFASTv2 global fits to the available transit and radial velocity data sets, together with the Gaia DR3 parallax, isochrones, and spectral energy distributions. To assess the potential for atmospheric characterisation, we compared the multicolour transit depths with a flat line and a clear atmosphere model. We consistently refined the transit and physical parameters. We improved the orbital period and ephemeris estimates, and found no evidence for TTVs or orbital decay. The MuSCAT2 broadband transmission spectra of HAT-P-19b and HAT-P-65b are consistent with previously published low-resolution transmission spectra. We also found that, except for HAT-P-65b, the assumption of a planetary atmosphere can improve the fit to the MuSCAT2 data. In particular, we identified HAT-P-55b as a priority target among these four planets for further atmospheric studies using transmission spectroscopy. Copyright © 2024, The Authors. All rights reserved.

关键词： Photometry

Robust joint registration of multiple stains and MRI for multimodal 3D histology reconstruction: Application to the Allen human brain atlas

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arXiv 2021年

作者： Casamitjana Díaz, Adrià Lorenzi, Marco Ferraris, Sebastiano Peter, Loïc Modat, Marc Stevens, Allison Fischl, Bruce Vercauteren, Tom Iglesias, Juan Eugenio Center for Medical Image Computing University College London United Kingdom Université Côte dÁzur Inria Epione Team Sophia Antipolis06902 France School of Biomedical Engineering & Imaging Sciences King’s College London United Kingdom Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States Program in Health Sciences and Technology Massachusetts Institute of Technology United States

Joint registration of a stack of 2D histological sections to recover 3D structure ("3D histology reconstruction") finds application in areas such as atlas building and validation of in vivo imaging. Straightforward pairwise registration of neighbouring sections yields smooth reconstructions but has well-known problems such as "banana effect" (straightening of curved structures) and "z-shift" (drift). While these problems can be alleviated with an external, linearly aligned reference (e.g., Magnetic Resonance (MR) images), registration is often inaccurate due to contrast differences and the strong nonlinear distortion of the tissue, including artefacts such as folds and tears. In this paper, we present a probabilistic model of spatial deformation that yields reconstructions for multiple histological stains that that are jointly smooth, robust to outliers, and follow the reference shape. The model relies on a spanning tree of latent transforms connecting all the sections and slices of the reference volume, and assumes that the registration between any pair of images can be see as a noisy version of the composition of (possibly inverted) latent transforms connecting the two images. Bayesian inference is used to compute the most likely latent transforms given a set of pairwise registrations between image pairs within and across modalities. We consider two likelihood models: Gaussian (`2 norm, which can be minimised in closed form) and Laplacian (`1 norm, minimised with linear programming). Results on synthetic deformations on multiple MR modalities, show that our method can accurately and robustly register multiple contrasts even in the presence of outliers. The framework is used for accurate 3D reconstruction of two stains (Nissl and parvalbumin) from the Allen human brain atlas, showing its benefits on real data with severe distortions. Moreover, we also provide the registration of the reconstructed volume to MNI space, bridging the gaps between two of the most widely

关键词： Brain

Correction: Evaluating the Environmental Impacts at the End Point Level of the Biodiesel Production Process from Multiple Feedstocks in Indonesia Through Life Cycle Assessment Methodology

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Waste and Biomass Valorization 2023年第1期15卷 523-523页

作者： Wahyono, Yoyon Hadiyanto, Hadiyanto Gheewala, Shabbir H. Budihardjo, Mochamad Arief Adiansyah, ·Joni Safaat Widayat, Widayat Christwardana, Marcelinus Doctoral Program of Environmental Science School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Chemical Engineering Faculty of Engineering Diponegoro University Semarang Indonesia Master Program of Energy School of Postgraduate Studies Diponegoro University Semarang Indonesia The Joint Graduate School of Energy and Environment King Mongkut’s University of Technology Thonburi Bangkok Thailand Center of Excellence on Energy Technology and Environment (CEE) Ministry of Higher Education Science Research and Innovation Bangkok Thailand Department of Environmental Engineering Faculty of Engineering Diponegoro University Semarang Indonesia Mining Engineering Department Faculty of Engineering Muhammadiyah University of Mataram Mataram Indonesia Department of Chemistry Faculty of Science and Mathematics Diponegoro University Semarang Indonesia

Correction : Low carbon futures: assessing the status of decarbonisation efforts at universities within a 2050 perspective

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Energy, Sustainability and Society 2023年第1期13卷 1-2页

作者： Walter Leal Filho Diogo Guedes Vidal Maria Alzira Pimenta Dinis Wim Lambrechts Claudio R. P. Vasconcelos Petra Molthan‑Hill Ismaila Rimi Abubakar Rachel M. Dunk Amanda Lange Salvia Ayyoob Sharifi European School of Sustainability Science and Research Hamburg University of Applied Sciences Ulmenliet 20 21033 Hamburg Germany Department of Natural Sciences Manchester Metropolitan University Chester Street Manchester M11 5GD UK Department of Life Sciences Faculty of Sciences and Technology Center for Functional Ecology‑Science for People and The Planet (CFE) TERRA Associate Laboratory University of Coimbra Calçada Martim de Freitas 3000‑456 Coimbra Portugal UFP Energy Environment and Health Research Unit (FP-ENAS) University Fernando Pessoa (UFP) Praça 9 de Abril 349 4249‑004 Porto Portugal Department of Marketing and Supply Chain Management Faculty of Management Open Universiteit The Netherlands (OUNL) Postbus 2960 6401 DL Heerlen The Netherlands Laboratory of Sustainability Engineering and Consumption Federal University of Paraíba João Pessoa PB Brazil Algoritmi Research Centre School of Engineering University of Minho 4800‑058 Guimarães Portugal Business Sustainability Nottingham Trent University England UK College of Architecture and Planning Imam Abdulrahman Bin Faisal University (Formerly University of Dammam) P.O. Box 1982 Dammam 31441 Saudi Arabia School of Science and the Environment Manchester Metropolitan University Chester Street Manchester M1 5GD UK Graduate Program in Civil and Environment Engineering University of Passo Fundo Campus I‑BR 285 São José Passo Fundo RS 99052‑900 Brazil Graduate School of Humanities and Social Sciences and Network for Education and Research on Peace and Sustainability Hiroshima University Higashi-Hiroshima 739-8530 Japan

Confidence intervals of prediction accuracy measures for multivariable prediction models based on the bootstrap-based optimism correction methods

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arXiv 2020年

作者： Noma, Hisashi Shinozaki, Tomohiro Iba, Katsuhiro Teramukai, Satoshi Furukawa, Toshi A. Department of Data Science The Institute of Statistical Mathematics Tokyo Japan Department of Information and Computer Technology Faculty of Engineering Tokyo University of Science Tokyo Japan Department of Statistical Science School of Multidisciplinary Sciences The Graduate University for Advanced Studies Tokyo Japan Office of Biostatistics Department of Biometrics Headquarters of Clinical Development Otsuka Pharmaceutical Co. Ltd. Tokyo Japan Department of Biostatistics Graduate School of Medical Science Kyoto Prefectural University of Medicine Kyoto Japan Department of Health Promotion and Human Behavior and of Clinical Epidemiology Kyoto University Graduate School of Medicine School of Public Health Kyoto Japan

In assessing prediction accuracy of multivariable prediction models, optimism corrections are essential for preventing biased results. However, in most published papers of clinical prediction models, the point estimates of the prediction accuracy measures are corrected by adequate bootstrap-based correction methods, but their confidence intervals are not corrected, e.g., the DeLong's confidence interval is usually used for assessing the Cstatistic. These naïve methods do not adjust for the optimism bias and do not account for statistical variability in the estimation of parameters in the prediction models. Therefore, their coverage probabilities of the true value of the prediction accuracy measure can be seriously below the nominal level (e.g., 95%). In this article, we provide two generic bootstrap methods, namely (1) location-shifted bootstrap confidence intervals and (2) two-stage bootstrap confidence intervals, that can be generally applied to the bootstrap-based optimism correction methods, i.e., the Harrell's bias correction, 0.632, and 0.632+ methods. In addition, they can be widely applied to various methods for prediction model development involving modern shrinkage methods such as the ridge and lasso regressions. Through numerical evaluations by simulations, the proposed confidence intervals showed favourable coverage performances. Besides, the current standard practices based on the optimism-uncorrected methods showed serious undercoverage properties. To avoid erroneous results, the optimism-uncorrected confidence intervals should not be used in practice, and the adjusted methods are recommended instead. We also developed the R package predboot for implementing these methods (https://***/nomahi/predboot). The effectiveness of the proposed methods are illustrated via applications to the GUSTO-I clinical trial. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

Representation matching for remote quantum computing

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arXiv 2020年

作者： Yang, Yuxiang Hayashi, Masahito Institute for Theoretical Physics ETH Zürich QICI Quantum Information and Computation Initiative Department of Computer Science University of Hong Kong Pokfulam Road Hong Kong Hong Kong Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China Shenzhen Key Laboratory of Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China Graduate School of Mathematics Nagoya University Nagoya464-8602 Japan

Many quantum computational tasks have inherent symmetries, suggesting a path to enhancing their efficiency and performance. Exploiting this observation, we propose representation matching, a generic probabilistic protocol for reducing the cost of quantum computation in a quantum network. We show that the representation matching protocol is capable of reducing the communication or memory cost to almost minimum in various tasks, including remote execution of unitary gate arrays, permutation gates and unitary conjugation, as well as the storage and retrieval of unitary gates. Copyright © 2020, The Authors. All rights reserved.

关键词： Cost reduction