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arXiv 2019年

作者： Ito, Noboru Kotorii, Yuka Takamura, Masashi Graduate School of Mathematical Sciences The University of Tokyo 3-8-1 Komaba Meguro-ku Tokyo153-8914 Japan Mathematics Program Graduate school of Advanced Science and Engineering Hiroshima University 1-7-1 Kagamiyama Higashi-hiroshima City Hiroshima739-8521 Japan 1-4-1 Nihonbashi Chuo-ku Tokyo103-0027 Japan RIKEN 2-1 Hirosawa Wako Saitama351-0198 Japan School of Social Informatics Aoyama Gakuin University 5-10-1 Fuchinobe Chuo-ku Kanagawa-ken Sagamihara-shi252-5258 Japan

Although it is known that the dimension of the Vassiliev invariants of degree three of long virtual knots is seven, the complete list of seven distinct Gauss diagram formulas have been unknown explicitly, where only one known formula was revised without proof. In this paper, we give seven Gauss diagram formulas to present the seven invariants of the degree three (Proposition 4). We further give 23 Gauss diagram formulas of classical knots (Proposition 5). In particular, the Polyak-Viro Gauss diagram formula [19] is not a long virtual knot invariant;however, it is included in the list of 23 formulas. It has been unknown whether this formula would be available by arrow diagram calculus automatically. In consequence, as it relates to the conjecture of Goussarov-Polyak-Viro [8, Conjecture 3.C], for all the degree three finite type long virtual knot invariants, each Gauss diagram formula is represented as those of Vassiliev invariants of classical knots (Theorem 1). Copyright © 2019, The Authors. All rights reserved.

关键词： Gaussian distribution

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Identification of gene expression signatures for psoriasis classification using machine learning techniques

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Medicine in Omics 2021年 1卷

作者： Nguyen Quoc Khanh Le Duyen Thi Do Trinh-Trung-Duong Nguyen Ngan Thi Kim Nguyen Truong Nguyen Khanh Hung Nguyen Thi Thu Trang Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei City 106 Taiwan Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei City 106 Taiwan Graduate Institute of Biomedical Informatics Taipei Medical University Taipei City 106 Taiwan Department of Computer Science and Engineering Yuan Ze University Chung-Li 32003 Taiwan School of Nutrition and Health Sciences Taipei Medical University Taipei City 110 Taiwan International Master/Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei City 110 Taiwan Orthopedic and Trauma Department Cho Ray Hospital Ho Chi Minh City Viet Nam International PhD Program for Cell Therapy and Regeneration Medicine College of Medicine Taipei Medical University Taipei City 110 Taiwan

Psoriasis classification requires the accurate identification of the lesional types for the early and effective diagnosis and it is worth interesting that the normal and psoriasis cell tissues exhibit different gene expression. Therefore, gene expression data is an effective source for psoriasis classification and there is a challenge regarding the selection of suitable gene signatures for its purpose. In this present study, the gene expression-based microarray data were used and 35 expression features linked with psoriasis were utilized to feed into our machine learning model. Overall, the performance of our model based on 35 mentioned-above features surpassed that of other state-of-the-art classifiers with an average accuracy of 98.3%, recall of 98.6%, and precision of 98% in 5-fold cross-validation tests. We also validate our model on two different sets of psoriasis and the performance results are significant. These results have suggested that our 35 expression signatures have been identified as key features for classifying samples between lesion and non-lesion. More specifically, the expression levels of few genes i.e., FABP5 , TGM1 , or BCAR3 are discovered as newly potential biomarkers for psoriasis classification and treatment with high confidence. This study, therefore, could shed light on developing the prediction models for psoriasis classification and treatment using gene expression profiles.

关键词： Psoriasis classification Random forest Gene expression Feature selection Machine learning FABP5 Dermatology

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Evidence of hadronic origin of the gamma-ray emission from the nova RS Oph by the MAGIC telescopes 38

Evidence of hadronic origin of the gamma-ray emission from t...

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38th International Cosmic Ray Conference, ICRC 2023

作者： Green, David M. Ramazani, Vandad Fallah Leone, Francesco López-Coto, Rubén López-Oramas, Alicia Sitarek, Julian Abe, H. Abe, S. Abhir, J. Acciari, V.A. Agudo, I. Aniello, T. Ansoldi, S. Antonelli, L.A. Arbet Engels, A. Arcaro, C. Artero, M. Asano, K. Baack, D. Babić, A. Baquero, A. Barres de Almeida, U. Barrio, J.A. Batković, I. Baxter, J. Becerra González, J. Bednarek, W. Bernardini, E. Bernardos, M. Bernete, J. Berti, A. Besenrieder, J. Bigongiari, C. Biland, A. Blanch, O. Bonnoli, G. Bošnjak, Ž. Burelli, I. Busetto, G. Campoy-Ordaz, A. Carosi, A. Carosi, R. Carretero-Castrillo, M. Castro-Tirado, A.J. Ceribella, G. Chai, Y. Chilingarian, A. Cifuentes, A. Cikota, S. Colombo, E. Contreras, J.L. Cortina, J. Covino, S. D’Amico, G. D’Elia, V. Da Vela, P. Dazzi, F. De Angelis, A. De Lotto, B. Del Popolo, A. Delfino, M. Delgado, J. Delgado Mendez, C. Depaoli, D. Di Pierro, F. Di Venere, L. Dominis Prester, D. Donini, A. Dorner, D. Doro, M. Elsaesser, D. Emery, G. Escudero, J. Fallah Ramazani, V. Fariña, L. Fattorini, A. Foffano, L. Font, L. Fröse, S. Fukami, S. Fukazawa, Y. García López, R.J. Garczarczyk, M. Gasparyan, S. Gaug, M. Giesbrecht Paiva, J.G. Giglietto, N. Giordano, F. Gliwny, P. Godinović, N. Grau, R. Green, D. Green, J.G. Hadasch, D. Hahn, A. Hassan, T. Heckmann, L. Herrera, J. Hrupec, D. Hütten, M. Imazawa, R. Inada, T. Iotov, R. Ishio, K. Jiménez Martínez, I. Jormanainen, J. Kerszberg, D. Kluge, G.W. Kobayashi, Y. Kouch, P.M. Kubo, H. Kushida, J. Láinez Lezáun, M. Lamastra, A. Lelas, D. Leone, F. Lindfors, E. Linhoff, L. Lombardi, S. Longo, F. López-Coto, R. López-Moya, M. López-Oramas, A. Loporchio, S. Lorini, A. Lyard, E. Machado de Oliveira Fraga, B. Majumdar, P. Makariev, M. Maneva, G. Mang, N. Manganaro, M. Mangano, S. Mannheim, K. Mariotti, M. Martínez, M. Martínez-Chicharro, M. Mas-Aguilar, A. Mazin, D. Menchiari, S. Mender, S. Mićanović, S. Miceli, D. Miener, T. Miranda, J.M. Mirzoyan, R. Bochum Germany Instituto de Astrofísica de Andalucía-CSIC Granada Spain Instituto de Astrofísica de Canarias Departamento de Astrofísica Universidad de La Laguna La Laguna Spain Department of Astrophysics Faculty of Physics and Applied Informatics University of Lodz Lodz Poland The University of Tokyo Chiba Kashiwa277-8582 Japan ETH Zürich ZürichCH-8093 Switzerland Instituto de Astrofísica de Canarias Dpto. de Astrofísica Universidad de La Laguna Tenerife La LagunaE-38200 Spain Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n Granada18008 Spain RomeI-00136 Italy Università di Udine INFN Trieste UdineI-33100 Italy Università di Padova INFN PadovaI-35131 Italy Bellaterra BarcelonaE-08193 Spain Technische Universität Dortmund DortmundD-44221 Germany Zagreb10000 Croatia IPARCOS Institute EMFTEL Department Universidad Complutense de Madrid MadridE-28040 Spain URCA RJ Rio de Janeiro22290-180 Brazil University of Lodz Faculty of Physics and Applied Informatics Department of Astrophysics Lodz90-236 Poland Centro de Investigaciones Energéticas Medioambientales y Tecnológicas MadridE-28040 Spain Departament de Física CERES-IEEC Universitat Autònoma de Barcelona BellaterraE-08193 Spain Università di Pisa INFN Pisa PisaI-56126 Italy Universitat de Barcelona ICCUB IEEC-UB BarcelonaE-08028 Spain Armenian MAGIC Group A. Alikhanyan National Science Laboratory Yerevan0036 Armenia Department for Physics and Technology University of Bergen Norway INFN MAGIC Group INFN Sezione di Catania Dipartimento di Fisica e Astronomia University of Catania CataniaI-95123 Italy INFN MAGIC Group INFN Sezione di Torino Università degli Studi di Torino TorinoI-10125 Italy INFN MAGIC Group INFN Sezione di Bari Dipartimento Interateneo di Fisica dell’Università e del Politecnico di Bari BariI-70125 Italy Croatian MAGIC Group University of Rijeka Faculty of Physics Rijeka51000 Croatia Universität Würzburg Würzbu

RS Ophiuchi (RS Oph) is a symbiotic recurrent nova that shows eruptive events roughly every 15 years. On August 8th, 2021, RS Oph erupted with its latest outburst. This event was detected by a wide range of multi-wavelength (MWL) instruments from radio up to very-high-energy (VHE) gamma rays. The MAGIC telescopes followed up on optical and high-energy triggers and initiated an observation campaign from August 9th till September 1st. RS Oph is the first nova detected in the VHE gamma-ray energy range. We report on the detection of VHE gamma rays at a significant level of 13.2σ during the first 4 days of RS Oph with the MAGIC telescopes. We combine the VHE emission detected by MAGIC with optical and high energy observations and conclude RS Oph accelerated hadrons during its eruption. We will present the MWL modeling revealing this hadronic emission, and its further implications for Galactic cosmic-rays. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Hadrons

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Using a Recurrent Neural Network Model for Classification of Tweets Conveyed Influenza-related Information 1

Using a Recurrent Neural Network Model for Classification of...

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1st International Workshop on Digital Disease Detection using Social Media, DDDSM 2017, co-located with the 8th International Joint Conference on Natural Language Processing, IJCNLP 2017

作者： Wang, Chen-Kai Singh, Onkar Tang, Zhao-Li Dai, Hong-Jie Graduate Institute of Biomedical Informatics Taipei Medical University Taipei Taiwan Bioinformatics Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei Taiwan Institute of Biomedical Informatics National Yang-Ming University Taipei Taiwan Department of Computer Science and Information Engineering National Taitung University Taitung Taiwan Interdisciplinary Program of Green and Information Technology National Taitung University Taitung Taiwan

ISBN: (纸本)9781948087070

Traditional disease surveillance systems depend on outpatient reporting and virological test results released by hospitals. These data have valid and accurate information about emerging outbreaks but it's often not timely. In recent years the exponential growth of users getting connected to social media provides immense knowledge about epidemics by sharing related information. Social media can now flag more immediate concerns related to outbreaks in real time. In this paper we apply the long short-term memory recurrent neural network (RNN) architecture to classify tweets conveyed influenza-related information and compare its performance with baseline algorithms including support vector machine (SVM), decision tree, naive Bayes, simple logistics, and naive Bayes multinomial. The developed RNN model achieved an F-score of 0.845 on the MedWeb task test set, which outperforms the F-score of SVM without applying the synthetic minority oversampling technique by 0.08. The F-score of the RNN model is within 1% of the highest score achieved by SVM with oversampling technique. © 2017 AFNLP

关键词： Support vector machines

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Application of Interpolation Image by using Bi-Cubic Algorithm

Application of Interpolation Image by using Bi-Cubic Algorit...

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2018 Workshop on Multidisciplinary and Its Applications: Applied Mathematics, Computer Science, Information Systems, and Information Technology, WMA-Mathcomtech 2018

作者： Abdullah, Dahlan Fajriana, F. Maryana, M. Rosnita, Lidya Utama Siahaan, Andysah Putera Rahim, Robbi Harliana, P. Harmayani, H. Ginting, Zainuddin Erliana, Cut Ita Irwansyah, Defi Zulmiardi, Z. Khaddafi, Muammar Milanie, Feby Aspan, Henry Huda, Ismul Saddhono, Kundharu Mulyaningsih, Indrya Moonti, Roy Marthen Parwito, P. Djanggih, Hardianto Amalia, Ainna Winarno, Edy Hadikurniawati, Wiwien Department of Informatics Universitas Malikussaleh Aceh Indonesia School of Computer and Communication Engineering Universiti Malaysia Perlis Malaysia Department of Informatics Engineering Sekolah Tinggi Teknik Harapan Medan Indonesia Department of Informatics AMIK Intel Com Global Indo Indonesia Department of Chemical Engineering Universitas Malikussaleh Aceh Indonesia Department of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Department of Mechanical Engineering Universitas Malikussaleh Aceh Indonesia Department of Accounting Universitas Malikussaleh Aceh Indonesia Universitas Pembangunan Panca Budi Medan Indonesia Universitas Syiah Kuala Aceh Indonesia Graduate Program Universitas Sebelas Maret Surakarta Indonesia Indonesian Language Teaching Department Institut Agama Islam Negeri Syekh Nurjati Cirebon Indonesia Department of Law Universitas Gorontalo Gorontalo Indonesia Department of Agrotechnology Universitas Ratu Samban Bengkulu Indonesia Department of Law Universitas Tompotika Luwuk Banggai Luwuk Indonesia Islamic Institut of Miftahul Ula Kertosono Nganjuk-East-Java Indonesia Faculty of Information Technology Universitas Stikubank Semarang Indonesia

Image magnification is one of the branches in digital image processing that is often required in various applications such as in the field of medicine, multimedia, and in satellite imagery. As technology grows, more and more methods are used for image enlargement. In this study, the image enlargement process performs by using Bi-Cubic spline interpolation method, and the result of image try to compare between the original one and picture after enlargement. © Published under licence by IOP Publishing Ltd.

关键词： Satellite imagery

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Study of initial guess influence on the quality of solutions on binary genetic algorithm in job shop scheduling problem 14

Study of initial guess influence on the quality of solutions...

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14th International Symposium on Operational Research, SOR 2017

作者： Ferreira da Cruz, Valmir Pereira, Fabio Henrique Universidade Nove de Julho Industrial Engineering Graduate Program Francisco Matarazzo Av. 612 São Paulo Brazil Universidade Nove de Julho Informatics and knowledge management Graduate Program Francisco Matarazzo Av. 612 São Paulo Brazil

ISBN: (纸本)9789616165501

The purpose of this study is to evaluate the influence of the initial guess to generate the Genetic Algorithm population of solutions of scheduling problems in relation to the quality and feasibility of the solutions. The scheduling problem is defined as to find the sequence of operations on the machines that optimize some performance measure as, for example, the use of resources and the total processing time (makespan). It is common to treat such problems with the use of metaheuristics as genetic algorithm mainly due to its computational complexity. This work carried out experiments with a set of literature instances, varying the sequencing rule used in the generation of initial solutions. Usually, rules from the literature have been tested and identified a hybrid rule that generates a smaller number of non-feasible solutions and the number of instances that have reached the optimal makespan. © 2017 Slovenian Society Informatika. All Rights Reserved.

关键词： Genetic algorithms

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Development of knowledge management system for determining organizational culture in micro, small and medium enterprises using organizational culture assessment instrument

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IOP Conference Series: Materials Science and engineering 2018年第1期403卷

作者： R Z A Aziz M F Azima S Y Irianto Informatics Engineering Department Graduate Program Informatics and Business Institute Darmajaya Bandar Lampung Indonesia

Organizations must improve their competitiveness due to global competition and free market. Organizational culture and continuous improvement are one of the main factors used not only to face the competitiveness in the free market but also to improve organizational performance and vision and mission achievement. Moreover, the organizational culture also has an important element for determining the direction of Micro, Small and Medium Enterprises (MSMEs). However, many MSMEs currently do not pay attention to their organizational culture so that difficulties emerge in determining the right direction and policy. To solve the problems, this research aimed to determine the companies' organizational culture by using organizational culture assessment instrument (OCAI) based on knowledge management system (KMS) in order to provide recommendations. In the developing the system, the researchers conducted surveys to several MSMEs and invite experts and practitioners to conduct the process of knowledge capture and literature study and to achieve the best organizational culture recommendations on MSMEs. This research produced a software used for determining the organizational culture based on KMS and OCAI principles. The developed software was tested to several companies and MSMEs to obtain results with a very high level of accuracy in providing recommendations.

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Comparison of time in processing frame as an error detection mechanism in network transmission 2

Comparison of time in processing frame as an error detection...

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2nd Nommensen International Conference on Technology and engineering, NICTE 2018

作者： Rahim, Robbi Manurung, Rosida Tiurma Napitupulu, Darmawan Ratnadewi, R. Nurdiyanto, Heri Rangka, Itsar Bolo Eterudin, Hamzah Susanto, Eko Hidayat, Arif Suseno, Nyoto School of Computer and Communication Engineering Universiti Malaysia Perlis Kubang Gajah Malaysia Graduate Program in Scientific Psychology Universitas Kristen Maranatha Bandung Indonesia Research Center for Quality System and Testing Technology Indonesian Institute of Sciences Jakarta Indonesia Electrical Engineering Universitas Kristen Maranatha Bandung Indonesia Department of Informatics STMIK Dharma Wacana Metro Lampung Indonesia Departement of Guidance and Counseling Universitas Indraprasta PGRI Jakarta Indonesia Departement of Electrical Universitas Lancang Kuning Pekanbaru Indonesia Universitas Muhammadiyah Metro Lampung Indonesia

Errors in data transmission cannot be known directly when the data transmission process is fulfilled, but error is often prevalent in data transmission. Faulty or missing frames or bits are standard errors and to control or check errors requires a unique method, in this case, the Stop-and-wait method, Go-Back-N and Selective Reject are methods that can be used to do so, comparison of time of error checking process with Stop-and-wait method, Go-Back-N and Selective Reject is very important to get the right method to check the ignorance, from testing performed that the method of Go-Back-N is much faster than the other methods. © Published under licence by IOP Publishing Ltd.

关键词： Errors

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Toward the data-driven 'smart' and 'green' hospital-care 9

Toward the data-driven 'smart' and 'green' hospital-care

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9th ITU Kaleidoscope Academic Conference: Challenges for a Data-Driven Society, ITU K 2017

作者： Spyropoulos, B. Alexandropoulos, A. Boci, N. Chatziapostolou, E. Frappa, E. Georgiadou, E. Louts, I. Pantelakis, I. Poultsaki, M. Xenaki, M. Department of Biomedical Engineering Technological Education Institute of Athens Athens Greece Graduate Program 'Information Technologies in Medicine and Biology' Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens Greece

ISBN: (纸本)9789261242916

The Hospital is the most complex and representative establishment of the society and nowadays, among the most costly ones. ICTs may rationalize personnel-efforts and reduce energy and material-wasting, to enable health-care coverage, of unprivileged social-groups. The aim of the paper is to present the most effective and efficient means and tools, reducing unnecessary cost, as Mobile IP-network, Service-oriented architecture, provided to other components, through a communication protocol, over a network, Ubiquitous-computing, Femtocells, small, low-power cellular base-stations, typically designed for use in a hospital-department, ward-room or unit, Wireless mesh-networks, made-up of radio-nodes, organized in a mesh-topology, supporting intra-hospital data-exchange, training Multimedia-sharing over wireless networks, for real-time or compressed data-streaming over IP/wireless-networks for communication or archiving. Green-computing in wireless-networks, are limiting people and material intrahospital 'circulation' and, thus, they are enabling the necessary environmentally friendly and smooth procedures. Standardization, policies and regulations for green communications and computing, in 'green' Hospitals are indispensable and ICTs enable procedures optimization, solving energy- and material-waste problems, reducing the overall operational-cost, in the emerging 'smart, green and networked' Hospital, in favor of the people in need. © 2017 ITU.

关键词： Standardization

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Understanding the origin of the extended γ-ray emission and the physical nature of HESS J1841-055 using observations at TeV energies with the MAGIC telescopes 37

Understanding the origin of the extended γ-ray emission and...

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37th International Cosmic Ray Conference, ICRC 2021

作者： Acciari, V.A. Ansoldi, S. Antonelli, L.A. Arbet Engels, A. Artero, M. Asano, K. Baack, D. Babić, A. Baquero, A. Barres de Almeida, U. Barrio, J.A. Batković, I. Becerra González, J. Bednarek, W. Bellizzi, L. Bernardini, E. Bernardos, M. Berti, A. Besenrieder, J. Bhattacharyya, W. Bigongiari, C. Biland, A. Blanch, O. Bökenkamp, H. Bonnoli, G. Bošnjak, Ž. Busetto, G. Carosi, R. Ceribella, G. Cerruti, M. Chai, Y. Chilingarian, A. Cikota, S. Colak, S.M. Colombo, E. Contreras, J.L. Cortina, J. Covino, S. D'Amico, G. D'Elia, V. Da Vela, P. Dazzi, F. De Angelis, A. De Lotto, B. Delfino, M. Delgado, J. Delgado Mendez, C. Depaoli, D. Di Pierro, F. Di Venere, L. Do Souto Espiñeira, E. Dominis Prester, D. Donini, A. Dorner, D. Doro, M. Elsaesser, D. Fallah Ramazani, V. Fattorini, A. Fonseca, M.V. Font, L. Fruck, C. Fukami, S. Fukazawa, Y. García López, R.J. Garczarczyk, M. Gasparyan, S. Gaug, M. Giglietto, N. Giordano, F. Gliwny, P. Godinović, N. Green, David Green, J.G. Hadasch, D. Hahn, A. Heckmann, L. Herrera, J. Hoang, J. Hrupec, D. Hütten, M. Inada, T. Ishio, K. Iwamura, Y. Jiménez Martínez, I. Jormanainen, J. Jouvin, L. Karjalainen, M. Kerszberg, D. Kobayashi, Y. Kubo, H. Kushida, J. Lamastra, A. Lelas, D. Leone, F. Lindfors, E. Linhoff, L. Lombardi, S. Longo, F. López-Coto, R. López-Moya, M. López-Oramas, Alicia Loporchio, S. Machado de Oliveira Fraga, B. Maggio, C. Majumdar, P. Makariev, M. Mallamaci, M. Maneva, G. Manganaro, M. Mannheim, K. Maraschi, L. Mariotti, M. Martínez, M. Mazin, D. Menchiari, S. Mender, S. Mićanović, S. Miceli, D. Miener, T. Miranda, J.M. Mirzoyan, R. Molina, E. Moralejo, A. Morcuende, D. Moreno, V. Moretti, E. Nakamori, T. Nava, L. Neustroev, V. Nigro, C. Nilsson, K. Nishijima, K. Noda, K. Nozaki, S. Ohtani, Y. Oka, T. Otero-Santos, J. Paiano, S. Palatiello, M. Paneque, D. Paoletti, R. Paredes, J.M. Pavletić, L. Peñil, P. Persic, M. Pihet, M. Prada Moroni, P.G. Prandini, E. Priyadarshi, C. Brazil Spain Croatian MAGIC Group Ruđer Bošković Institute Croatia Croatia Croatian MAGIC Group Josip Juraj Strossmayer University of Osijek Department of Physics Croatia Croatian MAGIC Group University of Rijeka Department of Physics Croatia Croatia Germany Technische Universität Dortmund Germany ETH Zürich Switzerland Finnish MAGIC Group Finnish Centre for Astronomy with ESO University of Turku Finland Finnish MAGIC Group Astronomy Research Unit University of Oulu Finland Instituto de Astrofísica de Andalucía-CSIC Spain Instituto de Astrofísica de Canarias La LagunaE-38200 Spain Universidad de La Laguna Dpto. Astrofísica La Laguna TenerifeE-38206 Spain Spain INFN MAGIC Group INFN Sezione di Torino Università degli Studi di Torino Italy INFN MAGIC Group INFN Sezione di Bari Dipartimento Interateneo di Fisica Università e del Politecnico di Bari Italy INFN MAGIC Group INFN Sezione di Perugia Italy INFN MAGIC Group INFN Roma Tor Vergata The University of Tokyo Japan Japanese MAGIC Group Department of Physics Kyoto University Japan Japanese MAGIC Group Department of Physics Tokai University Japan Japanese MAGIC Group: Physics Program Graduate School of Advanced Science and Engineering Hiroshima University Japan Japanese MAGIC Group Department of Physics Yamagata University Japan Japanese MAGIC Group Department of Physics Konan University Japan University of Lodz Faculty of Physics and Applied Informatics Department of Astrophysics Poland IPARCOS Institute EMFTEL Department Universidad Complutense de Madrid MadridE-28040 Spain Max-Planck-Institut für Physik MünchenD-80805 Germany Università di Padova INFN Italy Università di Pisa INFN Pisa Italy Università di Siena INFN Pisa Italy Saha Institute of Nuclear Physics India Inst. for Nucl. Research and Nucl. Energy Bulgarian Academy of Sciences Bulgaria Departament de Física CERES-IEEC Universitat Autónoma de Barcelona Spain Universitat de Barcelona ICCUB IEEC-UB Spain Università

With the improved sensitivity with respect to the previous generation, current space-borne and ground-based gamma-ray telescopes have made the number of γ-ray sources detected at GeV-TeV energies increase many folds over the last decade. Many of the detected extended gamma-ray sources are not associated with any known sources at other wavelengths. Understanding the nature of these sources and the origin of the observed high energy gamma-ray emission remains a great challenge. Using the MAGIC telescopes, we have observed one such unassociated gamma-ray source, named HESS J1841-055 at TeV energies. We investigate the physical nature and origin of the γ-ray emission from this extended source. In this paper, we present the results of our detailed investigation on this source using MAGIC data and other multi-waveband information on nearby sources. The results included in this proceeding and shown in this presentation were published in ref. [1]. The details about the analysis, interpretation and modelling can be found in the aforementioned reference. © 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： Gamma rays

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