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European radiology experimental 2022年第1期6卷 56页

作者： Orhun Utku Aydin Abdel Aziz Taha Adam Hilbert Ahmed A Khalil Ivana Galinovic Jochen B Fiebach Dietmar Frey Vince Istvan Madai CLAIM - Charité Lab for Artificial Intelligence in Medicine Charité Universitätsmedizin Berlin Berlin Germany. orhun-utku.aydin@charite.de. Research Studio Data Science Research Studios Austria Salzburg Austria. CLAIM - Charité Lab for Artificial Intelligence in Medicine Charité Universitätsmedizin Berlin Berlin Germany. Centre for Stroke Research Berlin Charité Universitätsmedizin Berlin Berlin Germany. Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany. Mind Brain Body Institute Berlin School of Mind and Brain Humboldt-Universität Berlin Berlin Germany. School of Computing and Digital Technology Faculty of Computing Engineering and the Built Environment Birmingham City University Birmingham UK.

Altering nodes types in controlling complex networks

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arXiv 2019年

作者： Zhang, Xizhe Zhu, Yuyan Zhao, Yongkang School of Computer Science and Engineering Northeastern University Shenyang Liaoning China Joint Laboratory of Artificial Intelligence Northeastern University Shenyang Liaoning China First Hospital of China Medical University Shenyang Liaoning China National Institute of Health and Medical Big Data China Medical University Shenyang Liaoning China

Controlling a complex network towards a desired state is of great importance in many applications. A network can be controlled by inputting suitable external signals into some selected nodes, which are called driver nodes. Previous works found there exist two control modes in dense networks: Distributed and centralized modes. For networks with the distributed mode, most of the nodes can be act as driver nodes;and those with the centralized mode, most of the nodes never be the driver nodes. Here we present an efficient algorithm to change the control type of nodes, from input nodes to redundant nodes, which is done by reversing edges of the network. We conclude four possible cases when reversing an edge and show the control mode can be changed by reversing very few in-edges of driver nodes. We evaluate the performance of our algorithm on both synthetic and real networks. The experimental results show that the control mode of a network can be easily changed by reversing a few elaborately selected edges, and the number of possible driver nodes is dramatically decreased. Our methods provide the ability to design the desired control modes of the network for different control scenarios, which may be used in many application regions. Copyright © 2019, The Authors. All rights reserved.

关键词： Complex networks

Online video streaming for human tracking based on weighted resampling particle filter

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Procedia computer science 2018年 144卷 2-12页

作者： Mukesh Prasad Liang-Cheng Chang Deepak Gupta Mahardhika Pratama Suresh Sundaram Chin-Teng Lin Centre for Artificial Intelligence School of Software FEIT University of Technology Sydney Australia Department of Computer Science and Engineering National Chiao Tung University Hsinchu Taiwan Department of Computer Science National Institute of Technology Arunachal Pradesh India School of Computer Science and Engineering Nanyang Technological University Singapore

This paper proposes a weighted resampling method for particle filter which is applied for human tracking on active camera. The proposed system consists of three major parts which are human detection, human tracking, and camera control. The codebook matching algorithm is used for extracting human region in human detection system, and the particle filter algorithm estimates the position of the human in every input image. The proposed system in this paper selects the particles with highly weighted value in resampling, because it provides higher accurate tracking features. Moreover, a proportional–integral–derivative controller (PID controller) controls the active camera by minimizing difference between center of image and the position of object obtained from particle filter. The proposed system also converts the position difference into pan-tilt speed to drive the active camera and keep the human in the field of view (FOV) camera. The intensity of image changes overtime while tracking human therefore the proposed system uses the Gaussian mixture model (GMM) to update the human feature model. As regards, the temporal occlusion problem is solved by feature similarity and the resampling particles. Also, the particle filter estimates the position of human in every input frames, thus the active camera drives smoothly. The robustness of the accurate tracking of the proposed system can be seen in the experimental results.

关键词： human tracking particle filter color distribution PID controller codebook matching

Handling of uncertainty in medical data using machine learning and probability theory techniques: A review of 30 years (1991-2020)

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arXiv 2020年

作者： Alizadehsani, Roohallah Roshanzamir, Mohamad Hussain, Sadiq Khosravi, Abbas Koohestani, Afsaneh Zangooei, Mohammad Hossein Abdar, Moloud Beykikhoshk, Adham Shoeibi, Afshin Zare, Assef Panahiazar, Maryam Nahavandi, Saeid Srinivasan, Dipti Atiya, Amir F. Acharya, U. Rajendra Deakin University Geelong Australia Department of Engineering Fasa Branch Islamic Azad University Post Box No 364 Fasa Fars*** Iran System Administrator Dibrugarh University Assam786004 India University of Texas Dallas United States Applied Artificial Intelligence Institute Deakin University Geelong Australia Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Faculty of Electrical and Computer Engineering Biomedical Data Acquisition Lab K. N. Toosi University of Technology Tehran Iran Faculty of Electrical Engineering Gonabad Branch Islamic Azad University Gonabad Iran Institute for Computational Health Sciences University of California San Francisco United States Dept. of Electrical and Computer Engineering National University of Singapore Singapore117576 Singapore Department of Computer Engineering Faculty of Engineering Cairo University Cairo12613 Egypt Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Singapore Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan

Understanding data and reaching valid conclusions are of paramount importance in the present era of big data. Machine learning and probability theory methods have widespread application for this purpose in different fields. One critically important yet less explored aspect is how data and model uncertainties are captured and analyzed. Proper quantification of uncertainty provides valuable information for optimal decision making. This paper reviewed related studies conducted in the last 30 years (from 1991 to 2020) in handling uncertainties in medical data using probability theory and machine learning techniques. Medical data is more prone to uncertainty due to the presence of noise in the data. So, it is very important to have clean medical data without any noise to get accurate diagnosis. The sources of noise in the medical data need to be known to address this issue. Based on the medical data obtained by the physician, diagnosis of disease, and treatment plan are prescribed. Hence, the uncertainty is growing in healthcare and there is limited knowledge to address these problems. We have little knowledge about the optimal treatment methods as there are many sources of uncertainty in medical science. Our findings indicate that there are few challenges to be addressed in handling the uncertainty in medical raw data and new models. In this work, we have summarized various methods employed to overcome this problem. Nowadays, application of novel deep learning techniques to deal such uncertainties have significantly increased. Copyright © 2020, The Authors. All rights reserved.

关键词： Monte Carlo methods

Logarithmic mean optimization a metaheuristic algorithm for global and case specific energy optimization

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Scientific reports 2025年第1期15卷 18155页

作者： Idriss Dagal Ambe Harrison Wulfran Fendzi Mbasso Fritz Nguemo Kemdoum Mohammad Khishe Pradeep Jangir Aseel Smerat Saad F Al-Gahtani Z M S Elbarbary Emmanuel Fendzi Donfack Hassan Abouobaida Electrical Engineering Ayazağa Mahallesi Beykent University Hadım Koruyolu Cd. No:19 Sarıyer Istanbul Turkey. idriss.dagal@std.yildiz.edu.tr. Department of Electrical and Electronics Engineering College of Technology (COT) University of Buea P.O. Box Buea 63 South West Province Cameroon. ambe.harrison@ubuea.cm. Centre for Research and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 1404010 Punjab India. ambe.harrison@ubuea.cm. Department of Biosciences Saveetha School of Engineering. Saveetha Institute of Medical and Technical Sciences Chennai 602 105 India. fendzi.wulfran@yahoo.fr. Technology and Applied Sciences Laboratory U.I.T. of Douala P.O. Box 8689 Douala Cameroon. fendzi.wulfran@yahoo.fr. Technology and Applied Sciences Laboratory U.I.T. of Douala P.O. Box 8689 Douala Cameroon. Department of Electrical Engineering Imam Khomeini Naval Science University of Nowshahr Nowshahr Iran. m_khishe@alumni.iust.ac.ir. Innovation Center for Artificial Intelligence Applications Yuan Ze University Taoyuan Taiwan. m_khishe@alumni.iust.ac.ir. Applied Science Research Center Applied Science Private University Amman 11937 Jordan. Innovation Center for Artificial Intelligence Applications Yuan Ze University Taoyuan 320315 Taiwan. Department of Electrical and Electronics Engineering J.J. College of Engineering and Technology Tiruchirappalli Tamilnadu India. Faculty of Educational Sciences Al-Ahliyya Amman University Amman 19328 Jordan. Centre for Research and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 1404010 Punjab India. Computer Technologies Engineering Mazaya University College Nasiriyah Iraq. Electrical Engineering Department Faculty of Engineering King Khalid University Abha Saudi Arabia. Department of Electrical Engineering College of Engineering King Khalid University P.O. Box 394 Abha 61421 KSA Saudi Arabia. Center for Engineering and Technology In

This study introduces a novel metaheuristic optimization algorithm named Logarithmic Mean-Based Optimization (LMO), designed to enhance convergence speed and global optimality in complex energy optimization problems. LMO leverages logarithmic mean operations to achieve a superior balance between exploration and exploitation. The algorithm's performance was benchmarked against six established methods-Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Grey Wolf Optimizer (GWO), Cuckoo Search Algorithm (CSA), and Firefly Algorithm (FA)-using the CEC 2017 suite of 23 high-dimensional functions. LMO achieved the best solution on 19 out of 23 benchmark functions, significantly outperforming all comparison algorithms. It demonstrated a mean improvement of 83% in convergence time and up to 95% better accuracy in optimal values over competitors. In a real-world application, LMO was employed to optimize a hybrid photovoltaic (PV) and wind energy system, achieving a 5000 kWh energy yield at a minimized cost of $20,000, outperforming all other algorithms in both efficiency and effectiveness. The results affirm LMO's capability for robust, scalable, and cost-effective optimization in renewable energy systems.

关键词： Global optimization Hybrid PV-Wind systems Logarithmic Mean-Based optimization (LMO) Metaheuristic techniques Photovoltaic optimization Renewable energy systems

Region-wise generative adversarial image inpainting for large missing areas

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arXiv 2019年

作者： Ma, Yuqing Liu, Xianglong Bai, Shihao Wang, Lei Liu, Aishan Tao, Dacheng Hancock, Edwin State Key Lab of Software Development Environment Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine UBTECH Sydney Artificial Intelligence Centre School of Information Technologies Faculty of Engineering and Information Technologies University of Sydney DarlingtonNSW2008 Australia Department of Computer Science University of York York United Kingdom

Recently deep neutral networks have achieved promising performance for filling large missing regions in image inpainting tasks. They usually adopted the standard convolutional architecture over the corrupted image, leading to meaningless contents, such as color discrepancy, blur and artifacts. Moreover, most inpainting approaches cannot well handle the large contiguous missing area cases. To address these problems, we propose a generic inpainting framework capable of handling with incomplete images on both contiguous and discontiguous large missing areas, in an adversarial manner. From which, region-wise convolution is deployed in both generator and discriminator to separately handle with the different regions, namely existing regions and missing ones. Moreover, a correlation loss is introduced to capture the non-local correlations between different patches, and thus guides the generator to obtain more information during inference. With the help of our proposed framework, we can restore semantically reasonable and visually realistic images. Extensive experiments on three widely-used datasets for image inpainting tasks have been conducted, and both qualitative and quantitative experimental results demonstrate that the proposed model significantly outperforms the state-of-the-art approaches, both on the large contiguous and discontiguous missing areas. Copyright © 2019, The Authors. All rights reserved.

关键词： Generative adversarial networks

MedShapeNet - A Large-Scale dataset of 3D Medical Shapes for computer Vision

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arXiv 2023年

作者： Li, Jianning Zhou, Zongwei Yang, Jiancheng Pepe, Antonio Gsaxner, Christina Luijten, Gijs Qu, Chongyu Zhang, Tiezheng Chen, Xiaoxi Li, Wenxuan Wodzinski, Marek Friedrich, Paul Xie, Kangxian Jin, Yuan Ambigapathy, Narmada Nasca, Enrico Solak, Naida Melito, Gian Marco Vu, Viet Duc Memon, Afaque R. Schlachta, Christopher De Ribaupierre, Sandrine Patel, Rajnikant Eagleson, Roy Chen, Xiaojun Mächler, Heinrich Kirschke, Jan Stefan de la Rosa, Ezequiel Christ, Patrick Ferdinand Li, Hongwei Bran Ellis, David G. Aizenberg, Michele R. Gatidis, Sergios Küstner, Thomas Shusharina, Nadya Heller, Nicholas Andrearczyk, Vincent Depeursinge, Adrien Hatt, Mathieu Sekuboyina, Anjany Löffler, Maximilian Liebl, Hans Dorent, Reuben Vercauteren, Tom Shapey, Jonathan Kujawa, Aaron Cornelissen, Stefan Langenhuizen, Patrick Ben-Hamadou, Achraf Rekik, Ahmed Pujades, Sergi Boyer, Edmond Bolelli, Federico Grana, Costantino Lumetti, Luca Salehi, Hamidreza Ma, Jun Zhang, Yao Gharleghi, Ramtin Beier, Susann Sowmya, Arcot Garza-Villarreal, Eduardo A. Balducci, Thania Angeles-Valdez, Diego Souza, Roberto Rittner, Leticia Frayne, Richard Ji, Yuanfeng Ferrari, Vincenzo Chatterjee, Soumick Dubost, Florian Schreiber, Stefanie Mattern, Hendrik Speck, Oliver Haehn, Daniel John, Christoph Nürnberger, Andreas Pedrosa, João Ferreira, Carlos Aresta, Guilherme Cunha, António Campilho, Aurélio Suter, Yannick Garcia, Jose Lalande, Alain Vandenbossche, Vicky Van Oevelen, Aline Duquesne, Kate Mekhzoum, Hamza Vandemeulebroucke, Jef Audenaert, Emmanuel Krebs, Claudia van Leeuwen, Timo Vereecke, Evie Heidemeyer, Hauke Röhrig, Rainer Hölzle, Frank Badeli, Vahid Krieger, Kathrin Gunzer, Matthias Chen, Jianxu van Meegdenburg, Timo Dada, Amin Balzer, Miriam Fragemann, Jana Jonske, Frederic Rempe, Moritz Malorodov, Stanislav Bahnsen, Fin H. Seibold, Constantin Jaus, Alexander Marinov, Zdravko Jaeger, Paul F. Stiefelhagen, Rainer Santos, Ana Sofia Lindo, Mariana Ferreira, André Alves, Victor Kamp, Michael Abourayya, Amr Nensa, Felix Hörst, Fabian Brehmer, Alexander Hein Girardetstraße 2 Essen45131 Germany Graz University of Technology Inffeldgasse 16c Graz8010 Austria Graz Austria The Department of Computer Science Johns Hopkins University Malone Hall 3400 N Charles St BaltimoreMD21218 United States Rte Cantonale Lausanne1015 Switzerland The Department of Measurement and Electronics AGH University of Science and Technology Krakow Poland Sierre Switzerland Department of Biomedical Engineering University of Basel Hegenheimermattweg Allschwil167C CH-4123 Switzerland The Boston College 140 Commonwealth Ave Chestnut HillMA02467 United States University Hospital B7-200 339 Windermere Road LondonN6A 5A5 Canada The Department of Radiology Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai200240 China The Research Center for Connected Healthcare Big Data ZhejiangLab Zhejiang Hangzhou311121 China The Faculty of Statistics Technical University Dortmund August-Schmidt-Straße 1 Dortmund44227 Germany The Institute of Mechanics Graz University of Technology Kopernikusgasse 24/IV Graz8010 Austria The Institute of Biomedical Manufacturing and Life Quality Engineering State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China The Department of Cardiac Surgery Medical University Graz Auenbruggerplatz 29 Graz8036 Austria The Department of Diagnostic and Interventional Radiology University Hospital Giessen Justus-Liebig-University Giessen Klinikstraße 33 Giessen35392 Germany The Department of Mechanical Engineering Mehran University of Engineering and Technology Jamshoro Sindh76062 Pakistan The Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China The Geschäftsführender Oberarzt Abteilung für Interventionelle und Diagnostische Neuroradiologie Universitätsklinikum der Technischen Universität München Ismaningerstr. 22 München81675 Germany icometrix Kolonel Be

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://***/ and https://***/Jianningli/*** Codes 68T01 © 2023, CC BY.

关键词： Large dataset

Flow-based sampling for multimodal and extended-mode distributions in lattice field theory

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arXiv 2021年

作者： Hackett, Daniel C. Hsieh, Chung-Chun Pontula, Sahil Albergo, Michael S. Boyda, Denis Chen, Jiunn-Wei Chen, Kai-Feng Cranmer, Kyle Kanwar, Gurtej Shanahan, Phiala E. Center for Theoretical Physics Massachusetts Institute of Technology CambridgeMA02139 United States The NSF AI Institute for Artificial Intelligence and Fundamental Interactions United States Fermi National Accelerator Laboratory BataviaIL60510 United States Department of Physics Center for Theoretical Physics National Taiwan University Taipei106 Taiwan Department of Physics University of Maryland College ParkMD20742 United States Maryland Center for Fundamental Physics University of Maryland College ParkMD20742 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Society of Fellows Harvard University CambridgeMA02138 United States Physics Division National Center for Theoretical Sciences Taipei10617 Taiwan Leung Center for Cosmology and Particle Astrophysics National Taiwan University Taipei106 Taiwan Department of Physics Data Science Institute University of Wisconsin–Madison MadisonWI53706 United States Higgs Centre for Theoretical Physics University of Edinburgh EdinburghEH9 3FD United Kingdom

Recent results have demonstrated that samplers constructed with flow-based generative models are a promising new approach for configuration generation in lattice field theory. In this paper, we present a set of training- and architecture-based methods to construct flow models for targets with multiple separated modes (i.e. vacua) as well as targets with extended/continuous modes. We demonstrate the application of these methods to modeling two-dimensional real and complex scalar field theories in their symmetry-broken phases. In this context we investigate different flow-based sampling algorithms, including a composite sampling algorithm where flow-based proposals are occasionally augmented by applying updates using traditional algorithms like HMC. Copyright © 2021, The Authors. All rights reserved.

关键词： Lattice theory

Reports of the workshops held at the 2019 aaai conference on artificial intelligence

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Reports of the workshops held at the 2019 aaai conference on...

作者： Barash, Guy Castillo-Effen, Mauricio Chhaya, Niyati Clark, Peter Espinoza, Huáscar Farchi, Eitan Geib, Christopher Gundersen, Odd Erik Heígeartaig, Seán Ó Hernández-Orallo, José Hori, Chiori Huang, Xiaowei Jaidka, Kokil Kapanipathi, Pavan Keren, Sarah Kim, Seokhwan Lanctot, Marc Lange, Danny Martinez, David Mattar, Marwan Mausam Mcauley, Julian Michalowski, Martin Mirsky, Reuth Mottaghi, Roozbeh Osborn, Joseph C. Pérolat, Julien Schmid, Martin Shaban-Nejad, Arash Shehory, Onn Srivastava, Biplav Streilein, William Talamadupula, Kartik Togelius, Julian Yoshino, Koichiro Zhang, Quanshi Zitouni, Imed Western Digital Lockheed Martin United States Adobe Research Allen Institute for Artificial Intelligence United States Commissariat àĺÉnergie Atomique France IBM Research Haifa Israel SIFT LLC Norwegian University of Science and Technology AI Officer at TrønderEnergi Norway University of Cambridge's Centre United Kingdom Universitat Politècnica de València Spain United States Department of Computer Science University of Liverpool United Kingdom Nanyang Tech University Singapore IBM Research AI Harvard University United States Research Scientist at Adobe Research United States Research scientist at DeepMind Alberta Edmonton Canada Unity Technologies University of California San Diego United States MIT Lincoln Laboratory Unity Technologies Computer Science at Indian Institute of Technology Delhi India University of Minnesota School of Nursing United States University of Texas United States Allen Institutem for Artificial Intelligence United States Pomona College United States DeepMind London United Kingdom DeepMind Alberta Edmonton Canada University of Tennessee Health Science Center-Oak Ridge National Laboratory United States Bar Ilan University Israel IBM's ArmonkNY United States IBM Research AI Tandon School of Engineering New York University United States Japan MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China Conversation understanding group at Microsoft

Affective Content Analysis and CL-Aff Shared Task: In Pursuit of Happiness The Affective Content Analysis workshop series held at the AAAI Conference on artificial intelligence is an interdisciplinary platform intended to engage the AI and machinelearning communities about open problems in affective content analysis and understanding, with a special focus on affect in language and text. The theme of this second workshop was modeling affect-in-action, with a shared task (CL-Aff-in pursuit of happiness) to encourage the development of new models and approaches for modeling happy moments. Copyright © 2019, Association for the Advancement of artificial intelligence.

关键词： artificial intelligence

VaTEST III: Validation of 8 Potential Super-Earths from TESS data

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arXiv 2023年

作者： Mistry, Priyashkumar Prasad, Aniket Maity, Mousam Pathak, Kamlesh Gharat, Sarvesh Lekkas, Georgios Bhattarai, Surendra Kumar, Dhruv Lissauer, Jack J. Twicken, Joseph D. Soubkiou, Abderahmane Pozuelos, Francisco J. Jenkins, Jon Horne, Keith Giacalone, Steven Barkaoui, Khalid Timmermans, Mathilde Watkins, Cristilyn N. Sefako, Ramotholo Collins, Karen A. Ciardi, David R. Clark, Catherine A. Safonov, Boris S. Shporer, Avi Schlieder, Joshua E. Benkhaldoun, Zouhair Stockdale, Chris Ziegler, Carl Gilbert, Emily A. Jehin, Emmanuël Murgas, Felipe Crossfield, Ian J.M. Paegert, Martin Lund, Michael B. Narita, Norio Schwarz, Richard P. Goeke, Robert F. Fajardo-Acosta, Sergio B. Howell, Steve B. Tan, Thiam-Guan Barclay, Thomas Kawai, Yugo Department of Physics Sardar Vallabhbhai National Institute of Technology Gujarat Surat395007 India School of Physics Faculty of Science University of New South Wales Kensington SydneyNSW2052 Australia Department of Earth and Space Science Indian Institute of Space Science and Technology Thiruvananthapuram India Department of Physics Presidency University West Bengal Kolkata700073 India Centre for Machine Intelligence and Data Science Indian Institute of Technology Bombay India Department of Computer Science International Hellenic University Kavala65404 Greece Department of Physics Indian Institute of Science Education and Research Kolkata West Bengal Mohanpur741246 India Department of Physics National Institute of Technology Tripura Agartala799046 India NASA Ames Research Center Moffett FieldCA94035 United States SETI Institute Mountain ViewCA94043 United States Oukaimeden Observatory High Energy Physics and Astrophysics Laboratory Cadi Ayyad University Marrakech Morocco Astrobiology Research Unit Université de Liège 19C Allée du 6 Août Liège4000 Belgium Glorieta de la Astronomía s/n Granada18008 Spain SUPA Physics and Astronomy University of St. Andrews FifeKY16 9SS United Kingdom Department of Astronomy University of California Berkeley BerkeleyCA94720 United States Department of Earth Atmospheric and Planetary Science Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Calle Vía Láctea s/n Tenerife La Laguna38200 Spain Center for Astrophysics Harvard & Smithsonian 60 Garden Street CambridgeMA02138 United States South African Astronomical Observatory P.O. Box 9 Observatory Cape Town7935 South Africa NASA Exoplanet Science Institute IPAC California Institute of Technology PasadenaCA91125 United States Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States Sternberg Astronomical Institute Lomonosov Moscow State University Russia Depart

NASA’s all-sky survey mission, the Transiting Exoplanet Survey Satellite (TESS), is specifically engineered to detect exoplanets that transit bright stars. Thus far, TESS has successfully identified approximately 400 transiting exoplanets, in addition to roughly 6000 candidate exoplanets pending confirmation. In this study, we present the results of our ongoing project, the Validation of Transiting Exoplanets using Statistical Tools (VaTEST). Our dedicated effort is focused on the confirmation and characterization of new exoplanets through the application of statistical validation tools. Through a combination of ground-based telescope data, high-resolution imaging, and the utilization of the statistical validation tool known as TRICERATOPS, we have successfully discovered eight potential super-Earths. These planets bear the designations: TOI-238b (1.61+0.09–0.10 R⊗), TOI-771b (1.42+0.11–0.09 R⊗), TOI-871b (1.66+0.11–0.11 R⊗), TOI-1467b (1.83+0.16–0.15 R⊗), TOI-1739b (1.69+0.10–0.08 R⊗), TOI-2068b (1.82+0.16–0.15 R⊗), TOI-4559b (1.42+0.13–0.11 R⊗), and TOI-5799b (1.62+0.19–0.13 R⊗). Among all these planets, six of them fall within the region known as’keystone planets,’ which makes them particularly interesting for study. Based on the location of TOI-771b and TOI-4559b below the radius valley we characterized them as likely super-Earths, though radial velocity mass measurements for these planets will provide more details about their characterization. It is noteworthy that planets within the size range investigated herein are absent from our own solar system, making their study crucial for gaining insights into the evolutionary stages between Earth and Neptune. © 2023, CC BY.

关键词： Satellites