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Institution

Romanian Academy

ArchiveBucharest, Romania
About: Romanian Academy is a archive organization based out in Bucharest, Romania. It is known for research contribution in the topics: Population & Nonlinear system. The organization has 3662 authors who have published 10491 publications receiving 146447 citations. The organization is also known as: Academia Română & Societatea Literară Română.


Papers
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Journal ArticleDOI
Gheorghe Paun1
TL;DR: It is proved that the P systems with the possibility of objects to cooperate characterize the recursively enumerable sets of natural numbers; moreover, systems with only two membranes suffice.

2,327 citations

Journal ArticleDOI
TL;DR: A new dataset, Human3.6M, of 3.6 Million accurate 3D Human poses, acquired by recording the performance of 5 female and 6 male subjects, under 4 different viewpoints, is introduced for training realistic human sensing systems and for evaluating the next generation of human pose estimation models and algorithms.
Abstract: We introduce a new dataset, Human3.6M, of 3.6 Million accurate 3D Human poses, acquired by recording the performance of 5 female and 6 male subjects, under 4 different viewpoints, for training realistic human sensing systems and for evaluating the next generation of human pose estimation models and algorithms. Besides increasing the size of the datasets in the current state-of-the-art by several orders of magnitude, we also aim to complement such datasets with a diverse set of motions and poses encountered as part of typical human activities (taking photos, talking on the phone, posing, greeting, eating, etc.), with additional synchronized image, human motion capture, and time of flight (depth) data, and with accurate 3D body scans of all the subject actors involved. We also provide controlled mixed reality evaluation scenarios where 3D human models are animated using motion capture and inserted using correct 3D geometry, in complex real environments, viewed with moving cameras, and under occlusion. Finally, we provide a set of large-scale statistical models and detailed evaluation baselines for the dataset illustrating its diversity and the scope for improvement by future work in the research community. Our experiments show that our best large-scale model can leverage our full training set to obtain a 20% improvement in performance compared to a training set of the scale of the largest existing public dataset for this problem. Yet the potential for improvement by leveraging higher capacity, more complex models with our large dataset, is substantially vaster and should stimulate future research. The dataset together with code for the associated large-scale learning models, features, visualization tools, as well as the evaluation server, is available online at http://vision.imar.ro/human3.6m .

2,209 citations

Journal ArticleDOI
Daniel Conroy-Beam1, David M. Buss2, Kelly Asao2, Agnieszka Sorokowska3, Agnieszka Sorokowska4, Piotr Sorokowski3, Toivo Aavik5, Grace Akello6, Mohammad Madallh Alhabahba7, Charlotte Alm8, Naumana Amjad9, Afifa Anjum9, Chiemezie S. Atama10, Derya Atamtürk Duyar11, Richard Ayebare, Carlota Batres12, Mons Bendixen13, Aicha Bensafia14, Boris Bizumic15, Mahmoud Boussena14, Marina Butovskaya16, Marina Butovskaya17, Seda Can18, Katarzyna Cantarero19, Antonin Carrier20, Hakan Cetinkaya21, Ilona Croy4, Rosa María Cueto22, Marcin Czub3, Daria Dronova17, Seda Dural18, İzzet Duyar11, Berna Ertuğrul23, Agustín Espinosa22, Ignacio Estevan24, Carla Sofia Esteves25, Luxi Fang26, Tomasz Frackowiak3, Jorge Contreras Garduño27, Karina Ugalde González, Farida Guemaz, Petra Gyuris28, Mária Halamová29, Iskra Herak20, Marina Horvat30, Ivana Hromatko31, Chin Ming Hui26, Jas Laile Suzana Binti Jaafar32, Feng Jiang33, Konstantinos Kafetsios34, Tina Kavčič35, Leif Edward Ottesen Kennair13, Nicolas Kervyn20, Truong Thi Khanh Ha19, Imran Ahmed Khilji36, Nils C. Köbis37, Hoang Moc Lan19, András Láng28, Georgina R. Lennard15, Ernesto León22, Torun Lindholm8, Trinh Thi Linh19, Giulia Lopez38, Nguyen Van Luot19, Alvaro Mailhos24, Zoi Manesi39, Rocio Martinez40, Sarah L. McKerchar15, Norbert Meskó28, Girishwar Misra41, Conal Monaghan15, Emanuel C. Mora42, Alba Moya-Garófano40, Bojan Musil30, Jean Carlos Natividade43, Agnieszka Niemczyk3, George Nizharadze, Elisabeth Oberzaucher44, Anna Oleszkiewicz4, Anna Oleszkiewicz3, Mohd Sofian Omar-Fauzee45, Ike E. Onyishi10, Barış Özener11, Ariela Francesca Pagani38, Vilmante Pakalniskiene46, Miriam Parise38, Farid Pazhoohi47, Annette Pisanski42, Katarzyna Pisanski48, Katarzyna Pisanski3, Edna Lúcia Tinoco Ponciano, Camelia Popa49, Pavol Prokop50, Pavol Prokop51, Muhammad Rizwan, Mario Sainz52, Svjetlana Salkičević31, Ruta Sargautyte46, Ivan Sarmány-Schuller53, Susanne Schmehl44, Shivantika Sharad41, Razi Sultan Siddiqui54, Franco Simonetti55, Stanislava Stoyanova56, Meri Tadinac31, Marco Antonio Correa Varella57, Christin-Melanie Vauclair25, Luis Diego Vega, Dwi Ajeng Widarini, Gyesook Yoo58, Marta Zaťková29, Maja Zupančič59 
University of California, Santa Barbara1, University of Texas at Austin2, University of Wrocław3, Dresden University of Technology4, University of Tartu5, Gulu University6, Middle East University7, Stockholm University8, University of the Punjab9, University of Nigeria, Nsukka10, Istanbul University11, Franklin & Marshall College12, Norwegian University of Science and Technology13, University of Algiers14, Australian National University15, Russian State University for the Humanities16, Russian Academy of Sciences17, İzmir University of Economics18, University of Social Sciences and Humanities19, Université catholique de Louvain20, Ankara University21, Pontifical Catholic University of Peru22, Cumhuriyet University23, University of the Republic24, ISCTE – University Institute of Lisbon25, The Chinese University of Hong Kong26, National Autonomous University of Mexico27, University of Pécs28, University of Constantine the Philosopher29, University of Maribor30, University of Zagreb31, University of Malaya32, Central University of Finance and Economics33, University of Crete34, University of Primorska35, Institute of Molecular and Cell Biology36, University of Amsterdam37, Catholic University of the Sacred Heart38, VU University Amsterdam39, University of Granada40, University of Delhi41, University of Havana42, Pontifical Catholic University of Rio de Janeiro43, University of Vienna44, Universiti Utara Malaysia45, Vilnius University46, University of British Columbia47, University of Sussex48, Romanian Academy49, Comenius University in Bratislava50, Slovak Academy of Sciences51, University of Monterrey52, SAS Institute53, DHA Suffa University54, Pontifical Catholic University of Chile55, South-West University "Neofit Rilski"56, University of São Paulo57, Kyung Hee University58, University of Ljubljana59
TL;DR: This work combines this large cross-cultural sample with agent-based models to compare eight hypothesized models of human mating markets and finds that this cross-culturally universal pattern of mate choice is most consistent with a Euclidean model of mate preference integration.
Abstract: Humans express a wide array of ideal mate preferences. Around the world, people desire romantic partners who are intelligent, healthy, kind, physically attractive, wealthy, and more. In order for these ideal preferences to guide the choice of actual romantic partners, human mating psychology must possess a means to integrate information across these many preference dimensions into summaries of the overall mate value of their potential mates. Here we explore the computational design of this mate preference integration process using a large sample of n = 14,487 people from 45 countries around the world. We combine this large cross-cultural sample with agent-based models to compare eight hypothesized models of human mating markets. Across cultures, people higher in mate value appear to experience greater power of choice on the mating market in that they set higher ideal standards, better fulfill their preferences in choice, and pair with higher mate value partners. Furthermore, we find that this cross-culturally universal pattern of mate choice is most consistent with a Euclidean model of mate preference integration.

1,827 citations

Journal ArticleDOI
Mircea Vinatoru1
TL;DR: This paper presents a review of the ultrasonically assisted extraction of bioactive principles from herbs under European community grants under the COPERNICUS programme and in a COST D10 network.

991 citations

Journal ArticleDOI
TL;DR: The Global energy system transition from fossil fuel to hydrogen utilization is described in this paper, and the benefits of the combustion of hydrogen are reported. And the atomic hydrogen/carbon ratio and chemical properties of hydrogen is described.

868 citations


Authors

Showing all 3740 results

NameH-indexPapersCitations
Cristina Popescu7428518434
Adrian Covic7357017379
Gheorghe Paun6539918513
Floriana Tuna6027111968
Arto Salomaa5637417706
Jan A. Bergstra5561613436
Alexandru T. Balaban5360514225
Cristian Sminchisescu5317312268
Maya Simionescu4719210608
Marius Andruh462398431
Werner Scheid465189186
Vicenţiu D. Rădulescu463607771
Cornelia Vasile442977108
Irinel Popescu444018448
Mihail Barboiu442395789
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202335
2022113
2021671
2020690
2019704
2018630