5916 research outputs found
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Residential satisfaction assessment of a 1970s modernist housing community in Istanbul: searching for the reasons behind the preservation of a housing community’s current architectural status against the urban transformation movements affecting the entire city
No full textGoing against the mainstream popularity of high-rise housing developments dominating the Turkish construction market in the late twentieth century, Haluk Baysal and Melih Birsel collaboratively built the Istanbul Yesilkoy Housing Community project in 1973 using the principles of modern architecture. Although the modern lifestyle dominant in the design of this housing project did not emerge from the principles of life inherent to Turkish domestic architecture, this study examines how this project could remain resistant to the destructive force of a possible earthquake in a location where the land value is higher than many other districts of Istanbul. This study's research question is whether or not the survival of this residential housing community could be attributed to residential satisfaction. The study's research method was a residential satisfaction survey and factor analyses. The results showed that the continued existence of the residential community in the midst of the urban transformation movement is because the community satisfied its residents' needs for safety, centrality, and neighbourliness
Patch-wise contrastive style learning for instagram filter removal
No full textImage-level corruptions and perturbations degrade the performance of CNNs on different downstream vision tasks. Social media filters are one of the most common resources of various corruptions and perturbations for real-world visual analysis applications. The negative effects of these dis-tractive factors can be alleviated by recovering the original images with their pure style for the inference of the downstream vision tasks. Assuming these filters substantially inject a piece of additional style information to the social media images, we can formulate the problem of recovering the original versions as a reverse style transfer problem. We introduce Contrastive Instagram Filter Removal Network (CIFR), which enhances this idea for Instagram filter removal by employing a novel multi-layer patch-wise contrastive style learning mechanism. Experiments show our proposed strategy produces better qualitative and quantitative results than the previous studies. Moreover, we present the results of our additional experiments for proposed architecture within different settings. Finally, we present the inference outputs and quantitative comparison of filtered and recovered images on localization and segmentation tasks to encourage the main motivation for this problem
InfraGAN: A GAN architecture to transfer visible images to infrared domain
No full textUtilizing both visible and infrared (IR) images in various deep learning based computer vision tasks has been a recent trend. Consequently, datasets having both visible and IR image pairs are desired in many applications. However, while large image datasets taken at the visible spectrum can be found in many domains, large IR-based datasets are not easily available in many domains. The lack of IR counterparts of the available visible image datasets limits existing deep algorithms to perform on IR images effectively. In this paper, to overcome with that challenge, we introduce a generative adversarial network (GAN) based solution and generate the IR equivalent of a given visible image by training our deep network to learn the relation between visible and IR modalities. In our proposed GAN architecture (InfraGAN), we introduce using structural similarity as an additional loss function. Furthermore, in our discriminator, we do not only consider the entire image being fake or real but also each pixel being fake or real. We evaluate our comparative results on three different datasets and report the state of the art results over five metrics when compared to Pix2Pix and ThermalGAN architectures from the literature. We report up to +16% better performance in Structural Similarity Index Measure (SSIM) over Pix2Pix and +8% better performance over ThermalGAN for VEDAI dataset. Further gains on different metrics and on different datasets are also reported in our experiments section.TÜBİTA
A coordinated repair routing problem for post-disaster recovery of interdependent infrastructure networks
No full textDisasters may cause significant damages and long-lasting failures in lifeline infrastructure networks (such as gas, power and water), which must be recovered quickly to resume providing essential services to the affected communities. While making repair plans, it is important to consider the interdependencies among network components to minimize recovery times. In this paper, we focus on post-disaster repair operations of multiple interdependent lifeline networks, which involve functional dependencies. We assume that each network component, whether damaged or not, becomes nonfunctional if it depends on another nonfunctional component, and it is recovered when all components that it depends on become functional. We introduce a post-disaster coordinated infrastructure repair routing problem, in which dedicated repair teams of each lifeline infrastructure travel through a road network to visit the sites with damaged network components. We present a mixed integer programming model that assigns repair teams to the sites and constructs routes for each team in order to minimize the sum of the recovery times for all network components. We develop a constructive heuristic and a simulated annealing algorithm to solve the proposed coordinated routing problem. We test the performance of the proposed solution algorithms on a set of instances that are developed based on two interdependent lifeline networks (e.g., power and gas). The computational results show that our heuristics can quickly find high-quality solutions. Our results also indicate that coordinating repair operations can significantly improve the overall recovery time of interdependent infrastructure networks
Türkiye Bina Deprem Yönetmeliği 2018'e göre tasarlanan merkezi X çaprazlı çelik çerçevelerin sismik performans değerlendirmesi
No full textConcentrically braced steel frame systems are frequently used lateral load resisting frame systems, especially in highly seismic regions. Those systems are preferred due to their significant contribution of lateral stiffness and strength of the structure. Other advantages of CBFs can be expressed as their elastic stiffness that provides to satisfy the serviceability deformation limits compared to other lateral load resisting frames, as well as their contribution to the ductility of the structure by allowing inelastic deformations within the system. However, they must be designed and detailed carefully to maintain the general stability of the system and to avoid a brittle and sudden local failure. These details and related design approaches have been specified by various design regulations and guidelines, including the Turkish Building Earthquake Code 2018 (TBEC 2018) which came to force in January 2019. With this code, not only a number of new design approaches that did not take place in the previous version were introduced, but also various definitions and approaches were revised. These changes can be expected to influence the dynamic behavior and seismic performance of CBFs designed per TBEC 2018. From this perspective, in this research it is aimed to investigate the lateral behavior and seismic performance of CBFs. A set of CBFs were designed to TBEC 2018 and a nonlinear numerical model was developed to simulate the designed CBFs. Afterwards, several nonlinear static and dynamic analysis procedures were followed to obtain and evaluate the dynamic behavior and seismic performance of CBF systems. Results and findings are reported at the end of each study and summarized in the conclusion.Merkezi çaprazlı çelik çerçeve tipi yanal yük taşıyan sistemler özellikle sismik hareketliliği ve riski yoğun olan bölgelerde sıkça karşılaşılan çerçevelerdir. Bu sistemler, sağladığı yüksek seviye rijitlik ve dayanım nedeniyle tercih edilirler. Aynı zamanda elastik rijitlikleri sayesinde özellikle servis yükleri altında deformasyon limitlerini diğer yanal yük taşıyıcı sistemlere kıyasla daha kolay sağlamaları, aynı zamanda da plastik yerdeğiştirmelere izin verip yapının sünek davranmasını sağlamaları bu sistemlerin diğer avantajlarındandır. Fakat yapının genel stabilitesini korumak ve yerel burkulmalardan doğabilecek gevrek ve ani burkulma veya yırtılmaları engellemek amacıyla bu sistemlerin sismik tasarım ve detaylandırması dikkatli bir şekilde yapılmalıdır. Bu detaylar ve ilgili tasarım yaklaşımları, Türkiye Bina Deprem Yönetmeliği 2018'in de dahil olduğu (TBDY 2018) çeşitli tasarım yönetmelikleri ve yönergelerince belirlenmiştir. TBDY 2018, Ocak 2019'da yayınlanmış ve bu yönetmelikle birlikte önceki yönetmelikte yer almayan bir dizi yeni tasarım yaklaşımları getirilmiş, aynı zamanda bazı kısımlar da revize edilmiştir. Bu revizyonların, TBDY 2018'e göre tasarlanan merkezi çaprazlı çelik çerçevelerin dinamik davranışlarını ve sismik performanslarını etkilemesi beklenebilir. Buradan yola çıkarak, bu araştırmada/tezde merkezi çaprazlı çelik çerçevelerin yanal davranışları ve deprem performanslarının incelenmesi hedeflenmiştir. Çeşitli bir dizi merkezi çaprazlı çelik çerçeve tasarımları TBDY 2018 göz önünde bulundurularak gerçekleştirilmiş ve bu tasarımlar doğrusal olmayan bir numerik model ile modellenmiştir. Sonrasında çeşitli statik ve dinamik analiz prosedürleri takip edilerek bu modellerin dinamik davranışları ve deprem performansları ölçülüp değerlendirilmiştir
Derin sıkılaştırma yönteminin PyTorch modelleri üzerinde, otonom sistemler için kullanılması
No full textApplications of artificial neural networks on low-cost embedded systems and microcontrollers (MCUs), has recently been attracting more attention than ever. Since MCUs have limited memory capacity as well as limited compute-speed compared to workstations, employment of current deep learning algorithms on MCUs becomes more practical with the help of model compression. This makes MCUs common and practical alternative solution for autonomous systems. In this paper, we add model compression, specifically Deep Compression, to an existing work, which efficiently deploys PyTorch models on MCUs, in order to increase neural network speed and save electrical power. First, we prune the weight values close to zero in convolutional and fully connected layers. Secondly, the remaining weights and activations are quantized to 8-bit integers from 32-bit floating-point. Finally, forward pass functions are compressed using special data structures for sparse matrices, which store only nonzero weights. In the case of the LeNet-5 model, the memory footprint was reduced by 12.5x, and the inference speed was boosted by 2.6x
Vestel'in vardiya seçimi, parti büyüklüğü belirleme ve işçi ataması problemi için matemetiksel modelleme ve matematiksel buluşsal yaklaşım
No full textThe simultaneous lot sizing and scheduling studies do not implement a worker assignment problem in the literature. This study provides a solution for worker assignment problems with overtime constraints of the Government. The study also provides fairness for overtime decision in a model which try to manage the unfairness between workers. The shift and overtime types are decided initially. Then, the production lot sizes are de ned and the demanded models are scheduled according to the due dates of the orders. This study examines the simultaneous lot sizing and scheduling problem of the serial production which has specific constraints that affect the lot size of the production set and schedule of the orders. The study presents two different models which are called combined Model and two-phase model. The comparison parameters between combined model and two-phase model are in terms of solution time, real cost difference, and achieving the purpose. The heuristic methods are combined with mixed-integer programming to solve the worker assignment problem. A fairness restriction has been proposed in the last section of the study. The models try to solve the problems in a reasonable time. The developed models are solved by Gurobi Optimizer and Python. The results show that the two-phase model provides very close output with a 0.27% difference in comparison to the combined model in a reasonable time. The results show also that the two-phase model can solve the problem faster time than the combined model in most of the real instances.Literatürdeki eş zamanlı parti büyüklüğü belirleme ve üretim zaman çizelgesi oluşturma çalışmaları işçi seçimini ihtiva etmemektedir. Bu çalışmada işçi ataması problemi için fazla mesai ile ilgili devlet sınırlamalarını göz önünde bulunduran ve aynı zamanda fazla mesai kararını tüm işçilere adil bir şekilde dağıtılmasını sağlayan bir model sunulmuştur. _Ilk olarak vardiya tipi ve fazla mesai kararları verilir. Üretim parti miktarları belirlenir ve talep edilen modeller son teslim tarihlerine göre sıraya alınır. Fabrikalar, üretim parti miktarlarını sınırlayan ve üretim çizelgesini etkileyen bazı özel kısıtlara sahiptir. Bu çalışma tüm bu kısıtları göz önünde bulundurarak biri bileşik diğeri ayrık olmak üzere iki model sunmakta ve kabul edilebilir bir çözüm sunmayı amaçlamaktadır. Çıkan sonuçlar çözüme ulaşma süresi, minimize edilen reel maliyet değeri ve doğru hedefe ulaşma açısından karşılaştırılmıştır. Sezgisel metotlar karışık tamsayı programlama metoduyla birleştirilerek işçi seçimi problem için alternatif bir çözüm sunan, fazla mesai kararlarında işçiler arası adaleti göz önünde bulunduran bir model sunulmuş ve kabul edilebilir zaman aralğında çözüm bulmaya çalışılmıştır. Geliştirilen model Gurobi Optimizer ve Python ile çözülmüştür. Sonuç olarak yan sanayi kısıtlarını ve sıfır stok presibinin getirdiği kısıtları göz önünde bulundurarak geliştirilen matematiksel modeller arasında ayrık olan model bileşik model ile kıyaslandığında yüzde 0.27 farkla benzer bir çözümü makul bir zaman içinde sunmuştur. Ayrıca iki fazlı ardışık modelin bir çok gerçek örnek değerlendirildiğinde birleşik modelden daha kısa sürede sonuç aldığı görülmüştür
Search for resonant production of strongly coupled dark matter in proton-proton collisions at 13 TeV
No full textThe first collider search for dark matter arising from a strongly coupled hidden sector is presented and uses a data sample corresponding to 138 fb−1, collected with the CMS detector at the CERN LHC, at s = 13 TeV. The hidden sector is hypothesized to couple to the standard model (SM) via a heavy leptophobic Z′ mediator produced as a resonance in proton-proton collisions. The mediator decay results in two “semivisible” jets, containing both visible matter and invisible dark matter. The final state therefore includes moderate missing energy aligned with one of the jets, a signature ignored by most dark matter searches. No structure in the dijet transverse mass spectra compatible with the signal is observed. Assuming the Z′ boson has a universal coupling of 0.25 to the SM quarks, an inclusive search, relevant to any model that exhibits this kinematic behavior, excludes mediator masses of 1.5–4.0 TeV at 95% confidence level, depending on the other signal model parameters. To enhance the sensitivity of the search for this particular class of hidden sector models, a boosted decision tree (BDT) is trained using jet substructure variables to distinguish between semivisible jets and SM jets from background processes. When the BDT is employed to identify each jet in the dijet system as semivisible, the mediator mass exclusion increases to 5.1 TeV, for wider ranges of the other signal model parameters. These limits exclude a wide range of strongly coupled hidden sector models for the first time.BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES and BNSF (Bulgaria); CERN; CAS, MoST, and NSFC (China); MINCIENCIAS (Colombia); MSES and CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); MoER, ERC PUT and ERDF (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRI (Greece); NKFIA (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); MES (Latvia); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom, RAS, RFBR, and NRC KI (Russia); MESTD (Serbia); MCIN/AEI and PCTI (Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); ThEPCenter, IPST, STAR, and NSTDA (Thailand); TUBITAK and TAEK (Turkey); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (U.S.A.).
Individuals have received support from the Marie-Curie program and the European Research Council and Horizon 2020 Grant, contract Nos. 675440, 724704, 752730, 758316, 765710, 824093, 884104, and COST Action CA16108 (European Union); the Leventis Foundation; the Alfred P. Sloan Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science Policy Office; the Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium) under the "Excellence of Science -EOS" -be.h project n. 30820817; the Beijing Municipal Science & Technology Commission, No. Z191100007219010; the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Deutsche Forschungsgemeinschaft (DFG), under Germany's Excellence Strategy -EXC 2121 "Quantum Universe" -390833306, and under project number 400140256 -GRK2497; the Lendulet ("Momentum") Program and the Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences, the New National Excellence Program UNKP, the NKFIA research grants 123842, 123959, 124845, 124850, 125105, 128713, 128786, and 129058 (Hungary); the Council of Science and Industrial Research, India; the Latvian Council of Science; the Ministry of Science and Higher Education and the National Science Center, contracts Opus 2014/15/B/ST2/03998 and 2015/19/B/ST2/02861 (Poland); the Fundacao para a Ciencia e a Tecnologia, grant CEECIND/01334/2018 (Portugal); the National Priorities Research Program by Qatar National Research Fund; the Ministry of Science and Higher Education, projects no. 14.W03.31.0026 and no. FSWW-2020-0008, and the Russian Foundation for Basic Research, project No.19-42-703014 (Russia); MCIN/AEI/10.13039/501100011033, ERDF "a way of making Europe", and the Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu, grant MDM-2017-0765 and Programa Severo Ochoa del Principado de Asturias (Spain); the Stavros Niarchos Foundation (Greece); the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University and the Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); the Kavli Foundation; the Nvidia Corporation; the SuperMicro Corporation; the Welch Foundation, contract C-1845; and the Weston Havens Foundation (U.S.A.).Publisher versio
Search for electroweak production of charginos and neutralinos in proton-proton collisions at s√ = 13 TeV
No full textA direct search for electroweak production of charginos and neutralinos is presented. Events with three or four leptons, with up to two hadronically decaying τ leptons, or two same-sign light leptons are analyzed. The data sample consists of 137 fb−1 of proton-proton collisions with a center of mass energy of 13 TeV, recorded with the CMS detector at the LHC. The results are interpreted in terms of several simplified models. These represent a broad range of production and decay scenarios for charginos and neutralinos. A parametric neural network is used to target several of the models with large backgrounds. In addition, results using orthogonal search regions are provided for all the models, simplifying alternative theoretical interpretations of the results. Depending on the model hypotheses, charginos and neutralinos with masses up to values between 300 and 1450 GeV are excluded at 95% confidence level.BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST, and NSFC (China); MINCIENCIAS (Colombia); MSES and CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); MoER, ERC PUT and ERDF (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRT (Greece); NKFIA (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); MES (Latvia); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom, RAS, RFBR, and NRC KI (Russia); MESTD (Serbia); SEIDI, CPAN, PCTI, and FEDER (Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); ThEPCenter, IPST, STAR, and NSTDA (Thailand); TUBITAK and TAEK (Turkey); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (U.S.A.).
Individuals have received support from the Marie-Curie program and the European Research Council and Horizon 2020 Grant, contract Nos. 675440, 724704, 752730, 765710 and 824093 (European Union); the Leventis Foundation; the Alfred P. Sloan Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science Policy Office; the Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium) under the "Excellence of Science -EOS" -be.h project n. 30820817; the Beijing Municipal Science & Technology Commission, No. Z191100007219010; the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Deutsche Forschungsgemeinschaft (DFG), under Germany's Excellence Strategy -EXC 2121 "Quantum Universe" -390833306, and under project number 400140256 GRK2497; the Lendulet ("Momentum") Program and the Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences, the New National Excellence Program UNKP, the NKFIA research grants 123842, 123959, 124845, 124850, 125105, 128713, 128786, and 129058 (Hungary); the Council of Science and Industrial Research, India; the Latvian Council of Science; the Ministry of Science and Higher Education and the National Science Center, contracts Opus 2014/15/B/ST2/03998 and 2015/19/B/ST2/02861 (Poland); the National Priorities Research Program by Qatar National Research Fund; the Ministry of Science and Higher Education, project no. 0723-2020-0041 (Russia); the Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu, grant MDM-2015-0509 and the Programa Severo Ochoa del Principado de Asturias; the Thalis and Aristeia programs cofinanced by EU-ESF and the Greek NSRF; the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University and the Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); the Kavli Foundation; the Nvidia Corporation; the SuperMicro Corporation; the Welch Foundation, contract C-1845; and the Weston Havens Foundation (U.S.A.).Publisher versio
Strategies and performance of the CMS silicon tracker alignment during LHC Run 2
No full textThe strategies for and the performance of the CMS silicon tracking system alignment during the 2015–2018 data-taking period of the LHC are described. The alignment procedures during and after data taking are explained. Alignment scenarios are also derived for use in the simulation of the detector response. Systematic effects, related to intrinsic symmetries of the alignment task or to external constraints, are discussed and illustrated for different scenarios.The Austrian Federal Ministry of Education, Science and Research and the Austrian Science Fund; the Belgian Fonds de la Recherche Scientifique, and Fonds voor Wetenschappelijk Onderzoek; the Brazilian Funding Agencies (CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP); the Bulgarian Ministry of Education and Science, and the Bulgarian National Science Fund; CERN; the Chinese Academy of Sciences, Ministry of Science and Technology, and National Natural Science Foundation of China; the Ministerio de Ciencia Tecnologia e Innovacion (MINCIENCIAS), Colombia; the Croatian Ministry of Science, Education and Sport, and the Croatian Science Foundation; the Research and Innovation Foundation, Cyprus; the Secretariat for Higher Education, Science, Technology and Innovation, Ecuador; the Ministry of Education and Research, Estonian Research Council via PRG780, PRG803 and PRG445 and European Regional Development Fund, Estonia; the Academy of Finland, Finnish Ministry of Education and Culture, and Helsinki Institute of Physics; the Institut National de Physique Nucleaire et de Physique des Particules/CNRS, and Commissariat a l'Energie Atomique et aux Energies Alternatives/CEA, France; the Bundesministerium fur Bildung und Forschung, the Deutsche Forschungsgemeinschaft (DFG), under Germany's Excellence Strategy -EXC 2121 "Quantum Universe"-390833306, and under project number 400140256 -GRK2497, and Helmholtz-Gemeinschaft Deutscher Forschungszentren, Germany; the General Secretariat for Research and Innovation, Greece; the National Research, Development and Innovation Fund, Hungary; the Department of Atomic Energy and the Department of Science and Technology, India; the Institute for Studies in Theoretical Physics and Mathematics, Iran; the Science Foundation, Ireland; the Istituto Nazionale di Fisica Nucleare, Italy; the Istituto Nazionale di Fisica Nucleare, Italy, and National Research Foundation (NRF), Republic of Korea; the Ministry of Education and Science of the Republic of Latvia; the Lithuanian Academy of Sciences; the Ministry of Education, and University of Malaya (Malaysia); the Ministry of Science of Montenegro; the Mexican Funding Agencies (BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI); the Ministry of Business, Innovation and Employment, New Zealand; the Pakistan Atomic Energy Commission; the Ministry of Science and Higher Education and the National Science Centre, Poland; the Fundacao para a Ciencia e a Tecnologia, grants CERN/FIS-PAR/0025/2019 and CERN/FIS-INS/0032/2019, Portugal; JINR, Dubna; the Ministry of Education and Science of the Russian Federation, the Federal Agency of Atomic Energy of the Russian Federation, Russian Academy of Sciences, the Russian Foundation for Basic Research, and the National Research Center "Kurchatov Institute"; the Ministry of Education, Science and Technological Development of Serbia; the Secretaria de Estado de Investigacion, Desarrollo e Innovacion, Programa Consolider-Ingenio 2010, Plan Estatal de Investigacion Cientifica.. Tecnica..
de Innovacion 2017-2020, research project IDI-2018-000174 del Principado de Asturias, and Fondo Europeo de Desarrollo Regional, Spain; the Ministry of Science, Technology and Research, Sri Lanka; the Swiss Funding Agencies (ETH Board, ETH Zurich, PSI, SNF, UniZH, Canton Zurich, and SER); the Ministry of Science and Technology, Taipei; the Thailand Center of Excellence in Physics, the Institute for the Promotion of Teaching Science and Technology of Thailand, Special Task Force for Activating Research and the National Science and Technology Development Agency of Thailand; the Scientific and Technical Research Council of Turkey, and Turkish Atomic Energy Authority; the National Academy of Sciences of Ukraine; the Science and Technology Facilities Council, UK; the US Department of Energy, and the US National Science Foundation. Individuals have received support from the Marie-Curie programme and the European Research Council and Horizon 2020 Grant, contract Nos. 675440, 724704, 752730, 758316, 765710, 824093, 884104, and COST Action CA16108 (European Union) the Leventis Foundation Leventis Foundation; the Alfred P. Sloan Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science Policy Office; the Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWTBelgium); the F.R.S.-FNRS and FWO (Belgium) under the "Excellence of Science -EOS'' -be.h project n. 30820817; the Beijing Municipal Science & Technology Commission, No. Z191100007219010; the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Lendulet ("Momentum'') Programme and the Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences, the New National Excellence Program UNKP, the NKFIA research grants 123842, 123959, 124845, 124850, 125105, 128713, 128786, and 129058 (Hungary); the Council of Scientific and Industrial Research, India; the Latvian Council of Science; the National Science Center (Poland), contracts Opus 2014/15/B/ST2/03998 and 2015/19/B/ST2/02861; the Fundacao para a Ciencia e a Tecnologia, grant FCT CEECIND/01334/2018; the National Priorities Research Program by Qatar National Research Fund; the Ministry of Science and Higher Education, projects no. 14.W03.31.0026 and no. FSWW-2020-0008, and the Russian Foundation for Basic Research, project No. 19-42-703014 (Russia); the Programa de Excelencia Maria de Maeztu, and the Programa Severo Ochoa del Principado de Asturias; the Stavros Niarchos Foundation Greece); the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University, and the Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); the Kavli Foundation; the Nvidia Corporation; the SuperMicro Corporation; the Welch Foundation, contract C-1845; and the Weston Havens Foundation (USA).Publisher versio