TOBB ETU GCRIS Database
Not a member yet
9778 research outputs found
Sort by
Search for tt¯H/A→tt¯tt¯ Production in Proton–proton Collisions at s=13 TeV With the ATLAS Detector
No full textA search is presented for a heavy scalar (H) or pseudo-scalar (A) predicted by the two-Higgs-doublet models, where the H/A is produced in association with a top-quark pair (tt¯H/A), and with the H/A decaying into a tt¯ pair. The full LHC Run 2 proton–proton collision data collected by the ATLAS experiment is used, corresponding to an integrated luminosity of 139fb-1. Events are selected requiring exactly one or two opposite-charge electrons or muons. Data-driven corrections are applied to improve the modelling of the tt¯+jets background in the regime with high jet and b-jet multiplicities. These include a novel multi-dimensional kinematic reweighting based on a neural network trained using data and simulations. An H/A-mass parameterised graph neural network is trained to optimise the signal-to-background discrimination. In combination with the previous search performed by the ATLAS Collaboration in the multilepton final state, the observed upper limits on the tt¯H/A→tt¯tt¯ production cross-section at 95% confidence level range between 14 fb and 5.0 fb for an H/A with mass between 400 GeV and 1000 GeV, respectively. Assuming that both the H and A contribute to the tt¯tt¯ cross-section, tanβ values below 1.7 or 0.7 are excluded for a mass of 400 GeV or 1000 GeV, respectively. The results are also used to constrain a model predicting the pair production of a colour-octet scalar, with the scalar decaying into a tt¯ pair. © The Author(s) 2025.Ministerio de Ciencia, Innovación y Universidades, MCIU; BNL; Australian Research Council, ARC; La Caixa Banking Foundation; Centre National pour la Recherche Scientifique et Technique, CNRST; Center for African Studies, CAS; Fundação para a Ciência e a Tecnologia, FCT; European Union, Future Artificial Intelligence Research; Georgia Health Initiative, HGF; Center for Advancing Research Impact in Society, ARIS; National Science Foundation, NSF; Science and Technology Facilities Council, STFC; H2020 Marie Skłodowska-Curie Actions, MSCM; Nederlandse Organisatie voor Wetenschappelijk Onderzoek, NWO; Leverhulme Trust; Baden-Württemberg Stiftung, BWS; MVZI; PROMETEO; Spine Education and Research Institute, SERI; Neubauer Family Foundation, NFF; IDUB AGH; Generalitat de Catalunya; Bundesministerium für Wissenschaft, Forschung und Wirtschaft, BMWFW; Austrian Science Fund, FFWF; Agencia Nacional de Investigación y Desarrollo, ANID; Bundesministerium für Bildung und Forschung, BMBF; Canada Foundation for Innovation, FCI; Danmarks Grundforskningsfond, DNRF; Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq; Forskningsrådet för hälsa, arbetsliv och välfärd, FORTE; Karlsruhe Institute of Technology, KIT; Canarie; GridKA; Horizon 2020 Framework Programme; Göran Gustafssons Stiftelser; CNY Arts, CRC; United States-Israel Binational Science Foundation, BSF; European Commission, EU; European Social Fund Plus, ESF; Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja, MEST; European Cooperation in Science and Technology, COST; International Council of Shopping Centers, ICSC; RGC; Duchenne Research Fund, DRF; Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP; Agencia Estatal de Investigación, AEI; Islamic Scholarship Fund, ISF; Institutul de Fizică Atomică, IFA; Natural Sciences and Engineering Research Council of Canada, CRSNG; Nella and Leon Benoziyo Center for Neurological Diseases, Weizmann Institute of Science; National Science and Technology Council, NSTC; Irish Rugby Football Union, IRFU; Cantons of Bern and Geneva; Defence Science Institute, DSI; MNE; Agencia Nacional de Promoción Científica y Tecnológica, ANPCyT; Royal Society; Minerva Foundation; Marcus och Amalia Wallenbergs minnesfond, MMW; CERN-CZ; National Research Foundation, NRF; Ministerstwo Edukacji i Nauki, MNiSW; Generalitat Valenciana, GVA; CERN, CERN; National Research Council Canada, CNRC; Alexander von Humboldt-Stiftung, AvH; Multiple Sclerosis Scientific Research Foundation, MSSRF; Horizon 2020; Istituto Nazionale di Fisica Nucleare, INFN; British Columbia Knowledge Development Fund, BCKDF; Ministry of Education, Culture, Sports, Science and Technology, MEXT; UK Research and Innovation, UKRI; Ministry of Science and Technology of the People's Republic of China, MOST, (MOST-2023YFA1609300, MOST-2023YFA1605700); Ministry of Science and Technology of the People's Republic of China, MOST; Investissements d’Avenir Labex, (ANR-11-LABX-0012); The Slovenian Research and Innovation Agency, ARRS, (J1-3010); The Slovenian Research and Innovation Agency, ARRS; European Regional Development Fund, ERDF, (IDIFEDER /2018/048); European Regional Development Fund, ERDF; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNSF, (RPG-2020-004, NIF-R1-231091, PCEFP2_194658); Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNSF; NextGenerationEU, NGEU, (PE00000013); NextGenerationEU, NGEU; North Dakota Game and Fish Department, NDGF, (CC-IN2P3); North Dakota Game and Fish Department, NDGF; Norges Forskningsråd, (RCN-314472); Norges Forskningsråd; MICIN, (RYC2019-028510-I, RYC2022-038164-I, PID2021-125273NB, RYC2021-031273-I, RYC2020-030254-I); Deutsche Forschungsgemeinschaft, DFG, (DFG - CR 312/5-2, DFG - 469666862); Deutsche Forschungsgemeinschaft, DFG; Ministero dell'Università e della Ricerca, MUR, (PRIN - 20223N7F8K - PNRR M4.C2.1.1); Ministero dell'Università e della Ricerca, MUR; H2020 European Research Council, CEI, (ERC - 101002463); H2020 European Research Council, CEI; DNSRC, (IN2P3-CNRS); Narodowa Agencja Wymiany Akademickiej, NAWA, (PPN/PPO/2020/1/00002/U/00001); Narodowa Agencja Wymiany Akademickiej, NAWA; Ministerio de Ciencia e Innovación, MICINN, (PCI2022-135018-2); Ministerio de Ciencia e Innovación, MICINN; U.S. Department of Energy, DOE, (ECA DE-AC02-76SF00515); U.S. Department of Energy, DOE; Ministerstvo Školství, Mládeže a Tělovýchovy, MEYS, (CZ.02.01.01/00/22_008/0004632, PRIMUS/21/SCI/017); Ministerstvo Školství, Mládeže a Tělovýchovy, MEYS; European Research Council, ERC, (101089007, 948254); European Research Council, ERC; National Natural Science Foundation of China, NNSF, (12275265, 12175119, NSFC-12075060); National Natural Science Foundation of China, NNSF; Norwegian Financial Mechanism, (2014-2021); Narodowe Centrum Nauki, NCN, (2021/42/E/ST2/00350, UMO-2021/40/C/ST2/00187, UMO-2023/ 49/B/ST2/04085, UMO-2020/37/B/ST2/01043, UMO-2019/34/E/ST2/00393, UMO-2022/47/O/ST2/00148, H2020 MSCA 945339, 2022/47/B/ST2/03059); Narodowe Centrum Nauki, NCN; GenT Programmes Generalitat Valenciana, (CIDEGENT/2019/027, CIDEGENT/2019/023); Grantová Agentura České Republiky, GACR, (GACR - 24-11373S); Grantová Agentura České Republiky, GACR; Agence Nationale de la Recherche, ANR, (ANR-20-CE31-0013, ANR-21-CE31-0013, ANR-22-EDIR-0002, ANR-21-CE31-0022); Agence Nationale de la Recherche, ANR; Carl Tryggers Stiftelse för Vetenskaplig Forskning, (CTS 22:2312); Carl Tryggers Stiftelse för Vetenskaplig Forskning; Vetenskapsrådet, VR, (VR 2022-04683, 2023-04654, VR 2022-03845, 2021-03651, VR 2023-03403, VR 2018-00482); Vetenskapsrådet, VR; MUCCA, (CHIST-ERA-19-XAI-00); Japan Society for the Promotion of Science, KAKENHI, (JP21H05085, JP22KK0227, JP22H04944, JP22H01227); Japan Society for the Promotion of Science, KAKENHI; DRAC, (21/SCI/017); Fondo Nacional de Desarrollo Científico y Tecnológico, FONDECYT, (1230987, 1210400, 1190886, 1230812); Fondo Nacional de Desarrollo Científico y Tecnológico, FONDECYT; Knut och Alice Wallenbergs Stiftelse, (KAW 2018.0157, KAW 2018.0458, KAW 2019.0447, KAW 2022.0358); Knut och Alice Wallenbergs Stiftels
Multiclass certainty mapped network for high-precision segmentation of high-altitude imagery
No full textProceedings Volume 13263, Land Surface and Cryosphere Remote Sensing VSatellites and high-altitude unmanned aerial vehicles are the top platforms for electro-optical remote sensing for both civilian and military applications. Since early 2000s high altitude electro-optical remote sensing platforms have been actively used for real-time and offline damage assessment following natural disasters such as earthquakes, floods, and landslides. High accuracy, multi-class automated object segmentation is one of the key processing blocks that makes such applications practical. Given the typical distances between target areas and high-altitude sensing platforms (10s to 1000s of kms) as well as the critical nature of the resulting assessments, the accuracy of segmentation maps is of key interest. In this work we present the Multi-Class Certainty Mapped Network (MCCM-Net) that uses multi-class per-pixel uncertainty to enhance segmentation performance. MCCM-Net explicitly models multi-class uncertainty as the entropy of class probability distribution. Pixel-level uncertainty is then used to iteratively enhance segmentation maps. Our experiments on publicly available benchmark datasets show that MCCM-Net provides state-of-the-art multi-class pixel-level segmentation performance
Search for Higgs Boson Decays Into a Pair of Pseudoscalar Particles in the Γγτhadτhad Final State Using pp Collisions at √s=13 Tev With the ATLAS Detector
No full textGaudio, Gabriella/0000-0002-6833-0933; Manhaes De Andrade Filho, Luciano/0000-0003-1792-6793; Pettee, Mariel/0000-0001-9208-3218; Bona, Marcella/0000-0002-9660-580X; Mcpherson, Robert/0000-0001-9211-7019; Terzo, Stefano/0000-0003-3388-3906; Moser, Brian/0000-0001-6750-5060; Schmitt, Stefan/0000-0001-8387-1853; Cranmer, Kyle/0000-0002-5769-7094; Stark, Giordon/0000-0001-6616-3433; Keeler, Richard/0000-0002-0510-4189; Islam, Wasikul/0000-0002-5624-5934; Smirnova, Oxana/0000-0003-2517-531X; Beau, Tristan/0000-0002-2022-2140; De La Torre Perez, Hector/0000-0002-4516-5269; Morii, Masahiro/0000-0001-9324-057X; Bella, Gideon/0000-0002-4009-0990; Sciandra, Andrea/0000-0001-7163-501X; Konstantinidis, Nikolaos/0000-0002-4140-6360; Bouhova-Thacker, Evelina/0000-0002-5103-1558; Jia, Jiangyong/0000-0002-5725-3397; Su, Dong/0000-0001-6980-0215; Tishelman-Charny, Abraham/0000-0002-7332-5098; Lloyd, Stephen/0000-0002-5073-2264; Fox, Harald/0000-0003-3089-6090; Vincter, Manuella/0000-0002-5338-8972; Kirk, Julie/0000-0001-8096-7577; Meloni, Federico/0000-0001-7075-2214; Simsek, Sinem/0000-0002-9650-3846; Kowalewski, Robert/0000-0002-7314-0990; Onofre, Antonio/0000-0003-3471-2703; Schultz-Coulon, Hans-Christian/0000-0002-0860-7240; Butterworth, Jonathan/0000-0002-5905-5394; Cheong, Sanha/0000-0002-2797-6383; Azuelos, Georges/0000-0003-4241-022X; Mckee, Shawn/0000-0002-4551-4502; Grinstein, Sebastian/0000-0002-6460-8694; Winter, Benedict Tobias/0000-0001-9606-7688; Kretzschmar, Jan/0000-0002-8515-1355; Carmignani, Joseph (Joe)/0000-0002-1705-1061A search for exotic decays of the 125 GeV Higgs boson into a pair of new spin-0 particles, H -> aa, where one decays into a photon pair and the other into a tau-lepton pair, is presented. Hadronic decays of the tau-leptons are considered and reconstructed using a dedicated tagger for collimated tau-lepton pairs. The search uses 140 fb(-1) of proton-proton collision data at a centre-of-mass energy of root s = 13TeV recorded between 2015 and 2018 by the ATLAS experiment at the Large Hadron Collider. The search is performed in the mass range of the a boson between 10 GeV and 60 GeV. No significant excess of events is observed above the Standard Model background expectation. Model-independent upper limits at 95% confidence level are set on the branching ratio of the Higgs boson to the gamma gamma tau tau final state, B(H -> aa -> gamma gamma tau tau), ranging from 0.2% to 2%, depending on the a-boson mass hypothesis.DOE, United States of America The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF/SFU (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), RAL (U.K.) and BNL (U.S.A.), the Tier-2 facilities worldwide and large non-WLCG resource providers. Major contributors of computing resources are listed in ref. [78]. We gratefully acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; ANID, Chile; CAS, MOST and NSFC, China; Minciencias, Colombia; MEYS CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS and CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF and MPG, Germany; GSRI, Greece; RGC and Hong Kong SAR, China; ICHEP and Academy of Sciences and Humanities, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW, Poland; FCT, Portugal; MNE/IFA, Romania; MSTDI, Serbia; MSSR, Slovakia; ARIS and MVZI, Slovenia; DSI/NRF, South Africa; MICIU/AEI, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; NSTC, Taipei; TENMAK, Turkiye; STFC/UKRI, United Kingdom; DOE and NSF, United States of America. Individual groups and members have received support from BCKDF, CANARIE, CRC and DRAC, Canada; CERN-CZ, FORTE and PRIMUS, Czech Republic; COST, ERC, ERDF, Horizon 2020, ICSC-NextGenerationEU and Marie Sklodowska-Curie Actions, European Union; Investissements d'Avenir Labex, Investissements d'Avenir Idex and ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and MINERVA, Israel; NCN and NAWA, Poland; La Caixa Banking Foundation, CERCA Programme Generalitat de Catalunya and PROMETEO and GenT Programmes Generalitat Valenciana, Spain; Goran Gustafssons Stiftelse, Sweden; The Royal Society and Leverhulme Trust, United Kingdom. In addition, individual members wish to acknowledge support from Armenia: Yerevan Physics Institute (FAPERJ); CERN: European Organization for Nuclear Research (CERN DOCT); Chile: Agencia Nacional de Investigacion y Desarrollo (FONDECYT 1230812, FONDECYT 1230987, FONDECYT 1240864); China: Chinese Ministry of Science and Technology (MOST-2023YFA1605700, MOST-2023YFA1609300), National Natural Science Foundation of China (NSFC -12175119, NSFC 12275265, NSFC-12075060); Czech Republic: Czech Science Foundation (GACR -24-11373S), Ministry of Education Youth and Sports (FORTE CZ.02.01.01/00/22_008/0004632), PRIMUS Research Programme (PRIMUS/21/SCI/017); EU: H2020 European Research Council (ERC -101002463); European Union: European Research Council (ERC -948254, ERC 101089007, ERC, BARD, 101116429), European Union, Future Artificial Intelligence Research (FAIR-NextGenerationEU PE00000013), Italian Center for High Performance Computing, Big Data and Quantum Computing (ICSC, NextGenerationEU); France: Agence Nationale de la Recherche (ANR-20-CE31-0013, ANR-21-CE31-0013, ANR-21-CE31-0022, ANR-22-EDIR-0002); Germany: Baden-Wurttemberg Stiftung (BW Stiftung-Postdoc Eliteprogramme), Deutsche Forschungsgemeinschaft (DFG -469666862, DFG -CR 312/5-2); Italy: Istituto Nazionale di Fisica Nucleare (ICSC, NextGenerationEU), Ministero dell'Universita e della Ricerca (PRIN -20223N7F8K -PNRR M4.C2.1.1); Japan: Japan Society for the Promotion of Science (JSPS KAKENHI JP22H01227, JSPS KAKENHI JP22H04944, JSPS KAKENHI JP22KK0227, JSPS KAKENHI JP23KK0245); Norway: Research Council of Norway (RCN-314472); Poland: Ministry of Science and Higher Education (IDUB AGH, POB8, D4 no. 9722), Polish National Agency for Academic Exchange (PPN/PPO/2020/1/00002/U/00001), Polish National Science Centre (NCN 2021/42/E/ST2/00350, NCN OPUS 2023/51/B/ST2/02507, NCN OPUS nr 2022/47/B/ST2/03059, NCN UMO-2019/34/E/ST2/00393, NCN ; H2020 MSCA 945339, UMO-2020/37/B/ST2/01043, UMO-2021/40/C/ST2/00187, UMO-2022/47/O/ST2/00148, UMO-2023/49/B/ST2/04085, UMO-2023/51/B/ST2/00920); Spain: Generalitat Valenciana (Artemisa, FEDER, IDIFEDER/2018/048), Ministry of Science and Innovation (MCIN ; NextGenEU PCI2022-135018-2, MICIN ; FEDER PID2021-125273NB, RYC2019-028510-I, RYC2020-030254-I, RYC2021-031273-I, RYC2022-038164-I); Sweden: Carl Trygger Foundation (Carl Trygger Foundation CTS 22:2312), Swedish Research Council (Swedish Research Council 2023-04654, VR 2018-00482, VR 2022-03845, VR 2022-04683, VR 2023-03403, VR grant 2021-03651), Knut and Alice Wallenberg Foundation (KAW 2018.0458, KAW 2019.0447, KAW 2022.0358); Switzerland: Swiss National Science Foundation (SNSF -PCEFP2_194658); United Kingdom: Leverhulme Trust (Leverhulme Trust RPG-2020-004), Royal Society (NIF-R1-231091); United States of America: U.S. Department of Energy (ECA DE-AC02-76SF00515), Neubauer Family Foundation.ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW, Austria; FWF, Austria; ANAS, Azerbaijan; CNPq, Brazil; FAPESP, Brazil; NSERC, Canada; CFI, Canada; NSFC, China; MEYS CR, Czech Republic; DNRF, Denmark; DNSRC, Denmark; IN2P3-CNRS, France; CEA-DRF/IRFU, France; BMBF, Germany; MPG, Germany; Hong Kong SAR, China; ISF, Israel; INFN, Italy; MEXT, Japan; JSPS, Japan; CNRST, Morocco; RCN, Norway; MEiN, Poland; FCT, Portugal; MNE/IFA, Romania; MESTD, Serbia; MSSR, Slovakia; ARRS, Slovenia; MIZS, Slovenia; MICINN, Spain; Wallenberg Foundation, Sweden; SERI, Switzerland; MOST, Taiwan; DOE, United States of America; NSF, United States of America; BCKDF, Canada; CANARIE, Canada; Compute Canada, Canada; Czech Republic [PRIMUS 21/SCI/017, UNCE SCI/013]; COST, European Union; ERC, European Union; ERDF, European Union; Horizon 2020, European Union; Marie Skodowska-Curie Actions, European Union; Investissements d'Avenir Labex, France; Investissements d'Avenir Idex , France; ANR, France; DFG , Germany; AvH Foundation, Germany; Herakleitos programme - EU-ESF, Greece; Thales programme - EU-ESF, Greece; Aristeia programme - EU-ESF, Greece; Greek NSRF, Greece; BSF-NSF, Israel; MINERVA, Israel; Norwegian Financial Mechanism 2014-2021, Norway; NCN, Poland; NAWA, Poland; La Caixa Banking Foundation, Spain; CERCA Programme Generalitat de Catalunya, Spain; PROMETEO Programme Generalitat Valenciana, Spain; GenT Programme Generalitat Valenciana, Spain; Goran Gustafssons Stiftelse, Sweden; Royal Society, United Kingdom; Leverhulme Trust, United Kingdom; STFC, United Kingdom; TENMAK, Turkiye; Canton of Geneva, Switzerland; Canton of Bern, Switzerland; SNSF, Switzerland; SRC, Sweden; DSI/NRF, South Africa; NWO, Netherlands; Benoziyo Center, Israel; RGC, China; GSRI, Greece; HGF, Germany; SRNSFG, Georgia; Minciencias, Colombia; MOST, China; CAS, China; ANID, Chile; CERN; NRC, Canada; CERN: European Organization for Nuclear Research (CERN PJAS); Chile: Agencia Nacional de Investigacion y Desarrollo [FONDECYT 1190886, FONDECYT 1210400, FONDECYT 1230812, FONDECYT 1230987]; China: National Natural Science Foundation of China [NSFC - 12175119, NSFC 12275265, NSFC-12075060]; Czech Republic: PRIMUS Research Programme [PRIMUS/21/SCI/017]; European Union: European Research Council [ERC - 948254]; European Union: Horizon 2020 Framework Programme [MUCCA - CHIST-ERA-19-XAI-00]; European Union, Future Artificial Intelligence Research (FAIR-NextGenerationEU) [PE00000013]; Italian Center for High Performance Computing, Big Data and Quantum Computing (ICSC, NextGenerationEU); Marie Sklodowska-Curie Actions (EU H2020 MSC IF GRANT) [101033496]; France: Agence Nationale de la Recherche [ANR-20-CE31-0013, ANR-21-CE31-0013, ANR-21-CE31-0022]; France: Investissements d'Avenir Idex [ANR-11-LABX-0012]; France: Investissements d'Avenir Labex [ANR-11-LABX-0012]; Germany: Baden-Wurttemberg Stiftung (BW Stiftung-Postdoc Eliteprogramme); Germany: Deutsche Forschungsgemeinschaft [DFG - 469666862, DFG - CR 312/5-1]; Italy: Istituto Nazionale di Fisica Nucleare (FELLINI) [754496]; Japan: Japan Society for the Promotion of Science (JSPS KAKENHI) [22KK0227, JP21H05085, JP22H01227, JP22H04944]; Netherlands: Netherlands Organisation for Scientific Research (NWO Veni 2020) [VI.Veni.202.179]; Norway: Research Council of Norway [RCN-314472]; Poland: Polish National Agency for Academic Exchange [PPN/PPO/2020/1/00002/U/00001]; Poland: Polish National Science Centre [NCN 2021/42/E/ST2/00350, 2022/47/B/ST2/03059, NCN UMO-2019/34/E/ST2/00393, UMO-2020/37/B/ST2/01043, UMO-2021/40/C/ST2/00187]; Slovenia: Slovenian Research Agency (ARIS grant) [J1-3010]; Spain: BBVA Foundation [LEO22-1-603]; Generalitat Valenciana (Artemisa, FEDER) [IDIFEDER/2018/048]; La Caixa Banking Foundation [LCF/BQ/PI20/11760025]; Ministry of Science and Innovation (MCIN NextGenEU) [PCI2022-135018-2]; Ministry of Science and Innovation (MICIN FEDER) [PID2021-125273NB, RYC2019-028510-I, RYC2020-030254-I, RYC2021-031273-I, RYC2022-038164-I]; PROMETEO Programme Generalitat Valenciana [CIDEGENT/2019/023, CIDEGENT/2019/027]; GenT Programme Generalitat Valenciana [CIDEGENT/2019/023, CIDEGENT/2019/027]; Sweden: Swedish Research Council [VR 2018-00482, VR 2022-03845, VR 2022-0468, 2021-03651]; Knut and Alice Wallenberg Foundation [KAW 2017.0100, KAW 2018.0157, KAW 2018.0458, KAW 2019.0447]; Switzerland: Swiss National Science Foundation [SNSF - PCEFP2_194658]; United Kingdom: Leverhulme Trust [RPG-2020-004]; United States of America: Neubauer Family Foundatio
Müşterek Radar ve Haberleşme Sistemleri
No full textSon yıllarda kablosuz haberleşme alanındaki hem askeri hem de sivil ürünlerin çeşitlenmesiyle birlikte frekans spektrumu yoğunlaşmıştır. Bu durum, haberleşme ağı sağlayıcılarını diğer kullanım konseptlerine atanmış frekans bantlarının değerlendirilmesine yönlendirmiştir. Radar bandı, geniş spektrumu ve ortak kullanıma uygun yapısı ile öne çıkmaktadır. Günümüzde 10 GHz ve altındaki radar bantlarının 5G yeni radyo (NR), Uzun Vadeli Evrim (LTE) ve Kablosuz Bağlantı Güvenliği (Wi-Fi) gibi sistemlerle ortak kullanımı yaygın hale gelmiş olup, gelecekte 6G sistemleri için milimetre dalga bantlarının radar sistemleriyle paylaşımı gündemdedir. Ancak, bu ortak kullanım frekans spektrumu yoğunluğunu azaltmak için uygun bir çözüm gibi görünse de enterferans yönetimi gibi teknik zorlukları da beraberinde getirmektedir. Entegre algılama ve haberleşme (ISAC) sistemleri, radar ve haberleşme işlevlerini birleştirerek aynı RF sinyali üzerinden hem algılama hem de haberleşme yapılmasını mümkün kılan yeni bir konsept olarak öne çıkmaktadır. ISAC, hücresel ağlardan insansız hava aracı (İHA) ağlarına, Wi-Fi ağlarından askeri haberleşme sistemlerine kadar geniş bir uygulama alanı sunmaktadır. Bu tez çalışmada ISAC konsepti için hem algılama hem de haberleşme işlevlerine uygun dalga biçimleri incelenmiştir. Bu kapsamda darbe içi cıvıltı, ikili faz ve Zadoff-Chu modülasyonlu, radar dalga biçimleri kullanılarak hem algılama hem de haberleşme fonksiyonlarını uygulamak üzere analizler gerçekleştirilmiştir. Radar alıcısında menzil ve hız tahmini performansı artırmak amacıyla yeni bir teknik önerilmiş, radar sinyali haberleşme sembolleri içerdiğinde ve içermediğinde performans analizleri yapılmıştır. Ayrıca, haberleşme alıcısında sembol hata oranı değerlendirilmiştir. Ardından, ISAC konseptinin uygulandığı bir kullanım durumu olarak hava savunma ağ yapısı ele alınmıştır. Modern hava tehditleri, yüksek hız, üstün manevra kabiliyeti, gelişmiş aviyonik ekipmanlar, bireysel ve koordineli elektronik harp kabiliyetleri ve düşük radar kesit alanı gibi hava savunma sistemlerinin tespit ve angajman kabiliyetlerini sınırlayan özelliklere sahiptir. Bu teknolojik gelişmeler, hava savunma sistemlerinde daha geniş kapsama alanı, müşterek operasyonlar, gelişmiş algılayıcı sistemleri, sensör füzyonu, elektronik harp tekniklerine karşı koruma ve bölgesel hava savunma sistemlerinin merkezi komuta kontrol sistemleriyle etkin haberleşme gibi yeteneklerin geliştirilmesini gerektirmektedir. Bu durum, tehditlerin evrilen kabiliyetleriyle birlikte savunulan bölgeler için tehlike alanlarının genişlemesini ve dolayısıyla hava savunma sistemlerinin menzilinin artırılmasını zorunlu kılmıştır. Bu kapsamda, bir hava savunma sistemi için korunan bölgeyi tarayan radarların sayısının artırılması ve bu radarların kaynakları etkin bir şekilde kullanmasını sağlayacak bir strateji planlaması gerekmektedir. Bu tez çalışması kapsamında darbe içi cıvıltı modülasyonu kullanılarak oluşturulan ISAC dalga biçimi kullanan bir müşterek radar ve haberleşme ağ yapısında kaynak tahsisi problemi ele alınmıştır.In recent years, the frequency spectrum has become increasingly congested due to the diversification of both military and civilian products in the field of wireless communication. This trend has prompted communication network providers to explore the utilization of frequency bands allocated to other usage concepts. Radar bands, with their wide spectrum and suitability for shared use, have emerged as a focal point. Currently, radar bands below 10 GHz are commonly shared with systems such as 5G New Radio (NR), Long-Term Evolution (LTE), and Wireless Fidelity (Wi-Fi). In the future, sharing millimeter-wave bands between radar systems and 6G systems is also on the agenda. However, although shared usage appears to be a suitable solution for reducing frequency spectrum congestion, it also introduces technical challenges, such as interference management. Integrated Sensing and Communication (ISAC) systems have emerged as an innovative concept enabling simultaneous sensing and communication using the same RF signal. ISAC systems find applications across a wide range of areas, from cellular networks to unmanned aerial vehicle (UAV) networks, Wi-Fi networks, and military communication systems. In this study, waveforms suitable for both sensing and communication functions under the ISAC concept were examined. The analyzed waveforms included intra-pulse chirp modulation, binary phase modulation, and Zadoff-Chu modulation for enabling dual functionalities. This thesis investigates the use of a chirp pulse radar waveform to implement both sensing and communication functionalities. A novel technique was proposed to enhance range and velocity estimation performance at the radar receiver, with performance analysis conducted for radar signals containing communication symbols and those that did not. Symbol error rate evaluations were also performed for the communication receiver. Additionally, as a case study of ISAC implementation, the air defense network structure was analyzed. Modern air threats possess advanced features, such as high speed, superior maneuverability, cutting-edge avionics, coordinated electronic warfare capabilities, and low radar cross-sections. These advancements challenge the detection and engagement capabilities of air defense systems and necessitate enhancements in coverage, interoperability, sensor fusion, electronic warfare resilience, and communication with centralized command systems. Consequently, extended ranges for air defense systems are required, increasing the number of radars in protected zones. This study addresses the resource allocation problem in radar networks with a monostatic ISAC concept that employs a chirp pulse modulation waveform. The findings contribute to optimizing resource usage and overcoming interference challenges in radar communication networks
Rate of Weak Convergence of Random Walk with a Generalized Reflecting Barrier
No full textIn this study, a random walk process with generalized reflecting barrier is considered and an inequality for rate of weak convergence of the stationary distribution of the process of interest is propounded. Though the rate of convergence is not thoroughly examined, the literature does provide a weak convergence theorem under certain conditions for the stationary distribution of the process under consideration. Nonetheless, one of the most crucial issues in probability theory is the convergence rate in limit theorems, as it affects the precision and effectiveness of using these theorems in practice. Therefore, for the rate of convergence for the examined process, comparatively simple inequality is represented. The obtained inequality demonstrates that the rate of convergence is correlated with the tail of the distribution of ladder heights of the random walk
Fer’î Müdahilin Tek Başına Kanun Yoluna Başvurmasının Mümkün Olduğuna İlişkin Danıştay İçtihadı Birleştirme Kararının Hukuk Yargılaması İle İlişkisi Ve Değerlendirilmesi
No full text[No Abstract Available
Search for Triple Higgs Boson Production in the 6b Final State Using pp Collisions at √s=13 TeV With the ATLAS Detector
No full textKonstantinidis, Nikolaos/0000-0002-4140-6360; Weber, Michele/0000-0002-2770-9031; Umaka, Ejiro/0000-0001-7725-8227; Calafiura, Paolo/0000-0002-1692-1678; Parajuli, Santosh/0000-0003-1499-3990; Berger, Nicolas/0000-0002-7963-9725; Panwar, Lata/0000-0003-2461-4907; Fanti, Marcello/0000-0002-8773-145X; Heinrich, Lukas/0000-0002-4048-7584; Da Fonseca Pinto, Joao Victor/0000-0003-1746-1914; Dam, Mogens/0000-0001-6278-9674; Bevan, Adrian/0000-0002-4105-9629; Chevalier, Laurent/0000-0003-3762-7264; Willocq, Stephane/0000-0002-4120-1453; Mete, Alaettin Serhan/0000-0002-5508-530X; Cindro, Vladimir/0000-0002-2037-7185; Longarini, Iacopo/0000-0002-0352-2854; Zhang, Rui/0000-0002-8265-474X; Hoya, Joaquin/0000-0002-7562-0234; Simsek, Sinem/0000-0002-9650-3846; Dao, Valerio/0000-0003-1645-8393; Beretta, Matteo Mario/0000-0002-7026-8171; Zamora-Saa, Jilberto/0000-0002-5030-7516; Chou, Yuan-Tang/0000-0002-2204-5731; Ballabene, Eric/0000-0001-9700-2587; Introzzi, Gianluca/0000-0002-1314-2580; Escobar Ibanez, Carlos/0000-0003-4442-4537; Doglioni, Caterina/0000-0002-1509-0390; Hays, Chris/0000-0003-2371-9723; Goussiou, Anna/0000-0001-6211-7122; Haas, Andrew/0000-0002-4832-0455; Deliot, Frederic/0000-0003-0777-6031; Komarek, Tomas/0000-0002-3047-3146; Sato, Koji/0000-0001-8988-4065; Kretzschmar, Jan/0000-0002-8515-1355; Liu, Bingxuan/0000-0002-0721-8331; Ould-Saada, Farid/0000-0002-9404-835X; Goossens, Luc/0000-0002-2536-4498; Carbone, Antonio/0000-0002-4117-3800; Tzovara, Eftychia/0000-0002-0410-0055; Ali, Babar/0000-0001-8653-5556; Romano, Marino/0000-0002-6609-7250; Zhang, Zhicai/0000-0002-1630-0986; Bellos, Panagiotis/0000-0003-2049-9622; Butterworth, Jonathan/0000-0002-5905-5394; Hulsken, Raphael/0000-0002-0095-1290; Varni, Carlo/0000-0001-6733-4310; Zhang, Zhiqing/0000-0002-7853-9079; Muanza, Steve/0000-0002-1786-2075; Balasubramanian, Rahul/0000-0001-5840-1788; Nasri, Salah/0000-0002-5985-4567; Kourlitis, Vangelis/0000-0001-6568-2047; Leeuw, Lerothodi/0000-0002-3365-6781; Leitgeb, Clara Elisabeth/0000-0002-0335-503X; Faraj, Mohammed/0000-0001-9442-7598; Rebuzzi, Daniela Marcella/0000-0003-4461-3880; D'Auria, Saverio/0000-0003-3393-6318; Maeda, Junpei/0000-0002-9084-3305; Worm, Steven/0000-0002-3865-4996; Ernani Martins Neto, Daniel/0000-0003-2793-5335; Weber, Christian/0000-0002-8659-5767; Mildner, Hannes/0000-0002-0384-6955; Chapon, Emilien/0000-0001-6968-9828; Javurkova, Martina/0000-0001-8798-808X; Beacham, James/0000-0003-3623-3335; Mueller, James/0000-0001-5099-4718; D'Uffizi, Matteo/0000-0003-2499-1649; Tu, Yanjun/0000-0002-5865-183X; Tariq, Khuram/0000-0002-0584-8700; Belfkir, Mohamed/0000-0001-9974-1527; Elsing, Markus/0000-0002-1213-0545; Yang, Siqi/0000-0002-0204-984X; Dell'Asta, Lidia/0000-0002-9601-4225; Garcia, Carmen/0000-0003-1625-7452; Cerri, Alessandro/0000-0002-1904-6661; Mazzeo, Elena/0000-0002-8406-0195; Nikiforou, Nikiforos/0000-0003-1267-7740; Mungo, Davide Pietro/0000-0002-2567-7857; D'Onofrio, Monica/0000-0003-2408-5099; Mckee, Shawn/0000-0002-4551-4502; Rummler, Andre/0000-0001-8945-8760; Albert, Justin/0000-0003-0253-2505; Artoni, Giacomo/0000-0002-3477-4499; Feligioni, Lorenzo/0000-0002-1403-0951; Pascual Dominguez, Luis/0000-0003-4701-9481; Tishelman-Charny, Abraham/0000-0002-7332-5098; Montejo Berlingen, Javier/0000-0001-9213-904X; Di Luca, Andrea/0000-0002-9074-2133; Guo, Yuxiang/0000-0002-6027-5132; Gonella, Laura/0000-0002-4919-0808; Meloni, Federico/0000-0001-7075-2214; Jackson, Paul/0000-0002-0847-402X; Zerradi, Soufiane/0000-0001-9101-3226; Mokgatitswane, Gaogalalwe/0000-0001-9878-4373; Ghosh, Aishik/0000-0003-0819-1553; Cristoforetti, Marco/0000-0002-0127-1342; Ragusa, Francesco/0000-0002-4064-0489; Mondal, Santu/0000-0002-6965-7380; De Almeida Dias, Flavia/0000-0001-6882-5402; Stevenson, Thomas/0000-0003-2399-8945; Kvam, Audrey/0000-0001-7243-0227; Dado, Tomas/0000-0002-7050-2669; Klein, Matthew Henry/0000-0002-9999-2534; Yabsley, Bruce/0000-0002-2680-0474; Gonski, Julia/0000-0003-2037-6315; Coelli, Simone/0000-0002-5145-3646; Miu, Ovidiu/0000-0002-0287-8293; Dong, Binbin/0000-0002-6075-0191; Dong, Qichen/0000-0002-0117-7831; Sandesara, Jay/0000-0002-6016-8011; Jones, Eleanor/0000-0001-6289-2292; Li, Zhelun/0000-0001-7096-2158; Grosse-Knetter, Jorn/0000-0003-3085-7067; Khwaira, Yahya/0000-0001-8538-1647; Martin-Haugh, Stewart/0000-0001-9457-1928; Clark, Allan/0000-0001-8341-5911; Gorisek, Andrej/0000-0002-3903-3438; Camarda, Stefano/0000-0003-0479-7689; Merlassino, Claudia/0000-0002-5445-5938; Angerami, Aaron/0000-0001-7834-8750; Chan, Jay/0000-0001-7069-0295; Moreno Martinez, Carlos/0000-0002-5719-7655; Wang, Zirui/0000-0002-0928-2070; Gutierrez Zagazeta, Luis Felipe/0000-0003-0374-1595; Iuppa, Roberto/0000-0001-5038-2762; Nitika, Nitika/0000-0003-0576-3122; Marjanovic, Marija/0000-0002-4468-0154; Shah, Aashaq/0000-0002-6157-2016; Saito, Masahiko/0000-0001-5564-0935; Alves, Fabio Lucio/0000-0002-1626-6255; Hank, Michael/0000-0002-4731-6120; Costanzo, Davide/0000-0003-4920-6264; Bortoletto, Daniela/0000-0002-1287-4712; Du, Dongshuo/0000-0002-6758-0113; Cepaitis, Vilius/0000-0002-4809-4056; Maleev, Victor/0000-0003-1028-8602; Sinha, Sukanya/0000-0002-2438-3785; Gaudio, Gabriella/0000-0002-6833-0933; Tanaka, Reisaburo/0000-0002-9929-1797; Huffman, B. Todd/0000-0002-5332-2738; Stanislaus, Beojan/0000-0001-9007-7658; Robson, Aidan/0000-0002-1659-8284; Pater, Joleen/0000-0002-0598-5035; Todorova, Sarka/0000-0003-2433-231X; Kumar, Mukesh/0000-0003-3681-1588; Gonzalez Sevilla, Sergio/0000-0003-4458-9403; Schopf, Elisabeth/0000-0002-9340-2214; Farrington, Sinead/0000-0001-5350-9271; Leblanc, Matt/0000-0001-5977-6418; Boudet, Leo/0000-0002-3613-3142; Gregor, Ingrid Maria/0000-0002-5976-7818; Yorita, Kohei/0000-0003-1988-8401; Ricci, Ester/0000-0002-4222-9976; Jia, Jiangyong/0000-0002-5725-3397; Petersen, Troels/0000-0003-0221-3037; Brandt, Oleg/0000-0001-5219-1417; Abramowicz, Halina/0000-0001-5329-6640; Schmitt, Christian/0000-0003-1471-690X; Barr, Alan/0000-0002-3533-3740; Valero, Alberto/0000-0002-9776-5880; Sciandra, Andrea/0000-0001-7163-501X; Volkotrub, Yuriy/0000-0002-3114-3798; Franklin, Melissa/0000-0002-6595-883X; Frattari, Guglielmo/0000-0002-7829-6564; Martin Dit Latour, Bertrand/0000-0003-3420-2105; Escalier, Marc/0000-0003-4270-2775; Vu, Ngoc Khanh/0000-0002-6251-1178; Gramstad, Eirik/0000-0001-5792-5352; Affolder, Anthony/0000-0002-9058-7217; Vittori, Camilla/0000-0001-9156-970X; Qian, Jianming/0000-0003-4813-8167; Taylor, Wendy/0000-0002-6596-9125; Gwilliam, Carl/0000-0002-9401-5304; Lister, Alison/0000-0002-1552-3651; Grivaz, Jean-Francois/0000-0003-4793-7995; Carmignani, Joseph (Joe)/0000-0002-1705-1061; Liu, Mingyi/0000-0002-0236-5404; Dziedzic, Bartosz/0000-0002-0805-9184; Vasile, Matei-Eugen/0000-0001-8415-0759; Salvador Salas, Adrian/0000-0001-5041-5659; Canbay, Ali Can/0000-0003-4602-473X; Munoz Sanchez, Francisca/0000-0002-6374-458X; Gonzalez Suarez, Rebeca/0000-0002-6126-7230; Das, Sruthy Jyothi/0000-0003-2693-3389; Cunha Sargedas Sousa, Mario Jose/0000-0001-7991-593X; Wendland, Bjorn/0000-0003-1623-3899; Ulloa Poblete, Pablo Augusto/0000-0002-0789-7581; Burdin, Sergey/0000-0003-4831-4132; Filthaut, Frank/0000-0003-3338-2247; Wei, Yingjie/0000-0001-9725-2316; Rousseau, David/0000-0001-7613-8063; Pollard, Christopher/0000-0002-3690-3960; Li, Liang/0000-0001-6411-6107; Bouquet, Romain/0000-0001-9683-7101; Doyle, Anthony/0000-0001-6322-6195; Aad, Georges/0000-0002-6665-4934; Ripellino, Giulia/0000-0002-4053-5144; Zenz, Seth/0000-0002-9720-1794; Snyder, Scott/0000-0001-8610-8423; Rompotis, Nikolaos/0000-0003-2577-1875; Pereira Sanchez, Laura/0000-0001-7913-3313; Potti, Harish/0000-0002-0800-9902; Navarro Gonzalez, Josep/0000-0002-4172-7965; Ali, Shahzad/0000-0001-5216-3133; Golling, Tobias/0000-0001-8535-6687; Sopczak, Andre/0000-0001-6981-0544; Gustavino, Giuliano/0000-0002-5938-4921; Fox, Harald/0000-0003-3089-6090; Raine, John/0000-0002-5987-4648; Poreba, Aleksandra/0000-0003-1250-0865; Hopkins, Walter/0000-0001-7814-8740; Shabalina, Elizaveta/0000-0003-4849-556X; Thompson, Emily Anne/0000-0001-7050-8203; Olivares, Sebastian/0000-0003-4616-6973; Malito, Davide/0000-0002-3996-4662; Uysal, Zekeriya/0000-0002-7110-8065; Stupak Iii, John/0000-0001-9610-0783; Wu, Xin/0000-0001-7655-389X; Carlson, Benjamin/0000-0002-7550-7821; Vormwald, Benedikt/0000-0003-2607-7287; Ran, Kunlin/0000-0003-3119-9924; Genest, Marie-Helene/0000-0002-4098-2024; Pianori, Elisabetta/0000-0001-9233-5892; Onofre, Antonio/0000-0003-3471-2703; Ciesla, Krzysztof/0000-0003-2751-3474; Saibel, Andrej/0000-0002-9932-7622; Islam, Wasikul/0000-0002-5624-5934; Mitsou, Vasiliki A./0000-0002-1533-8886; Moss, Joshua/0000-0002-6729-4803; Smirnov, Sergei/0000-0002-6778-073X; De La Torre Perez, Hector/0000-0002-4516-5269; Yang, Tianyi/0000-0002-4996-1924; Rodriguez Bosca, Sergi/0000-0002-4571-2509; Buckley, Andy/0000-0001-8355-9237; Brahimi, Nihal/0000-0003-0992-3509; Burghgrave, Blake/0000-0001-5686-0948; Ozturk, Nurcan/0000-0003-1125-6784; Zanzottera, Riccardo/0009-0006-5900-2539; Stockton, Mark/0000-0001-9679-0323; Maniatis, Ioannis/0000-0002-4362-0088; Poveda, Joaquin/0000-0001-8144-1964; Aoki, Masato/0000-0001-7498-0097; Wang, Shudong/0000-0001-7477-4955; Lari, Tommaso/0000-0002-1388-869X; Sadrozinski, Hartmut/0000-0003-0019-5410; Duda, Dominik/0000-0002-5916-3467; Ryzhov, Andrey/0000-0002-0623-7426; Lewicki, Maciej Piotr/0000-0002-8972-3066; Alderweireldt, Sara/0000-0002-8224-7036; Thomson, Evelyn/0000-0001-6031-2768; Duperrin, Arnaud/0000-0002-5789-9825; Resconi, Silvia/0000-0003-2313-4020; Nasella, Laura/0000-0002-4871-784X; Kowalewski, Robert/0000-0002-7314-0990; Nisati, Aleandro/0000-0002-5080-2293; Bhattarai, Prajita/0000-0001-9977-0416; Cai, Yizhou/0000-0003-2246-7456; Moreno Llacer, Maria/0000-0003-1113-3645; Lopez Solis, Alvaro/0000-0002-0511-4766; Bhattacharya, Deb Sankar/0000-0003-3837-4166; Romain, Madar/0000-0002-6875-6408; Keaveney, James/0000-0003-0766-5307; Alvarez Fernandez, Adrian/0000-0003-1525-4620; Cadamuro, Luca/0000-0001-8789-610X; Sedlaczek, Kevin/0000-0003-2052-2386; Liu, Xiaotian/0000-0003-1366-5530; Przybycien, Mariusz/0000-0002-9235-2649; Grinstein, Sebastian/0000-0002-6460-8694;A search for the production of three Higgs bosons (HHH) in the b (b) over barb (b) over barb (b) over bar final state is presented. The search uses 126 fb(-1) of proton-proton collision data at root s = 13 TeV collected with the ATLAS detector at the Large Hadron Collider. The analysis targets both nonresonant and resonant production of HHH. The resonant interpretations primarily consider a cascade decay topology of X -> SH -> HHH with masses of the new scalars X and S up to 1.5 and 1 TeV, respectively. In addition to scenarios where S is off-shell, the nonresonant interpretation includes a search for Standard Model HHH production, with limits on the trilinear and quartic Higgs self-coupling set. No evidence for HHH production is observed. An upper limit of 59 fb is set, at the 95% confidence level, on the cross section for Standard Model HHH production.CERN; NDGF (Denmark, Norway, Sweden); KIT/GridKA (Germany); INFN-CNAF (Italy); NL-T1 (Netherlands), PIC (Spain); RAL (UK); BNL (USA); ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW; FWF, Austria; ANAS; CNPq; FAPESP, Brazil; NSERC; CFI, Canada; MOST; NSFC, China; MEYS CR, Czech Republic; DNRF; DNSRC, Denmark; IN2P3-CNRS; CEADRF/IRFU, France; BMBF; MPG, Germany; RGC and Hong Kong SAR, China; ICHEP; Academy of Sciences and Humanities, Israel; INFN, Italy; MEXT; JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW, Poland; FCT, Portugal; MNE/IFA, Romania; MSSR, Slovakia; SRC; Wallenberg Foundation, Sweden; SNSF; NSTC, Taipei; DOE; NSF; BCKDF; CANARIE; CRC; DRAC, Canada; FORTE; PRIMUS, Czech Republic; ERC [101116429]; ERDF; Marie SklodowskaCurie Actions, European Union; Investissements d'Avenir Labex, Investissements d'Avenir Idex; ANR, France; DFG; AvH Foundation, Germany; Thales - EU-ESF; Greek NSRF, Greece; BSF-NSF; NCN [UMO-2020/37/B/ST2/01043, UMO-2021/40/C/ST2/00187, UMO-2022/47/O/ST2/00148, UMO-2023/49/B/ST2/04085, UMO-2023/51/B/ST2/00920]; La Caixa Banking Foundation; CERCA Programme Generalitat de Catalunya; PROMETEO; Generalitat Valenciana, Spain; Goran Gustafssons Stiftelse, Sweden; Royal Society [NIF-R1-231091]; Leverhulme Trust, United Kingdom; Armenia: Yerevan Physics Institute (FAPERJ); CERN: European Organization for Nuclear Research; Chile: Agencia Nacional de Investigacion y Desarrollo (FONDECYT) [1230812]; FONDECYT [1240864]; China: Chinese Ministry of Science and Technology [MOST-2023YFA1605700, MOST-2023YFA1609300]; National Natural Science Foundation of China [NSFC-12175119, NSFC-12275265, NSFC-12075060]; Czech Republic: Czech Science Foundation [GACR-24-11373S]; Ministry of Education Youth and Sports [FORTE CZ.02.01.01/00/22_008/0004632]; PRIMUS Research Programme [PRIMUS/21/SCI/017]; EU [ERC101002463]; European Union: European Research Council [ERC-948254, 101089007]; European Union [FAIR-NextGenerationEU PE00000013]; Italian Center for High-Performance Computing; France: Agence Nationale de la Recherche [ANR-20-CE31-0013, ANR-21-CE31-0013, ANR-21-CE31-0022, ANR-22-EDIR-0002]; Germany: Baden-Wurttemberg Stiftung; Deutsche Forschungsgemeinschaft [DFG-469666862]; Ministero dell'Universit; Japan Society for the Promotion of Science (JSPS KAKENHI) [JP22H01227, JP22H04944, JP22KK0227]; JSPS KAKENHI [JP23KK0245, RCN-314472, 9722]; Polish National Agency for Academic Exchange [PPN/PPO/2020/1/00002/U/00001]; Polish National Science Centre (NCN) [2021/42/E/ST2/00350]; NCN OPUS [2022/47/B/ST2/03059]; Spain: Generalitat Valenciana; FEDER [PID2021-125273NB, RYC2020-030254-I, RYC2021-031273-I]; Swedish Research Council (Swedish Research Council) [202304654, VR 2018-00482, VR 2022-03845, VR 202204683, VR 2023-03403, 2021-03651]; Knut and Alice Wallenberg Foundation [KAW 2018.0458, KAW 2019.0447, SNSFPCEFP2_194658]; Leverhulme Trust (Leverhulme Trust) [RPG-2020-004]; USA: U.S. Department of Energy; Neubauer Family FoundationWe thank CERN for the very successful operation of the LHC and its injectors, as well as the support staff at CERN and at our institutions worldwide, without whom ATLAS could not be operated efficiently. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF/SFU (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), RAL (UK), and BNL (USA), the Tier-2 facilities worldwide, and large non-WLCG resource providers. Major contributors of computing resources are listed in Ref. [71]. We gratefully acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; CNPq and FAPESP, Brazil; NSERC, NRC, and CFI, Canada; CERN; ANID, Chile; CAS, MOST, and NSFC, China; Minciencias, Colombia; MEYS CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS and CEADRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF, and MPG, Germany; GSRI, Greece; RGC and Hong Kong SAR, China; ICHEP and Academy of Sciences and Humanities, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW, Poland; FCT, Portugal; MNE/IFA, Romania; MSTDI, Serbia; MSSR, Slovakia; ARIS and MVZI, Slovenia; DSI/NRF, South Africa; MICIU/AEI, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF, and Cantons of Bern and Geneva, Switzerland; NSTC, Taipei; TENMAK, Turkiye; STFC/UKRI, United Kingdom; DOE and NSF, USA. Individual groups and members have received support from BCKDF, CANARIE, CRC, and DRAC, Canada; CERN-CZ, FORTE, and PRIMUS, Czech Republic; COST, ERC, ERDF, Horizon 2020, ICSC-NextGenerationEU, and Marie SklodowskaCurie Actions, European Union; Investissements d'Avenir Labex, Investissements d'Avenir Idex, and ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales, and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and MINERVA, Israel; NCN and NAWA, Poland; La Caixa Banking Foundation, CERCA Programme Generalitat de Catalunya, and PROMETEO and GenT Programmes Generalitat Valenciana, Spain; Goran Gustafssons Stiftelse, Sweden; The Royal Society and Leverhulme Trust, United Kingdom. In addition, individual members wish to acknowledge support from Armenia: Yerevan Physics Institute (FAPERJ); CERN: European Organization for Nuclear Research (CERN DOCT); Chile: Agencia Nacional de Investigacion y Desarrollo (FONDECYT 1230812, FONDECYT 1230987, and FONDECYT 1240864); China: Chinese Ministry of Science and Technology (MOST-2023YFA1605700 and MOST-2023YFA1609300), National Natural Science Foundation of China (NSFC-12175119, NSFC-12275265, and NSFC-12075060); Czech Republic: Czech Science Foundation (GACR-24-11373S), Ministry of Education Youth and Sports (FORTE CZ.02.01. 01/00/22_008/0004632), and PRIMUS Research Programme (PRIMUS/21/SCI/017); EU: H2020 European Research Council (ERC101002463); European Union: European Research Council (ERC-948254, ERC 101089007, and ERC, BARD, 101116429), European Union, Future Artificial Intelligence Research (FAIR-NextGenerationEU PE00000013), and Italian Center for High-Performance Computing, Big Data, and Quantum Computing (ICSC, NextGenerationEU); France: Agence Nationale de la Recherche (ANR-20-CE31-0013, ANR-21-CE31-0013, ANR-21-CE31-0022, and ANR-22-EDIR-0002); Germany: Baden-Wurttemberg Stiftung (BW StiftungPostdoc Eliteprogramme) and Deutsche Forschungsgemeinschaft (DFG-469666862 and DFG-CR 312/5-2); Italy: Istituto Nazionale di Fisica Nucleare (ICSC, NextGenerationEU) and Ministero dell'Universit`a e della Ricerca (PRIN-20223N7F8K-PNRR M4.C2.1.1); Japan: Japan Society for the Promotion of Science (JSPS KAKENHI JP22H01227, JSPS KAKENHI JP22H04944, JSPS KAKENHI JP22KK0227, and JSPS KAKENHI JP23KK0245); Norway: Research Council of Norway (RCN-314472); Poland: Ministry of Science and Higher Education (IDUB AGH, POB8, D4 No. 9722), Polish National Agency for Academic Exchange (PPN/PPO/2020/1/00002/U/00001), and the Polish National Science Centre (NCN 2021/42/E/ST2/00350, NCN OPUS 2023/51/B/ST2/02507, NCN OPUS No. 2022/47/B/ST2/03059, NCN UMO-2019/34/E/ST2/00393, NCN and H2020 MSCA 945339, UMO-2020/37/B/ST2/01043, UMO-2021/40/C/ST2/00187, UMO-2022/47/O/ST2/00148, UMO-2023/49/B/ST2/04085, and UMO-2023/51/B/ST2/00920); Spain: Generalitat Valenciana (Artemisa, FEDER, IDIFEDER/2018/048) and the Ministry of Science and Innovation (MCIN and NextGenEU PCI2022-135018-2, MICIN and FEDER PID2021-125273NB, RYC2019028510-I, RYC2020-030254-I, RYC2021-031273-I, and RYC2022-038164-I); Sweden: Carl Trygger Foundation (Carl Trygger Foundation CTS 22:2312), Swedish Research Council (Swedish Research Council 202304654, VR 2018-00482, VR 2022-03845, VR 202204683, VR 2023-03403, and VR grant 2021-03651), and the Knut and Alice Wallenberg Foundation (KAW 2018.0458, KAW 2019.0447, and KAW 2022.0358); Switzerland: Swiss National Science Foundation (SNSFPCEFP2_194658); United Kingdom: Leverhulme Trust (Leverhulme Trust RPG-2020-004) and the Royal Society (NIF-R1-231091); USA: U.S. Department of Energy (ECA DE-AC02-76SF00515) and the Neubauer Family Foundation
Colorectal Cancer Tumor Grade Segmentation: a New Dataset and Baseline Results
No full textRoutine pathology assessment for the tumor grading is currently performed under the microscope by experienced pathologists which might be prone to interpersonal variability and requiring years of experience. Over the past decade, with the help of whole-slide scanning technology, it is now possible to generate whole-slide images. Indeed, this provides an opportunity to extract vision-based information latent in these images and automate and assist pathologists in their daily workflow. In this process, key machine learning algorithms have been developed enabling an automatic segmentation of pathology slides. Here, in this study, we present a novel dataset for Colorectal Cancer Tumor Grade Segmentation, which contains a total of 103 whole-slide images. The ground-truth annotations for these images were obtained from two independent pathologists. The annotations include pixelwise segmentation masks for “Grade-1”, “Grade-2”, “Grade-3” tumor classes, and “Normal-mucosa” for the normal class. To establish baseline results for this dataset, we trained and evaluated prominent convolutional neural network and transformer models. Our results show that SwinT, a transformer-based model, achieves 63 % mean-dice score, outperforming other transformer-based models and all CNN based models, aligning with the recent success of transformer-based models in the field of computer vision. Most importantly, our new dataset addresses the absence of publicly available datasets for tumor segmentation. Taken together, the findings from our study indicate that integrating various deep neural network structures is promising at facilitating a more unbiased and consistent tumor grading of colorectal cancer using a novel dataset which is publicly available to all researchers. © 2025 The AuthorsAmazon Web Services, AWS; Türkiye Academy of Sciences, (118C197); Council of Higher Education Research Universities Support Program, (ADEP-108-2022-11202, ADEP-312-2024-11455
Nonlinear Approximation of Vector-Valued Functions by Shepard Operators Based on Max-Product and Max-Min Operations
No full textIn this paper, to approximate vector-valued and continuous functions on the unit hypercube, we modify the linear Shepard operators by using max-product and max-min operations. We also investigate the effects of some regular summability methods in the approximation, such as Ces ; agrave;ro summability and Abel summability. Furthermore, we give some interesting applications and graphical simulations verifying our theoretical results. For example, we approximate a torus surface, a helix curve, a fuzzy point and the LogSumExp function by means of these modified operators. Our applications show that the results obtained here are connected with not only the classical approximation theory but also the theory of fuzzy logic and machine learning algorithms
Emergency Response, and Community Impact After February 6, 2023 Kahramanmaraş Pazarcık and Elbistan Earthquakes: Reconnaissance Findings and Observations on Affected Region in Türkiye
No full textTürkiye has a long history of devastating earthquakes, and on February 6, 2023, the region experienced two major earthquakes with magnitudes of 7.7 and 7.6, striking Pazarcık and Elbistan, Kahramanmaraş, respectively, on the East Anatolian Fault Zone. These earthquakes resulted in significant loss of life and property, impacting multiple cities across 11 cities, and leaving a lasting impact on the country. The 2023 Kahramanmaraş Earthquakes rank among the deadliest and most damaging earthquakes in Türkiye, alongside the historical significance of the 1939 Erzincan Earthquake and the 1999 Marmara Earthquake. Despite reforms following the 1999 Marmara Earthquake in disaster policy and preparedness, the scale of damage from the February 6 earthquakes has been shocking, necessitating further insights and lessons for future earthquake management. This paper presents the outcomes of immediate response efforts organized after the 2023 Kahramanmaraş earthquakes to elucidate emergency response activities and their impacts on communities, considering the substantial size and severity of the damages. The study focuses on evaluating the emergency response provided within the first 24 h, 3 days, and 2 weeks after the earthquakes, aiming to promptly identify the nature and effectiveness of these responses, as well as the conditions that hindered their efficacy. By shedding light on the specific experiences and challenges faced during these crucial timeframes, the research aims to offer valuable insights and lessons learned. These findings contribute to improved preparedness strategies and more efficient emergency response measures needed in responding to future disaster scenarios. Ultimately, this study provides a useful resource for all stakeholders involved in emergency response and disaster management, offering valuable guidance to enhance resilience and preparedness in the face of seismic hazards. © The Author(s) 2024.Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK; Doğal Afetler Odaklı Saha Çalışması Acil Destek Programı; Scientific and Technological Research Institution of Türkiy