Essec\Faculty\Model\Profile {#2216
#_id: "B00820417"
#_source: array:39 [
"bid" => "B00820417"
"academId" => "34842"
"slug" => "traversi-emiliano"
"fullName" => "Emiliano TRAVERSI"
"lastName" => "TRAVERSI"
"firstName" => "Emiliano"
"title" => array:2 [
"fr" => "Professeur associé"
"en" => "Associate Professor"
]
"email" => "emiliano.traversi@essec.edu"
"status" => "ACTIF"
"campus" => "Campus de Cergy"
"departments" => []
"phone" => ""
"sites" => []
"facNumber" => "34842"
"externalCvUrl" => "https://faculty.essec.edu/en/cv/traversi-emiliano/pdf"
"googleScholarUrl" => "https://scholar.google.com/citations?user=T-6PKroAAAAJ&hl=en&oi=ao"
"facOrcId" => "https://orcid.org/"
"career" => array:1 [
0 => Essec\Faculty\Model\CareerItem {#2220
#_index: null
#_id: null
#_source: array:7 [
"startDate" => "2024-09-01"
"endDate" => null
"isInternalPosition" => true
"type" => array:2 [
"fr" => "Positions académiques principales"
"en" => "Full-time academic appointments"
]
"label" => array:2 [
"fr" => "Professeur associé"
"en" => "Associate Professor"
]
"institution" => array:2 [
"fr" => "ESSEC Business School"
"en" => "ESSEC Business School"
]
"country" => array:2 [
"fr" => "France"
"en" => "France"
]
]
+lang: "en"
+"parent": Essec\Faculty\Model\Profile {#2216}
}
]
"diplomes" => array:1 [
0 => Essec\Faculty\Model\Diplome {#2218
#_index: null
#_id: null
#_source: array:6 [
"diplome" => "DIPLOMA"
"type" => array:2 [
"fr" => "Diplômes"
"en" => "Diplomas"
]
"year" => "2023"
"label" => array:2 [
"en" => "Habilitation à diriger des recherches, Business administration, Business administration"
"fr" => "Habilitation à diriger des recherches, Sciences de Gestion, Science de gestion"
]
"institution" => array:2 [
"fr" => "Sorbonne Université"
"en" => "Sorbonne Université"
]
"country" => array:2 [
"fr" => "France"
"en" => "France"
]
]
+lang: "en"
+"parent": Essec\Faculty\Model\Profile {#2216}
}
]
"bio" => array:2 [
"fr" => "<p>Emiliano Traversi est professeur associé au département de systèmes d’information, sciences de la décision et statistiques à l’ESSEC. Le professeur Emiliano Traversi a obtenu un doctorat en operations research de l'Université de Bologne. Avant de rejoindre l’ESSEC, il était professeur titulaire au LIRMM, Université de Montpellier, en France. Ses domaines de recherche incluent l'optimisation mathématique, les méthodes de décomposition et le machine learning.</p>\n"
"en" => "<p>Emiliano Traversi is an associate professor in the Department of Information Systems, Data Analytics and Operations at ESSEC. Professor Emiliano Traversi earned a PhD in operations research from the University of Bologna. Before joining ESSEC, he was a full professor at LIRMM, University of Montpellier, in France. His research areas include mathematical optimization, decomposition methods and machine learning.</p>\n"
]
"department" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
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"fr" => null
"en" => null
]
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"distinctions" => []
"teaching" => []
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"theses" => []
"indexedAt" => "2025-12-06T05:21:23.000Z"
"contributions" => array:5 [
0 => Essec\Faculty\Model\Contribution {#2221
#_index: "academ_contributions"
#_id: "15482"
#_source: array:18 [
"id" => "15482"
"slug" => "15482-network-slicing-in-aerial-base-station-uav-bs-towards-coexistence-of-heterogeneous-5g-services"
"yearMonth" => "2025-04"
"year" => "2025"
"title" => "Network slicing in aerial base station (UAV-BS) towards coexistence of heterogeneous 5G services"
"description" => "MISHRA, D., TRAVERSI, E., TROTTA, A., RAUT, P., GALKIN, B., DI FELICE, M. et NATALIZIO, E. (2025). Network slicing in aerial base station (UAV-BS) towards coexistence of heterogeneous 5G services. <i>Computer Networks</i>, 261, pp. 111146."
"authors" => array:7 [
0 => array:3 [
"name" => "TRAVERSI Emiliano"
"bid" => "B00820417"
"slug" => "traversi-emiliano"
]
1 => array:1 [
"name" => "Mishra Debashisha"
]
2 => array:1 [
"name" => "Trotta Angelo"
]
3 => array:1 [
"name" => "Raut Prasanna"
]
4 => array:1 [
"name" => "Galkin Boris"
]
5 => array:1 [
"name" => "Di Felice Marco"
]
6 => array:1 [
"name" => "Natalizio Enrico"
]
]
"ouvrage" => ""
"keywords" => array:6 [
0 => "UAVbase station (UAV-BS)"
1 => "Network slicing"
2 => "EASIER"
3 => "5G and beyond network"
4 => "eMBB, uRLLC, mMTC"
5 => "Heterogeneous 5G services"
]
"updatedAt" => "2025-03-12 08:07:25"
"publicationUrl" => "https://doi.org/10.1016/j.comnet.2025.111146"
"publicationInfo" => array:3 [
"pages" => "111146"
"volume" => "261"
"number" => ""
]
"type" => array:2 [
"fr" => "Articles"
"en" => "Journal articles"
]
"support_type" => array:2 [
"fr" => "Revue scientifique"
"en" => "Scientific journal"
]
"countries" => array:2 [
"fr" => null
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"abstract" => array:2 [
"fr" => "Unmanned aerial vehicle base stations (UAV-BSs) empowered with network slicing capabilities are presented in this work to support three heterogeneous classes of 5G slice service types, namely enhanced mobile broadband (eMBB), ultra-reliable low-latency communication (uRLLC), massive machine-type communication (mMTC). The coexistence of eMBB, uRLLC and mMTC services multiplexed over common UAV-BS radio resources leads to an incredibly challenging downlink scheduling problem due to the underlying trade-off of end-user requirements in terms of coverage, traffic demand, data rates, latency, reliability, and UAV-specific constraints. To this end, a modular and customizable two-phase resource slicing optimization framework is proposed for UAV-BS known as gEneral rAn Slicing optImizEr fRamework (EASIER) decomposed into: (i) resource optimizer (RO) and (ii) scheduling validator (SV). The reciprocation of RO and SV guided by above split optimization model can generate efficient scheduling decisions that benefit constrained UAV platforms in terms of finite computation and endurance. Furthermore, prioritizing per slice user acceptance rate, our results show that EASIER not only adheres to slice-specific SLAs (service level agreements) specified by the slice owners (i.e., tenants), but also benefit from efficient UAV-BS positioning to improvise service offering by 15% as compared to a slice-agnostic “default” positioning."
"en" => "Unmanned aerial vehicle base stations (UAV-BSs) empowered with network slicing capabilities are presented in this work to support three heterogeneous classes of 5G slice service types, namely enhanced mobile broadband (eMBB), ultra-reliable low-latency communication (uRLLC), massive machine-type communication (mMTC). The coexistence of eMBB, uRLLC and mMTC services multiplexed over common UAV-BS radio resources leads to an incredibly challenging downlink scheduling problem due to the underlying trade-off of end-user requirements in terms of coverage, traffic demand, data rates, latency, reliability, and UAV-specific constraints. To this end, a modular and customizable two-phase resource slicing optimization framework is proposed for UAV-BS known as gEneral rAn Slicing optImizEr fRamework (EASIER) decomposed into: (i) resource optimizer (RO) and (ii) scheduling validator (SV). The reciprocation of RO and SV guided by above split optimization model can generate efficient scheduling decisions that benefit constrained UAV platforms in terms of finite computation and endurance. Furthermore, prioritizing per slice user acceptance rate, our results show that EASIER not only adheres to slice-specific SLAs (service level agreements) specified by the slice owners (i.e., tenants), but also benefit from efficient UAV-BS positioning to improvise service offering by 15% as compared to a slice-agnostic “default” positioning."
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2025-12-06T05:21:43.000Z"
]
+lang: "en"
+"_score": 7.561819
+"_ignored": array:2 [
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}
1 => Essec\Faculty\Model\Contribution {#2219
#_index: "academ_contributions"
#_id: "15607"
#_source: array:18 [
"id" => "15607"
"slug" => "15607-formation-analysis-for-a-fleet-of-drones-a-mathematical-framework"
"yearMonth" => "2025-04"
"year" => "2025"
"title" => "Formation Analysis for a Fleet of Drones: A Mathematical Framework"
"description" => "TRAVERSI, E., BARCIS, M., BELLONE, L., BARCIS, A., AHMIM-BONALDI, D., FERRANTE, E. et NATALIZIO, E. (2025). Formation Analysis for a Fleet of Drones: A Mathematical Framework. Dans: <i>Proceedings of the 17th International Conference on Agents and Artificial Intelligence</i>. Porto: SCITEPRESS - Science and Technology Publications, pp. 471-480."
"authors" => array:7 [
0 => array:3 [
"name" => "TRAVERSI Emiliano"
"bid" => "B00820417"
"slug" => "traversi-emiliano"
]
1 => array:1 [
"name" => "Barcis Michal"
]
2 => array:1 [
"name" => "Bellone Lorenzo"
]
3 => array:1 [
"name" => "Barcis Agata"
]
4 => array:1 [
"name" => "Ahmim-Bonaldi Dina"
]
5 => array:1 [
"name" => "Ferrante Eliseo"
]
6 => array:1 [
"name" => "Natalizio Enrico"
]
]
"ouvrage" => "Proceedings of the 17th International Conference on Agents and Artificial Intelligence"
"keywords" => array:3 [
0 => "Robot and Multi-Robot Systems"
1 => "Task Planning and Execution"
2 => "Formation Study"
]
"updatedAt" => "2025-04-23 09:52:32"
"publicationUrl" => "https://doi.org/10.5220/0013189400003890"
"publicationInfo" => array:3 [
"pages" => "471-480"
"volume" => "1"
"number" => ""
]
"type" => array:2 [
"fr" => "Actes d'une conférence"
"en" => "Conference Proceedings"
]
"support_type" => array:2 [
"fr" => "Editeur"
"en" => "Publisher"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => "We consider a dynamic coverage scenario, where a group of agents (e.g., Unmanned Aerial Vehicles (UAVs)) is exploring an environment in search of a moving target (e.g., survivors on a lifeboat). We assume UAVs are capable to achieve, maintain, and move in formation (e.g., to maintain connectivity). This paper addresses the question “Which formation maximizes the chance of finding the target?”. We propose a mathematical framework to answer this question. The proposed framework is generic and can be easily applied to various formations and missions. We show how the framework can identify which formation will result in better performance in the type of missions we consider. We analyze how different factors, namely the target speed relative to the group, affect the performance of the formations. We validate the framework against simulations of the considered scenarios. The supplementary video material including the real-world implementation is available at https://youtu.be/ mYmTnAJi-I?si"
"en" => "We consider a dynamic coverage scenario, where a group of agents (e.g., Unmanned Aerial Vehicles (UAVs)) is exploring an environment in search of a moving target (e.g., survivors on a lifeboat). We assume UAVs are capable to achieve, maintain, and move in formation (e.g., to maintain connectivity). This paper addresses the question “Which formation maximizes the chance of finding the target?”. We propose a mathematical framework to answer this question. The proposed framework is generic and can be easily applied to various formations and missions. We show how the framework can identify which formation will result in better performance in the type of missions we consider. We analyze how different factors, namely the target speed relative to the group, affect the performance of the formations. We validate the framework against simulations of the considered scenarios. The supplementary video material including the real-world implementation is available at https://youtu.be/ mYmTnAJi-I?si"
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2025-12-06T05:21:43.000Z"
]
+lang: "en"
+"_score": 7.561819
+"_ignored": array:3 [
0 => "abstract.en.keyword"
1 => "abstract.fr.keyword"
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}
2 => Essec\Faculty\Model\Contribution {#2223
#_index: "academ_contributions"
#_id: "15828"
#_source: array:18 [
"id" => "15828"
"slug" => "15828-a-trust-region-framework-for-derivative-free-mixed-integer-optimization"
"yearMonth" => "2024-09"
"year" => "2024"
"title" => "A trust-region framework for derivative-free mixed-integer optimization"
"description" => "TORRES, J.J., NANNICINI, G., TRAVERSI, E. et WOLFLER CALVO, R. (2024). A trust-region framework for derivative-free mixed-integer optimization. <i>Mathematical Programming Computation</i>, 16(3), pp. 369-422."
"authors" => array:4 [
0 => array:3 [
"name" => "TRAVERSI Emiliano"
"bid" => "B00820417"
"slug" => "traversi-emiliano"
]
1 => array:1 [
"name" => "Torres Juan J."
]
2 => array:1 [
"name" => "Nannicini Giacomo"
]
3 => array:1 [
"name" => "Wolfler Calvo Roberto"
]
]
"ouvrage" => ""
"keywords" => array:4 [
0 => "Derivative-free optimization"
1 => "Mixed-integer programming"
2 => "Nonlinear programming"
3 => "Trust-region methods"
]
"updatedAt" => "2025-07-08 10:54:24"
"publicationUrl" => "https://doi.org/10.1007/s12532-024-00260-0"
"publicationInfo" => array:3 [
"pages" => "369-422"
"volume" => "16"
"number" => "3"
]
"type" => array:2 [
"fr" => "Articles"
"en" => "Journal articles"
]
"support_type" => array:2 [
"fr" => "Revue scientifique"
"en" => "Scientific journal"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => "This paper overviews the development of a framework for the optimization of black-box mixed-integer functions subject to bound constraints. Our methodology is based on the use of tailored surrogate approximations of the unknown objective function, in combination with a trust-region method. To construct suitable model approximations, we assume that the unknown objective is locally quadratic, and we prove that this leads to fully-linear models in restricted discrete neighborhoods. We show that the proposed algorithm converges to a first-order mixed-integer stationary point according to several natural definitions of mixed-integer stationarity, depending on the structure of the objective function. We present numerical results to illustrate the computational performance of different implementations of this methodology in comparison with the state-of-the-art derivative-free solver NOMAD."
"en" => "This paper overviews the development of a framework for the optimization of black-box mixed-integer functions subject to bound constraints. Our methodology is based on the use of tailored surrogate approximations of the unknown objective function, in combination with a trust-region method. To construct suitable model approximations, we assume that the unknown objective is locally quadratic, and we prove that this leads to fully-linear models in restricted discrete neighborhoods. We show that the proposed algorithm converges to a first-order mixed-integer stationary point according to several natural definitions of mixed-integer stationarity, depending on the structure of the objective function. We present numerical results to illustrate the computational performance of different implementations of this methodology in comparison with the state-of-the-art derivative-free solver NOMAD."
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2025-12-06T05:21:43.000Z"
]
+lang: "en"
+"_score": 7.561819
+"_ignored": array:2 [
0 => "abstract.en.keyword"
1 => "abstract.fr.keyword"
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+"parent": null
}
3 => Essec\Faculty\Model\Contribution {#2215
#_index: "academ_contributions"
#_id: "15829"
#_source: array:18 [
"id" => "15829"
"slug" => "15829-branch-and-price-for-submodular-bin-packing"
"yearMonth" => "2023-03"
"year" => "2023"
"title" => "Branch and price for submodular bin packing"
"description" => "XU, L., D'AMBROSIO, C., HADDAD-VANIER, S. et TRAVERSI, E. (2023). Branch and price for submodular bin packing. <i>EURO Journal on Computational Optimization</i>, 11, pp. 100074."
"authors" => array:4 [
0 => array:3 [
"name" => "TRAVERSI Emiliano"
"bid" => "B00820417"
"slug" => "traversi-emiliano"
]
1 => array:1 [
"name" => "Xu Liding"
]
2 => array:1 [
"name" => "D'Ambrosio Claudia"
]
3 => array:1 [
"name" => "Haddad-Vanier Sonia"
]
]
"ouvrage" => ""
"keywords" => []
"updatedAt" => "2025-09-03 10:34:04"
"publicationUrl" => "https://doi.org/10.1016/j.ejco.2023.100074"
"publicationInfo" => array:3 [
"pages" => "100074"
"volume" => "11"
"number" => ""
]
"type" => array:2 [
"fr" => "Articles"
"en" => "Journal articles"
]
"support_type" => array:2 [
"fr" => "Revue scientifique"
"en" => "Scientific journal"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => "The Submodular Bin Packing (SMBP) problem asks for packing unsplittable items into a minimal number of bins for which the capacity utilization function is submodular. SMBP is equivalent to chance-constrained and robust bin packing problems under various conditions. SMBP is a hard binary nonlinear programming optimization problem. In this paper, we propose a branch-and-price algorithm to solve this problem. The resulting price subproblems are submodular knapsack problems, and we propose a tailored exact branch-and-cut algorithm based on a piece-wise linear relaxation to solve them. To speed up column generation, we develop a hybrid pricing strategy to replace the exact pricing algorithm with a fast pricing heuristic. We test our algorithms on instances generated as suggested in the literature. The computational results show the efficiency of our branch-and-price algorithm and the proposed pricing techniques."
"en" => "The Submodular Bin Packing (SMBP) problem asks for packing unsplittable items into a minimal number of bins for which the capacity utilization function is submodular. SMBP is equivalent to chance-constrained and robust bin packing problems under various conditions. SMBP is a hard binary nonlinear programming optimization problem. In this paper, we propose a branch-and-price algorithm to solve this problem. The resulting price subproblems are submodular knapsack problems, and we propose a tailored exact branch-and-cut algorithm based on a piece-wise linear relaxation to solve them. To speed up column generation, we develop a hybrid pricing strategy to replace the exact pricing algorithm with a fast pricing heuristic. We test our algorithms on instances generated as suggested in the literature. The computational results show the efficiency of our branch-and-price algorithm and the proposed pricing techniques."
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2025-12-06T05:21:43.000Z"
]
+lang: "en"
+"_score": 7.561819
+"_ignored": array:2 [
0 => "abstract.en.keyword"
1 => "abstract.fr.keyword"
]
+"parent": null
}
4 => Essec\Faculty\Model\Contribution {#2224
#_index: "academ_contributions"
#_id: "15901"
#_source: array:18 [
"id" => "15901"
"slug" => "15901-alleviating-the-quantum-big-m-problem"
"yearMonth" => "2025-12"
"year" => "2025"
"title" => "Alleviating the quantum Big-M problem"
"description" => "ALESSANDRONI, E., RAMOS-CALDERER, S., ROTH, I., TRAVERSI, E. et AOLITA, L. (2025). Alleviating the quantum Big-M problem. <i>npj Quantum Information</i>, 11(1), pp. Art. 125."
"authors" => array:5 [
0 => array:3 [
"name" => "TRAVERSI Emiliano"
"bid" => "B00820417"
"slug" => "traversi-emiliano"
]
1 => array:1 [
"name" => "Alessandroni Edoardo"
]
2 => array:1 [
"name" => "Ramos-Calderer Sergi"
]
3 => array:1 [
"name" => "Roth Ingo"
]
4 => array:1 [
"name" => "Aolita Leandro"
]
]
"ouvrage" => ""
"keywords" => array:3 [
0 => "Quantum Optimization"
1 => "Big-M Problem"
2 => "Combinatorial Optimization"
]
"updatedAt" => "2025-09-03 10:35:04"
"publicationUrl" => "https://doi.org/10.1038/s41534-025-01067-0"
"publicationInfo" => array:3 [
"pages" => "Art. 125"
"volume" => "11"
"number" => "1"
]
"type" => array:2 [
"fr" => "Articles"
"en" => "Journal articles"
]
"support_type" => array:2 [
"fr" => "Revue scientifique"
"en" => "Scientific journal"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => "A major obstacle for quantum optimizers is the reformulation of constraints as a quadratic unconstrained binary optimization (QUBO). Current QUBO translators exaggerate the weight M of the penalty terms. Classically known as the “Big-M” problem, the issue becomes even more daunting for quantum solvers, since it affects the physical energy scale. We take a systematic, encompassing look at the quantum big-M problem, revealing NP-hardness in finding the optimal M and establishing bounds on the Hamiltonian spectral gap Δ as a function of the weight M, inversely related to the expected run-time of quantum solvers. We propose a practical translation algorithm, based on SDP relaxation, that outperforms previous methods in numerical benchmarks. Our algorithm gives values of Δ orders of magnitude greater, e.g. for portfolio optimization instances. Solving such instances with an adiabatic algorithm on 6-qubits of an IonQ device, we observe significant advantages in time to solution and average solution quality. Our findings are relevant to quantum and quantum-inspired solvers alike."
"en" => "A major obstacle for quantum optimizers is the reformulation of constraints as a quadratic unconstrained binary optimization (QUBO). Current QUBO translators exaggerate the weight M of the penalty terms. Classically known as the “Big-M” problem, the issue becomes even more daunting for quantum solvers, since it affects the physical energy scale. We take a systematic, encompassing look at the quantum big-M problem, revealing NP-hardness in finding the optimal M and establishing bounds on the Hamiltonian spectral gap Δ as a function of the weight M, inversely related to the expected run-time of quantum solvers. We propose a practical translation algorithm, based on SDP relaxation, that outperforms previous methods in numerical benchmarks. Our algorithm gives values of Δ orders of magnitude greater, e.g. for portfolio optimization instances. Solving such instances with an adiabatic algorithm on 6-qubits of an IonQ device, we observe significant advantages in time to solution and average solution quality. Our findings are relevant to quantum and quantum-inspired solvers alike."
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2025-12-06T05:21:43.000Z"
]
+lang: "en"
+"_score": 7.561819
+"_ignored": array:2 [
0 => "abstract.en.keyword"
1 => "abstract.fr.keyword"
]
+"parent": null
}
]
"avatar" => "https://faculty.essec.edu/wp-content/uploads/avatars/B00820417.jpg"
"contributionCounts" => 5
"personalLinks" => array:2 [
0 => "<a href="https://orcid.org/" target="_blank">ORCID</a>"
1 => "<a href="https://scholar.google.com/citations?user=T-6PKroAAAAJ&hl=en&oi=ao" target="_blank">Google scholar</a>"
]
"docTitle" => "Emiliano TRAVERSI"
"docSubtitle" => "Associate Professor"
"docDescription" => "Department: Information Systems, Data Analytics and Operations<br>Campus de Cergy"
"docType" => "cv"
"docPreview" => "<img src="https://faculty.essec.edu/wp-content/uploads/avatars/B00820417.jpg"><span><span>Emiliano TRAVERSI</span><span>B00820417</span></span>"
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]
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}
