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Network slicing is included as a key feature of the 5G architecture in order to simultaneously support diverse service types with heterogeneous requirements. The realization of network slicing in the Radio Access Network (RAN) needs mechanisms that allow the distribution of the available capacity in the system in an efficient manner while satisfying the requirements of the different services. In this paper, a capacity sharing function is proposed, which is approached as a multi agent reinforcement learning based on the Deep Reinforcement Learning (DRL) algorithm Deep Q-Network (DQN). The proposed algorithm provides the capacity to be assigned to each RAN slice. Performance assessment reveals the promising behavior of the proposed solution. ; This work has been supported by the Spanish Research Council and FEDER funds under SONAR 5G grant (ref. TEC2017-82651-R), by the European Commission's Horizon 2020 research and innovation program under grant agreement #871428, 5G-CLARITY project, and by the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia under grant 2019FI_B1 00102. ; Peer Reviewed ; Postprint (author's final draft)

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. ; 5G is envisioned to simultaneously provide diverse service types with heterogeneous needs under very different application scenarios and business models. Therefore, network slicing is included as a key feature of the 5G architecture to allow sharing a common infrastructure among different tenants, such as mobile communication providers, vertical market players, etc. In order to provide the Radio Access Network (RAN) with network slicing capabilities, mechanisms that efficiently distribute the available capacity among the different tenants while satisfying their needs are required. For this purpose, this paper proposes a multi-agent reinforcement learning approach for RAN capacity sharing. It makes use of the Deep Q-Network algorithm in a way that each agent is associated to a different tenant and learns the capacity to be provided to this tenant in each cell while ensuring that the service level agreements are satisfied and that the available radio resources are efficiently used. The consideration of multiple agents contributes to a better scalability and higher learning speed in comparison to single-agent approaches. In this respect, results show that the policy learnt by the agent of one tenant can be generalised and directly applied by other agents, thus reducing the complexity of the training and making the proposed solution easily scalable, e.g., to add new tenants in the system. The proposed approach is well aligned with the on-going 3GPP standardization work and guidelines for the parametrization of the solution are provided, thus enforcing its practical applicability. ; This work was supported in part by the Spanish Research Council and FEDER funds under SONAR 5G Grant ref. TEC2017-82651-R, in part by the European Commission's Horizon 2020 5G-CLARITY project under Grant 871428, and in part by the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia under Grant 2020FI_B2 00075. ; Peer Reviewed ; Postprint (author's final draft)

One of the key features of the 5G architecture is network slicing, which allows the simultaneous support of diverse service types with heterogeneous requirements over a common network infrastructure. In order to support this feature in the Radio Access Network (RAN), it is required to have capacity sharing mechanisms that distribute the available capacity in each cell among the existing RAN slices while satisfying their requirements and efficiently using the available resources. Deep Reinforcement Learning (DRL) techniques are good candidates to deal with the complexity of capacity sharing in multi-cell scenarios where the traffic in the different cells can be heterogeneously distributed in the time and space domains. In this paper, a multi-agent reinforcement learning-based solution for capacity sharing in multi-cell scenarios is discussed and assessed under heterogeneous traffic conditions. Results show the capability of the solution to satisfy the requirements of the RAN slices while using the resources in the different cells efficiently. ; This work has been supported by the Spanish Research Council and FEDER funds under SONAR 5G grant (ref.TEC2017-82651-R), by the European Commission's Horizon 2020 5G-CLARITY project under grant agreement 871428 and by the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia under grant 2020FI_B2 00075. ; Peer Reviewed ; Postprint (author's final draft)

"This discussion memorandum is published with the financial assistance of the U.S. Department of Housing & Urban Development, Office of Policy Development and Research, Financial Management Capacity Sharing Program." ; "James S. Remis-Director, NCGA." ; Mode of access: Internet.

In this paper I argue that reform capacity, defined as the extent to which political institutions facilitate the adoption of socially efficient reforms, is not primarily determined by the number of veto players in the political system, but by the availability of institutional mechanisms that allow political agents to solve commitment problems associated with bargaining over reform. Specifically, I argue that power sharing is compatible with high reform capacity if policy areas involved in package deals are all controlled by the central government (or whatever level of government makes the reform), and if the 'losers' are confident that they will remain veto players in the future.

In this paper I argue that reform capacity, defined as the extent to which political institutions facilitate the adoption of socially efficient reforms, is not primarily determined by the number of veto players in the political system, but by the availability of institutional mechanisms that allow political agents to solve commitment problems associated with bargaining over reform. Specifically, I argue that power sharing is compatible with high reform capacity if policy areas involved in package deals are all controlled by the central government (or whatever level of government makes the reform), and if the 'losers' are confident that they will remain veto players in the future. [Reprinted by permission of Sage Publications Ltd., copyright holder.]

Skills augmentation, through individual and collective learning, building destination relationships and network linkages through knowledge sharing and industry-wide benchmarking are internationally recognised as leading economic drivers for regional growth in general and the tourism industry in particular. The aim of this paper is to share the authors' initial insights into regional tourism industry network and capacity building via online skilling and knowledge sharing. Applying an ICT and Internet-enhanced platform for the delivery of business skills, this paper discusses an online learning pilot for micro tourism businesses in regional Australia designed to offer tourism information and skills development towards industry accreditation. While a tripartite university-government-industry partnership was successfully established to share knowledge towards regional capacity building and industry benchmarking, the authors also highlight silo politics, cultural differences, lack of leadership and lack of trust as some of the main barriers to effective and bilateral knowledge sharing towards regional industry capacity building. ; E1

This decision making tool is designed to assist national policy makers and other stakeholders to identify appropriate measures to implement the Plant Treaty's multilateral system of access and benefit-sharing in their countries. It is the product of eight years of experience working with partners in numerous countries developing national policies under the overall framework of the FAO/Bioversity International/ITPGRFA Secretariat Joint Capacity Building Programme for Developing Countries on Implementation of the ITPGRFA and its Multilateral System of Access and Benefit-sharing. The decision-making tool is divided into the following eleven sections corresponding to issues that national-level policy actors need to address: 1. Who is responsible for promoting and coordinating national implementation? 2. What is facilitated access to PGRFA under the multilateral system and who has the right to facilitated access? 3. Who may authorize access to PGRFA under the multilateral system? 4. What processes and criteria should be followed to consider requests for PGRFA included in the multilateral system? 5. How to deal with requests for purposes that are (or may be) beyond the scope of the multilateral system? 6. What PGRFA are automatically included in the multilateral system? 7. How to encourage voluntary inclusions by natural and legal persons? 8. How to ensure legal space for the implementation of the multilateral system? 9. How to address benefit-sharing? 10. How to deal with reporting obligations regarding transfers and sales? 11. Who monitors the use of PGRFA under the multilateral system and enforces the multilateral system's terms and conditions? Each section is presented in a question-and-answer format. Among other things, each section considers: • if there are means of simultaneously implementing other parts of the ITPGRFA, for example conservation (Article 5), sustainable use (Article 6), farmers' rights (Article 9) or the global information system (article 17) • if there are important issues to consider related to the mutually supportive implementation of the Nagoya Protocol on access and benefit-sharing? • If it is useful, given the legal and political culture of the country concerned, to develop new policies or laws to implement the multilateral system (which underscoring that many countries are implementing the multilateral system without creating new laws) At the end of each section, draft 'baseline' legal provisions are provided. They can be adapted and incorporated into new laws and administrative guides, if and when they are considered to be useful. The publication is available in Spanish, French and Arabic: SP: https://hdl.handle.net/10568/97878 FR: https://hdl.handle.net/10568/98234 AR: https://hdl.handle.net/10568/101420 Interested users can request hard copies of the tool to Bioversity International by sending and email to Evelyn Clancy: e.clancy@cgiar.org

In urban settings where space is at a premium, bus lanes can often only be created via the conversion of existing general-use lanes. This can be politically infeasible in cities where bus flows are low, because the converted lanes would be under-utilized and thereby squander road space. The roadway bottlenecks would then as a result create even greater delays and queuing for car traffic. The present thesis explores novel ways in which buses and cars might share lanes within select bottlenecks. Details of these shared-lane strategies vary, depending upon a bottleneck's local operating conditions. In all cases, cars would be inserted into a shared lane in ways that impart few delays to the buses. These insertions would allow for the unused space between buses to be used, lessening the damage otherwise incurred by car traffic at bottlenecks. Hence, by reducing the car delay at bottlenecks it may become feasible to deploy ordinary bus-lane conversions elsewhere throughout a city's road network. The ordinary lane conversions would enable buses to bypass the car queues that still form at bottlenecks. Analytical assessments unveil the wide range of bus flows for which these strategies increase a bottleneck's car-carrying capacity, in comparison to reserving one of its lanes for buses only. Simulations of a real case study indicate that significant reductions in car delays can result while increasing bus speeds as much as ordinary conversions.