To detain, or not to detain: a functional approach to non-state armed groups’ activities in non-international armed conflicts

During armed conflict, non-State armed groups (NSAGs) deprive individuals of their liberty on a regular basis. Yet, examining these activities goes beyond their mere acknowledgment. This thesis explores the legal regulation of NSAGs’ detention activities in non-international armed conflicts (NIACs) from a functional perspective. It does so by concentrating on NSAGs’ practices in this field, presenting and testing the hypothesis that international humanitarian law (IHL) and, on certain occasions, international human rights law (IHRL) oblige these actors not to arbitrarily deprive individuals of their liberty. Furthermore, it argues that NSAGs must have a legal basis in order to undertake these activities. By relying on a “legal pluralistic” approach of international law, that basis is to be found in those “laws” and regulations established by the groups themselves. Other options are also possible, such as the adaption of the State’s domestic law or an agreement concluded with a third party. This proposal is grounded on the “functions” required by the aforementioned legal regimes to be respected, in particular for the parties (be they States or NSAGs) to adopt certain law-making or law-adapting functions with the goal of complying with some of the provisions contained therein.Exploring the Frontiers of International La

A learning approach for prioritized handoff channel allocation in mobile multimedia networks

An efficient channel allocation policy that prioritizes handoffs is an indispensable ingredient in future cellular networks in order to support multimedia traffic while ensuring quality of service requirements (QoS). In this paper we study the application of a reinforcement-learning algorithm to develop an alternative channel allocation scheme in mobile cellular networks that supports multiple heterogeneous traffic classes. The proposed scheme prioritizes handoff call requests over new calls and provides differentiated services for different traffic classes with diverse characteristics and quality of service requirements. Furthermore, it is asymptotically optimal, computationally inexpensive, model-free, and can adapt to changing traffic conditions. Simulations are provided to compare the effectiveness of the proposed algorithm with other known resource-sharing policies such as complete sharing and reservation policies

A learning approach for prioritized handoff channel allocation in mobile multimedia networks

An efficient channel allocation policy that prioritizes handoffs is an indispensable ingredient in future cellular networks in order to support multimedia traffic while ensuring quality of service requirements (QoS). In this paper we study the application of a reinforcement-learning algorithm to develop an alternative channel allocation scheme in mobile cellular networks that supports multiple heterogeneous traffic classes. The proposed scheme prioritizes handoff call requests over new calls and provides differentiated services for different traffic classes with diverse characteristics and quality of service requirements. Furthermore, it is asymptotically optimal, computationally inexpensive, model-free, and can adapt to changing traffic conditions. Simulations are provided to compare the effectiveness of the proposed algorithm with other known resource-sharing policies such as complete sharing and reservation policies