Intuitionistic common knowledge or belief

Starting off from the usual language of modal logic for multi-agent systems dealing with the agents’ knowledge/belief and common knowledge/belief we define so-called epistemic Kripke structures for intu- itionistic (common) knowledge/belief. Then we introduce corresponding deductive systems and show that they are sound and complete with respect to these semantics.

Systems medicine and integrated care to combat chronic noncommunicable diseases

We propose an innovative, integrated, cost-effective health system to combat major non-communicable diseases (NCDs), including cardiovascular, chronic respiratory, metabolic, rheumatologic and neurologic disorders and cancers, which together are the predominant health problem of the 21st century. This proposed holistic strategy involves comprehensive patient-centered integrated care and multi-scale, multi-modal and multi-level systems approaches to tackle NCDs as a common group of diseases. Rather than studying each disease individually, it will take into account their intertwined gene-environment, socio-economic interactions and co-morbidities that lead to individual-specific complex phenotypes. It will implement a road map for predictive, preventive, personalized and participatory (P4) medicine based on a robust and extensive knowledge management infrastructure that contains individual patient information. It will be supported by strategic partnerships involving all stakeholders, including general practitioners associated with patient-centered care. This systems medicine strategy, which will take a holistic approach to disease, is designed to allow the results to be used globally, taking into account the needs and specificities of local economies and health systems.

Spike-based decision learning of nash equilibria in two-player games

Humans and animals face decision tasks in an uncertain multi-agent environment where an agent's strategy may change in time due to the co-adaptation of others strategies. The neuronal substrate and the computational algorithms underlying such adaptive decision making, however, is largely unknown. We propose a population coding model of spiking neurons with a policy gradient procedure that successfully acquires optimal strategies for classical game-theoretical tasks. The suggested population reinforcement learning reproduces data from human behavioral experiments for the blackjack and the inspector game. It performs optimally according to a pure (deterministic) and mixed (stochastic) Nash equilibrium, respectively. In contrast, temporal-difference(TD)-learning, covariance-learning, and basic reinforcement learning fail to perform optimally for the stochastic strategy. Spike-based population reinforcement learning, shown to follow the stochastic reward gradient, is therefore a viable candidate to explain automated decision learning of a Nash equilibrium in two-player games.

Incremental Dynamic Updates with First-class Contexts

Highly available software systems occasionally need to be updated while avoiding downtime. Dynamic software updates reduce down-time, but still require the system to reach a quiescent state in which a global update can be performed. This can be difficult for multi-threaded systems. We present a novel approach to dynamic updates using first-class contexts, called Theseus. First-class contexts make global updates unnecessary: existing threads run to termination in an old context, while new threads start in a new, updated context; consistency between contexts is ensured with the help of bidirectional transformations. We show that for multi-threaded systems with coherent memory, first-class contexts offer a practical and flexible approach to dynamic updates, with acceptable overhead.

Adult Pleomorphic Rhabdomyosarcoma: A Multicentre Retrospective Study.

BACKGROUND Pleomorphic rhabdomyosarcoma (RMS) is a rare sub-type of RMS. Optimal treatment remains undefined. PATIENTS AND METHODS Between 1995 and 2014, 45 patients were diagnosed and treated in three tertiary sarcoma Centers (United Kingdom, Switzerland and Germany). Treatment characteristics and outcomes were analyzed. RESULTS The median age at diagnosis was 71.5 years (range=28.4-92.8 years). Median survival for those with localised (n=32, 71.1%) and metastatic disease (n=13, 28.9%) were 12.8 months (95% confidence interval=8.2-34.4) and 7.1 months (95% confidence interval=3.8-11.3) respectively. The relapse rate was 53.8% (four local and 10 distant relapses). In total, 14 (31.1%) patients received first line palliative chemotherapy including multi-agent paediatric chemotherapy schedules (n=3), ifosfamide-doxorubicin (n=4) and single-agent doxorubicin (n=7). Response to chemotherapy was poor (one partial remission with vincristine-actinomycin D-cyclophosphamide and six cases with stable disease). Median progression-free survival was 2.3 (range=1.2-7.3) months. CONCLUSION Pleomorphic RMS is an aggressive neoplasm mainly affecting older patients, associated with a high relapse rate, a poor and short-lived response to standard chemotherapy and an overall poor prognosis for both localised and metastatic disease.

SLA-Driven Simulation of Multi-Tenant Scalable Cloud-Distributed Enterprise Information Systems

Cloud Computing is an enabler for delivering large-scale, distributed enterprise applications with strict requirements in terms of performance. It is often the case that such applications have complex scaling and Service Level Agreement (SLA) management requirements. In this paper we present a simulation approach for validating and comparing SLA-aware scaling policies using the CloudSim simulator, using data from an actual Distributed Enterprise Information System (dEIS). We extend CloudSim with concurrent and multi-tenant task simulation capabilities. We then show how different scaling policies can be used for simulating multiple dEIS applications. We present multiple experiments depicting the impact of VM scaling on both datacenter energy consumption and dEIS performance indicators.