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.

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.

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.

Coupling biomechanics to a cellular level model: An approach to patient-specific image driven multi-scale and multi-physics tumor simulation

Modeling of tumor growth has been performed according to various approaches addressing different biocomplexity levels and spatiotemporal scales. Mathematical treatments range from partial differential equation based diffusion models to rule-based cellular level simulators, aiming at both improving our quantitative understanding of the underlying biological processes and, in the mid- and long term, constructing reliable multi-scale predictive platforms to support patient-individualized treatment planning and optimization. The aim of this paper is to establish a multi-scale and multi-physics approach to tumor modeling taking into account both the cellular and the macroscopic mechanical level. Therefore, an already developed biomodel of clinical tumor growth and response to treatment is self-consistently coupled with a biomechanical model. Results are presented for the free growth case of the imageable component of an initially point-like glioblastoma multiforme tumor. The composite model leads to significant tumor shape corrections that are achieved through the utilization of environmental pressure information and the application of biomechanical principles. Using the ratio of smallest to largest moment of inertia of the tumor material to quantify the effect of our coupled approach, we have found a tumor shape correction of 20\% by coupling biomechanics to the cellular simulator as compared to a cellular simulation without preferred growth directions. We conclude that the integration of the two models provides additional morphological insight into realistic tumor growth behavior. Therefore, it might be used for the development of an advanced oncosimulator focusing on tumor types for which morphology plays an important role in surgical and/or radio-therapeutic treatment planning.

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.