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We consider the problem of selecting the most influential members within a social network, in order to disseminate a message as widely as possible. This problem, also referred to as seed selection for influence maximization, has been under intensive investigation since the emergence of social networks. Nonetheless, a large body of existing research is based on the assumption that the network is completely known, whereas little work considers partially observable networks. Yet, due to many issues including the extremely large size of current networks and privacy considerations, assuming full knowledge of the network is rather unrealistic. Despite this, an influencer often wishes to distribute its message far beyond the boundaries of the known network. In this preliminary study, we propose a set of novel heuristic algorithms that specifically target nodes at this boundary, in order to maximize influence across the whole network. We show that these algorithms outperform the state of the art by up to 38% in networks with partial observability.

The aim of the MoreGrasp project is to develop a noninvasive, multimodal user interface including a brain-computer interface (BCI) for intuitive control of a grasp neuroprosthesis to support individuals with high spinal cord injury (SCI) in everyday activities. We describe the current state of the project, including the EEG system, preliminary results of natural movements decoding in people with SCI, the new electrode concept for the grasp neuroprosthesis, the shared control architecture behind the system and the implementation of a user-centered design.

In this paper, we study a service procurement problem with uncertainty as to whether service providers are capable of completing a given task within a specifie deadline. This type of setting is often encountered in large and dynamic multi-agent systems, such as computational Grids or clouds. To effectively deal with this uncertainty, the consumer may dynamically and redundantly procure multiple services over time, in order to increase the probability of success, while at the same time balancing this with the additional procurement costs. However, in order to do this optimally, the consumer requires information about the providers costs and their success probabilities over time. This information is typically held privately by the providers and they may have incentives to misreport this, so as to increase their own profits To address this problem, we introduce a novel mechanism that incentivises self-interested providers to reveal their true costs and capabilities, and we show that this mechanism is ex-post incentive compatible, efficien and individually rational. However, for these properties to hold, it generally needs to compute the optimal solution, which can be intractable in large settings. Therefore, we show how we can generate approximate solutions while maintaining the economic properties of the mechanism. This approximation admits a polynomial-time solution that can be computed in seconds even for hundreds of providers, and we demonstrate empirically that it performs as well as the optimal in typical scenarios. In particularly challenging settings, we show that it still achieves 97% or more of the optimal. Copyright © 2010, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.

Hemodynamic unloading using the ventricular assist device [VAD] results in partial functional recovery of failing hearts that show increased susceptibility to cardiomyocyte apoptosis. The caspase cascade is the central element of the apoptotic process in cells. We therefore tested expression shifts of left ventricular mRNA of caspases and their endogenous inhibitors from 15 patients with VAD support and successful bridging to transplantation using semiquantitative RT-PCR. Cardiac unloading was shown by the reduction in ventricular Pro-ANP mRNA under VAD. No alteration of mRNA expression under VAD could be observed for initiator caspases, for their selective inhibitors or for apoptotic signal molecules from the mitochondrial intermembrane space. Only two unselective cardiac IAPs (inhibitor of apoptosis protein) were increased under VAD with better recovery in younger patients. In conclusion, our findings indicate that successful hemodynamic unloading by VAD support causes only minor, age-dependent recovery in the expression of IAPs, while presumed alterations in antiapoptotic modulator systems upstream of the caspase cascade still remain to be identified.

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The rapid increase in the popularity of electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) is expected to place a considerable strain on the existing electricity grids, due to the high charging rates these vehicles require. In many places, the limited capacity of the local electricity distribution network will be exceeded if many such vehicles are plugged in and left to charge their batteries simultaneously. Thus, it will become increasingly important to schedule the charging of these vehicles, taking into account the vehicle owners’ preferences, and the local constraints on the network. In this paper, we address this setting using online mechanism design and develop a mechanism that incentivises agents (representing vehicle owners) to truthfully reveal their preferences, as well as when the vehicle is available for charging. Existing related online mechanisms assume that agent preferences can be described by a single parameter. However, this is not appropriate for our setting since agents are interested in acquiring multiple units of electricity and can have different preferences for these units, depending on factors such as their expected travel distance. To this end, we extend the state of the art in online mechanism design to multi-valued domains, where agents have non-increasing marginal valuations for each subsequent unit of electricity. Interestingly, we show that, in these domains, the mechanism occasionally requires leaving electricity unallocated to ensure truthfulness. We formally prove that the proposed mechanism is dominant-strategy incentive compatible, and furthermore, we empirically evaluate our mechanism using data from a real-world trial of electric vehicles in the UK. We show that our approach outperforms any fixed price mechanism in terms of allocation efficiency, while performing only slightly worse than a standard scheduling heuristic, which assumes non-strategic agents.

In a wide range of emerging applications, from disaster management to intelligent sensor networks, teams of software agents can be deployed to effectively solve complex distributed problems. To achieve this, agents typically need to communicate locally sensed information to each other. However, in many settings, there are heavy constraints on the communication infrastructure, making it infeasible for every agent to broadcast all relevant information to everyone else. To address this challenge, we investigate how agents can make good local decisions about what information to send to a set of communication channels with limited bandwidths such that the overall system utility is maximised. Specifically, to solve this problem efficiently in large-scale systems with hundreds or thousands of agents, we develop a novel decentralised algorithm. This combines multi-agent learning techniques with fast decision-theoretic reasoning mechanisms that predict the impact a single agent has on the entire system. We show empirically that our algorithm consistently achieves 85% of a hypothetical centralised optimal strategy with full information, and that it significantly outperforms a number of baseline benchmarks (by up to 600%). Copyright © 2012, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.

Liver receptor homologue-1 (LRH-1) is a nuclear receptor involved in the repression of inflammatory processes in the hepatointestinal tract. Here we report that LRH-1 is expressed in macrophages and induced by the Th2 cytokine IL-13 via a mechanism involving STAT6. We show that loss-of-function of LRH-1 in macrophages impedes IL-13-induced macrophage polarization due to impaired generation of 15-HETE PPAR gamma ligands. The incapacity to generate 15-HETE metabolites is at least partially caused by the compromised regulation of CYP1A1 and CYP1B1. Mice with LRH-1-deficient macrophages are, furthermore, highly susceptible to gastrointestinal and systemic Candida albicans infection. Altogether, these results identify LRH-1 as a critical component of the anti-inflammatory and fungicidal response of alternatively activated macrophages that acts upstream from the IL-13-induced 15-HETE/PPAR gamma axis.

Copyright © 2015, International Foundation for Autonomous Agents and Multiagent Systems.Environmental monitoring is important, as it allows authorities to understand the impact of potentially harmful environmental phenomena, such as air pollution, noise or temperature, on public health. To achieve this effectively, participatory sensing is a promising paradigm for large-scale data collection. In this approach, ordinary citizens (non-expert contributors) collect environmental data using low-cost mobile devices. However, these participants are generally self-interested agents having their own goals and making local decisions about where and when to take measurements, if any at all. This can lead to a highly inefficient outcome, where observations are either taken redundantly or do not provide sufficient information about key areas of interest. To address these challenges, a coordination system is necessary to guide and to coordinate participants. This paper proposes such a participatory sensing framework and presents a novel algorithm based on entropy and mutual information criteria, called Local Greedy Search (LGS), that takes into consideration knowledge about human mobility patterns and the inconvenience cost that is incurred by taking measurements. In particular, the algorithm uses a local search technique to map participants to measurements that need to be taken. We empirically evaluate our algorithm on real-world human mobility and air quality data and show that our coordination algorithm outperforms the state-of-the-art greedy and myopic algorithms. In particular, LGS gains 33.4% more information than the best benchmark in realistic city-scale scenarios with hundreds of agents.

In this paper, we develop a novel algorithm that allows service consumer agents to automatically select and provision service provider agents for their workflows in highly dynamic and uncertain computational service economies. In contrast to existing work, our algorithm reasons explicitly about the impact of failures on the overall feasibility of a workflow, and it mitigates them by proactively provisioning multiple providers in parallel for particularly critical tasks and by explicitly planning for contingencies. Furthermore, our algorithm provisions only part of its workflow at any given time, in order to retain flexibility and to decrease the potential for missing negotiated service time slots. We show empirically that current approaches are unable to achieve a high utility in such uncertain and dynamic environments; whereas our algorithm consistently outperforms them over a range of environments. Specifically, our approach can achieve up to a 27-fold increase in utility and successfully completes most workflows within a strict deadline, even when the majority of providers do not honour their contracts. Copyright © 2008, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.

Emerging service-oriented technologies allow software agents to automatically procure distributed services to complete complex tasks. However, in many application scenarios, service providers demand financial remuneration, execution times are uncertain and consumers have deadlines for their tasks. In this paper, we address these issues by developing a novel approach that dynamically procures multiple, redundant services over time, in order to ensure success by the deadline. Specifically, we first present an algorithm for finding optimal procurement solutions, as well as a heuristic algorithm that achieves over 99% of the optimal and is capable of handling thousands of providers. Using experiments, we show that these algorithms achieve an improvement of up to 130% over current strategies that procure only single services. Finally, we consider settings where service costs are not known to the consumer, and introduce several mechanisms that incentivise providers to reveal their costs truthfully and that still achieve up to 95% efficiency.

We introduce a novel online mechanism that schedules the allocation of an expiring and continuously-produced resource to self-interested agents with private preferences. A key application of our mechanism is the charging of pure electric vehicles, where owners arrive dynamically over time, and each owner requires a minimum amount of charge by its departure to complete its next trip. To truthfully elicit the agents preferences in this setting, we introduce the new concept of pre-commitment: Whenever an agent is selected, our mechanism pre-commits to charging the vehicle by its reported departure time, but maintains flexibility about when the charging takes place and at what rate. Furthermore, to make effective allocation decisions we use a model-based approach by modifying Consensus, a well-known online optimisation algorithm. We show that our pre-commitment mechanism with modified Consensus incentivises truthful reporting. Furthermore, through simulations based on real-world data, we show empirically that the average utility achieved by our mechanism is 93% or more of the offline optimal. Copyright © 2012, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.

Environmental monitoring allows authorities to understand the impact of potentially harmful phenomena, such as air pollution, excessive noise and radiation. Recently, there has been considerable interest in participatory sensing as a paradigm for such large-scale data collection because it is cost-effective and able to capture more fine-grained data than traditional approaches that use stationary sensors scattered in cities. In this approach, ordinary citizens (non-expert contributors) collect environmental data using low-cost mobile devices. However, these participants are generally self-interested actors that have their own goals and make local decisions about when and where to take measurements. This can lead to highly inefficient outcomes, where observations are either taken redundantly or do not provide sufficient information about key areas of interest. To address these challenges, it is necessary to guide and to coordinate participants, so they take measurements when it is most informative. To this end, we develop a computationally-efficient coordination algorithm (adaptive Best-Match) that suggests to users when and where to take measurements. Our algorithm exploits probabilistic knowledge of human mobility patterns, but explicitly considers the uncertainty of these patterns and the potential unwillingness of people to take measurements when requested to do so. In particular, our algorithm uses a local search technique, clustering and random simulations to map participants to measurements that need to be taken in space and time. We empirically evaluate our algorithm on a real-world human mobility and air quality dataset and show that it outperforms the current state of the art by up to 24% in terms of utility gained.

Excessive production of reactive oxygen species (ROS) contributes to progression of atherosclerosis, at least in part by causing endothelial dysfunction and inflammatory activation. The class III histone deacetylase SIRT1 has been implicated in extension of lifespan. In the vasculature,SIRT1 gain-of-function using SIRT1 overexpression or activation has been shown to improve endothelial function in mice and rats via stimulation of endothelial nitric oxide (NO) synthase (eNOS). However, the effects of SIRT1 loss-of-function on the endothelium in atherosclerosis remain to be characterized. Thus, we have investigated the endothelial effects of decreased endogenous SIRT1 in hypercholesterolemic ApoE-/- mice. We observed no difference in endothelial relaxation and eNOS (Ser1177) phosphorylation between 20-week old male atherosclerotic ApoE-/- SIRT1+/- and ApoE-/- SIRT1+/+ mice. However, SIRT1 prevented endothelial superoxide production, inhibited NF-kappaB signaling, and diminished expression of adhesion molecules. Treatment of young hypercholesterolemic ApoE-/- SIRT1+/- mice with lipopolysaccharide to boost NF-kappaB signaling led to a more pronounced endothelial expression of ICAM-1 and VCAM-1 as compared to ApoE-/- SIRT1+/+ mice. In conclusion, endogenous SIRT1 diminishes endothelial activation in ApoE-/- mice, but does not affect endothelium-dependent vasodilatation.