Bounded Decentralised Coordination over Multiple Objectives

We propose the bounded multi-objective max-sum algorithm (B-MOMS), the first decentralised coordination algorithm for multi-objective optimisation problems. B-MOMS extends the max-sum message-passing algorithm for decentralised coordination to compute bounded approximate solutions to multi-objective decentralised constraint optimisation problems (MO-DCOPs). Specifically, we prove the optimality of B-MOMS in acyclic constraint graphs, and derive problem dependent bounds on its approximation ratio when these graphs contain cycles. Furthermore, we empirically evaluate its performance on a multi-objective extension of the canonical graph colouring problem. In so doing, we demonstrate that, for the settings we consider, the approximation ratio never exceeds 2, and is typically less than 1.5 for less-constrained graphs. Moreover, the runtime required by B-MOMS on the problem instances we considered never exceeds 30 minutes, even for maximally constrained graphs with $100$ agents. Thus, B-MOMS brings the problem of multi-objective optimisation well within the boundaries of the limited capabilities of embedded agents

Fuzzy argumentation for trust

In an open Multi-Agent System, the goals of agents acting on behalf of their owners often conflict with each other. Therefore, a personal agent protecting the interest of a single user cannot always rely on them. Consequently, such a personal agent needs to be able to reason about trusting (information or services provided by) other agents. Existing algorithms that perform such reasoning mainly focus on the immediate utility of a trusting decision, but do not provide an explanation of their actions to the user. This may hinder the acceptance of agent-based technologies in sensitive applications where users need to rely on their personal agents. Against this background, we propose a new approach to trust based on argumentation that aims to expose the rationale behind such trusting decisions. Our solution features a separation of opponent modeling and decision making. It uses possibilistic logic to model behavior of opponents, and we propose an extension of the argumentation framework by Amgoud and Prade to use the fuzzy rules within these models for well-supported decisions

Active Learning of Gaussian Processes for Spatial Functions in Mobile Sensor Networks

This paper proposes a spatial function modeling approach using mobile sensor networks, which potentially can be used for environmental surveillance applications. The mobile sensor nodes are able to sample the point observations of an 2D spatial function. On the one hand, they will use the observations to generate a predictive model of the spatial function. On the other hand, they will make collective motion decisions to move into the regions where high uncertainties of the predictive model exist. In the end, an accurate predictive model is obtained in the sensor network and all the mobile sensor nodes are distributed in the environment with an optimized pattern. Gaussian process regression is selected as the modeling technique in the proposed approach. The hyperparameters of Gaussian process model are learned online to improve the accuracy of the predictive model. The collective motion control of mobile sensor nodes is based on a locational optimization algorithm, which utilizes an information entropy of the predicted Gaussian process to explore the environment and reduce the uncertainty of predictive model. Simulation results are provided to show the performance of the proposed approach. © 2011 IFAC

Decentralised coordination of information gathering agents

Unmanned sensors are rapidly becoming the de facto means of achieving situational awareness — the ability to make sense of, and predict what is happening in an environment — in disaster management, military reconnaissance, space exploration, and climate research. In these domains, and many others besides, their use reduces the need for exposing humans to hostile, impassable or polluted environments. Whilst these sensors are currently often pre-programmed or remotely controlled by human operators, there is a clear trend toward making these sensors fully autonomous, thus enabling them to make decisions without human intervention.Full autonomy has two clear benefits over pre-programming and human remote control. First, in contrast to sensors with pre-programmed motion paths, autonomous sensors are better able to adapt to their environment, and react to a priori unknown external events or hardware failure. Second, autonomous sensors can operate in large teams that would otherwise be too complex to control by human operators. The key benefit of this is that a team of cheap, small sensors can achieve through cooperation the same results as individual large, expensive sensors — with more flexibility and robustness.In light of the importance of autonomy and cooperation, we adopt an agent-based perspective on the operation of the sensors. Within this view, each sensor becomes an information gathering agent. As a team, these agents can then direct their collective activity towards collecting information from their environment with the aim of providingaccurate and up-to-date situational awareness.Against this background, the central problem we address in this thesis is that of achieving accurate situational awareness through the coordination of multiple information gathering agents. To achieve general and principled solutions to this problem, we formulate a generic problem definition, which captures the essential properties of dynamic environments. Specific instantiations of this generic problem span a broad spectrum of concrete application domains, of which we study three canonical examples: monitoring environmental phenomena, wide area surveillance, and search and patrol.The main contributions of this thesis are decentralised coordination algorithms that solve this general problem with additional constraints and requirements, and can be grouped into two categories. The first category pertains to decentralised coordination of fixed information gathering agents. For these agents, we study the application of decentralised coordination during two distinct phases of the agents’ life cycle: deployment and operation. For the former, we develop an efficient algorithm for maximising the quality of situational awareness, while simultaneously constructing a reliable communication network between the agents. Specifically, we present a novel approach to the NP-hard problem of frequency allocation, which deactivates certain agents such that the problem can be provably solved in polynomial time. For the latter, we address the challenge of coordinating these agents under the additional assumption that their control parameters are continuous. In so doing, we develop two extensions to the max-sum message passing algorithm for decentralised welfare maximisation, which constitute the first two algorithms for distributed constraint optimisation problems (DCOPs) with continuous variables—CPLF-MS (for linear utility functions) and HCMS (for non-linear utility functions).The second category relates to decentralised coordination of mobile information gathering agents whose motion is constrained by their environment. For these agents, we develop algorithms with a receding planning horizon, and a non-myopic planning horizon. The former is based on the max-sum algorithm, thus ensuring an efficient and scalable solution, and constitutes the first online agent-based algorithm for the domains of pursuit-evasion, patrolling and monitoring environmental phenomena. The second uses sequential decision making techniques for the offline computation of patrols — infinitely long paths designed to continuously monitor a dynamic environment — which are subsequently improved on at runtime through decentralised coordination.For both topics, the algorithms are designed to satisfy our design requirements of quality of situational awareness, adaptiveness (the ability to respond to a priori unknown events), robustness (the ability to degrade gracefully), autonomy (the ability of agents to make decisions without the intervention of a centralised controller), modularity (the ability to support heterogeneous agents) and performance guarantees (the ability to give a lower bound on the quality of the achieved situational awareness). When taken together, the contributions presented in this thesis represent an advance in the state of the art of decentralised coordination of information gathering agents, and a step towards achieving autonomous control of unmanned sensors

WS1.3 Respiratory microbiota dynamics in newborns with cystic fibrosis and healthy controls: A longitudinal study

IEEEMost malware are introduced into a computer system by applications that communicate with the outside world. These applications (called portals) are key components for system security. This paper presents an efficient anti-malware framework un

The Extracellular Matrix Component Psl Provides Fast-Acting Antibiotic Defense in Pseudomonas aeruginosa Biofilms

Bacteria within biofilms secrete and surround themselves with an extracellular matrix, which serves as a first line of defense against antibiotic attack. Polysaccharides constitute major elements of the biofilm matrix and are implied in surface adhesion and biofilm organization, but their contributions to the resistance properties of biofilms remain largely elusive. Using a combination of static and continuous-flow biofilm experiments we show that Psl, one major polysaccharide in the Pseudomonas aeruginosa biofilm matrix, provides a generic first line of defense toward antibiotics with diverse biochemical properties during the initial stages of biofilm development. Furthermore, we show with mixed-strain experiments that antibiotic-sensitive “non-producing” cells lacking Psl can gain tolerance by integrating into Psl-containing biofilms. However, non-producers dilute the protective capacity of the matrix and hence, excessive incorporation can result in the collapse of resistance of the entire community. Our data also reveal that Psl mediated protection is extendible to E. coli and S. aureus in co-culture biofilms. Together, our study shows that Psl represents a critical first bottleneck to the antibiotic attack of a biofilm community early in biofilm development.National Institutes of Health (U.S.). National Institute of Environmental Health Sciences (Training Grant in Toxicology 5 T32 ES7020-37

Antibiotic strategies for eradicating Pseudomonas aeruginosa in people with cystic fibrosis

Background: Respiratory tract infection with Pseudomonas aeruginosa occurs inmost people with cystic fibrosis. Once chronic infection is established, Pseudomonas aeruginosa is virtually impossible to eradicate and is associated with increased mortality and morbidity. Early infection may be easier to eradicate. This is an update of a Cochrane review first published in 2003, and previously updated in 2006 and 2009. Objectives: To determine whether antibiotic treatment of early Pseudomonas aeruginosa infection in children and adults with cystic fibrosis eradicates the organism, delays the onset of chronic infection, and results in clinical improvement. To evaluate whether there is evidence that a particular antibiotic strategy is superior to or more cost-effective than other strategies and to compare the adverse effects of different antibiotic strategies (including respiratory infection with other micro-organisms). Search methods: We searched the Cochrane Cystic Fibrosis and Genetic Disorders Group Trials Register comprising references identified from comprehensive electronic database searches and handsearches of relevant journals and abstract books of conference proceedings. Most recent search: 08 September 2014. Selection criteria: We included randomised controlled trials of people with cystic fibrosis, in whom Pseudomonas aeruginosa had recently been isolated from respiratory secretions. We compared combinations of inhaled, oral or intravenous antibiotics with placebo, usual treatment or other combinations of inhaled, oral or intravenous antibiotics. We excluded non-randomised trials, cross-over trials, and those utilising historical controls. Data collection and analysis: Both authors independently selected trials, assessed risk of bias and extracted data. Main results: The search identified 49 trials; seven trials (744 participants) with a duration between 28 days and 27 months were eligible for inclusion. Three of the trials are over 10 years old and their results may be less applicable today given the changes in standard treatment. Some of the trials had low numbers of participants and most had relatively short follow-up periods; however, there was generally a low risk of bias from missing data. In most trials it was difficult to blind participants and clinicians to treatment given the interventions and comparators used. Two trials were supported by the manufacturers of the antibiotic used. Evidence from two trials (38 participants) at the two-month time-point showed treatment of early Pseudomonas aeruginosa infection with inhaled tobramycin results in microbiological eradication of the organism from respiratory secretions more often than placebo, odds ratio 0.15 (95% confidence interval 0.03 to 0.65) and data from one of these trials, with longer follow up, suggested that this effect may persist for up to 12 months. One randomised controlled trial (26 participants) compared oral ciprofloxacin and nebulised colistin versus usual treatment. Results after two years suggested treatment of early infection results in microbiological eradication of Pseudomonas aeruginosa more often than no anti-pseudomonal treatment, odds ratio 0.12 (95% confidence interval 0.02 to 0.79). One trial comparing 28 days to 56 days treatment with nebulised tobramycin solution for inhalation in 88 participants showed that both treatments were effective and well-tolerated, with no notable additional improvement with longer over shorter duration of therapy. However, this trial was not powered to detect non- inferiority or equivalence. A trial of oral ciprofloxacin with inhaled colistin versus nebulised tobramycin solution for inhalation alone (223 participants) failed to show a difference between the two strategies, although it was underpowered to show this. A further trial of inhaled colistin with oral ciprofloxacin versus nebulised tobramycin solution for inhalation with oral ciprofloxacin also showed no superiority of the former, with increased isolation of Stenotrophomonas maltophilia in both groups. A recent, large trial in 306 children aged between one and 12 years compared cycled nebulised tobramycin solution for inhalation to culture-based therapy and also ciprofloxacin to placebo. The primary analysis showed no difference in time to pulmonary exacerbation or proportion of Pseudomonas aeruginosa positive cultures. An analysis performed in this review (not adjusted for age) showed fewer participants in the cycled therapy group with one or more isolates of Pseudomonas aeruginosa, odds ratio 0.51 (95% CI 0.31 to 0.28). Authors’ conclusions: We found that nebulised antibiotics, alone or in combination with oral antibiotics, were better than no treatment for early infection with Pseudomonas aeruginosa. Eradication may be sustained for up to two years. There is insufficient evidence to determine whether antibiotic strategies for the eradication of early Pseudomonas aeruginosa decrease mortality or morbidity, improve quality of life, or are associated with adverse effects compared to placebo or standard treatment. Four trials of two active treatments have failed to show differences in rates of eradication of Pseudomonas aeruginosa. There have been no published randomised controlled trials that investigate the efficacy of intravenous antibiotics to eradicate Pseudomonas aeruginosa in cystic fibrosis. Overall, there is still insufficient evidence from this review to state which antibiotic strategy should be used for the eradication of early Pseudomonas aeruginosa infection in cystic fibrosis

Yield of Targeted Polymerase Chain Reaction in Probable Early-Onset Sepsis:A Prospective Cohort Study in Term and Near-Term Neonates With Negative Blood Culture Results

Background:Discriminating noninfected from infected neonatal cases remains challenging, and subsequently many neonates are treated with antibiotics in the first week of life. We aimed to study the additional value of a targeted polymerase chain reaction (PCR) for group B streptococcus (GBS) and Escherichia coli on leftover blood culture media from term and near-term neonates with probable early-onset sepsis (EOS). Methods: Leftover blood culture material from neonates participating in the RAIN study was stored after 5 days of incubation. The RAIN study evaluated intravenous-oral antibiotic switch in probable bacterial infection, defined as risk factors and/or clinical signs and elevated inflammatory parameters but negative blood culture results. We applied 2 targeted PCRs for GBS and E coli, the main pathogens in EOS, and analyzed the samples batchwise in triplicate for each PCR. Results:PCR was performed in triplicate on blood culture media from 284 neonates. In 23 neonates, the PCR result was positive (3 cycle threshold values &lt;37) for GBS (n = 1) or E coli (n = 22). Inflammatory parameters did not discriminate for positive PCR result, nor did risk factors for sepsis, such as maternal GBS status and prolonged rupture of membranes. However, 96% of neonates with a positive PCR result were born vaginally vs 74% in the PCR-negative group (P = .05); furthermore, 96% vs 81% (P = .21) of neonates had clinical symptoms. Conclusions: Blood culture–negative “probable” EOS in neonates is accompanied by an 8% rate of PCR positivity, suggesting low-grade bacteriemia after birth with yet unclear clinical consequences. Further research should focus on how PCR can contribute to more targeted antibiotic use of neonates, specifically in those highly suspected of infection but in the absence of a positive blood culture result.</p

Stratified Management for Bacterial Infections in Late Preterm and Term Neonates:Current Strategies and Future Opportunities Toward Precision Medicine

Bacterial infections remain a major cause of morbidity and mortality in the neonatal period. Therefore, many neonates, including late preterm and term neonates, are exposed to antibiotics in the first weeks of life. Data on the importance of inter-individual differences and disease signatures are accumulating. Differences that may potentially influence treatment requirement and success rate. However, currently, many neonates are treated following a “one size fits all” approach, based on general protocols and standard antibiotic treatment regimens. Precision medicine has emerged in the last years and is perceived as a new, holistic, way of stratifying patients based on large-scale data including patient characteristics and disease specific features. Specific to sepsis, differences in disease susceptibility, disease severity, immune response and pharmacokinetics and -dynamics can be used for the development of treatment algorithms helping clinicians decide when and how to treat a specific patient or a specific subpopulation. In this review, we highlight the current and future developments that could allow transition to a more precise manner of antibiotic treatment in late preterm and term neonates, and propose a research agenda toward precision medicine for neonatal bacterial infections.</p