The forgiving tree: a self-healing distributed data structure

We consider the problem of self-healing in peer-to-peer networks that are under repeated attack by an omniscient adversary. We assume that the following process continues for up to n rounds where n is the total number of nodes initially in the network: the adversary deletesan arbitrary node from the network, then the network responds by quickly adding a small number of new edges.

We present a distributed data structure that ensures two key properties. First, the diameter of the network is never more than O(log Delta) times its original diameter, where Delta is the maximum degree of the network initially. We note that for many peer-to-peer systems, Delta is polylogarithmic, so the diameter increase would be a O(loglog n) multiplicative factor. Second, the degree of any node never increases by more than 3 over its original degree. Our data structure is fully distributed, has O(1) latency per round and requires each node to send and receive O(1) messages per round. The data structure requires an initial setup phase that has latency equal to the diameter of the original network, and requires, with high probability, each node v to send O(log n) messages along every edge incident to v. Our approach is orthogonal and complementary to traditional topology-based approaches to defending against attack.

Brief announcement: Maintaining large dense subgraphs on dynamic networks

In distributed networks, some groups of nodes may have more inter-connections, perhaps due to their larger bandwidth availability or communication requirements. In many scenarios, it may be useful for the nodes to know if they form part of a dense subgraph, e.g., such a dense subgraph could form a high bandwidth backbone for the network. In this work, we address the problem of self-awareness of nodes in a dynamic network with regards to graph density, i.e., we give distributed algorithms for maintaining dense subgraphs (subgraphs that the member nodes are aware of). The only knowledge that the nodes need is that of the dynamic diameter D, i.e., the maximum number of rounds it takes for a message to traverse the dynamic network. For our work, we consider a model where the number of nodes are fixed, but a powerful adversary can add or remove a limited number of edges from the network at each time step. The communication is by broadcast only and follows the CONGEST model in the sense that only messages of O(log n) size are permitted, where n is the number of nodes in the network. Our algorithms are continuously executed on the network, and at any time (after some initialization) each node will be aware if it is part (or not) of a particular dense subgraph. We give algorithms that approximate both the densest subgraph, i.e., the subgraph of the highest density in the network, and the at-least-k-densest subgraph (for a given parameter k), i.e., the densest subgraph of size at least k. We give a (2 + e)-approximation algorithm for the densest subgraph problem. The at-least-k-densest subgraph is known to be NP-hard for the general case in the centralized setting and the best known algorithm gives a 2-approximation. We present an algorithm that maintains a (3+e)-approximation in our distributed, dynamic setting. Our algorithms run in O(Dlog n) time. © 2012 Authors.

Selecting users in energy-efficient collaborative spectrum sensing

Cognitive radio network is defined as an intelligent wireless communication network that should be able to adaptively reconfigure its communication parameters to meet the demands of the transmission network or the user. In this context one possible way to utilize unused licensed spectrum without interfering with incumbent users is through spectrum sensing. Due to channel uncertainties, single cognitive (opportunistic) user cannot make a decision reliably and hence collaboration among multiple users is often required. Here collaboration among large number of users tends to increase power consumption and introduces large communication overheads. In this paper, the number of collaborating users is optimized in order to maximize the probability of detection for any given power budget in a cognitive radio network, while satisfying constraints on the false alarm probability. We show that for the maximum probability of detection, collaboration of only a subset of available opportunistic users is required. The robustness of our proposed spectrum sensing algorithm is also examined under flat Rayleigh fading and AWGN channel conditions.

Sublinear bounds for randomized leader election

This paper concerns randomized leader election in synchronous distributed networks. A distributed leader election algorithm is presented for complete n-node networks that runs in O(1) rounds and (with high probability) uses only O(√ √nlog^3/2n) messages to elect a unique leader (with high probability). When considering the "explicit" variant of leader election where eventually every node knows the identity of the leader, our algorithm yields the asymptotically optimal bounds of O(1) rounds and O(. n) messages. This algorithm is then extended to one solving leader election on any connected non-bipartite n-node graph G in O(τ(. G)) time and O(τ(G)n√log^3/2n) messages, where τ(. G) is the mixing time of a random walk on G. The above result implies highly efficient (sublinear running time and messages) leader election algorithms for networks with small mixing times, such as expanders and hypercubes. In contrast, previous leader election algorithms had at least linear message complexity even in complete graphs. Moreover, super-linear message lower bounds are known for time-efficient deterministic leader election algorithms. Finally, we present an almost matching lower bound for randomized leader election, showing that Ω(n) messages are needed for any leader election algorithm that succeeds with probability at least 1/. e+. ε, for any small constant ε. >. 0. We view our results as a step towards understanding the randomized complexity of leader election in distributed networks.

Women’s Perceptions and Experiences of Fetal Macrosomia

Objective
to explore women's perceptions and experiences of pregnancy and childbirth following birth of a macrosomic infant (birth weight ≥4000 g).

Methods
a qualitative design utilising interviews conducted 13–19 weeks post partum in women's homes. The study was conducted in one Health and Social Care Trust in Northern Ireland between January and September 2010. Participants were identified from a larger cohort of women recruited to a prospective study exploring the impact of physical activity and nutrition on macrosomia. Eleven women who delivered macrosomic infants participated in this phase of the study.

Findings
four overarching themes emerged: preparation for delivery; physical and emotional impact of macrosomia; professional relations and perceptions of macrosomia. Findings highlighted the importance of communication with health professionals in relation to both prediction of macrosomia and decision making about childbirth, and offers further understanding into the physical and emotional impact of having a macrosomic infant on women. Furthermore, there was evidence that beliefs and perceptions relating to macrosomia may influence birth experiences and uptake of health promotion messages.

Key conclusions and implications for practice
this study provides important insight into women's experiences of macrosomia throughout the perinatal period and how they were influenced by previous birth experiences, professional relations and personal perceptions and beliefs about macrosomia. Pregnant women at risk of having a macrosomic infant may require extra support throughout the antenatal period continuing into the postnatal period. Support needs to be tailored to the woman's information needs, with time allocated to explore previous birth experiences, beliefs about macrosomia and options for childbirth.

DEX: Self-Healing Expanders

We present a fully-distributed self-healing algorithm DEX, that maintains a constant degree expander network in a dynamic setting. To the best of our knowledge, our algorithm provides the first efficient distributed construction of expanders - whose expansion properties hold deterministically - that works even under an all-powerful adaptive adversary that controls the dynamic changes to the network (the adversary has unlimited computational power and knowledge of the entire network state, can decide which nodes join and leave and at what time, and knows the past random choices made by the algorithm). Previous distributed expander constructions typically provide only probabilistic guarantees on the network expansion which rapidly degrade in a dynamic setting, in particular, the expansion properties can degrade even more rapidly under adversarial insertions and deletions. Our algorithm provides efficient maintenance and incurs a low overhead per insertion/deletion by an adaptive adversary: only O(log n) rounds and O(log n) messages are needed with high probability (n is the number of nodes currently in the network). The algorithm requires only a constant number of topology changes. Moreover, our algorithm allows for an efficient implementation and maintenance of a distributed hash table (DHT) on top of DEX, with only a constant additional overhead. Our results are a step towards implementing efficient self-healing networks that have guaranteed properties (constant bounded degree and expansion) despite dynamic changes.

An Extended Event Reasoning Framework for Decision Support under Uncertainty

To provide in-time reactions to a large volume of surveil- lance data, uncertainty-enabled event reasoning frameworks for CCTV and sensor based intelligent surveillance system have been integrated to model and infer events of interest. However, most of the existing works do not consider decision making under uncertainty which is important for surveillance operators. In this paper, we extend an event reasoning framework for decision support, which enables our framework to predict, rank and alarm threats from multiple heterogeneous sources.

Development of a continuing professional development website to raise awareness of preconception counselling and pre-pregnancy care for Women with Diabetes

Aims: Pre-pregnancy care reduces the risk of adverse pregnancy outcomes in women with diabetes, yet the majority of women receive suboptimal care due to poor preconception counselling rates and a lack of awareness about the importance of specialised pre-pregnancy care. The primary aim was to develop a continuing professional development (CPD) resource for healthcare professionals (HCPs) who work with women with diabetes to facilitate preconception counselling with this group.

Methods: The website was developed under the direction of a multidisciplinary team, adhering to NICE guidelines. The tone, key messages and format are informed by the “Women with Diabetes” preconception counselling website, www.womenwithdiabetes.net, an existing resource which is effective in helping women to be better prepared for pregnancy.Results: This e-learning resource will give HCPs the necessary knowledge and tools to prepare women with diabetes to plan for pregnancy. The website features women with diabetes sharing their views and experiences, alongside an evidence-based commentary and key messages from research papers and clinical guidelines. It comprises two modules: “Planning for Pregnancy”, focusing on contraception, risks and planning; and “Diabetes and Pregnancy”, focusing on support during pregnancy with an overview of each trimester of pregnancy.

Conclusion: This website will be a useful CPD resource for all HCPs working with women with diabetes, providing a certificate on completion. This resource will empower HCPs to engage in preconception counselling with women with diabetes by providing the HCP with a greater understanding of the specific needs of women with diabetes both preconception and during pregnancy.

A Bayesian method for detecting stellar flares

We present a Bayesian-odds-ratio-based algorithm for detecting stellar flares in light-curve data. We assume flares are described by a model in which there is a rapid rise with a half-Gaussian profile, followed by an exponential decay. Our signal model also contains a polynomial background model required to fit underlying light-curve variations in the data, which could otherwise partially mimic a flare. We characterize the false alarm probability and efficiency of this method under the assumption that any unmodelled noise in the data is Gaussian, and compare it with a simpler thresholding method based on that used in Walkowicz et al. We find our method has a significant increase in detection efficiency for low signal-to-noise ratio (S/N) flares. For a conservative false alarm probability our method can detect 95 per cent of flares with S/N less than 20, as compared to S/N of 25 for the simpler method. We also test how well the assumption of Gaussian noise holds by applying the method to a selection of 'quiet' Kepler stars. As an example we have applied our method to a selection of stars in Kepler Quarter 1 data. The method finds 687 flaring stars with a total of 1873 flares after vetos have been applied. For these flares we have made preliminary characterizations of their durations and and S/N.

Reducing SCADA System Nuisance Alarms in the Water Industry in Northern Ireland

The advancement of telemetry control for the water industry has increased the difficulty of 14 managing large volumes of nuisance alarms (i.e. alarms that do not require a response). The aim 15 of this study was to identify and reduce the number of nuisance alarms that occur for Northern 16 Ireland (NI) Water by carrying-out alarm duration analysis to determine the appropriate length of 17 persistence (an advanced alarm management tool) that could be applied. All data was extracted 18 from TelemWeb (NI Water’s telemetry monitoring system) and analysed in Excel. Over a 6 19 week period, an average of 40,000 alarms occurred per week. The alarm duration analysis, which 20 has never been implemented before by NI Water, found that an average of 57% of NI Water 21 alarms had a duration of <5 minutes. Applying 5 minute persistence; therefore, could prevent an 22 average 26,816 nuisance alarms per week. Most of these alarms were from wastewater assets.

Fasciola hepatica vaccine: We may not be there yet but we're on the right road

Major advances have been made in identifying potential vaccine molecules for the control of fasciolosis in livestock but we have yet to reach the level of efficacy required for commercialisation. The pathogenesis of fasciolosis is associated with liver damage that is inflicted by migrating and feeding immature flukes as well as host inflammatory immune responses to parasite-secreted molecules and tissue damage alarm signals. Immune suppression/modulation by the parasites prevents the development of protective immune responses as evidenced by the lack of immunity observed in naturally and experimentally infected animals. In our opinion, future efforts need to focus on understanding how parasites invade and penetrate the tissues of their hosts and how they potentiate and control the ensuing immune responses, particularly in the first days of infection. Emerging 'omics' data employed in an unbiased approach are helping us understand liver fluke biology and, in parallel with new immunological data, to identify molecules that are essential to parasite development and accessible to vaccine-induced immune responses.

On the Complexity of Universal Leader Election

Electing a leader is a fundamental task in distributed computing. In its implicit version, only the leader must know who is the elected leader. This article focuses on studying the message and time complexity of randomized implicit leader election in synchronous distributed networks. Surprisingly, the most "obvious" complexity bounds have not been proven for randomized algorithms. In particular, the seemingly obvious lower bounds of Ω(m) messages, where m is the number of edges in the network, and Ω(D) time, where D is the network diameter, are nontrivial to show for randomized (Monte Carlo) algorithms. (Recent results, showing that even Ω(n), where n is the number of nodes in the network, is not a lower bound on the messages in complete networks, make the above bounds somewhat less obvious). To the best of our knowledge, these basic lower bounds have not been established even for deterministic algorithms, except for the restricted case of comparison algorithms, where it was also required that nodes may not wake up spontaneously and that D and n were not known. We establish these fundamental lower bounds in this article for the general case, even for randomized Monte Carlo algorithms. Our lower bounds are universal in the sense that they hold for all universal algorithms (namely, algorithms that work for all graphs), apply to every D, m, and n, and hold even if D, m, and n are known, all the nodes wake up simultaneously, and the algorithms can make any use of node's identities. To show that these bounds are tight, we present an O(m) messages algorithm. An O(D) time leader election algorithm is known. A slight adaptation of our lower bound technique gives rise to an Ω(m) message lower bound for randomized broadcast algorithms. 

An interesting fundamental problem is whether both upper bounds (messages and time) can be reached simultaneously in the randomized setting for all graphs. The answer is known to be negative in the deterministic setting. We answer this problem partially by presenting a randomized algorithm that matches both complexities in some cases. This already separates (for some cases) randomized algorithms from deterministic ones. As first steps towards the general case, we present several universal leader election algorithms with bounds that tradeoff messages versus time. We view our results as a step towards understanding the complexity of universal leader election in distributed networks.

Penser la guerre au XXIe siècle

Ce numéro était déjà sous presse quand, le 13 novembre 2015, Paris était une nouvelle fois la cible d’attentats terroristes d’une ampleur sans précédent, faisant plus d’une centaine de morts. Le Président François Hollande parla cette fois, de manière répétée, d’‘un acte de guerre’. Des voix solidaires se sont élevées des quatre coins de la planète, soulignant bien que, à travers la France, ce sont bien les valeurs qu’elle représente et qu’elle partage avec nombre de pays que les assassins de Daech visaient. Parmi tous les messages de solidarité, il nous semble important de souligner celui d’Hassan Rohani, le Président iranien, et celui d’Abdelaziz Bouteflika, le Président algérien: immédiatement, le premier ‘condamn[ait] avec vigueur ces crimes contre l'humanité et présent[ait] [s]es condoléance au peuple français endeuillé et au gouvernement’; le second dénonçait sans réserve ‘cette horreur planifiée [qui] constitue un véritable crime contre l'humanité’. Quant à Anouar Kbibech, le nouveau président du Conseil français du culte musulman, il ‘condamn[ait] avec la plus grande vigueur ces attaques inqualifiables’ et ‘appel[ait] à se regrouper autour de ces valeurs qui font la France’. Plus que jamais, il faut éviter les amalgames pour ne pas faire le jeu des minorités extrémistes.

This is Abuse… Or is it? Domestic abuse perpetrators’ responses to anti-domestic violence publicity

Social marketing has become a key component of policy initiatives aimed at reducing the incidence of domestic abuse. However, its efficacy remains debated, with most measures of effectiveness being somewhat crude. More subtle effects of social marketing, such as the boomerang effect whereby the message engenders the opposite effect to that intended, have been detected, suggesting a need for modes of analysis sensitive to the multiple ways in which viewers react to social opprobrium. This article attempts to deliver just this. It begins with a short history and critique of the concept of social marketing. It then proceeds to explore the utility of the more complex notion that viewers often identify with the subject positions thrown open by social marketing on a quite temporary basis, before reconfiguring them. Using the responses of domestic abuse perpetrators exposed to the UK Government’s This is Abuse campaign film, the article shows how contradictory identifications with both anti-violence messages and victim-blaming discourses are negotiated by those young men prone to perpetrating domestic abuse. The article concludes by exploring how effectiveness might be better conceptualised and assessed with regard to the impact of anti-violence social marketing that speaks to domestic abuse perpetrators.

Reconstructing ethical conduct within financial firms

This chapter explores whether ethical cultures can be created within a financial market context. Ongoing regulatory and legal actions, and press coverage of these, suggest that a definition of ethical problems in terms of ‘rogue traders’ and ‘bad apples’ would be inadequate, since entire business areas have been resorting to collusive illegal behaviour. The concept of ‘bad barrels’ seems to capture the situation rather better: the culture of firms fails to discourage transgression and indeed supports it. Unpacking the links between regulatory objectives and the cultural settings of firms and their employees, this chapter questions the chances of success of measures such as enhanced controls on individuals and restructured reward mechanisms. Financial firms typically have very flat, nodal structures, within which traders conceptualise themselves as an elite, in contrast to back office staff and also in contrast to managers. Traders’ functions and their occupational mobility mean that their linkages and attachments may be much stronger with others outside ‘their’ firm than their firm and those within it. Performance, camaraderie and their linkages are important in all work situations, yet all the more so for traders in financial markets. Thus, whether regulators and senior management combine to send a clear and consistent message to traders – or whether the logic of the financial marketplace leads some firms to continue send conflicting or ambivalent messages to them – misconduct is likely to continue to be a tough nut to crack.