Randomised Mutual Search

We study the efficiency of randomised solutions to the mutual search problem of finding k agents distributed over n nodes. For a restricted class of so-called linear randomised mutual search algorithms we derive a lower bound of k−1 k+1 (n+1) expected calls in the worst case. A randomised algorithm in the shared-coins model matching this bound is also presented. Finally we show that in general more adaptive randomized mutual algorithms perform better (using k−1+k−1k+1− k−2n(n−k) worst case expected calls in the shared coins model) than the lower bound for the restricted case, even when given only private coins. A lower bound of k − 1 + n−k k+1 for this case is also derived

A First Comparison Between LIGO and Virgo Inspiral Search Pipelines

This article reports on a project that is the first step the LIGO Scientific Collaboration and the Virgo Collaboration have taken to prepare for the mutual search for inspiral signals. The project involved comparing the analysis pipelines of the two collaborations on data sets prepared by both sides, containing simulated noise and injected events. The ability of the pipelines to detect the injected events was checked, and a first comparison of how the parameters of the events were recovered has been completed.Comment: GWDAW-9 proceeding

Subgraph Pattern Matching over Uncertain Graphs with Identity Linkage Uncertainty

There is a growing need for methods which can capture uncertainties and answer queries over graph-structured data. Two common types of uncertainty are uncertainty over the attribute values of nodes and uncertainty over the existence of edges. In this paper, we combine those with identity uncertainty. Identity uncertainty represents uncertainty over the mapping from objects mentioned in the data, or references, to the underlying real-world entities. We propose the notion of a probabilistic entity graph (PEG), a probabilistic graph model that defines a distribution over possible graphs at the entity level. The model takes into account node attribute uncertainty, edge existence uncertainty, and identity uncertainty, and thus enables us to systematically reason about all three types of uncertainties in a uniform manner. We introduce a general framework for constructing a PEG given uncertain data at the reference level and develop highly efficient algorithms to answer subgraph pattern matching queries in this setting. Our algorithms are based on two novel ideas: context-aware path indexing and reduction by join-candidates, which drastically reduce the query search space. A comprehensive experimental evaluation shows that our approach outperforms baseline implementations by orders of magnitude

The "Wedding-Ring"

In this paper we develop an agent-based marriage model based on social interaction. We build an population of interacting agents whose chances of marrying depend on the availability of partners, and whose willingness to marry depends on the share of relevant others in their social network who are already married. We then let the typical aggregate age pattern of marriage emerge from the bottom-up. The results of our simulation show that micro-level hypotheses founded on existing theory and evidence on social interaction can reproduce age-at-marriage patterns with both realistic shape and realistic micro-level dynamics.age at marriage, agent-based computational demography, marriage, micro-macro, models, social interaction

From where does the Red Tory speak?, Phillip Blond, theology and public discourse

This is the author's pdf version of an article published in Political Theology. The article can be found at www.politicaltheology.com/PT/This journal examines the role of theology in the public discourse of Philip Blond

Engaged Client-Centered Representation and the Moral Foundations of the Lawyer-Client Relationship

The field of legal ethics, as we know it today, has grown out of thoughtful, systematic grounding of lawyers’ duties in a comprehensive understanding of lawyers’ roles and the situating of lawyers’ roles in underlying theories of law, morality, and justice. Unfortunately, in the process, the field of theoretical legal ethics has mostly lost track of the thing that Freedman insisted was at the heart of a lawyers’ role: the integrity of the lawyer-client relationship. As I will discuss, the field of theoretical legal ethics has developed in ways that are deeply lawyer-centered rather than fundamentally client-centered. I am going to speak about how that happened. I am also going to share some of my ideas about what it would mean to ground a fundamentally client-centered conception of lawyers’ duties to represent a client zealously within the bounds of the law in moral, political, and jurisprudential theory

Connected and internal graph searching

This paper is concerned with the graph searching game. The search number es(G) of a graph G is the smallest number of searchers required to clear G. A search strategy is monotone (m) if no recontamination ever occurs. It is connected (c) if the set of clear edges always forms a connected subgraph. It is internal (i) if the removal of searchers is not allowed. The difficulty of the connected version and of the monotone internal version of the graph searching problem comes from the fact that, as shown in the paper, none of these problems is minor closed for arbitrary graphs, as opposed to all known variants of the graph searching problem. Motivated by the fact that connected graph searching, and monotone internal graph searching are both minor closed in trees, we provide a complete characterization of the set of trees that can be cleared by a given number of searchers. In fact, we show that, in trees, there is only one obstruction for monotone internal search, as well as for connected search, and this obstruction is the same for the two problems. This allows us to prove that, for any tree T, mis(T)= cs(T). For arbitrary graphs, we prove that there is a unique chain of inequalities linking all the search numbers above. More precisely, for any graph G, es(G)= is(G)= ms(G)leq mis(G)leq cs(G)= ics(G)leq mcs(G)=mics(G). The first two inequalities can be strict. In the case of trees, we have mics(G)leq 2 es(T)-2, that is there are exactly 2 different search numbers in trees, and these search numbers differ by a factor of 2 at most.Postprint (published version