MLGWSC-1: The first Machine Learning Gravitational-Wave Search Mock Data Challenge

We present the results of the first Machine Learning Gravitational-Wave Search Mock Data Challenge (MLGWSC-1). For this challenge, participating groups had to identify gravitational-wave signals from binary black hole mergers of increasing complexity and duration embedded in progressively more realistic noise. The final of the 4 provided datasets contained real noise from the O3a observing run and signals up to a duration of 20 seconds with the inclusion of precession effects and higher order modes. We present the average sensitivity distance and runtime for the 6 entered algorithms derived from 1 month of test data unknown to the participants prior to submission. Of these, 4 are machine learning algorithms. We find that the best machine learning based algorithms are able to achieve up to 95% of the sensitive distance of matched-filtering based production analyses for simulated Gaussian noise at a false-alarm rate (FAR) of one per month. In contrast, for real noise, the leading machine learning search achieved 70%. For higher FARs the differences in sensitive distance shrink to the point where select machine learning submissions outperform traditional search algorithms at FARs $\geq 200$ per month on some datasets. Our results show that current machine learning search algorithms may already be sensitive enough in limited parameter regions to be useful for some production settings. To improve the state-of-the-art, machine learning algorithms need to reduce the false-alarm rates at which they are capable of detecting signals and extend their validity to regions of parameter space where modeled searches are computationally expensive to run. Based on our findings we compile a list of research areas that we believe are the most important to elevate machine learning searches to an invaluable tool in gravitational-wave signal detection

MLGWSC-1: The first Machine Learning Gravitational-Wave Search Mock Data Challenge

We present the results of the first Machine Learning Gravitational-Wave Search Mock Data Challenge (MLGWSC-1). For this challenge, participating groups had to identify gravitational-wave signals from binary black hole mergers of increasing complexity and duration embedded in progressively more realistic noise. The final of the 4 provided datasets contained real noise from the O3a observing run and signals up to a duration of 20 seconds with the inclusion of precession effects and higher order modes. We present the average sensitivity distance and runtime for the 6 entered algorithms derived from 1 month of test data unknown to the participants prior to submission. Of these, 4 are machine learning algorithms. We find that the best machine learning based algorithms are able to achieve up to 95% of the sensitive distance of matched-filtering based production analyses for simulated Gaussian noise at a false-alarm rate (FAR) of one per month. In contrast, for real noise, the leading machine learning search achieved 70%. For higher FARs the differences in sensitive distance shrink to the point where select machine learning submissions outperform traditional search algorithms at FARs $\geq 200$ per month on some datasets. Our results show that current machine learning search algorithms may already be sensitive enough in limited parameter regions to be useful for some production settings. To improve the state-of-the-art, machine learning algorithms need to reduce the false-alarm rates at which they are capable of detecting signals and extend their validity to regions of parameter space where modeled searches are computationally expensive to run. Based on our findings we compile a list of research areas that we believe are the most important to elevate machine learning searches to an invaluable tool in gravitational-wave signal detection.Comment: 25 pages, 6 figures, 4 tables, additional material available at https://github.com/gwastro/ml-mock-data-challenge-

Efficient processing of XPath queries with structured overlay networks

Abstract. Non-trivial search predicates beyond mere equality are at the current focus of P2P research. Structured queries, as an important type of non-trivial search, have been studied extensively mainly for unstructured P2P systems so far. As unstructured P2P systems do not use indexing, structured queries are very easy to implement since they can be treated equally to any other type of query. However, this comes at the expense of very high bandwidth consumption and limitations in terms of guarantees and expressiveness that can be provided. Structured P2P systems are an efficient alternative as they typically offer logarithmic search complexity in the number of peers. Though the use of a distributed index (typically a distributed hash table) makes the implementation of structured queries more efficient, it also introduces considerable complexity, and thus only a few approaches exist so far. In this paper we present a first solution for efficiently supporting structured queries, more specifically, XPath queries, in structured P2P systems. For the moment we focus on supporting queries with descendant axes (“//”) and wildcards (“*”) and do not address joins. The results presented in this paper provide foundational basic functionalities to be used by higher-level query engines for more efficient, complex query support.

Query Reformulation in PDMS Based on Social Relevance

International audienceWe consider peer-to-peer data management systems (PDMS), where each peer maintains mappings between its schema and some acquaintances, along with social links with peer friends. In this context, we deal with reformulating conjunctive queries from a peer’s schema into other peer’s schemas. Precisely, queries against a peer node are rewritten into queries against other nodes using schema mappings thus obtaining query rewritings. Unfortunately, not all the obtained rewritings are relevant to a given query, as the information gain may be negligible or the peer is not worth exploring. On the other hand, the existence of social links with peer friends might be useful to get relevant rewritings. Therefore, we propose a new notion of ‘relevance’ of a query with respect to a mapping that encompasses both a local relevance (the relevance of the query w.r.t. the mapping) and a global relevance (the relevance of the query w.r.t. the entire network). Based on this notion, we have conceived a new query reformulation approach for social PDMS which achieves great accuracy and flexibility. To this purpose, we combine several techniques: (i) social links are expressed as FOAF (Friend of a Friend) links to characterize peer’s friendship; (ii) concise mapping summaries are used to obtain mapping descriptions; (iii) local semantic views (LSV) are special views that contain information about mappings captured from the network by using gossiping techniques. Our experimental evaluation, based on a prototype on top of PeerSim and a simulated network demonstrate that our solution yields greater recall, compared to traditional query translation approaches proposed in the literature