Searching for Gravitational Waves from the Inspiral of Precessing Binary Systems: New Hierarchical Scheme using "Spiky" Templates

In a recent investigation of the effects of precession on the anticipated detection of gravitational-wave inspiral signals from compact object binaries with moderate total masses, we found that (i) if precession is ignored, the inspiral detection rate can decrease by almost a factor of 10, and (ii) previously proposed ``mimic'' templates cannot improve the detection rate significantly (by more than a factor of 2). In this paper we propose a new family of templates that can improve the detection rate by factors of 5--6 in cases where precession is most important. Our proposed method for these new ``mimic'' templates involves a hierarchical scheme of efficient, two-parameter template searches that can account for a sequence of spikes that appear in the residual inspiral phase, after one corrects for the any oscillatory modification in the phase. We present our results for two cases of compact object masses (10 and 1.4 solar masses and 7 and 3 solar masses) as a function of spin properties. Although further work is needed to fully assess the computational efficiency of this newly proposed template family, we conclude that these ``spiky templates'' are good candidates for a family of precession templates used in realistic searches, that can improve detection rates of inspiral events.Comment: 17 pages, 22 figures, version accepted by PRD. Minor revision

TCtract-A Collocation Extraction Approach for Noun Phrases Using Shallow Parsing Rules and Statistic Models

PACLIC 20 / Wuhan, China / 1-3 November, 200

Extracting Hierarchies of Search Tasks & Subtasks via a Bayesian Nonparametric Approach

A significant amount of search queries originate from some real world information need or tasks. In order to improve the search experience of the end users, it is important to have accurate representations of tasks. As a result, significant amount of research has been devoted to extracting proper representations of tasks in order to enable search systems to help users complete their tasks, as well as providing the end user with better query suggestions, for better recommendations, for satisfaction prediction, and for improved personalization in terms of tasks. Most existing task extraction methodologies focus on representing tasks as flat structures. However, tasks often tend to have multiple subtasks associated with them and a more naturalistic representation of tasks would be in terms of a hierarchy, where each task can be composed of multiple (sub)tasks. To this end, we propose an efficient Bayesian nonparametric model for extracting hierarchies of such tasks \& subtasks. We evaluate our method based on real world query log data both through quantitative and crowdsourced experiments and highlight the importance of considering task/subtask hierarchies.Comment: 10 pages. Accepted at SIGIR 2017 as a full pape

Text authorship identified using the dynamics of word co-occurrence networks

The identification of authorship in disputed documents still requires human expertise, which is now unfeasible for many tasks owing to the large volumes of text and authors in practical applications. In this study, we introduce a methodology based on the dynamics of word co-occurrence networks representing written texts to classify a corpus of 80 texts by 8 authors. The texts were divided into sections with equal number of linguistic tokens, from which time series were created for 12 topological metrics. The series were proven to be stationary (p-value>0.05), which permits to use distribution moments as learning attributes. With an optimized supervised learning procedure using a Radial Basis Function Network, 68 out of 80 texts were correctly classified, i.e. a remarkable 85% author matching success rate. Therefore, fluctuations in purely dynamic network metrics were found to characterize authorship, thus opening the way for the description of texts in terms of small evolving networks. Moreover, the approach introduced allows for comparison of texts with diverse characteristics in a simple, fast fashion

"Mariage des Maillages": A new numerical approach for 3D relativistic core collapse simulations

We present a new 3D general relativistic hydrodynamics code for simulations of stellar core collapse to a neutron star, as well as pulsations and instabilities of rotating relativistic stars. It uses spectral methods for solving the metric equations, assuming the conformal flatness approximation for the three-metric. The matter equations are solved by high-resolution shock-capturing schemes. We demonstrate that the combination of a finite difference grid and a spectral grid can be successfully accomplished. This "Mariage des Maillages" (French for grid wedding) approach results in high accuracy of the metric solver and allows for fully 3D applications using computationally affordable resources, and ensures long term numerical stability of the evolution. We compare our new approach to two other, finite difference based, methods to solve the metric equations. A variety of tests in 2D and 3D is presented, involving highly perturbed neutron star spacetimes and (axisymmetric) stellar core collapse, demonstrating the ability to handle spacetimes with and without symmetries in strong gravity. These tests are also employed to assess gravitational waveform extraction, which is based on the quadrupole formula.Comment: 29 pages, 16 figures; added more information about convergence tests and grid setu

The interaction of knowledge sources in word sense disambiguation

Word sense disambiguation (WSD) is a computational linguistics task likely to benefit from the tradition of combining different knowledge sources in artificial in telligence research. An important step in the exploration of this hypothesis is to determine which linguistic knowledge sources are most useful and whether their combination leads to improved results. We present a sense tagger which uses several knowledge sources. Tested accuracy exceeds 94% on our evaluation corpus.Our system attempts to disambiguate all content words in running text rather than limiting itself to treating a restricted vocabulary of words. It is argued that this approach is more likely to assist the creation of practical systems

The modal particle ma 嘛: theoretical frames, analysis and interpretive perspectives

This article sets out to provide a semantic and pragmatic account of the modal particle ma 嘛, endeavouring to put into light new aspects in its function which, at present, remain widely unexplored in the literature. It presents an analysis of the particle ma by interrogating a written and a spoken corpus, showing how the semantic and the pragmatic levels are tightly interweaved in the functioning of ma: the results supported my hypothesis that the particle is plausibly a marker of interpersonal evidentiality (IE), a category set up by Tantucci (2013), used to signal a socially acknowledged piece of information, playing a fundamental role in the expression of politeness by safeguarding the interlocutors’ face; consequently, ma is always used with information that has an active or accessible status in the interlocutors’ mind and that is always pragmatically salient, independently of its position (at the end or inside the sentence), marking a Topic or a Focus. The particle performs pragmatic functions close to the ones of discourse markers since it increases the relevance of the marked information to the context, therefore also playing a contributing role in the coherence of discourse

[[alternative]]The Design and Construction of a Contrastive English-Chinese Verb Lexicon

計畫編號：NSC90-2411-H032-008研究期間：200108~200207研究經費：269,000[[sponsorship]]行政院國家科學委員