Symmetry-Based Search Space Reduction For Grid Maps

In this paper we explore a symmetry-based search space reduction technique which can speed up optimal pathfinding on undirected uniform-cost grid maps by up to 38 times. Our technique decomposes grid maps into a set of empty rectangles, removing from each rectangle all interior nodes and possibly some from along the perimeter. We then add a series of macro-edges between selected pairs of remaining perimeter nodes to facilitate provably optimal traversal through each rectangle. We also develop a novel online pruning technique to further speed up search. Our algorithm is fast, memory efficient and retains the same optimality and completeness guarantees as searching on an unmodified grid map

Discontinuous Molecular Dynamics for Semi-Flexible and Rigid Bodies

A general framework for performing event-driven simulations of systems with semi-flexible or rigid bodies interacting under impulsive torques and forces is outlined. Two different approaches are presented. In the first, the dynamics and interaction rules are derived from Lagrangian mechanics in the presence of constraints. This approach is most suitable when the body is composed of relatively few point masses or is semi-flexible. In the second method, the equations of rigid bodies are used to derive explicit analytical expressions for the free evolution of arbitrary rigid molecules and to construct a simple scheme for computing interaction rules. Efficient algorithms for the search for the times of interaction events are designed in this context, and the handling of missed interaction events is discussed.Comment: 16 pages, double column revte

GPU LSM: A Dynamic Dictionary Data Structure for the GPU

We develop a dynamic dictionary data structure for the GPU, supporting fast insertions and deletions, based on the Log Structured Merge tree (LSM). Our implementation on an NVIDIA K40c GPU has an average update (insertion or deletion) rate of 225 M elements/s, 13.5x faster than merging items into a sorted array. The GPU LSM supports the retrieval operations of lookup, count, and range query operations with an average rate of 75 M, 32 M and 23 M queries/s respectively. The trade-off for the dynamic updates is that the sorted array is almost twice as fast on retrievals. We believe that our GPU LSM is the first dynamic general-purpose dictionary data structure for the GPU.Comment: 11 pages, accepted to appear on the Proceedings of IEEE International Parallel and Distributed Processing Symposium (IPDPS'18

A neural network for mining large volumes of time series data

Efficiently mining large volumes of time series data is amongst the most challenging problems that are fundamental in many fields such as industrial process monitoring, medical data analysis and business forecasting. This paper discusses a high-performance neural network for mining large time series data set and some practical issues on time series data mining. Examples of how this technology is used to search the engine data within a major UK eScience Grid project (DAME) for supporting the maintenance of Rolls-Royce aero-engine are presented

Robust semicoherent searches for continuous gravitational waves with noise and signal models including hours to days long transients

The vulnerability to single-detector instrumental artifacts in standard detection methods for long-duration quasimonochromatic gravitational waves from nonaxisymmetric rotating neutron stars [continuous waves (CWs)] was addressed in past work [D. Keitel et al., Phys. Rev. D 89, 064023 (2014).] by a Bayesian approach. An explicit model of persistent single-detector disturbances led to a generalized detection statistic with improved robustness against such artifacts. Since many strong outliers in semicoherent searches of LIGO data are caused by transient disturbances that last only a few hours, we extend the noise model to cover such limited-duration disturbances, and demonstrate increased robustness in realistic simulated data. Besides long-duration CWs, neutron stars could also emit transient signals which, for a limited time, also follow the CW signal model (tCWs). As a pragmatic alternative to specialized transient searches, we demonstrate how to make standard semicoherent CW searches more sensitive to transient signals. Considering tCWs in a single segment of a semicoherent search, Bayesian model selection yields a new detection statistic that does not add significant computational cost. On simulated data, we find that it increases sensitivity towards tCWs, even of varying durations, while not sacrificing sensitivity to classical CW signals, and still being robust to transient or persistent single-detector instrumental artifacts.Comment: 16 pages, 6 figures, REVTeX4.

A Formal Framework for Linguistic Annotation

`Linguistic annotation' covers any descriptive or analytic notations applied to raw language data. The basic data may be in the form of time functions -- audio, video and/or physiological recordings -- or it may be textual. The added notations may include transcriptions of all sorts (from phonetic features to discourse structures), part-of-speech and sense tagging, syntactic analysis, `named entity' identification, co-reference annotation, and so on. While there are several ongoing efforts to provide formats and tools for such annotations and to publish annotated linguistic databases, the lack of widely accepted standards is becoming a critical problem. Proposed standards, to the extent they exist, have focussed on file formats. This paper focuses instead on the logical structure of linguistic annotations. We survey a wide variety of existing annotation formats and demonstrate a common conceptual core, the annotation graph. This provides a formal framework for constructing, maintaining and searching linguistic annotations, while remaining consistent with many alternative data structures and file formats.Comment: 49 page