Indexing Metric Spaces for Exact Similarity Search

With the continued digitalization of societal processes, we are seeing an explosion in available data. This is referred to as big data. In a research setting, three aspects of the data are often viewed as the main sources of challenges when attempting to enable value creation from big data: volume, velocity and variety. Many studies address volume or velocity, while much fewer studies concern the variety. Metric space is ideal for addressing variety because it can accommodate any type of data as long as its associated distance notion satisfies the triangle inequality. To accelerate search in metric space, a collection of indexing techniques for metric data have been proposed. However, existing surveys each offers only a narrow coverage, and no comprehensive empirical study of those techniques exists. We offer a survey of all the existing metric indexes that can support exact similarity search, by i) summarizing all the existing partitioning, pruning and validation techniques used for metric indexes, ii) providing the time and storage complexity analysis on the index construction, and iii) report on a comprehensive empirical comparison of their similarity query processing performance. Here, empirical comparisons are used to evaluate the index performance during search as it is hard to see the complexity analysis differences on the similarity query processing and the query performance depends on the pruning and validation abilities related to the data distribution. This article aims at revealing different strengths and weaknesses of different indexing techniques in order to offer guidance on selecting an appropriate indexing technique for a given setting, and directing the future research for metric indexes

Topological Photonic Phase in Chiral Hyperbolic Metamaterials

Recently the possibility of achieving one-way backscatter immune transportation of light by mimicking the topological order present within certain solid state systems, such as topological insulators, has received much attention. Thus far however, demonstrations of non-trivial topology in photonics have relied on photonic crystals with precisely engineered lattice structures, periodic on the scale of the operational wavelength and composed of finely tuned, complex materials. Here we propose a novel effective medium approach towards achieving topologically protected photonic surface states robust against disorder on all length scales and for a wide range of material parameters. Remarkably, the non-trivial topology of our metamaterial design results from the Berry curvature arising from the transversality of electromagnetic waves in a homogeneous medium. Our investigation therefore acts to bridge the gap between the advancing field of topological band theory and classical optical phenomena such as the Spin Hall effect of light. The effective medium route to topological phases will pave the way for highly compact one-way transportation of electromagnetic waves in integrated photonic circuits.Comment: 11 pages, 3 figures. To appear in PR

Q^2 Evolution of the Neutron Spin Structure Moments using a ^3He Target

We have measured the spin structure functions g_1 and g_2 of ^3He in a double-spin experiment by inclusively scattering polarized electrons at energies ranging from 0.862 to 5.058 GeV off a polarized ^3He target at a 15.5° scattering angle. Excitation energies covered the resonance and the onset of the deep inelastic regions. We have determined for the first time the Q^2 evolution of Γ_1(Q^2)=∫_0^1g_1(x,Q^2)dx, Γ_2(Q^2)=∫_0^1g_2(x,Q^2)dx, and d_2(Q^2)=∫_0^1x^2[2g_1(x,Q^2)+3g_2(x,Q^2)]dx for the neutron in the range 0.1 ≤ Q^2 ≤0.9  GeV^2 with good precision. Γ_1(Q^2) displays a smooth variation from high to low Q^2. The Burkhardt-Cottingham sum rule holds within uncertainties and d_2 is nonzero over the measured range

Q^2 Evolution of the Generalized Gerasimov-Drell-Hearn Integral for the Neutron using a ^3He Target

We present data on the inclusive scattering of polarized electrons from a polarized ^3He target at energies from 0.862 to 5.06 GeV, obtained at a scattering angle of 15.5°. Our data include measurements from the quasielastic peak, through the nucleon resonance region, and beyond, and were used to determine the virtual photon cross-section difference σ_(1/2)-σ_(3/2). We extract the extended Gerasimov-Drell-Hearn integral for the neutron in the range of four-momentum transfer squared Q^2 of 0.1–0.9   GeV^2

Location- and keyword-based querying of geo-textual data: a survey

With the broad adoption of mobile devices, notably smartphones, keyword-based search for content has seen increasing use by mobile users, who are often interested in content related to their geographical location. We have also witnessed a proliferation of geo-textual content that encompasses both textual and geographical information. Examples include geo-tagged microblog posts, yellow pages, and web pages related to entities with physical locations. Over the past decade, substantial research has been conducted on integrating location into keyword-based querying of geo-textual content in settings where the underlying data is assumed to be either relatively static or is assumed to stream into a system that maintains a set of continuous queries. This paper offers a survey of both the research problems studied and the solutions proposed in these two settings. As such, it aims to offer the reader a first understanding of key concepts and techniques, and it serves as an “index” for researchers who are interested in exploring the concepts and techniques underlying proposed solutions to the querying of geo-textual data.Agency for Science, Technology and Research (A*STAR)Ministry of Education (MOE)Nanyang Technological UniversityThis research was supported in part by MOE Tier-2 Grant MOE2019-T2-2-181, MOE Tier-1 Grant RG114/19, an NTU ACE Grant, and the Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU) that is funded by the Singapore Government through the Industry Alignment Fund Industry Collaboration Projects Grant, and by the Innovation Fund Denmark centre, DIREC

Collectively Simplifying Trajectories in a Database: A Query Accuracy Driven Approach

Increasing and massive volumes of trajectory data are being accumulated that may serve a variety of applications, such as mining popular routes or identifying ridesharing candidates. As storing and querying massive trajectory data is costly, trajectory simplification techniques have been introduced that intuitively aim to reduce the sizes of trajectories, thus reducing storage and speeding up querying, while preserving as much information as possible. Existing techniques rely mainly on hand-crafted error measures when deciding which point to drop when simplifying a trajectory. While the hope may be that such simplification affects the subsequent usability of the data only minimally, the usability of the simplified data remains largely unexplored. Instead of using error measures that indirectly may to some extent yield simplified trajectories with high usability, we adopt a direct approach to simplification and present the first study of query accuracy driven trajectory simplification, where the direct objective is to achieve a simplified trajectory database that preserves the query accuracy of the original database as much as possible. Specifically, we propose a multi-agent reinforcement learning based solution with two agents working cooperatively to collectively simplify trajectories in a database while optimizing query usability. Extensive experiments on four real-world trajectory datasets show that the solution is capable of consistently outperforming baseline solutions over various query types and dynamics.Comment: This paper has been accepted by ICDE 202