A Distributed Query Processing Engine

Wireless sensor networks (WSNs) are formed of tiny, highly energy-constrained sensor nodes that are equipped with wireless transceivers. They may be mobile and are usually deployed in large numbers in unfamiliar environments. The nodes communicate with one another by autonomously creating ad-hoc networks which are subsequently used to gather sensor data. WSNs also process the data within the network itself and only forward the result to the requesting node. This is referred to as in-network data aggregation and results in the substantial reduction of the amount of data that needs to be transmitted by any single node in the network. In this paper we present a framework for a distributed query processing engine (DQPE) which would allow sensor nodes to examine incoming queries and autonomously perform query optimisation using information available locally. Such qualities make a WSN the perfect tool to carryout environmental\ud monitoring in future planetary exploration missions in a reliable and cost effective manner

London dispersion forces without density distortion: a path to first principles inclusion in density functional theory

We analyse a path to construct density functionals for the dispersion interaction energy from an expression in terms of the ground state densities and exchange-correlation holes of the isolated fragments. The expression is based on a constrained search formalism for a supramolecular wavefunction that is forced to leave the diagonal of the many-body density matrix of each fragment unchanged, and is exact for the interaction between one-electron densities. We discuss several aspects: the needed features a density functional approximation for the exchange-correlation holes of the monomers should have, the optimal choice of the one-electron basis needed (named "dispersals"), and the functional derivative with respect to monomer density variations.Comment: 12 pages, 4 figure

DeepFactors: Real-time probabilistic dense monocular SLAM

The ability to estimate rich geometry and camera motion from monocular imagery is fundamental to future interactive robotics and augmented reality applications. Different approaches have been proposed that vary in scene geometry representation (sparse landmarks, dense maps), the consistency metric used for optimising the multi-view problem, and the use of learned priors. We present a SLAM system that unifies these methods in a probabilistic framework while still maintaining real-time performance. This is achieved through the use of a learned compact depth map representation and reformulating three different types of errors: photometric, reprojection and geometric, which we make use of within standard factor graph software. We evaluate our system on trajectory estimation and depth reconstruction on real-world sequences and present various examples of estimated dense geometry

Person Re-Identification by Discriminative Selection in Video Ranking

Current person re-identification (ReID) methods typically rely on single-frame imagery features, whilst ignoring space-time information from image sequences often available in the practical surveillance scenarios. Single-frame (single-shot) based visual appearance matching is inherently limited for person ReID in public spaces due to the challenging visual ambiguity and uncertainty arising from non-overlapping camera views where viewing condition changes can cause significant people appearance variations. In this work, we present a novel model to automatically select the most discriminative video fragments from noisy/incomplete image sequences of people from which reliable space-time and appearance features can be computed, whilst simultaneously learning a video ranking function for person ReID. Using the PRID2011, iLIDS-VID, and HDA+ image sequence datasets, we extensively conducted comparative evaluations to demonstrate the advantages of the proposed model over contemporary gait recognition, holistic image sequence matching and state-of-the-art single-/multi-shot ReID methods

Linear Constraints over Infinite Trees

Abstract. In this paper we consider linear arithmetic constraints over infinite trees whose nodes are labelled with nonnegative real numbers. These constraints arose in the context of resource inference for objectoriented programs but should be of independent interest. It is as yet open whether satisfiability of these constraint systems is at all decidable. For a restricted fragment motivated from the application to resource inference we are however able to provide a heuristic decision procedure based on regular trees. We also observe that the related problem of optimising linear objectives over these infinite trees falls into the area of convex optimisation

Compressed materialised views of semi-structured data

Query performance issues over semi-structured data have led to the emergence of materialised XML views as a means of restricting the data structure processed by a query. However preserving the conventional representation of such views remains a significant limiting factor especially in the context of mobile devices where processing power, memory usage and bandwidth are significant factors. To explore the concept of a compressed materialised view, we extend our earlier work on structural XML compression to produce a combination of structural summarisation and data compression techniques. These techniques provide a basis for efficiently dealing with both structural queries and valuebased predicates. We evaluate the effectiveness of such a scheme, presenting results and performance measures that show advantages of using such structures