Answering Top-k Queries Over a Mixture of Attractive and Repulsive Dimensions

In this paper, we formulate a top-k query that compares objects in a database to a user-provided query object on a novel scoring function. The proposed scoring function combines the idea of attractive and repulsive dimensions into a general framework to overcome the weakness of traditional distance or similarity measures. We study the properties of the proposed class of scoring functions and develop efficient and scalable index structures that index the isolines of the function. We demonstrate various scenarios where the query finds application. Empirical evaluation demonstrates a performance gain of one to two orders of magnitude on querying time over existing state-of-the-art top-k techniques. Further, a qualitative analysis is performed on a real dataset to highlight the potential of the proposed query in discovering hidden data characteristics.Comment: VLDB201

Branch-and-Bound Ranked Search by Minimizing Parabolic Polynomials

The Branch-and-Bound Ranked Search algorithm (BRS) is an efficient method for answering top-k queries based on R-trees using multivariate scoring functions. To make BRS effective with ascending rankings, the algorithm must be able to identify lower bounds of the scoring functions for exploring search partitions. This paper presents BRS supporting parabolic polynomials. These functions are common to minimize combined scores over different attributes and cover a variety of applications. To the best of our knowledge the problem to develop an algorithm for computing lower bounds for the BRS method has not been well addressed yet

Quasi-Convex Scoring Functions in Branch-and-Bound Ranked Search

For answering top-k queries in which attributes are aggregated to a scalar value for defining a ranking, usually the well-known branch-and-bound principle can be used for efficient query answering. Standard algorithms (e.g., Branch-and-Bound Ranked Search, BRS for short) require scoring functions to be monotone, such that a top-k ranking can be computed in sublinear time in the average case. If monotonicity cannot be guaranteed, efficient query answering algorithms are not known. To make branch-and-bound effective with descending or ascending rankings (maximum top-k or minimum top-k queries, respectively), BRS must be able to identify bounds for exploring search partitions, and only for monotonic ranking functions this is trivial. In this paper, we investigate the class of quasi-convex functions used for scoring objects, and we examine how bounds for exploring data partitions can correctly and efficiently be computed for quasi-convex functions in BRS for maximum top-k queries. Given that quasi-convex scoring functions can usefully be employed for ranking objects in a variety of applications, the mathematical findings presented in this paper are indeed significant for practical top-k query answering

Classifying complex topics using spatial-semantic document visualization: An evaluation of an interaction model to support open-ended search tasks

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.In this dissertation we propose, test and develop a novel search interaction model to address two key problems associated with conducting an open-ended search task within a classical information retrieval system: (i) the need to reformulate the query within the context of a shifting conception of the problem and (ii) the need to integrate relevant results across a number of separate results sets. In our model the user issues just one highrecall query and then performs a sequence of more focused, distinct aspect searches by browsing the static structured context of a spatial-semantic visualization of this retrieved document set. Our thesis is that unsupervised spatial-semantic visualization can automatically classify retrieved documents into a two-level hierarchy of relevance. In particular we hypothesise that the locality of any given aspect exemplar will tend to comprise a sufficient proportion of same-aspect documents to support a visually guided strategy for focused, same-aspect searching that we term the aspect cluster growing strategy. We examine spatial-semantic classification and potential aspect cluster growing performance across three scenarios derived from topics and relevance judgements from the TREC test collection. Our analyses show that the expected classification can be represented in spatial-semantic structures created from document similarities computed by a simple vector space text analysis procedure. We compare two diametrically opposed approaches to layout optimisation: a global approach that focuses on preserving the all similarities and a local approach that focuses only on the strongest similarities. We find that the local approach, based on a minimum spanning tree of similarities, produces a better classification and, as observed from strategy simulation, more efficient aspect cluster growing performance in most situations, compared to the global approach of multidimensional scaling. We show that a small but significant proportion of aspect clustering growing cases can be problematic, regardless of the layout algorithm used. We identify the characteristics of these cases and, on this basis, demonstrate a set of novel interactive tools that provide additional semantic cues to aid the user in locating same-aspect documents

Low-energy scattering on the lattice

In this thesis we present precision benchmark calculations for two-component fermions in the unitarity limit using an ab initio method, namely Hamiltonian lattice formalism. We calculate the ground state energy for unpolarized four particles (Fermi gas) in a periodic cube as a fraction of the ground state energy of the non-interacting system for two independent representations of the lattice Hamiltonians. We obtain the values 0.211(2) and 0.210(2). These results are in full agreement with the Euclidean lattice and fixed-node diffusion Monte Carlo calculations. We also give an expression for the energy corrections to the binding energy of a bound state in a moving frame. These corrections contain information about the mass and number of the constituents and are topological in origin and will have a broad applications to the lattice calculations of nucleons, nuclei, hadronic molecules and cold atoms. As one of its applications we use this expression and determine the low-energy parameters for the fermion dimer elastic scattering in shallow binding limit. For our lattice calculations we use Lüscher’s finite volume method. From the lattice calculations we find 1.174(9) for the scattering length and -0.029(13) for the effective range. These results are confirmed by the continuum calculations using the Skorniakov-Ter-Martirosian integral equation which gives 1.17907(1) and -0.0383(3) for the scattering length and effective range, respectively. Both results for the fermion dimer effective range are not in agreement with the previous calculation [86] which have found 0.08(1) for the effective range

BISPHOSOHOGLYCERTAE MUTASE: A POTENTIAL TARGET FOR SICKLE CELL DISEASE

Bisphosphoglycerate mutase (BPGM) is a part of the erythrocyte glycolysis system. Specifically, it is a central enzyme in the Rapoport-Leubering pathway, a side glycolytic pathway involved in the regulation of the concentration of the natural allosteric effector of hemoglobin (Hb), 2,3-bisphosphoglycerate (2,3-BPG). BPGM catalyses the synthesis and hydrolysis of 2,3-BPG through its synthase and phosphatase activities. The synthase activity is the main role of BPGM, while the phosphatase activity is low and is activated by the physiological effector, 2-phosphoglycolate (2-PG) with the latter mechanism poorly understood. BPGM activity and 2,3-BPG levels in red blood cells (RBCs) have a significant role in sickle cell disease (SCD) pathology. SCD patients experience a constant state of hypoxia that results in increasing the level of 2,3-BPG as a compensatory mechanism to enhance oxygen delivery to tissues. However, the abnormal increase in 2,3-BPG in RBCs of SCD patients exacerbates the disease’s primary pathophysiology, which is the hypoxia-driven deoxygenated-sickle hemoglobin (HbS) polymerization, that in turn leads to RBCs sickling and consequent numerous downstream multi-organ adverse effects. Reducing the levels of 2,3-BPG by activating BPGM phosphatase activity using 2-PG has been proposed as a potential therapeutic approach for SCD as 2-PG was found to have an anti-sickling property. Nonetheless, the actual activation mechanism of 2-PG on the phosphatase activity or the binding mode of 2-PG to BPGM is not clear. Moreover, no drug screening studies have been performed to identify small molecules against BPGM for therapeutic purposes. The objectives of this project are to characterize the steady-state kinetics of BPGM synthase and phosphatase activities, understand the mechanism of phosphatase activation, and elucidate the atomic interaction of BPGM with 2-PG and other effectors such as citrate that can provide valuable insight into their mechanism of actions and provide a framework for developing small molecules with potential SCD therapeutic benefit. In addition, we aim to identify ligands that modulate either BPGM phosphatase and/or synthase activity to reduce 2,3-BPG concentration in RBCs. First, the steady-state kinetics of BPGM synthase and phosphatase activities were characterized using the previously reported coupled spectrophotometric synthase and phosphatase activities assays. These assays were also optimized for drug screening experiments. Both assays have limitations and proved challenging for drug screening. We also employed the colorimetric malachite green assay to study BPGM phosphatase activity, as well as for compound screening. Next, we elucidated the mechanism of phosphatase activity activation by 2-PG using kinetic and X-ray crystallography studies. The kinetic study showed the mechanism of 2-PG activation of BPGM to be mixed-type of noncompetitive and competitive, suggesting the binding of 2-PG to the active site and to an allosteric or non-catalytic site of the enzyme. The crystal structures of BPGM with 2-PG in the presence and absence of the substrate 2,3-BPG showed binding of the 2-PG and/or 3-PGA (the reaction product of 2,3-BPG) at the expected active site, and at a novel non-catalytic site at the dimer interface, in agreement with the kinetic analysis. The structural studies of BPGM also showed conformational nonequivalence of the two monomeric active sites: one site in a close catalytic conformation, and the second site in an open conformation, with the residues at the entrance of the active site, including Arg100, Arg116, and Arg117, and the C-terminus region disordered, which we propose to be induced by the dimer interface binding. In order to gain further insight into the BPGM mechanism of action, we also co-crystallized BPGM with citrate, a known BPGM phosphatase inhibitor. The co-crystal structure of BPGM with citrate showed citrate binding to only one of the dimer active sites and to the dimer interface. The kinetic and crystallographic findings suggest for the first time an allosterism or cooperativity across monomers, in which the binding of a ligand at the dimer interface induces negative cooperativity affecting the affinity of ligand binding at the second active site. In the BPGM•citrtate binary complex, an extreme form of negative cooperativity, where half of the site reactivity is observed, shows that only one active site appears to be functional. Toward the objective of identifying small molecules modulators of BPGM activity for therapeutic purposes, we identified several compounds that target the active site of BPGM using (1) in-house pharmacophore-based virtual screening and molecular docking; (2) machine learning-based molecular screening in collaboration with the pharmaceutical company Atomwise, and (3) PGM1-004A, a known inhibitor of the homologous enzyme, phosphoglycerate mutase 1 (PGAM1). The compounds were tested for their effect on BPGM synthase and phosphatase activities. Unfortunately, the compounds did not show any modulation except for PGMI-004A, which shows a dose-dependent synthase inhibition with IC50 (50±11 µM). Several attempts were made to co-crystallize BPGM with PGMI-004A but were unsuccessful. The novel allosteric site at the dimer interface was also docked against a library of compounds, which identified several potential binders. The top-scoring compounds will be obtained and tested in the near future

Document Collection Visualization and Clustering Using An Atom Metaphor for Display and Interaction

Visual Data Mining have proven to be of high value in exploratory data analysis and data mining because it provides an intuitive feedback on data analysis and support decision-making activities. Several visualization techniques have been developed for cluster discovery such as Grand Tour, HD-Eye, Star Coordinates, etc. They are very useful tool which are visualized in 2D or 3D; however, they have not simple for users who are not trained. This thesis proposes a new approach to build a 3D clustering visualization system for document clustering by using k-mean algorithm. A cluster will be represented by a neutron (centroid) and electrons (documents) which will keep a distance with neutron by force. Our approach employs quantified domain knowledge and explorative observation as prediction to map high dimensional data onto 3D space for revealing the relationship among documents. User can perform an intuitive visual assessment of the consistency of the cluster structure

Structure and dynamics of nanoconfined water and aqueous solutions

This review is devoted to discussing recent progress on the structure, thermodynamic, reactivity, and dynamics of water and aqueous systems confined within different types of nanopores, synthetic and biological. Currently, this is a branch of water science that has attracted enormous attention of researchers from different fields interested to extend the understanding of the anomalous properties of bulk water to the nanoscopic domain. From a fundamental perspective, the interactions of water and solutes with a confining surface dramatically modify the liquid's structure and, consequently, both its thermodynamical and dynamical behaviors, breaking the validity of the classical thermodynamic and phenomenological description of the transport properties of aqueous systems. Additionally, man-made nanopores and porous materials have emerged as promising solutions to challenging problems such as water purification, biosensing, nanofluidic logic and gating, and energy storage and conversion, while aquaporin, ion channels, and nuclear pore complex nanopores regulate many biological functions such as the conduction of water, the generation of action potentials, and the storage of genetic material. In this work, the more recent experimental and molecular simulations advances in this exciting and rapidly evolving field will be reported and critically discussed

Classifying complex topics using spatial-semantic document visualization : an evaluation of an interaction model to support open-ended search tasks

In this dissertation we propose, test and develop a novel search interaction model to address two key problems associated with conducting an open-ended search task within a classical information retrieval system: (i) the need to reformulate the query within the context of a shifting conception of the problem and (ii) the need to integrate relevant results across a number of separate results sets. In our model the user issues just one highrecall query and then performs a sequence of more focused, distinct aspect searches by browsing the static structured context of a spatial-semantic visualization of this retrieved document set. Our thesis is that unsupervised spatial-semantic visualization can automatically classify retrieved documents into a two-level hierarchy of relevance. In particular we hypothesise that the locality of any given aspect exemplar will tend to comprise a sufficient proportion of same-aspect documents to support a visually guided strategy for focused, same-aspect searching that we term the aspect cluster growing strategy. We examine spatial-semantic classification and potential aspect cluster growing performance across three scenarios derived from topics and relevance judgements from the TREC test collection. Our analyses show that the expected classification can be represented in spatial-semantic structures created from document similarities computed by a simple vector space text analysis procedure. We compare two diametrically opposed approaches to layout optimisation: a global approach that focuses on preserving the all similarities and a local approach that focuses only on the strongest similarities. We find that the local approach, based on a minimum spanning tree of similarities, produces a better classification and, as observed from strategy simulation, more efficient aspect cluster growing performance in most situations, compared to the global approach of multidimensional scaling. We show that a small but significant proportion of aspect clustering growing cases can be problematic, regardless of the layout algorithm used. We identify the characteristics of these cases and, on this basis, demonstrate a set of novel interactive tools that provide additional semantic cues to aid the user in locating same-aspect documents.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Classifying complex topics using spatial-semantic document visualization : an evaluation of an interaction model to support open-ended search tasks

In this dissertation we propose, test and develop a novel search interaction model to address two key problems associated with conducting an open-ended search task within a classical information retrieval system: (i) the need to reformulate the query within the context of a shifting conception of the problem and (ii) the need to integrate relevant results across a number of separate results sets. In our model the user issues just one highrecall query and then performs a sequence of more focused, distinct aspect searches by browsing the static structured context of a spatial-semantic visualization of this retrieved document set. Our thesis is that unsupervised spatial-semantic visualization can automatically classify retrieved documents into a two-level hierarchy of relevance. In particular we hypothesise that the locality of any given aspect exemplar will tend to comprise a sufficient proportion of same-aspect documents to support a visually guided strategy for focused, same-aspect searching that we term the aspect cluster growing strategy. We examine spatial-semantic classification and potential aspect cluster growing performance across three scenarios derived from topics and relevance judgements from the TREC test collection. Our analyses show that the expected classification can be represented in spatial-semantic structures created from document similarities computed by a simple vector space text analysis procedure. We compare two diametrically opposed approaches to layout optimisation: a global approach that focuses on preserving the all similarities and a local approach that focuses only on the strongest similarities. We find that the local approach, based on a minimum spanning tree of similarities, produces a better classification and, as observed from strategy simulation, more efficient aspect cluster growing performance in most situations, compared to the global approach of multidimensional scaling. We show that a small but significant proportion of aspect clustering growing cases can be problematic, regardless of the layout algorithm used. We identify the characteristics of these cases and, on this basis, demonstrate a set of novel interactive tools that provide additional semantic cues to aid the user in locating same-aspect documents.EThOS - Electronic Theses Online ServiceGBUnited Kingdo