Improving peer-to-peer search performance through intelligent social search

As a large amount of information is added onto the Internet on a daily basis, the efficiency of peer-to-peer (P2P) search has become increasingly important. However, how to quickly discover the right resource in a large-scale P2P network without generating too much network traffic remains highly challenging. In this paper, we propose a novel P2P search method, by applying the concept of social grouping and intelligent social search; we derive peers into social groups in a P2P network to improve search performance. Through a super-peer-based architecture, we establish and maintain virtual social groups on top of a P2P network. The interactions between the peers in the P2P network are used to incrementally build the social relationships between the peers in the associated social groups. In such a P2P network, a search query is propagated along the social groups in the overlay social network. Our preliminary experiments have demonstrated that our method can significantly shorten search routes and result in a higher peer search performance. In addition, our method also enhances the trustworthiness of search results because searches go through trusted peers.     

Lidar-based mapping of flood control levees in South Louisiana

Flood protection in south Louisiana is largely dependent on earthen levees, and in the aftermath of Hurricane Katrina the state’s levee system has received intense scrutiny. Accurate elevation data along the levees are critical to local levee district managers responsible for monitoring and maintaining the extensive system of non-federal levees in coastal Louisiana. In 2012, high resolution airborne lidar data were acquired over levees in Lafourche Parish, Louisiana, and a mobile terrestrial lidar survey was conducted for selected levee segments using a terrestrial lidar scanner mounted on a truck. The mobile terrestrial lidar data were collected to test the feasibility of using this relatively new technology to map flood control levees and to compare the accuracy of the terrestrial and airborne lidar. Metrics assessing levee geometry derived from the two lidar surveys are also presented as an efficient, comprehensive method to quantify levee height and stability. The vertical root mean square error values of the terrestrial lidar and airborne lidar digital-derived digital terrain models were 0.038 m and 0.055 m, respectively. The comparison of levee metrics derived from the airborne and terrestrial lidar-based digital terrain models showed that both types of lidar yielded similar results, indicating that either or both surveying techniques could be used to monitor geomorphic change over time. Because airborne lidar is costly, many parts of the USA and other countries have never been mapped with airborne lidar, and repeat surveys are often not available for change detection studies. Terrestrial lidar provides a practical option for conducting repeat surveys of levees and other terrain features that cover a relatively small area, such as eroding cliffs or stream banks, and dunes.     

A Quantum Monte Carlo method at fixed energy

In this paper we explore ways to study the zero temperature limit of quantum statistical mechanics using Quantum Monte Carlo simulations. We develop a Quantum Monte Carlo method in which one fixes the ground state energy as a parameter. The Hamiltonians we consider are of the form H=H₀+λV with ground state energy E. For fixed H₀ and V, one can view E as a function of λ whereas we view λ as a function of E. We fix E and define a path integral Quantum Monte Carlo method in which a path makes no reference to the times (discrete or continuous) at which transitions occur between states. For fixed E we can determine λ(E) and other ground state properties of H.     

Using ICESat's Geoscience Laser Altimeter System (GLAS) to assess large-scale forest disturbance caused by hurricane Katrina

In 2005, hurricane Katrina resulted in a large disturbance to U.S. forests. Recent estimates of damage from hurricane Katrina have relied primarily on optical remote sensing and field data. This paper is the first large-scale study to use satellite-based lidar data to quantify changes in forest structure from that event. GLAS data for the years prior to and following hurricane Katrina were compared to wind speed, forest cover, and damage data to assess the adequacy of sensor sampling, and to estimate changes in Mean Canopy Height (MCH) over all areas that experienced tropical force winds and greater. Statistically significant decreases in MCH post-Katrina were found to increase with wind intensity: Tropical Storm ?MCH = − 0.5 m, Category 1 ?MCH = − 2 m, and Category 2 ?MCH = − 4 m. A strong relationship was also found between changes in non-photosynthetic vegetation (?NPV), a metric previously shown to be related to storm damage, and post-storm MCH. The season of data acquisition was shown to influence calculations of MCH and MCH loss, but did not preclude the detection of major large-scale patterns of damage. Results from this study show promise for using space-borne lidar for large-scale assessments of forest disturbance, and highlight the need for future data on vegetation structure from space.     

Indexing expensive functions for efficient multi-dimensional similarity search

Similarity search is important in information retrieval applications where objects are usually represented as vectors of high dimensionality. This leads to the increasing need for supporting the indexing of high-dimensional data. On the other hand, indexing structures based on space partitioning are powerless because of the well-known “curse of dimensionality”. Linear scan of the data with approximation is more efficient in the high-dimensional similarity search. However, approaches so far have concentrated on reducing I/O, and ignored the computation cost. For an expensive distance function such as L _p norm with fractional p, the computation cost becomes the bottleneck. We propose a new technique to address expensive distance functions by “indexing the function” by pre-computing some key values of the function once. Then, the values are used to develop the upper/lower bounds of the distance between a data vector and the query vector. The technique is extremely efficient since it avoids most of the distance function computations; moreover, it does not involve any extra secondary storage because no index is constructed and stored. The efficiency is confirmed by cost analysis, as well as experiments on synthetic and real data.     

Amperometric biosensor for formic acid in air

The possibility of developing a simple, inexpensive and specific personal passive “real-time” air sampler incorporating a biosensor for formic acid was investigated. The sensor is based on the enzymatic reaction between formic acid and formate dehydrogenase (FDH) with nicotinamide adenine dinucleotide (NAD⁺) as a co-factor and Meldola's blue as mediator. An effective way to immobilise the enzyme, co-factor and Meldola's blue on screen-printed, disposable, electrodes was found to be in a mixture of glycerol and phosphate buffer covered with a gas-permeable membrane. Steady-state current was reached after 4–15 min and the limit of detection was calculated to be below 1 mg/m³. However, the response decreased by 50% after storage at −15°C for 1 day.     

Consistency properties and set based logic programming

Blair et al. (2001) developed an extension of logic programming called set based logic programming. In the theory of set based logic programming the atoms represent subsets of a fixed universe X and one is allowed to compose the one-step consequence operator with a monotonic idempotent operator O so as to ensure that the analogue of stable models in the theory are always closed under O. Marek et al. (1992, Ann Pure Appl Logic 96:231–276 1999) developed a generalization of Reiter’s normal default theories that can be applied to both default theories and logic programs which is based on an underlying consistency property. In this paper, we show how to extend the normal logic programming paradigm of Marek, Nerode, and Remmel to set based logic programming. We also show how one can obtain a new semantics for set based logic programming based on a consistency property.     

A data mining proxy approach for efficient frequent itemset mining

Data mining has attracted a lot of research efforts during the past decade. However, little work has been reported on the efficiency of supporting a large number of users who issue different data mining queries periodically when there are new needs and when data is updated. Our work is motivated by the fact that the pattern-growth method is one of the most efficient methods for frequent pattern mining which constructs an initial tree and mines frequent patterns on top of the tree. In this paper, we present a data mining proxy approach that can reduce the I/O costs to construct an initial tree by utilizing the trees that have already been resident in memory. The tree we construct is the smallest for a given data mining query. In addition, our proxy approach can also reduce CPU cost in mining patterns, because the cost of mining relies on the sizes of trees. The focus of the work is to construct an initial tree efficiently. We propose three tree operations to construct a tree. With a unique coding scheme, we can efficiently project subtrees from on-disk trees or in-memory trees. Our performance study indicated that the data mining proxy significantly reduces the I/O cost to construct trees and CPU cost to mine patterns over the trees constructed.     

Graphical histories for visualization: supporting analysis, communication, and evaluation

Interactive history tools, ranging from basic undo and redo to branching timelines of user actions, facilitate iterative forms of interaction. In this paper, we investigate the design of history mechanisms for information visualization. We present a design space analysis of both architectural and interface issues, identifying design decisions and associated trade-offs. Based on this analysis, we contribute a design study of graphical history tools for Tableau, a database visualization system. These tools record and visualize interaction histories, support data analysis and communication of findings, and contribute novel mechanisms for presenting, managing, and exporting histories. Furthermore, we have analyzed aggregated collections of history sessions to evaluate Tableau usage. We describe additional tools for analyzing users’ history logs and how they have been applied to study usage patterns in Tableau.     

Molecular recognition in the sphingosine 1-phosphate receptor family

Computational modeling and its application in ligand screening and ligand receptor interaction studies play important roles in structure-based drug design. A series of sphingosine 1-phosphate (S1P) receptor ligands with varying potencies and receptor selectivities were docked into homology models of the S1P(1-5) receptors. These studies provided molecular insights into pharmacological trends both across the receptor family as well as at single receptors. This study identifies ligand recognition features that generalize across the S1P receptor family, features unique to the S1P(4) and S1P(5) receptors, and suggests significant structural differences of the S1P(2) receptor. Docking results reveal a previously unknown sulfur-aromatic interaction between the S1P(4) C5.44 sulfur atom and the phenyl ring of benzimidazole as well as pi-pi interaction between F3.33 of S1P(1,4,5) and aromatic ligands. The findings not only confirm the importance of a cation-pi interaction between W4.64 and the ammonium of S1P at S1P(4) but also predict the same interaction at S1P(5). S1P receptor models are validated for pharmacophore development including database mining and new ligand discovery and serve as tools for ligand optimization to improve potency and selectivity.