A cost-effective lexical acquisition process for large-scale thesaurus translation

Thesauri and controlled vocabularies facilitate access to digital collections by explicitly representing the underlying principles of organization. Translation of such resources into multiple languages is an important component for providing multilingual access. However, the specificity of vocabulary terms in most thesauri precludes fully-automatic translation using general-domain lexical resources. In this paper, we present an efficient process for leveraging human translations to construct domain-specific lexical resources. This process is illustrated on a thesaurus of 56,000 concepts used to catalog a large archive of oral histories. We elicited human translations on a small subset of concepts, induced a probabilistic phrase dictionary from these translations, and used the resulting resource to automatically translate the rest of the thesaurus. Two separate evaluations demonstrate the acceptability of the automatic translations and the cost-effectiveness of our approach.

Monte Carlo simulations of microchannel plate detectors. I. Steady-state voltage bias results

X-ray detectors based on straight-channel microchannel plates (MCPs) are a powerful diagnostic tool for two-dimensional, time-resolved imaging and time-resolved x-ray spectroscopy in the fields of laser-driven inertial confinement fusion and fast Z-pinch experiments. Understanding the behavior of microchannel plates as used in such detectors is critical to understanding the data obtained. The subject of this paper is a Monte Carlo computer code we have developed to simulate the electron cascade in a MCP under a static applied voltage. Also included in the simulation is elastic reflection of low-energy electrons from the channel wall, which is important at lower voltages. When model results were compared to measured MCP sensitivities, good agreement was found. Spatial resolution simulations of MCP-based detectors were also presented and found to agree with experimental measurements.     

Meta-analysis of linkage data under worst-case conditions: a demonstration using the human OB region

To date, few methods have been developed explicitly for meta-analysis of linkage analyses. Moreover, the methods that have been developed or suggested generally depend on certain ideal situations and have not been widely applied. In this article, we apply standard statistical theory and meta-analytic techniques in novel ways to five published papers discussing the evidence of linkage of body mass index (BMI) to the region of the human genome containing the OB gene. These methods are "inference based," meaning that they allow one to make statements about the statistical significance of the entire body of evidence. As currently developed, they do not allow specific statements to be made about the amount of variance explained by any putative locus or allow precise confidence intervals to be placed around the putative location of a linked locus. By applying these techniques to the literature on linkage in the human OB gene region, we are able to show that the evidence for linkage somewhere in the region is extremely strong (P = 1.5 x 10[-5]).     

Beyond Market Baskets: Generalizing Association Rules to Dependence Rules

One of the more well-studied problems in data mining is the search for association rules in market basket data. Association rules are intended to identify patterns of the type: A customer purchasing item A often also purchases item B. Motivated partly by the goal of generalizing beyond market basket data and partly by the goal of ironing out some problems in the definition of association rules, we develop the notion of dependence rules that identify statistical dependence in both the presence and absence of items in itemsets. We propose measuring significance of dependence via the chi-squared test for independence from classical statistics. This leads to a measure that is upward-closed in the itemset lattice, enabling us to reduce the mining problem to the search for a border between dependent and independent itemsets in the lattice. We develop pruning strategies based on the closure property and thereby devise an efficient algorithm for discovering dependence rules. We demonstrate our algorithm's effectiveness by testing it on census data, text data (wherein we seek term dependence), and synthetic data.     

Contribution of various substrates to total citric acid cycle flux and ]anaplerosis as determined by13C isotopomer analysis and O2 consumption in the heart

A simple relationship between parameters derived from a¹³C NMR isotopomer analysis and O₂ consumption is presented that allows measurement of the absolute rate of acetyl-CoA oxidation and anaplerotic flux in tissues oxidizing a mixture of four substrates. The method was first applied in a study of the effects of work state and -adrenergic stimulation on net acetate oxidation and anaplerosis in the isolated working rat heart. The results demonstrate that the anticipated ratio of 2 between O₂ consumption and TCA cycle flux for hearts oxidizing only acetate holds at low workload when anaplerosis is low, but deviates toward a factor of 3 under high workload conditions when anaplerosis is increased. This analysis was also extended to hearts that oxidize a more physiological mixture of substrates including long-chain fatty acids, acetoacetate, lactate, pyruvate, and glucose. We show that the contribution each substrate makes to total TCA cycle flux can be determined by combined¹³C NMR and O₂ consumption measurements. The present study also demonstrates that stimulation of anaplerosis (by addition of propionate) can significantly alter the relative contribution each substrate makes to total TCA cycle flux. We conclude that if¹³C labeling patterns are selected appropriately, a comprehensive picture of flux through all major metabolic pathways feeding the cycle can be determined in a single experiment even when complex physiological mixtures of substrates are provided.     

On the Complexity of Computing Winning Strategies for Finite Poset Games

This paper is concerned with the complexity of computing winning strategies for poset games. While it is reasonably clear that such strategies can be computed in PSPACE, we give a simple proof of this fact by a reduction to the game of geography. We also show how to formalize the reasoning about poset games in Skelley’s theory W₁¹\mathbf{W}_{1}^{1} for PSPACE reasoning. We conclude that W₁¹\mathbf{W}_{1}^{1} can use the “strategy stealing argument” to prove that in poset games with a supremum the first player always has a winning strategy.     

Effects of soil composition and mineralogy on remote sensing of crop residue cover

The management of crop residues (non-photosynthetic vegetation) in agricultural fields influences soil erosion and soil carbon sequestration. Remote sensing methods can efficiently assess crop residue cover and related tillage intensity over many fields in a region. Although the reflectance spectra of soils and crop residues are often similar in the visible, near infrared, and the lower part of the shortwave infrared (400-1900 nm) wavelength region, specific diagnostic chemical absorption features are evident in the upper shortwave infrared (1900-2500 nm) region. Two reflectance band height indices used for estimating residue cover are the Cellulose Absorption Index (CAI) and the Lignin-Cellulose Absorption (LCA) index, both of which use reflectances in the upper shortwave infrared (SWIR). Soil mineralogy and composition will affect soil spectral properties and may limit the usefulness of these spectral indices in certain areas. Our objectives were to (1) identify minerals and soil components with absorption features in the 2000 nm to 2400 nm wavelength region that would affect CAI and LCA and (2) assess their potential impact on remote sensing estimates of crop residue cover. Most common soil minerals had CAI values ≤ 0.5, whereas crop residues were always > 0.5, allowing for good contrast between soils and residues. However, a number of common soil minerals had LCA values > 0.5, and, in some cases, the mineral LCA values were greater than those of the crop residues, which could limit the effectiveness of LCA for residue cover estimation. The LCA of some dry residues and live corn canopies were similar in value, unlike CAI. Thus, the Normalized Difference Vegetation Index (NDVI) or similar method should be used to separate out green vegetation pixels. Mineral groups, such as garnets and chlorites, often have wide ranges of CAI and LCA values, and thus, mineralogical analyses often do not identify individual mineral species required for precise CAI estimation. However, these methods are still useful for identifying mineral soils requiring additional scrutiny. Future advanced multi- and hyperspectral remote sensing platforms should include CAI bands to allow for crop residue cover estimation.     

The logic of message-passing

Message-passing is a key ingredient of concurrent programming. The purpose of this paper is to describe the equivalence between the proof theory, the categorical semantics, and term calculus of message-passing. In order to achieve this we introduce the categorical notion of a linear actegory and the related polycategorical notion of a poly-actegory. Not surprisingly the notation used for the term calculus borrows heavily from the (synchronous) π-calculus. The cut-elimination procedure for the system provides an operational semantics.     

Comparison of bathymetry and seagrass mapping with hyperspectral imagery and airborne bathymetric lidar in a shallow estuarine environment

We compared hyperspectral imagery and single-wavelength airborne bathymetric light detection and ranging (lidar) for shallow water (<2 m) bathymetry and seagrass mapping. Both the bathymetric results from hyperspectral imagery and airborne bathymetric lidar reveal that the presence of a strongly reflecting benthic layer under seagrass affects the elevation estimates towards the bottom depth instead of the top of seagrass canopy. Full waveform lidar was also investigated for bathymetry and similar performance to discrete lidar was observed. A provisional classification was performed with limited ground reference samples and four supervised classifiers were applied in the study to investigate the capability of airborne bathymetric lidar and hyperspectral imagery to identify seagrass genera. The overall classification accuracy is highly variable and strongly dependent on the classification strategy used. Features from bathymetric lidar alone are not sufficient for substrate classification, while hyperspectral imagery alone showed significant capability for substrate classification with over 95% overall accuracy. The fusion of hyperspectral imagery and bathymetric lidar only marginally improved the overall accuracy of seagrass classification.