A heuristic file reorganization algorithm based on record clustering

The problem of file organization which we consider involves altering the placement of records on pages of a secondary storage device. In addition, we want this reorganization to be done in-place, i.e., using the file's original storage space for the newly reorganized file. The motivation for such a physical change is to improve the database system's performance. For example, by placing frequently and jointly accessed records on the same page or pages, we can try to minimize the number of page accesses made in answering a set of queeries. The optimal assignment (or reassignment) of records to clusters is exactly what record clustering algorithms attempt to do. However, record clustering algorithms usually do not solve the entire problem, i.e., they do not specify how to efficiently reorganize the file to reflect the clustering assignment which they determine. Our algorithm is a companion to general record clustering algorithms since it actually transforms the file. The problem of optimal file reorganization isNP-hard. Consequently, our reorganization algorithm is based on heuristics. The algorithm's time and space requirements are reasonable and its solution is near optimal. In addition, the reorganization problem which we consider in this paper is similar to the problem of join processing when indexes are used.The research of this author was partially supported by the National Science Foundation under grant IST-8696157.     

Bioassay-directed fractionation for discovery of bioactive neutral lipids guided by relative mass defect filtering and multiplexed collision-induced dissociation

We report a synergistic method using bioassay‐directed liquid chromatography fractionation and time‐of‐flight mass spectrometry to guide and accelerate bioactive compound discovery. To steer purification and assays toward anticipated neutral lipid activators of a constitutive androstane receptor splice variant, a relative mass defect filter was calculated, based on the ratio of the mass defect to the measured ion mass, and used to reduce the number of candidate ion masses. Mass measurements often lack sufficient accuracy to provide unambiguous assignments of elemental compositions, and since the relative mass defect reflects fractional hydrogen content of ions, this value is largely determined by the hydrogen content of a compound's biosynthetic precursors. A relative mass defect window ranging from 600–1000 ppm, consistent with an assortment of lipids, was chosen to assess the number of candidate ions in fractions of fetal bovine serum. This filter reduced the number of candidate ion m/z values from 1345 to 892, which was further reduced to 21 by intensity and isotope filtering. Accurate mass measurements from time‐of‐flight mass spectrometry and fragment ion masses generated using nonselective collision‐induced dissociation suggested dioctyl phthalate as one of few neutral lipid constituents in the active fraction. The identity of this compound was determined to be di(2‐ethylhexyl) phthalate using GC/MS, and it was ranked as a promising candidate for reporter assay screening. Copyright © 2010 John Wiley & Sons, Ltd.     

The first three anisotropy constants of nickel

The technique of ferromagnetic resonance at 23 GHz has been used to determine the first three anisotropy constants of pure Ni down to 4.2K. A temperature and orientation dependent linewidth has also been observed.     

Manifold preprocessing for laser‐induced breakdown spectroscopy under Mars conditions

Laser‐induced breakdown spectroscopy (LIBS) is currently being used onboard the Mars Science Laboratory rover Curiosity to predict elemental abundances in dust, rocks, and soils using a partial least squares regression model developed by the ChemCam team. Accuracy of that model is constrained by the number of samples needed in the calibration, which grows exponentially with the dimensionality of the data, a phenomenon known as the curse of dimensionality. LIBS data are very high dimensional, and the number of ground‐truth samples (i.e., standards) recorded with the ChemCam before departing for Mars was small compared with the dimensionality, so strategies to optimize prediction accuracy are needed. In this study, we first use an existing machine learning algorithm, locally linear embedding (LLE), to combat the curse of dimensionality by embedding the data into a low‐dimensional manifold subspace before regressing. LLE constructs its embedding by maintaining local neighborhood distances and discarding large global geodesic distances between samples, in an attempt to preserve the underlying geometric structure of the data. We also introduce a novel supervised version, LLE for regression (LLER), which takes into account the known chemical composition of the training data when embedding. LLER is shown to outperform traditional LLE when predicting most major elements. We show the effectiveness of both algorithms using three different LIBS datasets recorded under Mars‐like conditions. Copyright © 2015 John Wiley & Sons, Ltd.