Nonparametric Bayesian Modeling for Automated Database Schema Matching

The problem of merging databases arises in many government and commercial applications. Schema matching, a common first step, identifies equivalent fields between databases. We introduce a schema matching framework that builds nonparametric Bayesian models for each field and compares them by computing the probability that a single model could have generated both fields. Our experiments show that our method is more accurate and faster than the existing instance-based matching algorithms in part because of the use of nonparametric Bayesian models

Olfactory experience shapes the evaluation of odour similarity in ants - a behavioural and computational analysis

Perceptual similarity between stimuli is often assessed via generalisation, the response to stimuli that are similar to the one which was previously conditioned. Although conditioning procedures are variable, studies on how this variation may affect perceptual similarity remain scarce. Here, we use a combination of behavioural and computational analyses to investigate the influence of olfactory conditioning procedures on odour generalisation in ants. Insects were trained following either absolute conditioning, in which a single odour (an aldehyde) was rewarded with sucrose, or differential conditioning, in which one odour (the same aldehyde) was similarly rewarded and another odour (an aldehyde differing in carbon-chain length) was punished with quinine. The response to the trained odours and generalisation to other aldehydes were assessed. We show that olfactory similarity, rather than being immutable, varies with the conditioning procedure. Compared to absolute conditioning, differential conditioning enhances olfactory discrimination. This improvement is best described by a multiplicative interaction between two independent processes, the excitatory and inhibitory generalisation gradients induced by the rewarded and the punished odour, respectively. We show that olfactory similarity is dramatically shaped by an individual’s perceptual experience and suggest a new hypothesis for the nature of stimulus interactions underlying experience-dependent changes in perceptual similarity

Quantization and Compressive Sensing

Quantization is an essential step in digitizing signals, and, therefore, an indispensable component of any modern acquisition system. This book chapter explores the interaction of quantization and compressive sensing and examines practical quantization strategies for compressive acquisition systems. Specifically, we first provide a brief overview of quantization and examine fundamental performance bounds applicable to any quantization approach. Next, we consider several forms of scalar quantizers, namely uniform, non-uniform, and 1-bit. We provide performance bounds and fundamental analysis, as well as practical quantizer designs and reconstruction algorithms that account for quantization. Furthermore, we provide an overview of Sigma-Delta ($\Sigma\Delta$) quantization in the compressed sensing context, and also discuss implementation issues, recovery algorithms and performance bounds. As we demonstrate, proper accounting for quantization and careful quantizer design has significant impact in the performance of a compressive acquisition system.Comment: 35 pages, 20 figures, to appear in Springer book "Compressed Sensing and Its Applications", 201

A Once and Future Gulf of Mexico Ecosystem: Restoration Recommendations of an Expert Working Group

The Deepwater Horizon (DWH) well blowout released more petroleum hydrocarbons into the marine environment than any previous U.S. oil spill (4.9 million barrels), fouling marine life, damaging deep sea and shoreline habitats and causing closures of economically valuable fisheries in the Gulf of Mexico. A suite of pollutants — liquid and gaseous petroleum compounds plus chemical dispersants — poured into ecosystems that had already been stressed by overfishing, development and global climate change. Beyond the direct effects that were captured in dramatic photographs of oiled birds in the media, it is likely that there are subtle, delayed, indirect and potentially synergistic impacts of these widely dispersed, highly bioavailable and toxic hydrocarbons and chemical dispersants on marine life from pelicans to salt marsh grasses and to deep-sea animals. As tragic as the DWH blowout was, it has stimulated public interest in protecting this economically, socially and environmentally critical region. The 2010 Mabus Report, commissioned by President Barack Obama and written by the secretary of the Navy, provides a blueprint for restoring the Gulf that is bold, visionary and strategic. It is clear that we need not only to repair the damage left behind by the oil but also to go well beyond that to restore the anthropogenically stressed and declining Gulf ecosystems to prosperity-sustaining levels of historic productivity. For this report, we assembled a team of leading scientists with expertise in coastal and marine ecosystems and with experience in their restoration to identify strategies and specific actions that will revitalize and sustain the Gulf coastal economy. Because the DWH spill intervened in ecosystems that are intimately interconnected and already under stress, and will remain stressed from global climate change, we argue that restoration of the Gulf must go beyond the traditional “in-place, in-kind” restoration approach that targets specific damaged habitats or species. A sustainable restoration of the Gulf of Mexico after DWH must: 1. Recognize that ecosystem resilience has been compromised by multiple human interventions predating the DWH spill; 2. Acknowledge that significant future environmental change is inevitable and must be factored into restoration plans and actions for them to be durable; 3. Treat the Gulf as a complex and interconnected network of ecosystems from shoreline to deep sea; and 4. Recognize that human and ecosystem productivity in the Gulf are interdependent, and that human needs from and effects on the Gulf must be integral to restoration planning. With these principles in mind, we provide the scientific basis for a sustainable restoration program along three themes: 1. Assess and repair damage from DWH and other stresses on the Gulf; 2. Protect existing habitats and populations; and 3. Integrate sustainable human use with ecological processes in the Gulf of Mexico. Under these themes, 15 historically informed, adaptive, ecosystem-based restoration actions are presented to recover Gulf resources and rebuild the resilience of its ecosystem. The vision that guides our recommendations fundamentally imbeds the restoration actions within the context of the changing environment so as to achieve resilience of resources, human communities and the economy into the indefinite future

Once and Future Gulf of Mexico Ecosystem: Restoration Recommendations of an Expert Working Group

The Deepwater Horizon (DWH) well blowout released more petroleum hydrocarbons into the marine environment than any previous U.S. oil spill (4.9 million barrels), fouling marine life, damaging deep sea and shoreline habitats and causing closures of economically valuable fisheries in the Gulf of Mexico. A suite of pollutants—liquid and gaseous petroleum compounds plus chemical dispersants—poured into ecosystems that had already been stressed by overfishing, development and global climate change. Beyond the direct effects that were captured in dramatic photographs of oiled birds in the media, it is likely that there are subtle, delayed, indirect and potentially synergistic impacts of these widely dispersed, highly bioavailable and toxic hydrocarbons and chemical dispersants on marine life from pelicans to salt marsh grasses and to deep-sea animals. As tragic as the DWH blowout was, it has stimulated public interest in protecting this economically, socially and environmentally critical region. The 2010 Mabus Report, commissioned by President Barack Obama and written by the secretary of the Navy, provides a blueprint for restoring the Gulf that is bold, visionary and strategic. It is clear that we need not only to repair the damage left behind by the oil but also to go well beyond that to restore the anthropogenically stressed and declining Gulf ecosystems to prosperity-sustaining levels of historic productivity. For this report, we assembled a team of leading scientists with expertise in coastal and marine ecosystems and with experience in their restoration to identify strategies and specific actions that will revitalize and sustain the Gulf coastal economy. Because the DWH spill intervened in ecosystems that are intimately interconnected and already under stress, and will remain stressed from global climate change, we argue that restoration of the Gulf must go beyond the traditional "in-place, in-kind" restoration approach that targets specific damaged habitats or species. A sustainable restoration of the Gulf of Mexico after DWH must: 1. Recognize that ecosystem resilience has been compromised by multiple human interventions predating the DWH spill; 2. Acknowledge that significant future environmental change is inevitable and must be factored into restoration plans and actions for them to be durable; 3. Treat the Gulf as a complex and interconnected network of ecosystems from shoreline to deep sea; and 4. Recognize that human and ecosystem productivity in the Gulf are interdependent, and that human needs from and effects on the Gulf must be integral to restoration planning. With these principles in mind, the authors provide the scientific basis for a sustainable restoration program along three themes: 1. Assess and repair damage from DWH and other stresses on the Gulf; 2. Protect existing habitats and populations; and 3. Integrate sustainable human use with ecological processes in the Gulf of Mexico. Under these themes, 15 historically informed, adaptive, ecosystem-based restoration actions are presented to recover Gulf resources and rebuild the resilience of its ecosystem. The vision that guides our recommendations fundamentally imbeds the restoration actions within the context of the changing environment so as to achieve resilience of resources, human communities and the economy into the indefinite future

Sparsity and Incoherence in Compressive Sampling

We consider the problem of reconstructing a sparse signal $x^0\in\R^n$ from a limited number of linear measurements. Given $m$ randomly selected samples of $U x^0$, where $U$ is an orthonormal matrix, we show that $\ell_1$ minimization recovers $x^0$ exactly when the number of measurements exceeds $$m\geq \mathrm{Const}\cdot\mu^2(U)\cdot S\cdot\log n,$$ where $S$ is the number of nonzero components in $x^0$, and $\mu$ is the largest entry in $U$ properly normalized: $\mu(U) = \sqrt{n} \cdot \max_{k,j} |U_{k,j}|$. The smaller $\mu$, the fewer samples needed. The result holds for ``most'' sparse signals $x^0$ supported on a fixed (but arbitrary) set $T$. Given $T$, if the sign of $x^0$ for each nonzero entry on $T$ and the observed values of $Ux^0$ are drawn at random, the signal is recovered with overwhelming probability. Moreover, there is a sense in which this is nearly optimal since any method succeeding with the same probability would require just about this many samples

A non-synonymous single-nucleotide polymorphism in the gene encoding Toll-like Receptor 3 (TLR3) is associated with sero-negative Rheumatoid Arthritis (RA) in a Danish population

BACKGROUND: It has been suggested that polymorphisms in Toll-like Receptors (TLRs) are associated with Rheumatoid Arthritis (RA), but the implicated alleles have differed between studies. The aim of this investigation was to explore whether polymorphisms of TLR genes are associated with RA in a predominantly Caucasian population from Denmark using a case–control approach. FINDINGS: DNA samples (3 university hospital outpatient clinics) were obtained from patients with RA (n = 704) and healthy controls (n = 639) in a Danish population. TLR single nucleotide polymorphisms (SNPs) were selected based on the previously reported associations with chronic autoimmune diseases. Genotyping for the TLR SNPs was performed using Sequenom Multiplex technology. We identified one SNP in TLR3, [(rs3775291, P = 0.02, OR (95% CI) 1.31 (1.1087-1.5493)] significantly associated with the whole RA cohort. Subgroup analysis according to IgM rheumatoid factor (RF) and anti-cyclic citrinullated peptide (CCP) status suggested a significant association of sero-negative RA with the rs3775291 A allele and disease activity in this subset. CONCLUSION: These observations on a RA population of Danish ancestry suggest that variations in the TLR3 locus may be implicated in the pathogenesis of sero-negative RA. Since this TLR3 SNP has previously been associated with systemic lupus erythematous (SLE), the present findings support the notion that TLR3 genetic variants may represent a common risk factor in different chronic inflammatory conditions, including RA and SLE

Laser ablation loading of a radiofrequency ion trap

The production of ions via laser ablation for the loading of radiofrequency (RF) ion traps is investigated using a nitrogen laser with a maximum pulse energy of 0.17 mJ and a peak intensity of about 250 MW/cm^2. A time-of-flight mass spectrometer is used to measure the ion yield and the distribution of the charge states. Singly charged ions of elements that are presently considered for the use in optical clocks or quantum logic applications could be produced from metallic samples at a rate of the order of magnitude 10^5 ions per pulse. A linear Paul trap was loaded with Th+ ions produced by laser ablation. An overall ion production and trapping efficiency of 10^-7 to 10^-6 was attained. For ions injected individually, a dependence of the capture probability on the phase of the RF field has been predicted. In the experiment this was not observed, presumably because of collective effects within the ablation plume.Comment: submitted to Appl. Phys. B., special issue on ion trappin