An approximate dynamic programming approach for improving accuracy of lossy data compression by Bloom filters

Bloom filters are a data structure for storing data in a compressed form. They offer excellent space and time efficiency at the cost of some loss of accuracy (so-called lossy compression). This work presents a yes-no Bloom filter, which as a data structure consisting of two parts: the yes-filter which is a standard Bloom filter and the no-filter which is another Bloom filter whose purpose is to represent those objects that were recognised incorrectly by the yes-filter (that is, to recognise the false positives of the yes-filter). By querying the no-filter after an object has been recognised by the yes-filter, we get a chance of rejecting it, which improves the accuracy of data recognition in comparison with the standard Bloom filter of the same total length. A further increase in accuracy is possible if one chooses objects to include in the no-filter so that the no-filter recognises as many as possible false positives but no true positives, thus producing the most accurate yes-no Bloom filter among all yes-no Bloom filters. This paper studies how optimization techniques can be used to maximize the number of false positives recognised by the no-filter, with the constraint being that it should recognise no true positives. To achieve this aim, an Integer Linear Program (ILP) is proposed for the optimal selection of false positives. In practice the problem size is normally large leading to intractable optimal solution. Considering the similarity of the ILP with the Multidimensional Knapsack Problem, an Approximate Dynamic Programming (ADP) model is developed making use of a reduced ILP for the value function approximation. Numerical results show the ADP model works best comparing with a number of heuristics as well as the CPLEX built-in solver (B&B), and this is what can be recommended for use in yes-no Bloom filters. In a wider context of the study of lossy compression algorithms, our researchis an example showing how the arsenal of optimization methods can be applied to improving the accuracy of compressed data.

Creativity, entrepreneurship and economic development: city-level evidence on creativity spillover of entrepreneurship

We examine the black box of creativity, entrepreneurship and economic development by asking about the mechanisms through which creativity can influence economic development in cities. We propose that, like the knowledge spillover theory of entrepreneurship, creativity spillovers occur and can be slowed by a creativity filter. We examine how creativity and entrepreneurship, and creativity and a melting pot environment, interact to influence urban economic development. Using data on 187 cities in 15 European countries for the period 1999–2009, we advance the extant literature by providing evidence on the existence and dynamics of a creativity filter.

Implicit equal-weights particle filter

Filter degeneracy is the main obstacle for the implementation of particle filter in non-linear high-dimensional models. A new scheme, the implicit equal-weights particle filter (IEWPF), is introduced. In this scheme samples are drawn implicitly from proposal densities with a different covariance for each particle, such that all particle weights are equal by construction. We test and explore the properties of the new scheme using a 1,000-dimensional simple linear model, and the 1,000-dimensional non-linear Lorenz96 model, and compare the performance of the scheme to a Local Ensemble Kalman Filter. The experiments show that the new scheme can easily be implemented in high-dimensional systems and is never degenerate, with good convergence properties in both systems.

The role of life history traits in mammalian invasion success

Why some organisms become invasive when introduced into novel regions while others fail to even establish is a fundamental question in ecology. Barriers to success are expected to filter species at each stage along the invasion pathway. No study to date, however, has investigated how species traits associate with success from introduction to spread at a large spatial scale in any group. Using the largest data set of mammalian introductions at the global scale and recently developed phylogenetic comparative methods, we show that human-mediated introductions considerably bias which species have the opportunity to become invasive, as highly productive mammals with longer reproductive lifespans are far more likely to be introduced. Subsequently, greater reproductive output and higher introduction effort are associated with success at both the establishment and spread stages. High productivity thus supports population growth and invasion success, with barriers at each invasion stage filtering species with progressively greater fecundity.

Are mortgage lenders guilty of the housing bubble? A UK perspective

Existing theoretical models of house prices and credit rely on continuous rationality of consumers, an assumption that has been frequently questioned in recent years. Meanwhile, empirical investigations of the relationship between prices and credit are often based on national-level data, which is then tested for structural breaks and asymmetric responses, usually with subsamples. Earlier author argues that local markets are structurally different from one another and so the coefficients of any estimated housing market model should vary from region to region. We investigate differences in the price–credit relationship for 12 regions of the UK. Markov-switching is introduced to capture asymmetric market behaviours and turning points. Results show that credit abundance had a large impact on house prices in Greater London and nearby regions alongside a strong positive feedback effect from past house price movements. This impact is even larger in Greater London and the South East of England when house prices are falling, which are the only instances where the credit effect is more prominent than the positive feedback effect. A strong positive feedback effect from past lending activity is also present in the loan dynamics. Furthermore, bubble probabilities extracted using a discrete Kalman filter neatly capture market turning points.

THE GEMINI NICI PLANET-FINDING CAMPAIGN: DISCOVERY OF A SUBSTELLAR L DWARF COMPANION TO THE NEARBY YOUNG M DWARF CD-35 2722

We present the discovery of a wide (67 AU) substellar companion to the nearby (21 pc) young solar-metallicity M1 dwarf CD-35 2722, a member of the approximate to 100 Myr AB Doradus association. Two epochs of astrometry from the NICI Planet-Finding Campaign confirm that CD-35 2722 B is physically associated with the primary star. Near-IR spectra indicate a spectral type of L4 +/- 1 with a moderately low surface gravity, making it one of the coolest young companions found to date. The absorption lines and near-IR continuum shape of CD-35 2722 B agree especially well the dusty field L4.5 dwarf 2MASS J22244381-0158521, while the near-IR colors and absolute magnitudes match those of the 5 Myr old L4 planetary-mass companion, 1RXS J160929.1-210524 b. Overall, CD-35 2722 B appears to be an intermediate-age benchmark for L dwarfs, with a less peaked H-band continuum than the youngest objects and near-IR absorption lines comparable to field objects. We fit Ames-Dusty model atmospheres to the near-IR spectra and find T(eff) = 1700-1900 K and log(g) = 4.5 +/- 0.5. The spectra also show that the radial velocities of components A and B agree to within +/- 10 km s(-1), further confirming their physical association. Using the age and bolometric luminosity of CD-35 2722 B, we derive a mass of 31 +/- 8 M(Jup) from the Lyon/Dusty evolutionary models. Altogether, young late-M to mid-L type companions appear to be overluminous for their near-IR spectral type compared with field objects, in contrast to the underluminosity of young late-L and early-T dwarfs.

Suppression of small baryonic structures due to a primordial magnetic field

We investigate the impact of the existence of a primordial magnetic field on the filter mass, characterizing the minimum baryonic mass that can form in dark matter (DM) haloes. For masses below the filter mass, the baryon content of DM haloes are severely depressed. The filter mass is the mass when the baryon to DM mass ratio in a halo is equal to half the baryon to DM ratio of the Universe. The filter mass has previously been used in semi-analytic calculations of galaxy formation, without taking into account the possible existence of a primordial magnetic field. We examine here its effect on the filter mass. For homogeneous comoving primordial magnetic fields of B(0) similar to 1 or 2 nG and a re-ionization epoch that starts at a redshift z(s) = 11 and is completed at z(r) = 8, the filter mass is increased at redshift 8, for example, by factors of 4.1 and 19.8, respectively. The dependence of the filter mass on the parameters describing the re-ionization epoch is investigated. Our results are particularly important for the formation of low-mass galaxies in the presence of a homogeneous primordial magnetic field. For example, for B(0) similar to 1 nG and a re-ionization epoch of z(s) similar to 11 and z(r) similar to 7, our results indicate that galaxies of total mass M similar to 5 x 108 M(circle dot) need to form at redshifts z(F) greater than or similar to 2.0, and galaxies of total mass M similar to 108 M(circle dot) at redshifts z(F) greater than or similar to 7.7.

Analysis of total visual and CCD V-broadband observations of Comet C/1995 O1 (Hale-Bopp): 1995-2001

We developed a general method for determination of water production rates from groundbased visual observations and applied it to Comet Hale-Bopp. Our main objective is to extend the method to include total visual magnitude observations obtained with CCD detector and V filter in the analysis of total visual magnitudes. We compare the CCD V-broadband careful observations of Liller [Liller, W. Pre-perihelion CCD photometry of Comet 1995 01 (Hale-Bopp). Planet. Space Sci. 45, 1505-1513, 1997; Liller, W. CCD photometry of Comet C/1995 O1 (Hale-Bopp): 1995-2000. Int. Comet Quart. 23(3), 93-97, 2001] with the total visual magnitude observations from experienced international observers found in the International Comet Quarterly (ICQ) archive. A data set of similar to 400 CCD observations covering about the same 6 years time span of the similar to 12,000 ICQ selected total visual magnitude observations were used in the analysis. A least-square method applied to the water production rates, yields power laws as a function of the heliocentric distances for the pre- and post-perihelion phases. The average dimension of the nucleus as well as its effective active area is determined and compared with values published in the literature. (C) 2009 COSPAR. Published by Elsevier Ltd. All rights reserved.

The Diffusion Kernel Filter

A particle filter method is presented for the discrete-time filtering problem with nonlinear ItA ` stochastic ordinary differential equations (SODE) with additive noise supposed to be analytically integrable as a function of the underlying vector-Wiener process and time. The Diffusion Kernel Filter is arrived at by a parametrization of small noise-driven state fluctuations within branches of prediction and a local use of this parametrization in the Bootstrap Filter. The method applies for small noise and short prediction steps. With explicit numerical integrators, the operations count in the Diffusion Kernel Filter is shown to be smaller than in the Bootstrap Filter whenever the initial state for the prediction step has sufficiently few moments. The established parametrization is a dual-formula for the analysis of sensitivity to gaussian-initial perturbations and the analysis of sensitivity to noise-perturbations, in deterministic models, showing in particular how the stability of a deterministic dynamics is modeled by noise on short times and how the diffusion matrix of an SODE should be modeled (i.e. defined) for a gaussian-initial deterministic problem to be cast into an SODE problem. From it, a novel definition of prediction may be proposed that coincides with the deterministic path within the branch of prediction whose information entropy at the end of the prediction step is closest to the average information entropy over all branches. Tests are made with the Lorenz-63 equations, showing good results both for the filter and the definition of prediction.

Towards improving cluster-based feature selection with a simplified silhouette filter

This paper proposes a filter-based algorithm for feature selection. The filter is based on the partitioning of the set of features into clusters. The number of clusters, and consequently the cardinality of the subset of selected features, is automatically estimated from data. The computational complexity of the proposed algorithm is also investigated. A variant of this filter that considers feature-class correlations is also proposed for classification problems. Empirical results involving ten datasets illustrate the performance of the developed algorithm, which in general has obtained competitive results in terms of classification accuracy when compared to state of the art algorithms that find clusters of features. We show that, if computational efficiency is an important issue, then the proposed filter May be preferred over their counterparts, thus becoming eligible to join a pool of feature selection algorithms to be used in practice. As an additional contribution of this work, a theoretical framework is used to formally analyze some properties of feature selection methods that rely on finding clusters of features. (C) 2011 Elsevier Inc. All rights reserved.

A Floating-point Extended Kalman Filter Implementation for Autonomous Mobile Robots

Localization and Mapping are two of the most important capabilities for autonomous mobile robots and have been receiving considerable attention from the scientific computing community over the last 10 years. One of the most efficient methods to address these problems is based on the use of the Extended Kalman Filter (EKF). The EKF simultaneously estimates a model of the environment (map) and the position of the robot based on odometric and exteroceptive sensor information. As this algorithm demands a considerable amount of computation, it is usually executed on high end PCs coupled to the robot. In this work we present an FPGA-based architecture for the EKF algorithm that is capable of processing two-dimensional maps containing up to 1.8 k features at real time (14 Hz), a three-fold improvement over a Pentium M 1.6 GHz, and a 13-fold improvement over an ARM920T 200 MHz. The proposed architecture also consumes only 1.3% of the Pentium and 12.3% of the ARM energy per feature.

Aerosol inorganic composition at a tropical site: Discrepancies between filter-based sampling and a semi-continuous method

The concentrations of the water-soluble inorganic aerosol species, ammonium (NH4+), nitrate (NO3-), chloride (Cl-), and sulfate (SO42-), were measured from September to November 2002 at a pasture site in the Amazon Basin (Rondnia, Brazil) (LBA-SMOCC). Measurements were conducted using a semi-continuous technique (Wet-annular denuder/Steam-Jet Aerosol Collector: WAD/SJAC) and three integrating filter-based methods, namely (1) a denuder-filter pack (DFP: Teflon and impregnated Whatman filters), (2) a stacked-filter unit (SFU: polycarbonate filters), and (3) a High Volume dichotomous sampler (HiVol: quartz fiber filters). Measurements covered the late dry season (biomass burning), a transition period, and the onset of the wet season (clean conditions). Analyses of the particles collected on filters were performed using ion chromatography (IC) and Particle-Induced X-ray Emission spectrometry (PIXE). Season-dependent discrepancies were observed between the WAD/SJAC system and the filter-based samplers. During the dry season, when PM2.5 (D-p <= 2.5 mu m) concentrations were similar to 100 mu g m(-3), aerosol NH4+ and SO42- measured by the filter-based samplers were on average two times higher than those determined by the WAD/SJAC. Concentrations of aerosol NO3- and Cl- measured with the HiVol during daytime, and with the DFP during day- and nighttime also exceeded those of the WAD/SJAC by a factor of two. In contrast, aerosol NO3- and Cl- measured with the SFU during the dry season were nearly two times lower than those measured by the WAD/SJAC. These differences declined markedly during the transition period and towards the cleaner conditions during the onset of the wet season (PM2.5 similar to 5 mu g m(-3)); when filter-based samplers measured on average 40-90% less than the WAD/SJAC. The differences were not due to consistent systematic biases of the analytical techniques, but were apparently a result of prevailing environmental conditions and different sampling procedures. For the transition period and wet season, the significance of our results is reduced by a low number of data points. We argue that the observed differences are mainly attributable to (a) positive and negative filter sampling artifacts, (b) presence of organic compounds and organosulfates on filter substrates, and (c) a SJAC sampling efficiency of less than 100%.

IxV Curves of Boron and Nitrogen Doping Zigzag Graphene Nanoribbons

We investigate the transport properties (IxV curves and zero bias transmittance) of pristine graphene nanoribbons (GNRs) as well as doped with boron and nitrogen using an approach that combines nonequilibrium Green`s functions and density functional theory (DFT) [NEGF-DFT]. Even for a pristine nanoribbon we verify a spin-filter effect under finite bias voltage when the leads have an antiparallel magnetization. The presence of the impurities at the edges of monohydrogenated zigzag GNRs changes dramatically the charge transport properties inducing a spin-polarized conductance. The IxV curves for these systems show that depending on the bias voltage the spin polarization can be inverted. (C) 2010 Wiley Periodicals, Inc. Int J Quantum Chem 111: 1379-1386, 2011

Mice and larvae tracking using a particle filter with an auto-adjustable observation model

This paper proposes a novel way to combine different observation models in a particle filter framework. This, so called, auto-adjustable observation model, enhance the particle filter accuracy when the tracked objects overlap without infringing a great runtime penalty to the whole tracking system. The approach has been tested under two important real world situations related to animal behavior: mice and larvae tracking. The proposal was compared to some state-of-art approaches and the results show, under the datasets tested, that a good trade-off between accuracy and runtime can be achieved using an auto-adjustable observation model. (C) 2009 Elsevier B.V. All rights reserved.

Correlations between chemical composition and provenance of Justino site ceramics by INAA

Instrumental neutron activation analysis (INAA), have been used for the definition of compositional groups of potteries from Justino site, Brazil, according to the chemical similarities of ceramic paste. The outliers were identified by means of robust Mahalanobis distance. The temper effect in the ceramic paste was studied by means of modified Mahalanobis filter. The results were interpreted by means of cluster, principal components, and discriminant analyses. This work provides contributions for the reconstruction of the prehistory of baixo Sao Francisco region, and for the reconstitution of the Brazilian Northeast ceramist population of general frame.