9,524 research outputs found

    Soil Moisture

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    Naive Bayes ant colony optimization for designing high dimensional experiments

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    In a large number of experimental problems, high dimensionality of the search area and economical constraints can severely limit the number of experimental points that can be tested. Within these constraints, classical optimization techniques perform poorly, in particular, when little a priori knowledge is available. In this work we investigate the possibility of combining approaches from statistical modeling and bio-inspired algorithms to effectively explore a huge search space, sampling only a limited number of experimental points. To this purpose, we introduce a novel approach, combining ant colony optimization (ACO) and naive Bayes classifier (NBC) that is, the naive Bayes ant colony optimization (NACO) procedure. We compare NACO with other similar approaches developing a simulation study. We then derive the NACO procedure with the goal to design artificial enzymes with no sequence homology to the extant one. Our final aim is to mimic the natural fold of 200 amino acids 1AGY serine esterase from Fusarium solani

    The cross-entropy method for continuous multi-extremal optimization

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    In recent years, the cross-entropy method has been successfully applied to a wide range of discrete optimization tasks. In this paper we consider the cross-entropy method in the context of continuous optimization. We demonstrate the effectiveness of the cross-entropy method for solving difficult continuous multi-extremal optimization problems, including those with non-linear constraints

    Dispersive flies optimisation and medical imaging

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    One of the main sources of inspiration for techniques applicable to complex search space and optimisation problems is nature. This paper introduces a new metaheuristic—Dispersive Flies Optimisation (DFO)—whose inspiration is beckoned from the swarming behaviour of flies over food sources in nature. The simplicity of the algorithm facilitates the analysis of its behaviour. A series of experimental trials confirms the promising performance of the optimiser over a set of benchmarks, as well as its competitiveness when compared against three other well-known population based algorithms. The convergence-independent diversity of DFO algorithm makes it a potentially suitable candidate for dynamically changing environment. In addition to diversity, the performance of the newly introduced algorithm is investigated using the three performance measures of accuracy, efficiency and reliability and its outperformance is demonstrated in the paper. Then the proposed swarm intelligence algorithm is used as a tool to identify microcalcifications on the mammographs. This algorithm is adapted for this particular purpose and its performance is investigated by running the agents of the swarm intelligence algorithm on sample mammographs whose status have been determined by the experts. Two modes of the algorithms are introduced in the paper, each providing the clinicians with a different set of outputs, highlighting the areas of interest where more attention should be given by those in charge of the care of the patients

    Ownership and control in a competitive industry

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    We study a differentiated product market in which an investor initially owns a controlling stake in one of two competing firms and may acquire a non-controlling or a controlling stake in a competitor, either directly using her own assets, or indirectly via the controlled firm. While industry profits are maximized within a symmetric two product monopoly, the investor attains this only in exceptional cases. Instead, she sometimes acquires a noncontrolling stake. Or she invests asymmetrically rather than pursuing a full takeover if she acquires a controlling one. Generally, she invests indirectly if she only wants to affect the product market outcome, and directly if acquiring shares is profitable per se. --differentiated products,separation of ownership and control,private benefits of control

    A Study of Time-Dependent CP-Violating Asymmetries and Flavor Oscillations in Neutral B Decays at the Upsilon(4S)

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    We present a measurement of time-dependent CP-violating asymmetries in neutral B meson decays collected with the BABAR detector at the PEP-II asymmetric-energy B Factory at the Stanford Linear Accelerator Center. The data sample consists of 29.7 fb1{\rm fb}^{-1} recorded at the Υ(4S)\Upsilon(4S) resonance and 3.9 fb1{\rm fb}^{-1} off-resonance. One of the neutral B mesons, which are produced in pairs at the Υ(4S)\Upsilon(4S), is fully reconstructed in the CP decay modes J/ψKS0J/\psi K^0_S, ψ(2S)KS0\psi(2S) K^0_S, χc1KS0\chi_{c1} K^0_S, J/ψK0J/\psi K^{*0} (K0KS0π0K^{*0}\to K^0_S\pi^0) and J/ψKL0J/\psi K^0_L, or in flavor-eigenstate modes involving D()π/ρ/a1D^{(*)}\pi/\rho/a_1 and J/ψK0J/\psi K^{*0} (K0K+πK^{*0}\to K^+\pi^-). The flavor of the other neutral B meson is tagged at the time of its decay, mainly with the charge of identified leptons and kaons. The proper time elapsed between the decays is determined by measuring the distance between the decay vertices. A maximum-likelihood fit to this flavor eigenstate sample finds Δmd=0.516±0.016(stat)±0.010(syst)ps1\Delta m_d = 0.516\pm 0.016 {\rm (stat)} \pm 0.010 {\rm (syst)} {\rm ps}^{-1}. The value of the asymmetry amplitude sin2β\sin2\beta is determined from a simultaneous maximum-likelihood fit to the time-difference distribution of the flavor-eigenstate sample and about 642 tagged B0B^0 decays in the CP-eigenstate modes. We find sin2β=0.59±0.14(stat)±0.05(syst)\sin2\beta=0.59\pm 0.14 {\rm (stat)} \pm 0.05 {\rm (syst)}, demonstrating that CP violation exists in the neutral B meson system. (abridged)Comment: 58 pages, 35 figures, submitted to Physical Review

    Inclusive Search for Anomalous Production of High-pT Like-Sign Lepton Pairs in Proton-Antiproton Collisions at sqrt{s}=1.8 TeV

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    We report on a search for anomalous production of events with at least two charged, isolated, like-sign leptons with pT > 11 GeV/c using a 107 pb^-1 sample of 1.8 TeV ppbar collisions collected by the CDF detector. We define a signal region containing low background from Standard Model processes. To avoid bias, we fix the final cuts before examining the event yield in the signal region using control regions to test the Monte Carlo predictions. We observe no events in the signal region, consistent with an expectation of 0.63^(+0.84)_(-0.07) events. We present 95% confidence level limits on new physics processes in both a signature-based context as well as within a representative minimal supergravity (tanbeta = 3) model.Comment: 15 pages, 4 figures. Minor textual changes, cosmetic improvements to figures and updated and expanded reference
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