Single-particle momentum distributions of Efimov states in mixed-species systems

We solve the three-body bound-state problem in three dimensions for mass imbalanced systems of two identical bosons and a third particle in the universal limit where the interactions are assumed to be of zero range. The system displays the Efimov effect and we use the momentum-space wave equation to derive formulas for the scaling factor of the Efimov spectrum for any mass ratio assuming either that two or three of the two-body subsystems have a bound state at zero energy. We consider the single-particle momentum distribution analytically and numerically and analyze the tail of the momentum distribution to obtain the three-body contact parameter. Our findings demonstrate that the functional form of the three-body contact term depends on the mass ratio, and we obtain an analytic expression for this behavior. To exemplify our results, we consider mixtures of lithium with either two caesium or rubidium atoms which are systems of current experimental interest. © 2013 American Physical Society.

Single-Particle Momentum Distributions for Bosonic Trimer States in Two and Three Dimensions

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Dimensional Compactification and Two-Particle Binding

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Universal zero-bias conductance for the single-electron transistor

The thermal dependence of the zero-bias conductance for the single electron transistor is the target of two independent renormalization-group approaches, both based on the spin-degenerate Anderson impurity model. The first approach, an analytical derivation, maps the Kondo-regime conductance onto the universal conductance function for the particle-hole symmetric model. Linear, the mapping is parametrized by the Kondo temperature and the charge in the Kondo cloud. The second approach, a numerical renormalization-group computation of the conductance as a function the temperature and applied gate voltages offers a comprehensive view of zero-bias charge transport through the device. The first approach is exact in the Kondo regime; the second, essentially exact throughout the parametric space of the model. For illustrative purposes, conductance curves resulting from the two approaches are compared.

Multifractal characterization of saprolite particle-size distributions after topsoil removal

Multifractal analysis is now increasingly used to characterize soil properties as it may provide more information than a single fractal model. During the building of a large reservoir on the Parana River (Brazil), a highly weathered soil profile was excavated to a depth between 5 and 8 m. Excavation resulted in an abandoned area with saprolite materials and, in this area, an experimental field was established to assess the effectiveness of different soil rehabilitation treatments. The experimental design consisted of randomized blocks. The aim of this work was to characterize particle-size distributions of the saprolite material and use the information obtained to assess between-block variability. Particle-size distributions of the experimental plots were characterized by multifractal techniques. Ninety-six soil samples were analyzed routinely for particle-size distribution by laser diffractometry in a range of scales, varying from 0.390 to 2000 mu m. Six different textural classes (USDA) were identified with a clay content ranging from 16.9% to 58.4%. Multifractal models described reasonably well the scaling properties of particle-size distributions of the saprolite material. This material exhibits a high entropy dimension, D-1. Parameters derived from the left side (q > 0) of the f(alpha) spectra, D-1, the correlation dimension (D-2) and the range (alpha(0)-alpha(q+)), as well as the total width of the spectra (alpha(max) - alpha(min)) all showed dependence on the clay content. Sand, silt and clay contents were significantly different among treatments as a consequence of soil intrinsic variability. The D, and the Holder exponent of order zero, alpha(0), were not significantly different between treatments; in contrast, D-2 and several fractal attributes describing the width of the f(alpha) spectra were significantly different between treatments. The only parameter showing significant differences between sampling depths was (alpha(0) - alpha(q+)). Scale independent fractal attributes may be useful for characterizing intrinsic particle-size distribution variability. (c) 2006 Elsevier B.V. All rights reserved.

Particle Competition and Cooperation in Networks for Semi-Supervised Learning with Concept Drift

Concept drift is a problem of increasing importance in machine learning and data mining. Data sets under analysis are no longer only static databases, but also data streams in which concepts and data distributions may not be stable over time. However, most learning algorithms produced so far are based on the assumption that data comes from a fixed distribution, so they are not suitable to handle concept drifts. Moreover, some concept drifts applications requires fast response, which means an algorithm must always be (re) trained with the latest available data. But the process of labeling data is usually expensive and/or time consuming when compared to unlabeled data acquisition, thus only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are also based on the assumption that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenge in machine learning. Recently, a particle competition and cooperation approach was used to realize graph-based semi-supervised learning from static data. In this paper, we extend that approach to handle data streams and concept drift. The result is a passive algorithm using a single classifier, which naturally adapts to concept changes, without any explicit drift detection mechanism. Its built-in mechanisms provide a natural way of learning from new data, gradually forgetting older knowledge as older labeled data items became less influent on the classification of newer data items. Some computer simulation are presented, showing the effectiveness of the proposed method.

Synthesis and electrochemical behavior of single-crystal magnetite nanoparticles

Water-dispersed magnetite nanoparticle synthesis from iron(II) chloride in dimethyl sulfoxide (DMSO)-water solution at different DMSO-water ratios in alkaline medium was reported. TEM and XRD results suggest a single-crystal formation with mean particle size in the range 4-27 nm. Magnetic nanoparticles are formed by the oxidative hydrolysis reaction from green rust species that leads to FeOOH formation, followed by autocatalysis of the adsorbed available Fe(II) on the FeOOH surfaces. The available hydroxyl groups seem to be dependent on the DMSO-water ratio due to strong molecular interactions presented by the solvent mixture. Goethite phase on the magnetite surface was observed by XRD data only for sample synthesized in the absence of DMSO. In addition, cyclic voltammetry with carbon paste electroactive electrode (CV-CPEE) results reveal two reduction peaks near 0 and +400 mV associated with the presence of iron(III) in different chemical environments related to the surface composition of magnetite nanoparticles. The peak near +400 mV is related to a passivate thin layer surface such as goethite on the magnetite nanoparticle, assigned to the intensive hydrolysis reaction due to strong interactions between DMSO-water molecules in the initial solvent mixture that result in a hydroxyl group excess in the medium. Pure magnetite phase was only observed in the samples prepared at 30% (30W) and 80% (80W) water in DMSO in agreement with the structured molecular solvent cluster formation. The goethite phase present on the, magnetite nanoparticle surface like a thin passivate layer only was detectable using CV-CPEE, which is a very efficient, cheap, and powerful tool for surface characterization, and it is able to determine the passivate oxyhydroxide or oxide thin layer presence on the nanoparticle surface.

Measurement of neutral strange particle production in the underlying event in proton-proton collisions at √s=7 TeV

Measurements are presented of the production of primary KS0 and Λ particles in proton-proton collisions at √s=7 TeV in the region transverse to the leading charged-particle jet in each event. The average multiplicity and average scalar transverse momentum sum of KS0 and Λ particles measured at pseudorapidities |η|<2 rise with increasing charged-particle jet pT in the range 1-10 GeV/c and saturate in the region 10-50 GeV/c. The rise and saturation of the strange-particle yields and transverse momentum sums in the underlying event are similar to those observed for inclusive charged particles, which confirms the impact-parameter picture of multiple parton interactions. The results are compared to recent tunes of the pythia Monte Carlo event generator. The pythia simulations underestimate the data by 15%-30% for KS0 mesons and by about 50% for Λ baryons, a deficit similar to that observed for the inclusive strange-particle production in non-single-diffractive proton-proton collisions. The constant strange- to charged-particle activity ratios with respect to the leading jet pT and similar trends for mesons and baryons indicate that the multiparton-interaction dynamics is decoupled from parton hadronization, which occurs at a later stage. © 2013 CERN, for the CMS Collaboration Published by the American Physical Society under the terms of the Creative Commons Attribution 3.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

Search for top-squark pair production in the single-lepton final state in pp collisions at

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Combined active and semi-supervised learning using particle walking temporal dynamicscombined active and semi-supervised learning using particle walking temporal dynamics

Both Semi-Supervised Leaning and Active Learning are techniques used when unlabeled data is abundant, but the process of labeling them is expensive and/or time consuming. In this paper, those two machine learning techniques are combined into a single nature-inspired method. It features particles walking on a network built from the data set, using a unique random-greedy rule to select neighbors to visit. The particles, which have both competitive and cooperative behavior, are created on the network as the result of label queries. They may be created as the algorithm executes and only nodes affected by the new particles have to be updated. Therefore, it saves execution time compared to traditional active learning frameworks, in which the learning algorithm has to be executed several times. The data items to be queried are select based on information extracted from the nodes and particles temporal dynamics. Two different rules for queries are explored in this paper, one of them is based on querying by uncertainty approaches and the other is based on data and labeled nodes distribution. Each of them may perform better than the other according to some data sets peculiarities. Experimental results on some real-world data sets are provided, and the proposed method outperforms the semi-supervised learning method, from which it is derived, in all of them.

Semi-supervised learning with concept drift using particle dynamics applied to network intrusion detection data

Concept drift, which refers to non stationary learning problems over time, has increasing importance in machine learning and data mining. Many concept drift applications require fast response, which means an algorithm must always be (re)trained with the latest available data. But the process of data labeling is usually expensive and/or time consuming when compared to acquisition of unlabeled data, thus usually only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are based on assumptions that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenging task in machine learning. Recently, a particle competition and cooperation approach has been developed to realize graph-based semi-supervised learning from static data. We have extend that approach to handle data streams and concept drift. The result is a passive algorithm which uses a single classifier approach, naturally adapted to concept changes without any explicit drift detection mechanism. It has built-in mechanisms that provide a natural way of learning from new data, gradually "forgetting" older knowledge as older data items are no longer useful for the classification of newer data items. The proposed algorithm is applied to the KDD Cup 1999 Data of network intrusion, showing its effectiveness.

Bridging hydraulic diffusivity from aquifer to particle-size scale: a study on loess sediments from southwest Hungary

In situ megascale hydraulic diffusivities (D) of a confined loess aquifer were estimated at various scales (10 <= L <= 1500 m) by a finite difference model, and laboratory microscale diffusivities of a loess sample by empirical formulas. A scatter plot reveals that D fits to a single power function of L, providing that microscale diffusivities are assigned to L = 1 m and that differences in diffusivity observed between micro- and megascales are assigned to medium heterogeneity appraised by variations in the curvature and slope of natural hydraulic head waves propagating through the aquifer. Subsequently, a general power relationship between D and L is defined where the base and exponent terms stand for the aquifer storage capability under a confined regime of flow, for the microscale hydraulic conductivity and specific yield of loess, and for the changes in curvature and slope of hydraulic head waves relative to values defined at unit scale.[GRAPHICS]Editor Z.W. Kundzewicz