Quasi-Dirac neutrinos and solar neutrino data

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Bright solitons in a quasi-one-dimensional reduced model of a dipolar Bose-Einstein condensate with repulsive short-range interactions

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Lattice effects in the quasi-two-dimensional valence-bond-solid Mott insulator EtMe3P[Pd(dmit)(2)](2)

The organic charge-transfer salt EtMe3P[Pd(dmit)(2)](2) is a quasi-two-dimensional Mott insulator with localized spins S = 1/2 residing on a distorted triangular lattice. Here we report measurements of the uniaxial thermal expansion coefficients alpha(i) along the in-plane i = a and c axis as well as along the out-of-plane b axis for temperatures 1.4 K <= T <= 200 K. Particular attention is paid to the lattice effects around the phase transition at T-VBS = 25 K into a low-temperature valence-bond-solid phase and the paramagnetic regime above where effects of short-range antiferromagnetic correlations can be expected. The salient results of our study include (i) the observation of strongly anisotropic lattice distortions accompanying the formation of the valence-bond-solid phase, and (ii) a distinct anomaly in the thermal expansion coefficients in the paramagnetic regime around 40 K. Our results demonstrate that upon cooling through T-VBS the in-plane c axis, along which the valence bonds form, contracts while the second in-plane a axis elongates by the same relative amount. Surprisingly, the dominant effect is observed for the out-of-plane b axis which shrinks significantly upon cooling through T-VBS. The pronounced anomaly in alpha(i) around 40 K is attributed to short-range magnetic correlations. It is argued that the position of this maximum, relative to that in the magnetic susceptibility around 70 K, speaks in favor of a more anisotropic triangular-lattice scenario for this compound than previously thought.

Phase space properties and chaotic transport for a particle moving in a time dependent step potential well

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

An alternative approach for quasi-truth

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

Particle-vortex and Maxwell duality in the AdS(4) x CP3/ABJM correspondence

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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

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

Particle creation in a f(R) theory with cosmological constraints

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Quasi-exactly-solvable confining solutions for spin-1 and spin-0 bosons in (1+1)-dimensions with a scalar linear potential

We point out a misleading treatment in the recent literature regarding confining solutions for a scalar potential in the context of the Duffin-Kemmer-Petiau theory. We further present the proper bound-state solutions in terms of the generalized Laguerre polynomials and show that the eigenvalues and eigenfunctions depend on the solutions of algebraic equations involving the potential parameter and the quantum number. (C) 2014 Elsevier Inc. All rights reserved.

On the effects of each term of the geopotential perturbation along the time I: Quasi-circular orbits

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

EVALUATION OF ROUGHNESS, WETTABILITY, AND MORPHOLOGY OF AN YTTRIA-STABILIZED TETRAGONAL ZIRCONIA POLYCRYSTAL CERAMIC AFTER DIFFERENT AIRBORNE-PARTICLE ABRASION PROTOCOL

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Photoluminescence activity of Ba1-xCaxTiO3: dependence on particle size and morphology

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

CCDM model from quantum particle creation: constraints on dark matter mass

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.