Jet and underlying event properties as a function of charged-particle multiplicity in proton-proton collisions at root s=7 TeV

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Pattern recognition issues on anisotropic smoothed particle hydrodynamics

This is a preliminary theoretical discussion on the computational requirements of the state of the art smoothed particle hydrodynamics (SPH) from the optics of pattern recognition and artificial intelligence. It is pointed out in the present paper that, when including anisotropy detection to improve resolution on shock layer, SPH is a very peculiar case of unsupervised machine learning. On the other hand, the free particle nature of SPH opens an opportunity for artificial intelligence to study particles as agents acting in a collaborative framework in which the timed outcomes of a fluid simulation forms a large knowledge base, which might be very attractive in computational astrophysics phenomenological problems like self-propagating star formation.

Influence of air-particle deposition protocols on the surface topography and adhesion of resin cement to zirconia

Objectives: This study evaluated the influence of air-particle abrasion protocols on the surface roughness (SR) of zirconia and the shear bond strength (SBS) of dual-polymerized resin cement to this ceramic. Materials and methods. Sintered zirconia blocks (n = 115) (Lava, 3M ESPE) were embedded in acrylic resin and polished. The specimens were divided according to the 'particle type' (Al: 110 mu m Al2O3; Si: 110 mu m SiO2) and 'pressure' factors (2.5 or 3.5 bar) (n = 3 per group): (a) Control (no air-abrasion); (b) Al2.5; (c) Si2.5; (d) Al3.5; (e) Si3.5. SR (Ra) was measured 3-times from each specimen after 20 s of air-abrasion (distance: 10 mm) using a digital optical profilometer. Surface topography was evaluated under SEM analyses. For the SBS test, 'particle type', 'pressure' and 'thermocycling' (TC) factors were considered (n = 10; n = 10 per group): Control (no air-abrasion); Al2.5; Si2.5; Al3.5; Si3.5; Control(TC); Al2.5(TC); Si2.5(TC); Al3.5(TC); Si3.5(TC). After silane application, resin cement (Panavia F2.0) was bonded and polymerized. Specimens were thermocycled (6.000 cycles, 5-55 degrees C) and subjected to SBS (1 mm/min). Data were analyzed using ANOVA, Tukey's and Dunnett tests (5%). Results. 'Particle' (p = 0.0001) and 'pressure' (p = 0.0001) factors significantly affected the SR. All protocols significantly increased the SR (Al2.5: 0.45 +/- 0.02; Si2.5: 0.39 +/- 0.01; Al3.5: 0.80 +/- 0.01; Si3.5: 0.64 +/- 0.01 mu m) compared to the control group (0.16 +/- 0.01 mu m). For SBS, only 'particle' factor significantly affected the results (p = 0.015). The SiO2 groups presented significantly higher SBS results than Al2O3 (Al2.5: 4.78 +/- 1.86; Si2.5: 7.17 +/- 2.62; Al3.5: 4.97 +/- 3.74; Si3.5: 9.14 +/- 4.09 MPa) and the control group (3.67 +/- 3.0 MPa). All TC specimens presented spontaneous debondings. SEM analysis showed that Al2O3 created damage in zirconia in the form of grooves, different from those observed with SiO2 groups. Conclusions. Air-abrasion with 110 mu m Al2O3 resulted in higher roughness, but air-abrasion protocols with SiO2 promoted better adhesion.

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)

Chemical Modification of Palm Fibres Surface with Zirconium Oxychloride

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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)

Modeling 3-D desiccation soil crack networks using a mesh fragmentation technique

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.