Bi-metric theory of gravity from the non-chiral Plebanski action

We study a modification of the Plebanski action for general relativity, which leads to a modified theory of gravity with eight degrees of freedom. We show how the action can be recasted as a bi-metric theory of gravity, and expanding around a bi-flat background we identify the six extra degrees of freedom with a second, massive graviton and a scalar mode.Comment: 28 pages. v2 minor typos correcte

Tour-based Travel Mode Choice Estimation based on Data Mining and Fuzzy Techniques

This paper extends tour-based mode choice model, which mainly includes individual trip level interactions, to include linked travel modes of consecutive trips of an individual. Travel modes of consecutive trip made by an individual in a household have strong dependency or co-relation because individuals try to maintain their travel modes or use a few combinations of modes for current and subsequent trips. Traditionally, tour based mode choice models involved nested logit models derived from expert knowledge. There are limitations associated with this approach. Logit models assumes i) specific model structure (linear utility model) in advance; and, ii) it holds across an entire historical observations. These assumptions about the predefined model may be representative of reality, however these rules or heuristics for tour based mode choice should ideally be derived from the survey data rather than based on expert knowledge/ judgment. Therefore, in this paper, we propose a novel data-driven methodology to address the issues identified in tour based mode choice. The proposed methodology is tested using the Household Travel Survey (HTS) data of Sydney metropolitan area and its performances are compared with the state-of-the-art approaches in this area

Neutrino Masses and GUT Baryogenesis

We reconsider the GUT-baryogenesis mechanism for generating the baryon asymmetry of the Universe. The baryon asymmetry is produced by the out of equilibrium decay of coloured Higgs bosons at the GUT scale, conserving B-L. If neutrinos are Majorana particles, lepton number violating interactions erase the lepton number excess, but part of the baryon asymmetry may be preserved, provided those interactions are not in thermal equilibrium when the sphaleron processes become effective, at $T \sim 10^{12}~ GeV$. We analyse whether this mechanism for baryogenesis is feasible in a variety of GUT models of fermion masses proposed in the literature, based on horizontal symmetries.Comment: Talk presented at AHEP2003, Valencia, Spain, October 200

Dynamical windows for real-time evolution with matrix product states

We propose the use of a dynamical window to investigate the real-time evolution of quantum many-body systems in a one-dimensional lattice. In a recent paper [H. Phien et al, arxiv:????.????], we introduced infinite boundary conditions (IBC) in order to investigate real-time evolution of an infinite system under a local perturbation. This was accomplished by restricting the update of the tensors in the matrix product state to a finite window, with left and right boundaries held at fixed positions. Here we consider instead the use of a dynamical window, namely a window where the positions of left and right boundaries are allowed to change in time. In this way, all simulation efforts can be devoted to the space-time region of interest, which leads to a remarkable reduction in computational costs. For illustrative purposes, we consider two applications in the context of the spin-1 antiferromagnetic Heisenberg model in an infinite spin chain: one is an expanding window, with boundaries that are adjusted to capture the expansion in time of a local perturbation of the system; the other is a moving window of fixed size, where the position of the window follows the front of a propagating wave

Automatic learning of gait signatures for people identification

This work targets people identification in video based on the way they walk (i.e. gait). While classical methods typically derive gait signatures from sequences of binary silhouettes, in this work we explore the use of convolutional neural networks (CNN) for learning high-level descriptors from low-level motion features (i.e. optical flow components). We carry out a thorough experimental evaluation of the proposed CNN architecture on the challenging TUM-GAID dataset. The experimental results indicate that using spatio-temporal cuboids of optical flow as input data for CNN allows to obtain state-of-the-art results on the gait task with an image resolution eight times lower than the previously reported results (i.e. 80x60 pixels).Comment: Proof of concept paper. Technical report on the use of ConvNets (CNN) for gait recognition. Data and code: http://www.uco.es/~in1majim/research/cnngaitof.htm

Multiplicação vegetativa de plantas forrageiras: recomendações para plantio

Escolha da espécie/cultivar e obtenção do material de propagação; Escolha da área inicial de multiplicação; Preparo de solo.bitstream/item/55792/1/DT73.pd

Aplicador manual de herbicida por contato: enxada química.

bitstream/item/63859/1/CO67.pd

Controle de plantas indesejáveis em pastagens: uso da tecnologia campo limpo.

bitstream/item/31725/1/CO-72-online.pd