23,829 research outputs found
Non-Minimal Flavored Left-Right Symmetric Model
We propose a non-minimal left-right symmetric model (LRSM) with Parity
Symmetry where the fermion mixings arise as result of imposing an flavor symmetry, and an extra
symmetry is considered to suppress some Yukawa couplings in the lepton sector.
As a consequence, the effective neutrino mass matrix possesses approximately
the symmetry. The breaking of the symmetry induces
sizable non zero , and the deviation of from
is strongly controlled by an free parameter and the
complex neutrino masses. Then, an analytic study on the extreme Majorana phases
is done since these turn out to be relevant to enhance or suppress the reactor
and atmospheric angle. So that we have constrained the parameter space for the
parameter and the lightest neutrino mass that accommodate the mixing
angles. The highlighted results are: a) the normal hierarchy is ruled out since
the reactor angle comes out being tiny, for any values of the Majorana phases;
b) for the inverted hierarchy there is one combination in the extreme phases
where the values of the reactor and atmospheric angles are compatible up to of C. L., but the parameter space is tight; c) the model favors the
degenerate ordering for one combination in the extreme Majorana phases. In this
case, the reactor and atmospheric angle are compatible with the experimental
data for a large set of values of the free parameters. Therefore, this model
may be testable by the future result that the Nova and KamLAND-Zen
collaborations will provide.Comment: Version three. I added references and corrected typos. 19 pages. All
figures replaced, section II changed. Analytic study improved. Conclusions
unchange
Hierarchical path-finding for Navigation Meshes (HNA*)
Path-finding can become an important bottleneck as both the size of the virtual environments and the number of agents navigating them increase. It is important to develop techniques that can be efficiently applied to any environment independently of its abstract representation. In this paper we present a hierarchical NavMesh representation to speed up path-finding. Hierarchical path-finding (HPA*) has been successfully applied to regular grids, but there is a need to extend the benefits of this method to polygonal navigation meshes. As opposed to regular grids, navigation meshes offer representations with higher accuracy regarding the underlying geometry, while containing a smaller number of cells. Therefore, we present a bottom-up method to create a hierarchical representation based on a multilevel k-way partitioning algorithm (MLkP), annotated with sub-paths that can be accessed online by our Hierarchical NavMesh Path-finding algorithm (HNA*). The algorithm benefits from searching in graphs with a much smaller number of cells, thus performing up to 7.7 times faster than traditional A¿ over the initial NavMesh. We present results of HNA* over a variety of scenarios and discuss the benefits of the algorithm together with areas for improvement.Peer ReviewedPostprint (author's final draft
A non-projective greedy dependency parser with bidirectional LSTMs
The LyS-FASTPARSE team presents BIST-COVINGTON, a neural implementation of
the Covington (2001) algorithm for non-projective dependency parsing. The
bidirectional LSTM approach by Kipperwasser and Goldberg (2016) is used to
train a greedy parser with a dynamic oracle to mitigate error propagation. The
model participated in the CoNLL 2017 UD Shared Task. In spite of not using any
ensemble methods and using the baseline segmentation and PoS tagging, the
parser obtained good results on both macro-average LAS and UAS in the big
treebanks category (55 languages), ranking 7th out of 33 teams. In the all
treebanks category (LAS and UAS) we ranked 16th and 12th. The gap between the
all and big categories is mainly due to the poor performance on four parallel
PUD treebanks, suggesting that some `suffixed' treebanks (e.g. Spanish-AnCora)
perform poorly on cross-treebank settings, which does not occur with the
corresponding `unsuffixed' treebank (e.g. Spanish). By changing that, we obtain
the 11th best LAS among all runs (official and unofficial). The code is made
available at https://github.com/CoNLL-UD-2017/LyS-FASTPARSEComment: 12 pages, 2 figures, 5 table
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