25,044 research outputs found
Generalized Spinfoams
We reconsider the spinfoam dynamics that has been recently introduced, in the
generalized Kaminski-Kisielowski-Lewandowski (KKL) version where the foam is
not dual to a triangulation. We study the Euclidean as well as the Lorentzian
case. We show that this theory can still be obtained as a constrained BF theory
satisfying the simplicity constraint, now discretized on a general oriented
2-cell complex. This constraint implies that boundary states admit a (quantum)
geometrical interpretation in terms of polyhedra, generalizing the tetrahedral
geometry of the simplicial case. We also point out that the general solution to
this constraint (imposed weakly) depends on a quantum number r_f in addition to
those of loop quantum gravity. We compute the vertex amplitude and recover the
KKL amplitude in the Euclidean theory when r_f=0. We comment on the eventual
physical relevance of r_f, and the formal way to eliminate it.Comment: 16 pages, 3 figure
Rumba : a Python framework for automating large-scale recursive internet experiments on GENI and FIRE+
It is not easy to design and run Convolutional Neural Networks (CNNs) due to: 1) finding the optimal number of filters (i.e., the width) at each layer is tricky, given an architecture; and 2) the computational intensity of CNNs impedes the deployment on computationally limited devices. Oracle Pruning is designed to remove the unimportant filters from a well-trained CNN, which estimates the filters’ importance by ablating them in turn and evaluating the model, thus delivers high accuracy but suffers from intolerable time complexity, and requires a given resulting width but cannot automatically find it. To address these problems, we propose Approximated Oracle Filter Pruning (AOFP), which keeps searching for the least important filters in a binary search manner, makes pruning attempts by masking out filters randomly, accumulates the resulting errors, and finetunes the model via a multi-path framework. As AOFP enables simultaneous pruning on multiple layers, we can prune an existing very deep CNN with acceptable time cost, negligible accuracy drop, and no heuristic knowledge, or re-design a model which exerts higher accuracy and faster inferenc
LHC Phenomenology of Type II Seesaw: Nondegenerate Case
In this paper, we thoroughly investigate the LHC phenomenology of the type II
seesaw mechanism for neutrino masses in the nondegenerate case where the
triplet scalars of various charge () have
different masses. Compared with the degenerate case, the cascade decays of
scalars lead to many new, interesting signal channels. In the positive scenario
where , the four-lepton signal is still
the most promising discovery channel for the doubly-charged scalars
. The five-lepton signal is crucial to probe the mass spectrum of
the scalars, for which, for example, a reach at 14 TeV LHC for
with requires an integrated
luminosity of 76/fb. And the six-lepton signal can be used to probe the neutral
scalars , which are usually hard to detect in the degenerate case. In
the negative scenario where , the
detection of is more challenging, when the cascade decay
is dominant. The most important channel is the
associated production in the final state
, which requires a luminosity of 109/fb
for a discovery, while the final state
is less promising. Moreover, the
associated production can give same signals as the standard model
Higgs pair production. With a much larger cross section, the
production in the final state could reach
significance at 14 TeV LHC with a luminosity of 300/fb. In summary, with an
integrated luminosity of order 500/fb, the triplet scalars can be fully
reconstructed at 14 TeV LHC in the negative scenario.Comment: 41 pages, 20 figures, 7 tables. Version 2 accepted by PRD. 41 pages,
18 figures. Main changes are, (1) rewording in secs III and IV, removing 2
figs and quoting ref [34]; (2) a paragraph added before eq (10) to clarify
constraints from electroweak precision data; (3) a paper added to ref [11].
No changes in result
LHC Phenomenology of the Type II Seesaw Mechanism: Observability of Neutral Scalars in the Nondegenerate Case
This is a sequel to our previous work on LHC phenomenology of the type II
seesaw model in the nondegenerate case. In this work, we further study the pair
and associated production of the neutral scalars H^0/A^0. We restrict ourselves
to the so-called negative scenario characterized by the mass order
M_{H^{\pm\pm}}>M_{H^\pm}>M_{H^0/A^0}, in which the H^0/A^0 production receives
significant enhancement from cascade decays of the charged scalars
H^{\pm\pm},~H^\pm. We consider three important signal
channels---b\bar{b}\gamma\gamma, b\bar{b}\tau^+\tau^-,
---and perform detailed simulations. We find
that at the 14 TeV LHC with an integrated luminosity of 3000/fb, a 5\sigma mass
reach of 151, 150, and 180 GeV, respectively, is possible in the three channels
from the pure Drell-Yan H^0A^0 production, while the cascade-decay-enhanced
H^0/A^0 production can push the mass limit further to 164, 177, and 200 GeV.
The neutral scalars in the negative scenario are thus accessible at LHC run II.Comment: v1: 32 pages, 17 figures, 3 tables. v2: added 2 refs (2nd in [61] and
[66]), revised Acknowledgments, and corrected grammatical errors according to
proofs; no other change
WordSup: Exploiting Word Annotations for Character based Text Detection
Imagery texts are usually organized as a hierarchy of several visual
elements, i.e. characters, words, text lines and text blocks. Among these
elements, character is the most basic one for various languages such as
Western, Chinese, Japanese, mathematical expression and etc. It is natural and
convenient to construct a common text detection engine based on character
detectors. However, training character detectors requires a vast of location
annotated characters, which are expensive to obtain. Actually, the existing
real text datasets are mostly annotated in word or line level. To remedy this
dilemma, we propose a weakly supervised framework that can utilize word
annotations, either in tight quadrangles or the more loose bounding boxes, for
character detector training. When applied in scene text detection, we are thus
able to train a robust character detector by exploiting word annotations in the
rich large-scale real scene text datasets, e.g. ICDAR15 and COCO-text. The
character detector acts as a key role in the pipeline of our text detection
engine. It achieves the state-of-the-art performance on several challenging
scene text detection benchmarks. We also demonstrate the flexibility of our
pipeline by various scenarios, including deformed text detection and math
expression recognition.Comment: 2017 International Conference on Computer Visio
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