7,810 research outputs found
Practical Block-wise Neural Network Architecture Generation
Convolutional neural networks have gained a remarkable success in computer
vision. However, most usable network architectures are hand-crafted and usually
require expertise and elaborate design. In this paper, we provide a block-wise
network generation pipeline called BlockQNN which automatically builds
high-performance networks using the Q-Learning paradigm with epsilon-greedy
exploration strategy. The optimal network block is constructed by the learning
agent which is trained sequentially to choose component layers. We stack the
block to construct the whole auto-generated network. To accelerate the
generation process, we also propose a distributed asynchronous framework and an
early stop strategy. The block-wise generation brings unique advantages: (1) it
performs competitive results in comparison to the hand-crafted state-of-the-art
networks on image classification, additionally, the best network generated by
BlockQNN achieves 3.54% top-1 error rate on CIFAR-10 which beats all existing
auto-generate networks. (2) in the meanwhile, it offers tremendous reduction of
the search space in designing networks which only spends 3 days with 32 GPUs,
and (3) moreover, it has strong generalizability that the network built on
CIFAR also performs well on a larger-scale ImageNet dataset.Comment: Accepted to CVPR 201
Probing Transverse Momentum Broadening via Dihadron and Hadron-jet Angular Correlations in Relativistic Heavy-ion Collisions
Dijet, dihadron, hadron-jet angular correlations have been reckoned as
important probes of the transverse momentum broadening effects in relativistic
nuclear collisions. When a pair of high-energy jets created in hard collisions
traverse the quark-gluon plasma produced in heavy-ion collisions, they become
de-correlated due to the vacuum soft gluon radiation associated with the
Sudakov logarithms and the medium-induced transverse momentum broadening. For
the first time, we employ the systematical resummation formalism and establish
a baseline calculation to describe the dihadron and hadron-jet angular
correlation data in and peripheral collisions where the medium effect
is negligible. We demonstrate that the medium-induced broadening and the so-called jet quenching parameter can be
extracted from the angular de-correlations observed in collisions. A
global analysis of dihadron and hadron-jet angular correlation data
renders the best fit for a
quark jet at RHIC top energy. Further experimental and theoretical efforts
along the direction of this work shall significantly advance the quantitative
understanding of transverse momentum broadening and help us acquire
unprecedented knowledge of jet quenching parameter in relativistic heavy-ion
collisions.Comment: 6 pages, 3 figure
A model explaining neutrino masses and the DAMPE cosmic ray electron excess
We propose a flavored neutrino mass and dark matter~(DM) model
to explain the recent DArk Matter Particle Explorer (DAMPE) data, which feature
an excess on the cosmic ray electron plus positron flux around 1.4 TeV. Only
the first two lepton generations of the Standard Model are charged under the
new gauge symmetry. A vector-like fermion , which is our DM
candidate, annihilates into and via the new gauge boson
exchange and accounts for the DAMPE excess. We have found that the data
favors a mass around 1.5~TeV and a mass around 2.6~TeV, which can
potentially be probed by the next generation lepton colliders and DM direct
detection experiments.Comment: 7 pages, 3 figures. V2: version accepted by Physics Letters
Predicting the epidemic threshold of the susceptible-infected-recovered model
Researchers have developed several theoretical methods for predicting
epidemic thresholds, including the mean-field like (MFL) method, the quenched
mean-field (QMF) method, and the dynamical message passing (DMP) method. When
these methods are applied to predict epidemic threshold they often produce
differing results and their relative levels of accuracy are still unknown. We
systematically analyze these two issues---relationships among differing results
and levels of accuracy---by studying the susceptible-infected-recovered (SIR)
model on uncorrelated configuration networks and a group of 56 real-world
networks. In uncorrelated configuration networks the MFL and DMP methods yield
identical predictions that are larger and more accurate than the prediction
generated by the QMF method. When compared to the 56 real-world networks, the
epidemic threshold obtained by the DMP method is closer to the actual epidemic
threshold because it incorporates full network topology information and some
dynamical correlations. We find that in some scenarios---such as networks with
positive degree-degree correlations, with an eigenvector localized on the high
-core nodes, or with a high level of clustering---the epidemic threshold
predicted by the MFL method, which uses the degree distribution as the only
input parameter, performs better than the other two methods. We also find that
the performances of the three predictions are irregular versus modularity
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