4,719 research outputs found
Cosmic e^\pm, \bar p, \gamma and neutrino rays in leptocentric dark matter models
Dark matter annihilation is one of the leading explanations for the recently
observed excesses in cosmic rays by PAMELA, ATIC, FERMI-LAT and HESS.
Any dark matter annihilation model proposed to explain these data must also
explain the fact that PAMELA data show excesses only in spectrum but
not in anti-proton. It is interesting to ask whether the annihilation mode into
anti-proton is completely disallowed or only suppressed at low energies. Most
models proposed have negligible anti-protons in all energy ranges. We show that
the leptocentric dark matter model can explain the
excesses with suppressed anti-proton mode at low energies, but at higher
energies there are sizable anti-proton excesses. Near future data from PAMELA
and AMS can provide crucial test for this type of models. Cosmic ray
data can further rule out some of the models. We also show that this model has
interesting cosmic neutrino signatures.Comment: Latex 20 pages and five figures. References adde
Generating Adversarial Examples with Adversarial Networks
Deep neural networks (DNNs) have been found to be vulnerable to adversarial
examples resulting from adding small-magnitude perturbations to inputs. Such
adversarial examples can mislead DNNs to produce adversary-selected results.
Different attack strategies have been proposed to generate adversarial
examples, but how to produce them with high perceptual quality and more
efficiently requires more research efforts. In this paper, we propose AdvGAN to
generate adversarial examples with generative adversarial networks (GANs),
which can learn and approximate the distribution of original instances. For
AdvGAN, once the generator is trained, it can generate adversarial
perturbations efficiently for any instance, so as to potentially accelerate
adversarial training as defenses. We apply AdvGAN in both semi-whitebox and
black-box attack settings. In semi-whitebox attacks, there is no need to access
the original target model after the generator is trained, in contrast to
traditional white-box attacks. In black-box attacks, we dynamically train a
distilled model for the black-box model and optimize the generator accordingly.
Adversarial examples generated by AdvGAN on different target models have high
attack success rate under state-of-the-art defenses compared to other attacks.
Our attack has placed the first with 92.76% accuracy on a public MNIST
black-box attack challenge.Comment: Accepted to IJCAI201
Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks
Factorization Machines (FMs) are a supervised learning approach that enhances
the linear regression model by incorporating the second-order feature
interactions. Despite effectiveness, FM can be hindered by its modelling of all
feature interactions with the same weight, as not all feature interactions are
equally useful and predictive. For example, the interactions with useless
features may even introduce noises and adversely degrade the performance. In
this work, we improve FM by discriminating the importance of different feature
interactions. We propose a novel model named Attentional Factorization Machine
(AFM), which learns the importance of each feature interaction from data via a
neural attention network. Extensive experiments on two real-world datasets
demonstrate the effectiveness of AFM. Empirically, it is shown on regression
task AFM betters FM with a relative improvement, and consistently
outperforms the state-of-the-art deep learning methods Wide&Deep and DeepCross
with a much simpler structure and fewer model parameters. Our implementation of
AFM is publicly available at:
https://github.com/hexiangnan/attentional_factorization_machineComment: 7 pages, 5 figure
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