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 $e^\pm$ 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 $e^\pm$ 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 $U(1)_{B-3L_i}$ dark matter model can explain the $e^\pm$ 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 $\gamma$ 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 $8.6\%$ 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