Sneutrino DM in the NMSSM with inverse seesaw mechanism

In supersymmetric theories like the Next-to-Minimal Supersymmetric Standard Model (NMSSM), the lightest neutralino with bino or singlino as its dominant component is customarily taken as dark matter (DM) candidate. Since light Higgsinos favored by naturalness can strength the couplings of the DM and thus enhance the DM-nucleon scattering rate, the tension between naturalness and DM direct detection results becomes more and more acute with the improved experimental sensitivity. In this work, we extend the NMSSM by inverse seesaw mechanism to generate neutrino mass, and show that in certain parameter space the lightest sneutrino may act as a viable DM candidate, i.e. it can annihilate by multi-channels to get correct relic density and meanwhile satisfy all experimental constraints. The most striking feature of the extension is that the DM-nucleon scattering rate can be naturally below its current experimental bounds regardless of the higgsino mass, and hence it alleviates the tension between naturalness and DM experiments. Other interesting features include that the Higgs phenomenology becomes much richer than that of the original NMSSM due to the relaxed constraints from DM physics and also due to the presence of extra neutrinos, and that the signatures of sparticles at colliders are quite different from those with neutralino as DM candidate.Comment: 33 page

RFDforFin: Robust Deep Forgery Detection for GAN-generated Fingerprint Images

With the rapid development of the image generation technologies, the malicious abuses of the GAN-generated fingerprint images poses a significant threat to the public safety in certain circumstances. Although the existing universal deep forgery detection approach can be applied to detect the fake fingerprint images, they are easily attacked and have poor robustness. Meanwhile, there is no specifically designed deep forgery detection method for fingerprint images. In this paper, we propose the first deep forgery detection approach for fingerprint images, which combines unique ridge features of fingerprint and generation artifacts of the GAN-generated images, to the best of our knowledge. Specifically, we firstly construct a ridge stream, which exploits the grayscale variations along the ridges to extract unique fingerprint-specific features. Then, we construct a generation artifact stream, in which the FFT-based spectrums of the input fingerprint images are exploited, to extract more robust generation artifact features. At last, the unique ridge features and generation artifact features are fused for binary classification (\textit{i.e.}, real or fake). Comprehensive experiments demonstrate that our proposed approach is effective and robust with low complexities.Comment: 10 pages, 8 figure

Distraction-Aware Feature Learning for Human Attribute Recognition via Coarse-to-Fine Attention Mechanism

Recently, Human Attribute Recognition (HAR) has become a hot topic due to its scientific challenges and application potentials, where localizing attributes is a crucial stage but not well handled. In this paper, we propose a novel deep learning approach to HAR, namely Distraction-aware HAR (Da-HAR). It enhances deep CNN feature learning by improving attribute localization through a coarse-to-fine attention mechanism. At the coarse step, a self-mask block is built to roughly discriminate and reduce distractions, while at the fine step, a masked attention branch is applied to further eliminate irrelevant regions. Thanks to this mechanism, feature learning is more accurate, especially when heavy occlusions and complex backgrounds exist. Extensive experiments are conducted on the WIDER-Attribute and RAP databases, and state-of-the-art results are achieved, demonstrating the effectiveness of the proposed approach.Comment: 8 pages, 5 figures, accepted by AAAI-20 as an oral presentatio

Relation Embedding based Graph Neural Networks for Handling Heterogeneous Graph

Heterogeneous graph learning has drawn significant attentions in recent years, due to the success of graph neural networks (GNNs) and the broad applications of heterogeneous information networks. Various heterogeneous graph neural networks have been proposed to generalize GNNs for processing the heterogeneous graphs. Unfortunately, these approaches model the heterogeneity via various complicated modules. This paper aims to propose a simple yet efficient framework to make the homogeneous GNNs have adequate ability to handle heterogeneous graphs. Specifically, we propose Relation Embedding based Graph Neural Networks (RE-GNNs), which employ only one parameter per relation to embed the importance of edge type relations and self-loop connections. To optimize these relation embeddings and the other parameters simultaneously, a gradient scaling factor is proposed to constrain the embeddings to converge to suitable values. Besides, we theoretically demonstrate that our RE-GNNs have more expressive power than the meta-path based heterogeneous GNNs. Extensive experiments on the node classification tasks validate the effectiveness of our proposed method

Privacy Leaks through Data Hijacking Attack on Mobile Systems

To persistently eavesdrop on the mobile devices, attackers may obtain the elevated privilege and inject malicious modules into the user devices. Unfortunately, the attackers may not be able to obtain the privilege for a long period of time since the exploitable vulnerabilities may be fixed or the malware may be removed. In this paper, we propose a new data hijacking attack for the mobile apps. By employing the proposed method, the attackers are only required to obtain the root privilege of the user devices once, and they can persistently eavesdrop without any change to the original device. Specifically, we design a new approach to construct a shadow system by hijacking user data files. In the shadow system, attackers possess the identical abilities to the victims. For instance, if a victim has logged into the email app, the attacker can also access the email server in the shadow system without authentication in a long period of time. Without reauthentication of the app, it is difficult for victims to notice the intrusion since the whole eavesdropping is performed on other devices (rather than the user devices). In our experiments, we evaluate the effectiveness of the proposed attack and the result demonstrates that even the Android apps released by the top developers cannot resist this attack. Finally, we discuss some approaches to defend the proposed attack

The nonequilibrium evolution near the phase boundary

We study the nonequilibrium evolution near the phase boundary of the 3D Ising model, and find that the average of relaxation time (RT) near the first-order phase transition line (1st-PTL) is significantly larger than that near the critical point (CP). As the system size increases, the average of RT near the 1st-PTL increases at a higher power compared to that near the CP. We further show that RT near the 1st-PTL is not only non-self-averaging, but actually self-diverging: relative variance of RT increases with system size. The presence of coexisting and metastable states results in a substantial increase in randomness near the 1st-PTL, making it difficult to achieve equilibrium.Comment: 6 pages, 3 figure

Structure, Mechanical and Electrochemical Properties of Thermally Reduced Graphene Oxide-poly (Vinyl Alcohol) Foams

Graphene oxide foams with a wide range of poly (vinyl alcohol) contents were synthesized by freeze casting, and then thermally reduced at 300ºC in argon atmosphere. Their thermal stability, microstructure, composition and chemical states of constituents, mechanical and electrical properties were investigated by X-ray diffraction, scanning electron microscopy, X-ray photoelectron spectroscopy, thermogravimetry, compressive testing and electrochemical analysis. The results indicated that the PVA content highly influenced the crystallinity and microstructure, resulting in different mechanical properties. After thermal reduction, not only graphene oxide was reduced to graphene, but also PVA was subjected to partial pyrolysis. With the increase of the PVA content, the intensity of the sp2 C-C bond decreased while the sp3 C-C bond increased. Although the mechanical properties decreased after thermal reduction, the composite foams still showed high cyclic structure stability up to 18 % compression strain. Meanwhile, the reduced foams exhibited high electrical conductivity. Applying as anodes in lithium ion battery, the initial discharge capacity for the foams can reach 1822 mA h g-1 and it remained more than 330 mA h g-1 after 50 cycles

1, 25-D3 Protects From Cerebral Ischemia by Maintaining BBB Permeability via PPAR-γ Activation

The blood-brain barrier (BBB) is a physical and biochemical barrier that maintains cerebral homeostasis. BBB dysfunction in an ischemic stroke, results in brain injury and subsequent neurological impairment. The aim of this study was to determine the possible protective effects of 1, 25-dihydroxyvitamin D3 [1, 25(OH)2D3, 1, 25-D3, vit D] on BBB dysfunction, at the early stages of an acute ischemic brain injury. We analyzed the effects of 1, 25-D3 on BBB integrity in terms of histopathological changes, the neurological deficit, infarct size and the expression of brain derived neurotrophic factor (BDNF), in a middle cerebral artery occlusion/reperfusion (MCAO/R) rat model. BBB permeability and the expression of permeability-related proteins in the brain were also evaluated by Evans blue (EB) staining and Western blotting respectively. To determine the possible mechanism underlying the role of 1, 25-D3 in BBB maintenance, after MCAO/R, the rats were treated with the specific peroxisome proliferator-activated receptor gamma (PPARγ) inhibitor GW9662. Supplementation with 1, 25-D3 markedly improved the neurological scores of the rats, decreased the infarct volume, prevented neuronal deformation and upregulated the expression of the tight junction (TJ) and BDNF proteins in their brains. Furthermore, it activated PPARγ but downregulated neuro-inflammatory cytokines such as nuclear factor kappa-B (NF-κB) and tumor necrosis factor-α (TNF-α), after MCAO/R. Taken together, 1, 25-D3 protects against cerebral ischemia by maintaining BBB permeability, upregulating the level of BDNF and inhibiting PPARγ-mediated neuro-inflammation

Seroprevalence of avian influenza A (H5N1) virus among poultry workers in Jiangsu Province, China: an observational study

<p>Abstract</p> <p>Background</p> <p>Since 2003 to 06 Jan 2012, the number of laboratory confirmed human cases of infection with avian influenza in China was 41 and 27 were fatal. However, the official estimate of the H5N1 case-fatality rate has been described by some as an over estimation since there may be numerous undetected asymptomatic/mild cases of H5N1 infection. This study was conducted to better understand the real infection rate and evaluate the potential risk factors for the zoonotic spread of H5N1 viruses to humans.</p> <p>Methods</p> <p>A seroepidemiological survey was conducted in poultry workers, a group expected to have the highest level of exposure to H5N1-infected birds, from 3 counties with habitat lakes of wildfowl in Jiangsu province, China. Serum specimens were collected from 306 participants for H5N1 serological test. All participants were interviewed to collect information about poultry exposures.</p> <p>Results</p> <p>The overall seropositive rate was 2.61% for H5N1 antibodies. The poultry number was found associated with a 2.39-fold significantly increased subclinical infection risk after adjusted with age and gender.</p> <p>Conclusions</p> <p>Avian-to -human transmission of avian H5N1 virus remained low. Workers associated with raising larger poultry flocks have a higher risk on seroconversion.</p