Familial Clustering For Weakly-labeled Android Malware Using Hybrid Representation Learning

IEEE Labeling malware or malware clustering is important for identifying new security threats, triaging and building reference datasets. The state-of-the-art Android malware clustering approaches rely heavily on the raw labels from commercial AntiVirus (AV) vendors, which causes misclustering for a substantial number of weakly-labeled malware due to the inconsistent, incomplete and overly generic labels reported by these closed-source AV engines, whose capabilities vary greatly and whose internal mechanisms are opaque (i.e., intermediate detection results are unavailable for clustering). The raw labels are thus often used as the only important source of information for clustering. To address the limitations of the existing approaches, this paper presents ANDRE, a new ANDroid Hybrid REpresentation Learning approach to clustering weakly-labeled Android malware by preserving heterogeneous information from multiple sources (including the results of static code analysis, the metainformation of an app, and the raw-labels of the AV vendors) to jointly learn a hybrid representation for accurate clustering. The learned representation is then fed into our outlieraware clustering to partition the weakly-labeled malware into known and unknown families. The malware whose malicious behaviours are close to those of the existing families on the network, are further classified using a three-layer Deep Neural Network (DNN). The unknown malware are clustered using a standard density-based clustering algorithm. We have evaluated our approach using 5,416 ground-truth malware from Drebin and 9,000 malware from VIRUSSHARE (uploaded between Mar. 2017 and Feb. 2018), consisting of 3324 weakly-labeled malware. The evaluation shows that ANDRE effectively clusters weaklylabeled malware which cannot be clustered by the state-of-theart approaches, while achieving comparable accuracy with those approaches for clustering ground-truth samples

A computational study of fluid transport characteristics in the brain parenchyma of dementia subtypes

The cerebral environment is a complex system consisting of parenchymal tissue and multiple fluids. Dementia is a common class of neurodegenerative diseases, caused by structural damages and functional deficits in the cerebral environment. In order to better understand the pathology of dementia from a cerebral fluid transport angle and provide clearer evidence that could help differentiate between dementia subtypes, such as Alzheimer's disease and vascular dementia, we conducted fluid–structure interaction modelling of the brain using a multiple-network poroelasticity model, which considers both neuropathological and cerebrovascular factors. The parenchyma was further subdivided and labelled into parcellations to obtain more localised and detailed data. The numerical results were converted to computed functional images by an in-house workflow. Different cerebral blood flow (CBF) and cerebrospinal fluid (CSF) clearance abnormalities were identified in the modelling results, when comparing Alzheimer's disease and vascular dementia. This paper presents our preliminary results as a proof of concept for a novel clinical diagnostic tool, and paves the way for a larger clinical study

Chandra Observation of the Cluster of Galaxies MS 0839.9+2938 at z=0.194: the Central Excess Iron and SN Ia Enrichment

We present the Chandra study of the intermediately distant cluster of galaxies MS 0839.9+2938. By performing both the projected and deprojected spectral analyses, we find that the gas temperature is approximately constant at about 4 keV in 130-444h_70^-1 kpc. In the inner regions, the gas temperature descends towards the center, reaching <~ 3 keV in the central 37h_70^-1 kpc. This infers that the lower and upper limits of the mass deposit rate are 9-34 M_sun yr^-1 and 96-126 M_sun yr^-1, respectively within 74h_70^-1 kpc where the gas is significantly colder. Along with the temperature drop, we detect a significant inward iron abundance increase from about 0.4 solar in the outer regions to about 1 solar within the central 37h_70^-1 kpc. Thus MS 0839.9+2938 is the cluster showing the most significant central iron excess at z>~ 0.2. We argue that most of the excess iron should have been contributed by SNe Ia. By utilizing the observed SN Ia rate and stellar mass loss rate, we estimate that the time needed to enrich the central region with excess iron is 6.4-7.9 Gyr, which is similar to those found for the nearby clusters. Coinciding with the optical extension of the cD galaxy (up to about 30h_70^-1 kpc), the observed X-ray surface brightness profile exhibits an excess beyond the distribution expected by either the beta model or the NFW model, and can be well fitted with an empirical two-beta model that leads to a relatively flatter mass profile in the innermost region.Comment: Accepted for publication in Ap

Atmospheric reactive nitrogen concentrations at ten sites with contrasting land use in an arid region of central Asia

Atmospheric concentrations of reactive nitrogen (N&lt;sub&gt;r&lt;/sub&gt;) species from 2009 to 2011 are reported for ten sites in Xinjiang, China, an arid region of central Asia. Concentrations of NH&lt;sub&gt;3&lt;/sub&gt;, NO&lt;sub&gt;2&lt;/sub&gt;, particulate ammonium and nitrate (&lt;i&gt;p&lt;/i&gt;NH&lt;sub&gt;4&lt;/sub&gt;&lt;sup&gt;+&lt;/sup&gt; and &lt;i&gt;p&lt;/i&gt;NO&lt;sub&gt;3&lt;/sub&gt;&lt;sup&gt;&amp;minus;&lt;/sup&gt;) showed large spatial and seasonal variation and averaged 7.71, 9.68, 1.81 and 1.13 μg N m&lt;sup&gt;−3&lt;/sup&gt;, and PM&lt;sub&gt;10&lt;/sub&gt; concentrations averaged 249.2 μg m&lt;sup&gt;−3&lt;/sup&gt; across all sites. Lower NH&lt;sub&gt;3&lt;/sub&gt; concentrations and higher NO&lt;sub&gt;2&lt;/sub&gt;, &lt;i&gt;p&lt;/i&gt;NH&lt;sub&gt;4&lt;/sub&gt;&lt;sup&gt;+&lt;/sup&gt; and &lt;i&gt;p&lt;/i&gt;NO&lt;sub&gt;3&lt;/sub&gt;&lt;sup&gt;&amp;minus;&lt;/sup&gt; concentrations were found in winter, reflecting serious air pollution due to domestic heating in winter and other anthropogenic sources such as increased emissions from motor traffic and industry. The increasing order of total concentrations of N&lt;sub&gt;r&lt;/sub&gt; species was alpine grassland; desert, desert-oasis ecotone; desert in an oasis; farmland; suburban and urban ecosystems. Lower ratios of secondary particles (NH&lt;sub&gt;4&lt;/sub&gt;&lt;sup&gt;+&lt;/sup&gt; and NO&lt;sub&gt;3&lt;/sub&gt;&lt;sup&gt;&amp;minus;&lt;/sup&gt;) were found in the desert and desert-oasis ecotone, while urban and suburban areas had higher ratios, which implied that anthropogenic activities have greatly influenced local air quality and must be controlled

U-DADA:Unsupervised Deep Action Domain Adaptation

The problem of domain adaptation has been extensively studied for object classification task. However, this problem has not been as well studied for recognizing actions. While, object recognition is well understood, the diverse variety of videos in action recognition make the task of addressing domain shift to be more challenging. We address this problem by proposing a new novel adaptation technique that we term as unsupervised deep action domain adaptation (U-DADA). The main concept that we propose is that of explicitly modeling density based adaptation and using them while adapting domains for recognizing actions. We show that these techniques work well both for domain adaptation through adversarial learning to obtain invariant features or explicitly reducing the domain shift between distributions. The method is shown to work well using existing benchmark datasets such as UCF50, UCF101, HMDB51 and Olympic Sports. As a pioneering effort in the area of deep action adaptation, we are presenting several benchmark results and techniques that could serve as baselines to guide future research in this area.</p

An immersed boundary-lattice Boltzmann method for fluid-structure interaction problems involving viscoelastic fluids and complex geometries

An immersed boundary-lattice Boltzmann method (IB-LBM) for fluid-structure interaction (FSI) problems involving viscoelastic fluids and complex geometries is presented in this paper. In this method, the fluid dynamics and the constitutive equations of viscoelastic fluids are both solved using the lattice Boltzmann method. In order to enhance numerical stability in solving the constitutive equations, an artificial damping is introduced which does not affect the numerical results if the damping effect is much smaller than the relaxation and the convective effects. The structural dynamics including 2D and 3D capsules, 2D and 3D rigid particles and flags, are solved by the finite difference method (2D capsules, 2D and 3D rigid particles and flags) and the finite element method (3D capsules). The interaction between the solid structure and the fluid is enforced by an immersed boundary method. The overall framework of this method is very simple, enabling modelling FSI problems involving viscoelastic fluids and the inertia of both fluids and structures. It is very efficient for FSI problems involving high Weissenberg numbers flows, large deformations and complicated geometries without any preconditioner. This work uses IB-LBM to solve for the first time, flows involving viscoelastic fluids coupled with non-massless deforming structures. The method is also capable of solving very high Weissenberg number problems, as demonstrated by simulations of flexible particle flows at . The present method and models are validated by several cases including a 2D rigid particle migration in a Giesekus Couette flow, a spherical particle rotation in an Oldroyd-B shear flow, a spherical particle settling in a FENE-CR fluid, 2D and 3D capsules deformation in a Newtonian shear flow, and a 3D flag flapping in a Newtonian free stream. In addition, the present method is also applied to simulate the deformation of 2D and 3D capsules in an Oldroyd-B shear flow, a 3D flag flapping in an Oldroyd-B free stream, and elastic capsule movement in a contraction-expansion channel filled with an Oldroyd-B fluid. Deformation of the capsules decreases with the increase of the Weissenberg number and the capsules experience monotonically increasing deformation when the Weissenberg number is above a critical value which is respectively 10 for 2D and 2 for 3D simulations. Viscoelasticity of the Oldroyd-B fluid hinders the flapping motion of the 3D flag. For elastic capsules passing through a periodic contraction-expansion channel, the capsules mix up after a short-term evolution, and then migrate to the bottom of the channel and almost follow two steady trajectories after a long-term evolution. The validations and applications provide extensive data which may be used to expand the currently limited database available for FSI benchmark studies

Nuclear receptors in vascular biology

Nuclear receptors sense a wide range of steroids and hormones (estrogens, progesterone, androgens, glucocorticoid, and mineralocorticoid), vitamins (A and D), lipid metabolites, carbohydrates, and xenobiotics. In response to these diverse but critically important mediators, nuclear receptors regulate the homeostatic control of lipids, carbohydrate, cholesterol, and xenobiotic drug metabolism, inflammation, cell differentiation and development, including vascular development. The nuclear receptor family is one of the most important groups of signaling molecules in the body and as such represent some of the most important established and emerging clinical and therapeutic targets. This review will highlight some of the recent trends in nuclear receptor biology related to vascular biology

Coincidence between transcriptome analyses on different microarray platforms using a parametric framework

A parametric framework for the analysis of transcriptome data is demonstrated to yield coincident results when applied to data acquired using two different microarray platforms. Discrepancies among transcriptome studies are frequently reported, casting doubt on the reliability of collected data. The inconsistency among observations can be largely attributed to differences among the analytical frameworks employed for data analysis. The existing frameworks normalizes data against a standard determined from the data to be analyzed. In the present study, a parametric framework based on a strict model for normalization is applied to data acquired using an in-house printed chip and GeneChip. The framework is based on a common statistical characteristic of microarray data, and each data is normalized on the basis of a linear relationship with this model. In the proposed framework, the expressional changes observed and genes selected are coincident between platforms, achieving superior universality of data compared to other methods