Statistical analysis driven optimized deep learning system for intrusion detection

Attackers have developed ever more sophisticated and intelligent ways to hack information and communication technology systems. The extent of damage an individual hacker can carry out upon infiltrating a system is well understood. A potentially catastrophic scenario can be envisaged where a nation-state intercepting encrypted financial data gets hacked. Thus, intelligent cybersecurity systems have become inevitably important for improved protection against malicious threats. However, as malware attacks continue to dramatically increase in volume and complexity, it has become ever more challenging for traditional analytic tools to detect and mitigate threat. Furthermore, a huge amount of data produced by large networks has made the recognition task even more complicated and challenging. In this work, we propose an innovative statistical analysis driven optimized deep learning system for intrusion detection. The proposed intrusion detection system (IDS) extracts optimized and more correlated features using big data visualization and statistical analysis methods (human-in-the-loop), followed by a deep autoencoder for potential threat detection. Specifically, a pre-processing module eliminates the outliers and converts categorical variables into one-hot-encoded vectors. The feature extraction module discard features with null values and selects the most significant features as input to the deep autoencoder model (trained in a greedy-wise manner). The NSL-KDD dataset from the Canadian Institute for Cybersecurity is used as a benchmark to evaluate the feasibility and effectiveness of the proposed architecture. Simulation results demonstrate the potential of our proposed system and its outperformance as compared to existing state-of-the-art methods and recently published novel approaches. Ongoing work includes further optimization and real-time evaluation of our proposed IDS.Comment: To appear in the 9th International Conference on Brain Inspired Cognitive Systems (BICS 2018

Inbuilt Tendency of the eIF2 Regulatory System to Counteract Uncertainties

Eukaryotic initiation factor 2 (eIF2) plays a fundamental role in the regulation of protein synthesis. Investigations have revealed that the regulation of eIF2 is robust against intrinsic uncertainties and is able to efficiently counteract them. The robustness properties of the eIF2 pathway against intrinsic disturbances is also well known. However the reasons for this ability to counteract stresses is less well understood. In this article, the robustness conferring properties of the eIF2 dependent regulatory system is explored with the help of a mathematical model. The novelty of the work presented in this article lies in articulating the possible reason behind the inbuilt robustness of the highly engineered eIF2 system against intrinsic perturbations. Our investigations reveal that the robust nature of the eIF2 pathway may originate from the existence of an attractive natural sliding surface within the system satisfying reaching and sliding conditions that are well established in the domain of control engineering

Impact of glycaemic control on circulating endothelial progenitor cells and arterial stiffness in patients with type 2 diabetes mellitus

Topics: Basic science, translational and clinical researchPoster PresentationThis journal supplement contains abstracts from the 17th MRC; Dept. of Medicine, The University of Hong Kong, Queen Mary Hospital, Hong KongINTRODUCTION: Patients with type 2 diabetes mellitus (DM) have increased risk of endothelial dysfunction and arterial stiffness. Levels of circulating endothelial progenitor cells (EPCs) are also reduced in hyperglycaemic states. However, the relationships between glycaemic control, levels of EPCs and arterial stiffness are unknown. METHODS: We measured circulating EPCs and …published_or_final_versionThe 17th Medical Research Conference (MRC), Department of Medicine, University of Hong Kong, Hong Kong, 14 January 2012. In Hong Kong Medical Journal, 2012, v. 18 suppl. 1, p. 63, abstract no. 10

Effect of sieving on ex-situ soil respiration of soils from three land use types

This study aims to investigate the effect of sieving on ex situ soil respiration (CO2 flux) measurements from different land use types. We collected soils (0–10 cm) from arable, grassland and woodland sites, allocated them to either sieved (4-mm mesh, freshly sieved) or intact core treatments and incubated them in gas-tight jars for 40 days at 10 °C. Headspace gas was collected on days 1, 3, 17, 24, 31 and 38 and CO2 analysed. Our results showed that sieving (4 mm) did not significantly influence soil respiration measurements, probably because micro aggregates (< 0.25 mm) remain intact after sieving. However, soils collected from grassland soil released more CO2 compared with those collected from woodland and arable soils, irrespective of sieving treatments. The higher CO2 from grassland soil compared with woodland and arable soils was attributed to the differences in the water holding capacity and the quantity and stoichiometry of the organic matter between the three soils. We conclude that soils sieved prior to ex situ respiration experiments provide realistic respiration measurements. This finding lends support to soil scientists planning a sampling strategy that better represents the inhomogeneity of field conditions by pooling, homogenising and sieving samples, without fear of obtaining unrepresentative CO2 flux measurements caused by the disruption of soil architecture

Modelling upper respiratory viral load dynamics of SARS-CoV-2

Relationships between viral load, severity of illness, and transmissibility of virus, are fundamental to understanding pathogenesis and devising better therapeutic and prevention strategies for COVID-19. Here we present within-host modelling of viral load dynamics observed in the upper respiratory tract (URT), drawing upon 2172 serial measurements from 605 subjects, collected from 17 different studies. We developed a mechanistic model to describe viral load dynamics and host response, and contrast this with simpler mixed-effects regression analysis of peak viral load and its subsequent decline. We observed wide variation in URT viral load between individuals, over 5 orders of magnitude, at any given point in time since symptom onset. This variation was not explained by age, sex, or severity of illness, and these variables were not associated with the modelled early or late phases of immune-mediated control of viral load. We explored the application of the mechanistic model to identify measured immune responses associated with control of viral load. Neutralizing antibody correlated strongly with modelled immune-mediated control of viral load amongst subjects who produced neutralizing antibody. Our models can be used to identify host and viral factors which control URT viral load dynamics, informing future treatment and transmission blocking interventions

Inner Space Preserving Generative Pose Machine

Image-based generative methods, such as generative adversarial networks (GANs) have already been able to generate realistic images with much context control, specially when they are conditioned. However, most successful frameworks share a common procedure which performs an image-to-image translation with pose of figures in the image untouched. When the objective is reposing a figure in an image while preserving the rest of the image, the state-of-the-art mainly assumes a single rigid body with simple background and limited pose shift, which can hardly be extended to the images under normal settings. In this paper, we introduce an image "inner space" preserving model that assigns an interpretable low-dimensional pose descriptor (LDPD) to an articulated figure in the image. Figure reposing is then generated by passing the LDPD and the original image through multi-stage augmented hourglass networks in a conditional GAN structure, called inner space preserving generative pose machine (ISP-GPM). We evaluated ISP-GPM on reposing human figures, which are highly articulated with versatile variations. Test of a state-of-the-art pose estimator on our reposed dataset gave an accuracy over 80% on PCK0.5 metric. The results also elucidated that our ISP-GPM is able to preserve the background with high accuracy while reasonably recovering the area blocked by the figure to be reposed.Comment: http://www.northeastern.edu/ostadabbas/2018/07/23/inner-space-preserving-generative-pose-machine

Permian (Artinskian to Wuchapingian) conodont biostratigraphy in the Tieqiao section, Laibin area, South China

Permian strata from the Tieqiao section (Jiangnan Basin, South China) contain several distinctive conodont assemblages. Early Permian (Cisuralian) assemblages are dominated by the genera Sweetognathus, Pseudosweetognathus and Hindeodus with rare Neostreptognathodus and Gullodus. Gondolellids are absent until the end of the Kungurian stage—in contrast to many parts of the world where gondolellids and Neostreptognathodus are the dominant Kungurian conodonts. A conodont changeover is seen at Tieqiao and coincided with a rise of sea level in the late Kungurian to the early Roadian: the previously dominant sweetognathids were replaced by mesogondolellids. The Middle and Late Permian (Guadalupian and Lopingian Series) witnessed dominance of gondolellids (Jinogondolella and Clarkina), the common presence of Hindeodus and decimation of Sweetognathus. Twenty main and seven subordinate conodont zones are recognised at Tieqiao, spanning the lower Artinskian to the middle Wuchiapingian Stage. The main (first appearance datum) zones are, in ascending order by stage: the Sweetognathus (Sw.) whitei, Sw. toriyamai, and Sw. asymmetrica n. sp. Zones for the Artinskian; the Neostreptognathodus prayi, Sw. guizhouensis, Sw. iranicus, Sw. adjunctus, Sw. subsymmeticus and Sw. hanzhongensis Zones for the Kungurian; the Jinogondolella (J.) nankingensis Zone for the Roadian; the J. aserrata Zone for the Wordian; the J. postserrata, J. shannoni, J. altudaensis, J. prexuanhanensis, J. xuanhanensis, J. granti and Clarkina (C.) hongshuiensis Zones for the Capitanian and the C. postbitteri Zone and C. transcaucasica Zone for the base and middle of the Wuchiapingian. The subordinate (interval) zones are the Pseudosweetognathus (Ps.) costatus, Ps. monocornus, Hindeodus (H.) gulloides, Pseudohindeodus ramovsi, Gullodus (G.) sicilianus, G. duani and H. excavates Zones. In addition, three new species, Gullodus tieqiaoensis n. sp., Pseudohindeodus elliptica n. sp. and Sweetognathus asymmetrica n. sp. are described. Age assignments for less common species (e.g., G. duani, H. catalanoi and Pseudosweetognathus monocornus etc.) are reassessed based on a rich conodont collection

Solution-Processed Epitaxial Growth of Arbitrary Surface Nanopatterns on Hybrid Perovskite Monocrystalline Thin Films.

Semiconductor surface patterning at the nanometer scale is crucial for high-performance optical, electronic, and photovoltaic devices. To date, surface nanostructures on organic-inorganic single-crystal perovskites have been achieved mainly through destructive methods such as electron-beam lithography and focused ion beam milling. Here, we present a solution-based epitaxial growth method for creating nanopatterns on the surface of perovskite monocrystalline thin films. We show that high-quality monocrystalline arbitrary nanopatterns can form in solution with a low-cost simple setup. We also demonstrate controllable photoluminescence from nanopatterned perovskite surfaces by adjusting the nanopattern parameters. A seven-fold enhancement in photoluminescence intensity and a three-time reduction of the surface radiative recombination lifetime are observed at room temperature for nanopatterned MAPbBr3 monocrystalline thin films. Our findings are promising for the cost-effective fabrication of monocrystalline perovskite on-chip electronic and photonic circuits down to the nanometer scale with finely tunable optoelectronic properties