Efficient Analysis of High Dimensional Data in Tensor Formats

In this article we introduce new methods for the analysis of high dimensional data in tensor formats, where the underling data come from the stochastic elliptic boundary value problem. After discretisation of the deterministic operator as well as the presented random fields via KLE and PCE, the obtained high dimensional operator can be approximated via sums of elementary tensors. This tensors representation can be effectively used for computing different values of interest, such as maximum norm, level sets and cumulative distribution function. The basic concept of the data analysis in high dimensions is discussed on tensors represented in the canonical format, however the approach can be easily used in other tensor formats. As an intermediate step we describe efficient iterative algorithms for computing the characteristic and sign functions as well as pointwise inverse in the canonical tensor format. Since during majority of algebraic operations as well as during iteration steps the representation rank grows up, we use lower-rank approximation and inexact recursive iteration schemes

Hierarchical reinforcement learning for efficient and effective automated penetration testing of large networks

Penetration testing (PT) is a method for assessing and evaluating the security of digital assets by planning, generating, and executing possible attacks that aim to discover and exploit vulnerabilities. In large networks, penetration testing becomes repetitive, complex and resource consuming despite the use of automated tools. This paper investigates reinforcement learning (RL) to make penetration testing more intelligent, targeted, and efficient. The proposed approach called Intelligent Automated Penetration Testing Framework (IAPTF) utilizes model-based RL to automate sequential decision making. Penetration testing tasks are treated as a partially observed Markov decision process (POMDP) which is solved with an external POMDP-solver using different algorithms to identify the most efficient options. A major difficulty encountered was solving large POMDPs resulting from large networks. This was overcome by representing networks hierarchically as a group of clusters and treating each cluster separately. This approach is tested through simulations of networks of various sizes. The results show that IAPTF with hierarchical network modeling outperforms previous approaches as well as human performance in terms of time, number of tested vectors and accuracy, and the advantage increases with the network size. Another advantage of IAPTF is the ease of repetition for retesting similar networks, which is often encountered in real PT. The results suggest that IAPTF is a promising approach to offload work from and ultimately replace human pen testing

Molecular detection of Grapevine fleck virus-like viruses

Molecular reagents have been developed for virus-specific and simultaneous (virus-non-specific) detection of Grapevine fleck virus (GFkV) and allied viruses, ie. Grapevine asteroid mosaic-associated virus (GAMaV) and Grapevine red globe virus (GRGV). Degenerate primers designed on nucleotide sequences of the RNA-dependent RNA polymerase (RD) and methyltransferase (MTR) domains of the GFkV genome, were able to give amplification products of the expected size from total nucleic acid extracts of:vines infected with GFkV, GAMaV, and GRGV;a Californian grapevine accession infected by a marafi-like virus;Greek grapevine accessions infected by an unidentified agent that induced symptoms reminiscent of those elicited by GAMaV in Vitis rupestris.Degenerate primers designed on the nucleotide sequence of the helicase (HEL) domain of the GFLV genome recognized all the above viruses except for GAMaV and the unidentified Greek viral agent. RD primer set worked well also with crude grapevine cortical scrapings, thus constituting a useful universal reagent for the non-specific molecular identification of GFkV-like viruses in Vitis . The marafi-like virus from California was amplified by all sets of primers, but was recognized only by the GRGV-specific probe, suggesting that it is a likely isolate of GRGV: Likewise, the unidentified virus from Greek vines shared sequence homology with GFkV and allied viruses (GAMaV and GRGV) but exhibited differences relevant enough that call for further investigations to establish its taxonomic position. While GRGV was identified, though with a very low incidence, in some 11 southern Italian grapevine cultivars, no evidence was obtained for infection by GAMaV in any of 50 cultivars analyzed.

Inverse Problems in a Bayesian Setting

In a Bayesian setting, inverse problems and uncertainty quantification (UQ) --- the propagation of uncertainty through a computational (forward) model --- are strongly connected. In the form of conditional expectation the Bayesian update becomes computationally attractive. We give a detailed account of this approach via conditional approximation, various approximations, and the construction of filters. Together with a functional or spectral approach for the forward UQ there is no need for time-consuming and slowly convergent Monte Carlo sampling. The developed sampling-free non-linear Bayesian update in form of a filter is derived from the variational problem associated with conditional expectation. This formulation in general calls for further discretisation to make the computation possible, and we choose a polynomial approximation. After giving details on the actual computation in the framework of functional or spectral approximations, we demonstrate the workings of the algorithm on a number of examples of increasing complexity. At last, we compare the linear and nonlinear Bayesian update in form of a filter on some examples.Comment: arXiv admin note: substantial text overlap with arXiv:1312.504

Exhaustive and Efficient Constraint Propagation: A Semi-Supervised Learning Perspective and Its Applications

This paper presents a novel pairwise constraint propagation approach by decomposing the challenging constraint propagation problem into a set of independent semi-supervised learning subproblems which can be solved in quadratic time using label propagation based on k-nearest neighbor graphs. Considering that this time cost is proportional to the number of all possible pairwise constraints, our approach actually provides an efficient solution for exhaustively propagating pairwise constraints throughout the entire dataset. The resulting exhaustive set of propagated pairwise constraints are further used to adjust the similarity matrix for constrained spectral clustering. Other than the traditional constraint propagation on single-source data, our approach is also extended to more challenging constraint propagation on multi-source data where each pairwise constraint is defined over a pair of data points from different sources. This multi-source constraint propagation has an important application to cross-modal multimedia retrieval. Extensive results have shown the superior performance of our approach.Comment: The short version of this paper appears as oral paper in ECCV 201

Anatomical and morphological features of Pinus sylvestris growing on the dumps of the mining industry in the middle urals

The results of the study of anatomical and morphological parameters of Pinus sylvestris L., growing on the serpentine dumps of Anatol'sko-Shilovsky Mining, are presented. Adaptive morphological and anatomical changes that contribute to the survival of the species in extreme environmental conditions have been identified. A significant decrease in P. sylvestris morphometic parameters (tree height, the annual growth of the tree, and the branches) under dump conditions were established. Under the influence of unfavorable factors on the dumps (lack of nutrients and water, high rockiness of the substrate), the length and the cross-sectional area of the needles decreased. Of the anatomical features, it is important to note a decrease in the number of resin ducts with a tendency to increase their diameter. © 2021 Author(s)

Assessment of charged AuNPs: from synthesis to innate immune recognition

Gold nanoparticle (AuNP) physicochemical characteristics, mainly size and charge, modulate their biodistribution, cytotoxicity, and immunorecognition as reported from in vitro and in vivo studies. While data from in vitro studies could be biased by several factors including activation of cells upon isolation and lack of sera proteins in the microenvironment of primary generated cell lines, in vivo studies are costly and time-consuming and require ethics consideration. In this study, we developed a simple and novel in vivo-like method to test for NP immunorecognition from freshly withdrawn human blood samples. AuNPs with a size range of 30 ± 5 nm coated with cationic poly(L-lysine) (PLL) dendrigraft and slightly negative poly(vinyl alcohol) (PVA) were synthesized in water. PLL-capped AuNPs were further coated with poly(ethylene glycol) (PEG) to obtain nearly neutrally charged PEG-AuNPs. Physicochemical properties were determined using zeta potential measurements, UV-Vis spectroscopy, dynamic light scattering (DLS), and scanning electron microscopy (SEM). Gel electrophoretic separation, zeta potential, and DLS were also used to characterize our NPs after human blood plasma treatment. PLL-AuNPs showed similar variation in charge and binding affinity to plasma proteins in comparison with PVA-AuNPs. However, PLL-AuNPs.protein complexes revealed a drastic change in size compared to the other tested particles. Results obtained from the neutrophil function test and pyridine formazan extraction revealed the highest activation level of neutrophils (~70%) by 50 μg/mL of PLL-AuNPs compared to a null induction by PEG- and PVA-AuNPs. This observation was further verified by flow cytometry analysis of polymorphonuclear cell size variation in the presence of coated AuNPs. Overall, our in vivo-like method, to test for NP immunorecognition, proved to be reliable and effective. Finally, our data supports the use of PEG-AuNPs as promising vehicles for drug delivery, as they exhibit minimal protein adsorption affinity and insignificant charge and size variation once introduced in whole blood

Addressing Climate Change Impacts on Health

Climate change is a global health emergency, with impacts felt most acutely by vulnerable populations and communities. This paper explores health risks from climate change in a global context, setting out key risks and actions towards addressing these. In the context of COP27, it draws in a focus on Egypt as a case study throughout to exemplify the risks faced by countries which are particularly vulnerable to the health impacts of climate change. This policy working paper has been produced by the Academy of Scientific Research and Technology in Egypt, with contributions from the UK Universities Climate Network, through an academic collaboration ahead of COP27 in Egypt in 2022