Kevlar: A Mapping-Free Framework for Accurate Discovery of De Novo Variants.

De novo genetic variants are an important source of causative variation in complex genetic disorders. Many methods for variant discovery rely on mapping reads to a reference genome, detecting numerous inherited variants irrelevant to the phenotype of interest. To distinguish between inherited and de novo variation, sequencing of families (parents and siblings) is commonly pursued. However, standard mapping-based approaches tend to have a high false-discovery rate for de novo variant prediction. Kevlar is a mapping-free method for de novo variant discovery, based on direct comparison of sequences between related individuals. Kevlar identifies high-abundance k-mers unique to the individual of interest. Reads containing these k-mers are partitioned into disjoint sets by shared k-mer content for variant calling, and preliminary variant predictions are sorted using a probabilistic score. We evaluated Kevlar on simulated and real datasets, demonstrating its ability to detect both de novo single-nucleotide variants and indels with high accuracy

Message-Passing Inference on a Factor Graph for Collaborative Filtering

This paper introduces a novel message-passing (MP) framework for the collaborative filtering (CF) problem associated with recommender systems. We model the movie-rating prediction problem popularized by the Netflix Prize, using a probabilistic factor graph model and study the model by deriving generalization error bounds in terms of the training error. Based on the model, we develop a new MP algorithm, termed IMP, for learning the model. To show superiority of the IMP algorithm, we compare it with the closely related expectation-maximization (EM) based algorithm and a number of other matrix completion algorithms. Our simulation results on Netflix data show that, while the methods perform similarly with large amounts of data, the IMP algorithm is superior for small amounts of data. This improves the cold-start problem of the CF systems in practice. Another advantage of the IMP algorithm is that it can be analyzed using the technique of density evolution (DE) that was originally developed for MP decoding of error-correcting codes

Generalized Method Of Designing Unmanned Remotely Operated Complexes Based On The System Approach

Self-propelled underwater systems belong to the effective means of marine robotics. The advantages of their use include the ability to perform underwater work in real time with high quality and without risk to the life of a human operator. At present, the design of such complexes is not formalized and is carried out separately for each of the components – a remotely operated vehicle, a tether-cable and cable winch, a cargo device and a control and energy device. As a result, the time spent on design increases and its quality decreases. The system approach to the design of remotely operated complexes ensures that the features of the interaction of the components of the complex are taken into account when performing its main operating modes. In this paper, the system interaction between the components of the complex is proposed to take into account in the form of decomposition of “underwater tasks (mission) – underwater technology of its implementation – underwater work on the selected technology – task for the executive mechanism of the complex” operations. With this approach, an information base is formed for the formation of a list of mechanisms of the complex, the technical appearance of its components is being formed, which is important for the early design stages. Operative, creative and engineering phases of the design of the complex are proposed. For each phase, a set of works has been formulated that cover all the components of the complex and use the author's existence equations for these components as a tool for system analysis of technical solutions.The perspective of the scientific task of the creative phase to create accurate information models of the functioning of the components of the complex and models to support the adoption of design decisions based on a systematic approach is shown.The obtained results form the theoretical basis for finding effective technical solutions in the early stages of designing remotely operated complexes and for automating the design with the assistance of modern applied computer research and design packages

HyBIS: Windows Guest Protection through Advanced Memory Introspection

Effectively protecting the Windows OS is a challenging task, since most implementation details are not publicly known. Windows has always been the main target of malwares that have exploited numerous bugs and vulnerabilities. Recent trusted boot and additional integrity checks have rendered the Windows OS less vulnerable to kernel-level rootkits. Nevertheless, guest Windows Virtual Machines are becoming an increasingly interesting attack target. In this work we introduce and analyze a novel Hypervisor-Based Introspection System (HyBIS) we developed for protecting Windows OSes from malware and rootkits. The HyBIS architecture is motivated and detailed, while targeted experimental results show its effectiveness. Comparison with related work highlights main HyBIS advantages such as: effective semantic introspection, support for 64-bit architectures and for latest Windows (8.x and 10), advanced malware disabling capabilities. We believe the research effort reported here will pave the way to further advances in the security of Windows OSes