Integrated Development and Parallelization of Automated Dicentric Chromosome Identification Software to Expedite Biodosimetry Analysis

Manual cytogenetic biodosimetry lacks the ability to handle mass casualty events. We present an automated dicentric chromosome identification (ADCI) software utilizing parallel computing technology. A parallelization strategy combining data and task parallelism, as well as optimization of I/O operations, has been designed, implemented, and incorporated in ADCI. Experiments on an eight-core desktop show that our algorithm can expedite the process of ADCI by at least four folds. Experiments on Symmetric Computing, SHARCNET, Blue Gene/Q multi-processor computers demonstrate the capability of parallelized ADCI to process thousands of samples for cytogenetic biodosimetry in a few hours. This increase in speed underscores the effectiveness of parallelization in accelerating ADCI. Our software will be an important tool to handle the magnitude of mass casualty ionizing radiation events by expediting accurate detection of dicentric chromosomes

Ontology-based data semantic management and application in IoT- and cloud-enabled smart homes

The application of emerging technologies of Internet of Things (IoT) and cloud computing have increasing the popularity of smart homes, along with which, large volumes of heterogeneous data have been generating by home entities. The representation, management and application of the continuously increasing amounts of heterogeneous data in the smart home data space have been critical challenges to the further development of smart home industry. To this end, a scheme for ontology-based data semantic management and application is proposed in this paper. Based on a smart home system model abstracted from the perspective of implementing users’ household operations, a general domain ontology model is designed by defining the correlative concepts, and a logical data semantic fusion model is designed accordingly. Subsequently, to achieve high-efficiency ontology data query and update in the implementation of the data semantic fusion model, a relational-database-based ontology data decomposition storage method is developed by thoroughly investigating existing storage modes, and the performance is demonstrated using a group of elaborated ontology data query and update operations. Comprehensively utilizing the stated achievements, ontology-based semantic reasoning with a specially designed semantic matching rule is studied as well in this work in an attempt to provide accurate and personalized home services, and the efficiency is demonstrated through experiments conducted on the developed testing system for user behavior reasoning

Current noise in a vibrating quantum dot array

We develop methods for calculating the zero-frequency noise for quantum shuttles, i.e. nanoelectromechanical devices where the mechanical motion is quantized. As a model system we consider a three-dot array, where the internal electronic coherence both complicates and enriches the physics. Two different formulations are presented: (i) quantum regression theorem, and (ii) the counting variable approach. It is demonstrated, both analytically and numerically, that the two formulations yield identical results, when the conditions of their respective applicability are fulfilled. We describe the results of extensive numerical calculations for current and current noise (Fano factor), based on a solution of a Markovian generalized master equation. The results for the current and noise are further analyzed in terms of Wigner functions, which help to distinguish different transport regimes (in particular, shuttling vs. cotunneling). In the case of weak inter-dot coupling, the electron transport proceeds via sequential tunneling between neighboring dots. A simple rate equation with the rates calculated analytically from the P(E)-theory is developed and shown to agree with the full numerics.Comment: 22 two-column pages, 9 figure

A discrete geometric approach for simulating the dynamics of thin viscous threads

We present a numerical model for the dynamics of thin viscous threads based on a discrete, Lagrangian formulation of the smooth equations. The model makes use of a condensed set of coordinates, called the centerline/spin representation: the kinematical constraints linking the centerline's tangent to the orientation of the material frame is used to eliminate two out of three degrees of freedom associated with rotations. Based on a description of twist inspired from discrete differential geometry and from variational principles, we build a full-fledged discrete viscous thread model, which includes in particular a discrete representation of the internal viscous stress. Consistency of the discrete model with the classical, smooth equations is established formally in the limit of a vanishing discretization length. The discrete models lends itself naturally to numerical implementation. Our numerical method is validated against reference solutions for steady coiling. The method makes it possible to simulate the unsteady behavior of thin viscous jets in a robust and efficient way, including the combined effects of inertia, stretching, bending, twisting, large rotations and surface tension

Towards a hybrid parallelization of lattice Boltzmann methods

AbstractOngoing research towards the development of a hybrid parallelization concept for lattice Boltzmann methods is presented. It allows coping with platforms sharing both the properties of shared and distributed architectures. The proposed approach relies on spatial domain decomposition where each domain represents a basic block entity which is solved on a symmetric multi-processing (SMP) system. Emphasis is placed on the software design and the reworking needed to achieve good performance using OpenMP in that context. Those ideas are implemented in the C++ project OpenLB, which is also sketched in this article. The efficiency of the proposed approaches is tested on a 3D benchmark problem and compared with a purely MPI based approach

Implicit Decomposition for Write-Efficient Connectivity Algorithms

The future of main memory appears to lie in the direction of new technologies that provide strong capacity-to-performance ratios, but have write operations that are much more expensive than reads in terms of latency, bandwidth, and energy. Motivated by this trend, we propose sequential and parallel algorithms to solve graph connectivity problems using significantly fewer writes than conventional algorithms. Our primary algorithmic tool is the construction of an $o(n)$-sized "implicit decomposition" of a bounded-degree graph $G$ on $n$ nodes, which combined with read-only access to $G$ enables fast answers to connectivity and biconnectivity queries on $G$. The construction breaks the linear-write "barrier", resulting in costs that are asymptotically lower than conventional algorithms while adding only a modest cost to querying time. For general non-sparse graphs on $m$ edges, we also provide the first $o(m)$ writes and $O(m)$ operations parallel algorithms for connectivity and biconnectivity. These algorithms provide insight into how applications can efficiently process computations on large graphs in systems with read-write asymmetry