Parallel Recursive State Compression for Free

This paper focuses on reducing memory usage in enumerative model checking, while maintaining the multi-core scalability obtained in earlier work. We present a tree-based multi-core compression method, which works by leveraging sharing among sub-vectors of state vectors. An algorithmic analysis of both worst-case and optimal compression ratios shows the potential to compress even large states to a small constant on average (8 bytes). Our experiments demonstrate that this holds up in practice: the median compression ratio of 279 measured experiments is within 17% of the optimum for tree compression, and five times better than the median compression ratio of SPIN's COLLAPSE compression. Our algorithms are implemented in the LTSmin tool, and our experiments show that for model checking, multi-core tree compression pays its own way: it comes virtually without overhead compared to the fastest hash table-based methods.Comment: 19 page

Orientation-dependent deformation mechanisms of bcc niobium nanoparticles

Nanoparticles usually exhibit pronounced anisotropic properties, and a close insight into the atomic-scale deformation mechanisms is of great interest. In present study, atomic simulations are conducted to analyze the compression of bcc nanoparticles, and orientation-dependent features are addressed. It is revealed that surface morphology under indenter predominantly governs the initial elastic response. The loading curve follows the flat punch contact model in [110] compression, while it obeys the Hertzian contact model in [111] and [001] compressions. In plastic deformation regime, full dislocation gliding is dominated in [110] compression, while deformation twinning is prominent in [111] compression, and these two mechanisms coexist in [001] compression. Such deformation mechanisms are distinct from those in bulk crystals under nanoindentation and nanopillars under compression, and the major differences are also illuminated. Our results provide an atomic perspective on the mechanical behaviors of bcc nanoparticles and are helpful for the design of nanoparticle-based components and systems.Comment: 21 pages, 11 figure

Modeling turbulent energy behavior and sudden viscous dissipation in compressing plasma turbulence

We present a simple model for the turbulent kinetic energy behavior of subsonic plasma turbulence undergoing isotropic three-dimensional compression, such as may exist in various inertial confinement fusion experiments or astrophysical settings. The plasma viscosity depends on both the temperature and the ionization state, for which many possible scalings with compression are possible. For example, in an adiabatic compression the temperature scales as $1/L^2$, with $L$ the linear compression ratio, but if thermal energy loss mechanisms are accounted for, the temperature scaling may be weaker. As such, the viscosity has a wide range of net dependencies on the compression. The model presented here, with no parameter changes, agrees well with numerical simulations for a range of these dependencies. This model permits the prediction of the partition of injected energy between thermal and turbulent energy in a compressing plasma.Comment: 9 pages, 2 figures, 1 tabl

Efficient classification using parallel and scalable compressed model and Its application on intrusion detection

In order to achieve high efficiency of classification in intrusion detection, a compressed model is proposed in this paper which combines horizontal compression with vertical compression. OneR is utilized as horizontal com-pression for attribute reduction, and affinity propagation is employed as vertical compression to select small representative exemplars from large training data. As to be able to computationally compress the larger volume of training data with scalability, MapReduce based parallelization approach is then implemented and evaluated for each step of the model compression process abovementioned, on which common but efficient classification methods can be directly used. Experimental application study on two publicly available datasets of intrusion detection, KDD99 and CMDC2012, demonstrates that the classification using the compressed model proposed can effectively speed up the detection procedure at up to 184 times, most importantly at the cost of a minimal accuracy difference with less than 1% on average

Micromechanics of composite laminate compression failure

The Dugdale analysis for metals loaded in tension was adapted to model the failure of notched composite laminates loaded in compression. Compression testing details, MTS alignment verification, and equipment needs were resolved. Thus far, only 2 ductile material systems, HST7 and F155, were selected for study. A Wild M8 Zoom Stereomicroscope and necessary attachments for video taping and 35 mm pictures were purchased. Currently, this compression test system is fully operational. A specimen is loaded in compression, and load vs shear-crippling zone size is monitored and recorded. Data from initial compression tests indicate that the Dugdale model does not accurately predict the load vs damage zone size relationship of notched composite specimens loaded in compression