This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Robustness is a key property for critical systems that run in uncertain environments, to ensure that small input perturbations can cause only small output changes. Current critical systems often involve lots of floating-point computations which are inexact. Robustness analysis of floating-point programs needs to consider both the uncertain inputs and the inexact computation.

BAO module updated to support DM measurements, measurement files without error columns, reading either covariance or inverse covariance, support for multiple specified zeff.

BKP nuisance parameters now used as fast parameters.

These are now propagated to output chain. Planck lensing and BKP likelihoods are native and included with cosmomc, others can be linked to the Planck likelihood code.

Added SZ likelihood, szcounts. Generally much more accurate, and also works with multi-modal distributions. Added warning if attempting to run with PICO when compiled with -openmp fails for unknown reason; just remove openmp form Makefile and rebuild.

CAMB updated to January version, with support for more transfer function options, sigma8 variations, halofit version selection, and new derived parameters Data Updates: New optional script parameters to select more specifically in various ways deleteJobs.

Re-ordered derived parameters, and added new tensor derived parameters r0. Uses new Interpolation module with reusable 2D bicubic interpolation class.

Double interpolation is now avoided, and combinations of requirements for different likelihoods are automatically internally combined. This is recommended for Planck.

Does not need to be an integer, works well for Planck. Both true default is usually fastest. Grids of models and python utilities A new suite of python scripts for running CosmoMC for grids of models, running GetDist over the result, management, analysis and plotting.

See the python scripts readme Structure Restructuring using Fortran features probably does not compile on gfortran, ifort 13 OK ; likelihoods and theory now only re-computed as required as parameters change enables the fast-slow sampling method to work.

Each likelihood specifies its own theory and nuisance parameter requirements. Data likelihoods now specify their own. Combining theory and data parameters is done internally, there's no need to change hard-coded parameter numbers when new nuisance parameters are added, and unused likelihood nuisance parameter are no longer output.

Each likelihood function is passed the array of its nuisance parameters, completely independent of what other likelihoods are doing but multiple likelihood functions can use the same nuisance parameter names when they are the same parameter. Base parameters changed to use mnu rather than fnu; several new chain and derived parameters supported and output by default.

Note the code is not identical, cosmomc computes the drag sound horizon numerically recalibrated to be consistent in the fiducial model ; also be aware of correlation with Wigglez. HST data updated to Riess et al: Best-fit parameter values written out including derived parameters.

January Fixed generation of.

Added nnu effective number of neutrinos and YHe helium fraction to cmbtypes. Transfer of names and labels between cosmomc chains and getdist via.The signal to the switch may reflect operational ranges of the plant occurring at certain times and indicate the gain desired from the system relative to the output and input signals of the plant at those times.

where is the vector of state variables (nx1), is the time derivative of the state vector (nx1), is the input or control vector (px1), is the output vector (qx1), is the system matrix (nxn), is the input matrix (nxp), is the output matrix (qxn), and is the feedforward matrix (qxp).

A computer-implemented method for augmenting SAT-based BMC to handle embedded memory designs without explicitly modeling memory bits.

As is known, verifying designs having large embedded memories is typically handled by abstracting out (over-approximating) the memories. Such abstraction is not useful for finding real bugs. SAT-based BMC, as of now, is incapable of handling designs with. This method is intended to reduce the required number of Automatic Test Equipment (ATE) output channels compared to the number of scan-in input pins We propose a method for reducing test data volume of integrated circuits or cores in a System-on-Chip.

We consider the problem of computing bounded-degree lightweight plane spanners of unit disk graphs in the local distributed model of computation. We are motivated by the hypothesis that such subgraphs can provide the underlying network topology for efficient.

By Lyapunovâ€™s Theorem, the local stability of the uncertain discrete-time system with state delay and bounded delay variation on time - is ensured with the control gain given by and always the state trajectories of the closed-loop system evolve inside the set with, .

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Control Tutorials for MATLAB and Simulink -