Computing derivative-based global sensitivity measures using polynomial chaos expansions

In the field of computer experiments sensitivity analysis aims at quantifying the relative importance of each input parameter (or combinations thereof) of a computational model with respect to the model output uncertainty. Variance decomposition methods leading to the well-known Sobol' indices are recognized as accurate techniques, at a rather high computational cost though. The use of polynomial chaos expansions (PCE) to compute Sobol' indices has allowed to alleviate the computational burden though. However, when dealing with large dimensional input vectors, it is good practice to first use screening methods in order to discard unimportant variables. The {\em derivative-based global sensitivity measures} (DGSM) have been developed recently in this respect. In this paper we show how polynomial chaos expansions may be used to compute analytically DGSMs as a mere post-processing. This requires the analytical derivation of derivatives of the orthonormal polynomials which enter PC expansions. The efficiency of the approach is illustrated on two well-known benchmark problems in sensitivity analysis

Rapid detection of trace bacteria in biofluids using porous monoliths in microchannels

We present advancements in microfluidic technology for rapid detection of as few as 10 rickettsial organisms in complex biological samples. An immuno-reactive filter, macroporous polyacrylamide monolith (PAM), fabricated within a microfluidic channel enhances solid-phase immuno-capture, staining and detection of targeted bacteria. Bacterial cells in samples flowing through the channel are forced to interact with the PAM filter surface due to size exclusion, overcoming common transport and kinetic limitations for rapid (min), high-efficiency (~100%) capture. In the process, targeted cells in sample volumes of 10 ?l to >100 ?l are concentrated within a sub-50 nl region at the PAM filter edge in the microchannel, thus concentrating them over 1000-fold. This significantly increases sensitivity, as the hydrophilic PAM also yields low non-specific immuno-fluorescence backgrounds with samples including serum, blood and non-targeted bacteria. The concentrated target cells are detected using fluorescently-labeled antibodies. With a single 2.0�0�3 mm PAM filter, as few as 10 rickettsial organisms per 100 祃 of lysed blood sample can be analyzed within 60 min, as compared to hours or even days needed for conventional detection methods. This method is highly relevant to rapid, multiplexed, low-cost point of care diagnostics at early stages of infection where diagnostics providing more immediate and actionable test results are needed to improve patient outcomes and mitigate potential natural and non-natural outbreaks or epidemics of rickettsial diseases

Delineation of the Native Basin in Continuum Models of Proteins

We propose two approaches for determining the native basins in off-lattice models of proteins. The first of them is based on exploring the saddle points on selected trajectories emerging from the native state. In the second approach, the basin size can be determined by monitoring random distortions in the shape of the protein around the native state. Both techniques yield the similar results. As a byproduct, a simple method to determine the folding temperature is obtained.Comment: REVTeX, 6 pages, 5 EPS figure

Finitely generated free Heyting algebras via Birkhoff duality and coalgebra

Algebras axiomatized entirely by rank 1 axioms are algebras for a functor and thus the free algebras can be obtained by a direct limit process. Dually, the final coalgebras can be obtained by an inverse limit process. In order to explore the limits of this method we look at Heyting algebras which have mixed rank 0-1 axiomatizations. We will see that Heyting algebras are special in that they are almost rank 1 axiomatized and can be handled by a slight variant of the rank 1 coalgebraic methods

New Development of the J -Based Fracture Testing Technique for Ceramic-Matrix Composites

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65733/1/j.1151-2916.1994.tb09756.x.pd

TAI-SARNET: Deep Transferred Atrous-Inception CNN for Small Samples SAR ATR

Since Synthetic Aperture Radar (SAR) targets are full of coherent speckle noise, the traditional deep learning models are difficult to effectively extract key features of the targets and share high computational complexity. To solve the problem, an effective lightweight Convolutional Neural Network (CNN) model incorporating transfer learning is proposed for better handling SAR targets recognition tasks. In this work, firstly we propose the Atrous-Inception module, which combines both atrous convolution and inception module to obtain rich global receptive fields, while strictly controlling the parameter amount and realizing lightweight network architecture. Secondly, the transfer learning strategy is used to effectively transfer the prior knowledge of the optical, non-optical, hybrid optical and non-optical domains to the SAR target recognition tasks, thereby improving the model\u2019s recognition performance on small sample SAR target datasets. Finally, the model constructed in this paper is verified to be 97.97% on ten types of MSTAR datasets under standard operating conditions, reaching a mainstream target recognition rate. Meanwhile, the method presented in this paper shows strong robustness and generalization performance on a small number of randomly sampled SAR target datasets