Multiresolution fMRI activation detection using translation invariant wavelet transform and statistical analysis based on resampling

A new method is proposed for activation detection in event-related functional magnetic resonance imaging (fMRI). The method is based on the analysis of selected resolution levels (a subspace) in translation invariant wavelet transform (TIWT) domain. Using a priori knowledge about the activation signal and trends, we analyze their power in different resolution levels in TIWT domain and select an optimal set of resolution levels. A randomization-based statistical test is then applied in the wavelet domain for activation detection. This approach suppresses the effects of trends and enhances the detection sensitivity. In addition, since TIWT is insensitive to signal translations, the power analysis is robust with respect to signal shifts. The randomization test alleviates the need for assumptions about fMRI noise. The method has been applied to simulated and experimental fMRI datasets. Comparisons have been made between the results of the proposed method, a similar method in the time domain and the cross-correlation method. The proposed method has shown superior sensitivity compared to the other methods.     

Exploiting symmetries for scaling loopy belief propagation and relational training

Judging by the increasing impact of machine learning on large-scale data analysis in the last decade, one can anticipate a substantial growth in diversity of the machine learning applications for “big data” over the next decade. This exciting new opportunity, however, also raises many challenges. One of them is scaling inference within and training of graphical models. Typical ways to address this scaling issue are inference by approximate message passing, stochastic gradients, and MapReduce, among others. Often, we encounter inference and training problems with symmetries and redundancies in the graph structure. A prominent example are relational models that capture complexity. Exploiting these symmetries, however, has not been considered for scaling yet. In this paper, we show that inference and training can indeed benefit from exploiting symmetries. Specifically, we show that (loopy) belief propagation (BP) can be lifted. That is, a model is compressed by grouping nodes together that send and receive identical messages so that a modified BP running on the lifted graph yields the same marginals as BP on the original one, but often in a fraction of time. By establishing a link between lifting and radix sort, we show that lifting is MapReduce-able. Still, in many if not most situations training relational models will not benefit from this (scalable) lifting: symmetries within models easily break since variables become correlated by virtue of depending asymmetrically on evidence. An appealing idea for such situations is to train and recombine local models. This breaks long-range dependencies and allows to exploit lifting within and across the local training tasks. Moreover, it naturally paves the way for the first scalable lifted training approaches based on stochastic gradients, both in an online and a MapReduced fashion. On several datasets, the online training, for instance, converges to the same quality solution over an order of magnitude faster, simply because it starts optimizing long before having seen the entire mega-example even once.     

An optimal algorithm for two robots path planning problem on the grid

This paper is a study on the problem of path planning for two robots on a grid. We consider the objective of minimizing the maximum path length which corresponds to minimizing the arrival time of the last robot at its goal position. We propose an optimal algorithm that solves the problem in linear time with respect to the size of the grid. We show that the algorithm is complete; meaning that it is sure to find an optimal solution or report if any does not exist.     

Genetic-based random key generator (GRKG): a new method for generating more-random keys for one-time pad cryptosystem

In this paper, a new algorithm for generating more-randomized keys for symmetrical cipher one-time pad (OTP) according to the linear congruential (LCG) method based on the idea of genetic algorithm is proposed. The method, genetic-based random key generator, is proposed for generating keys for the OTP method with a high degree of key randomness; this adds more strength to the OTP method against breaking this cryptosystem. This algorithm is composed of two parts. Initially, the first population is being generated by LCG method, and then, genetic operators for generating the next populations are being used. Generating random keys with the presented method requires seven-parameter key that increases the security of communication between the transceivers.     

Ultrasonic Attenuation Due to Grain Boundary Scattering in Pure Niobium

Experimental confirmation of various grain scattering theories exist but there are still outstanding questions concerning the characterization of microstructure using ultrasound. In this study, high purity niobium serves as a model material devoid of extrinsic scattering centers (e.g., voids, precipitates, second phase particles etc.) A range of microstructures were obtained by annealing in ultrahigh vacuum at different temperatures (600–800°C) after routine cold-rolling and recrystallization heat treatment. Ultrasonic attenuation is measured as a function of a frequency for each sample. For the samples with an intermediate grain size (typically ∼50 μm), attenuation follows a power law dependence on frequency with an exponent of n∼1.6, which is close to the prediction, n=2, of classical Stochastic scattering theory. However, quantitative comparison shows that the observed attenuation is higher than predicted by the classical theory. The Stanke-Kino unified grain scattering theory may provide an explanation for the lower frequency dependence than traditional theories predict, though it still under predicts the magnitude of the attenuation. In any event, the resulting empirical relations provide a useful approach for practical grain size measurement with an acceptable level of uncertainty. The effect of a layered microstructure typical of some sheets/plates is discussed.     

In situ STM and EQCM studies of tantalum electrodeposition from TaF5 in the air- and water-stable ionic liquid 1-butyl-1-methylpyrrolidinium bis(trifluoromethylsulfonyl)amide

The electroreduction of 0.5 M TaF₅ on Au(1 1 1) and on polycrystalline gold substrates was investigated at room temperature in the ionic liquid 1-butyl-1-methylpyrrolidinium bis(trifluoromethylsulfonyl)amide, [Py_1,4]TFSA, by cyclic voltammetry, in situ scanning tunneling microscopy (STM) and electrochemical quartz crystal microbalance (EQCM). The electrochemical reduction of TaF₅ in the employed ionic liquid occurs in several steps. The first redox process is attributed to the reduction of TaF₅ to TaF₃, which likely occurs in the solution, as EQCM indicates no mass change. The electrodeposition of tantalum occurs only in a very narrow potential window and is preceded by the formation of various non-stoichiometric tantalum subhalides. Attempts to deposit micrometer thick tantalum layers at room temperature fail, presumably because of kinetic reasons.     

Generation of arbitrary optical waveforms by Fourier synthesisusing three continuous-wave semiconductor lasers

Arbitrary optical waveforms have been successfully generated by Fourier synthesis using three independent continuous-wave semiconductor lasers. The lasers were phase-locked to each other using a nonlinear phase-locking scheme. By controlling their amplitude and phase, triangular and trapezoidal waveforms as well as ordinary pulses have been generated     

A transformation technique to estimate the process capability index for non-normal processes

Estimating the process capability index (PCI) for non-normal processes has been discussed by many researches. There are two basic approaches to estimating the PCI for non-normal processes. The first commonly used approach is to transform the non-normal data into normal data using transformation techniques and then use a conventional normal method to estimate the PCI for transformed data. This is a straightforward approach and is easy to deploy. The alternate approach is to use non-normal percentiles to calculate the PCI. The latter approach is not easy to implement and a deviation in estimating the distribution of the process may affect the efficacy of the estimated PCI. The aim of this paper is to estimate the PCI for non-normal processes using a transformation technique called root transformation. The efficacy of the proposed technique is assessed by conducting a simulation study using gamma, Weibull, and beta distributions. The root transformation technique is used to estimate the PCI for each set of simulated data. These results are then compared with the PCI obtained using exact percentiles and the Box-Cox method. Finally, a case study based on real-world data is presented.