On computer generation of random vectors by transformations of uniformly distributed vectors

Copulas are used in finance and insurance for modeling stochastic dependency. They comprehend the entire dependence structure, not only the correlations. Here they are estimated from measured samples of random vectors. The copula and the marginal distributions of the vector elements define a multivariate distribution of the sample which can be used to generate random vectors with this distribution. This can be applied as well to time series. A programmed algorithm is proposed. It is fast and allows for random vectors with high dimension, for example 100.

Parallel VLSI generation of random analog vectors with controlled statistics from deterministic chaos is the key to applications such as analog encryption and secure communications, analog built-in self-test, stochastic neural networks, and simulated annealing optimization as well as perturbation learning in neural hardware. We present a class of analog additive cellular automata which generate parallel streams of statistically independent, uniformly distributed random analog values. The underlying noise-shaping mechanism is essentially that of a MASH cascade of delta-sigma modulators. We present theory on the noise-shaping properties, scalable parallel VLSI architectures, and include experimental results from an analog VLSI prototype with 65 channels. The cell for each channel implements a switched-capacitor delta-sigma modulator, and measures 100 /spl mu/m/spl times/120 /spl mu/m in 2 /spl mu/m CMOS technology. The 65 cells are connected in a chain on a 2-D grid, and can be rearranged for use in various VLSI applications that require a parallel supply of random analog vectors.

On generation of elliptical distributions with Gaussian form covariance matrix

Inversive methods provide an attractive alternative to linear methods for pseudorandom number and vector generation. We present the first nontrivial results on the distribution of sequences of digital inversive pseudorandom numbers and inversive pseudorandom vectors in parts of the period. The proofs are based on a new bound for certain exponential sums over finite fields.

Current verification methods for on-chip memory have been implemented using coverpoints that are generated based on a single operation. These coverpoints cannot consider the influence of other memory banks in a busy state. In this paper, we propose a method in which the coverpoints account for all operations executed on different memory banks. In addition, a new constrained random vector generation method is proposed to reduce the required random vectors for the multi-operation-based coverpoints. The simulation results on NAND flash memory show 100% coverage with 496,541 constrained random vectors indicating a reduction of 96.4% compared with conventional random vectors.

The NORTA method for multivariate generation is a fast general purpose method for generating samples of a random vector with given marginal distributions and given product-moment or rank correlation matrix. However, this method has been shown to fail to work for some feasible correlation matrices. (A matrix is feasible if there exists a random vector with the given marginal distributions and the matrix as the correlation matrix.) We investigate how this feasibility problem behaves as the dimension of the random vector is increased and find the problem to become acute rapidly. We also find that a modified NORTA procedure, augmented by a semidefinite program (SDP) that aims to generate a correlation matrix close to the desired one, performs well with increasing dimension.

Genetic transformation is an important technology that provides unique opportunities to find, isolate, and analyze genes, as well as to create organisms with unique functional characteristics. Insect biologists have been developing genetic transformation technologies that rely extensively on transposable elements. A number of class II transposable elements isolated originally from insects have been converted into broad host range insect gene vectors. Class II transposable elements are particularly amenable to gene vector development, although they suffer from some limitations such as low rates of recombination. Use of these gene vectors requires the physical introduction of the vectors into developing insect embryos by microinjection. Microinjection methods vary to accommodate the unique physical and developmental characteristics of the target insects. All methods rely on the use of fine glass needles in conjunction with micromanipulators and a microscope. A serious constraint on the use of existing systems can be the inefficiency of successfully delivering the gene vectors to the germ cells of the developing embryo. The general method for vector delivery to insect germ cells is described, as well as variations that are useful under some conditions.

The digital circuits should be tested thoroughly in order to find the faults due to manufacturing defects or errors. The generation of test vectors is one of main task of circuit test, the test vectors can be obtained by finding appropriate logic assignments to the circuit primary inputs such that the given faults can be detected. The power consumption during test process may be higher than that power consumption during normal operations of circuits. Therefore it is necessary to design the test vectors with low power. In this paper, a new method is proposed for the generation of test vectors with low power. The method makes use of the co-evolution algorithms, first of all, the coding of an individual corresponding to the circuit primary inputs is given, secondly, the populations consisting of a lot of individuals are built, the better individuals corresponding to the test vectors with low power are obtained by applying the evolutionary operations to the populations. A lot of experimental results show that the test generation method in this paper is able to produce the test vectors with lower power, and can obtain higher fault coverage.KeywordsDigital circuitstest vectorslow powerfaults detectionevolutionary algorithms

We present the “trivariate reduction extension” (TREx)—an exact algorithm for the fast generation of bivariate Poisson random vectors. Like the normal-to-anything (NORTA) procedure, TREx has two phases: a preprocessing phase when the required algorithm parameters are identified, and a generation phase when the parameters identified during the preprocessing phase are used to generate the desired Poisson vector. We prove that the proposed algorithm covers the entire range of theoretically feasible correlations, and we provide efficient-computation directives and rigorous bounds for truncation error control. We demonstrate through extensive numerical tests that TREx, being a specialized algorithm for Poisson vectors, has a preprocessing phase that is uniformly a hundred to a thousand times faster than a fast implementation of NORTA. The generation phases of TREx and NORTA are comparable in speed, with that of TREx being marginally faster. All code is publicly available.

Five different methods for computer generation of Dirichlet (multivariate 2-parameter beta) random vectors are studied. The performance of these five methods are compared in terms of Central Processing Unit (CPU) time. For parameter values suspected to be less than 1.0, the modified acceptance-rejection method is preferable and for parameter values greater than 1.0, the beta method is recommended.

Solute transport within and through unsaturated soils are important processes that can affect agricultural systems and groundwater supply and contamination. Nitrate (NO−3) leaching from three 10‐ha experimental plots under different agricultural practices was studied. A Geographic Information System (GIS) model, ARC/INFO was used to help decompose the three hydrologic environments into 34 subenvironments. Each subenvironment was discretized vertically as three modeling units. Within each modeling unit, soil physical and chemical properties were described by a random multivariate normal (MVN) vector with conditional probability. A one‐dimensional, field‐scale, continuous flow model, Groundwater Loading Effects of Agricultural Management Systems (GLEAMS), was then stochastically applied to each subenvironment by using generated MVN realizations for every modeling unit. Observed NO−3 leaching below 1‐m depth was estimated with a mass balance approach based on suction lysimeter data. The observed NO−3 loads were then compared to predictions from the stochastic simulation. Statistical analysis of the effects of agricultural practice and soil type on NO−3 leaching was conducted based on modeling results. Observed and predicted results showed (i) no significant difference between mean values of observed and predicted mean NO−3 leaching below the 1‐m depth; (ii) agricultural practice and soil type as well as their interaction had an effect on NO−3 leaching; and (iii) a large amount of spatial variability with respect to NO−3 leaching. The results are important for better understanding solute leaching from heterogeneous agricultural fields and useful in decision making.

Assessing the effectiveness of the noise addition method of preserving confidentiality in the multivariate normal case

Visual scanpaths of multiple humans on an image represent the process by which they capture the information in it. State-of-the-art models to predict visual scanpaths on images learn directly from recorded human visual scanpaths. However, the generation of multiple visual scanpaths on an image having diversity like human visual scanpaths has not been explicitly considered. In this paper, we propose a deep network for predicting multiple diverse visual scanpaths on an image. Image-specific hidden Markov model based generative data augmentation is performed in the beginning to increase the number of image-visual scanpath training pairs. Considering a similarity between our generative data augmentation process and the use of long short-term memory (LSTM) for prediction, we propose an LSTM based visual scanpath predictor. A network to predict a single visual scanpath on an image is designed first. The network is then modified to predict multiple diverse visual scanpaths representing different viewer varieties by using a parameter indicating the uniqueness of a viewer. A random vector is also employed for subtle variations within scanpaths of the same viewer variety. Our models are evaluated on three standard datasets using multiple performance measures, which demonstrate the superiority of the proposed approach over the state-of-the-art. Empirical studies are also given indicating the significance of our generative data augmentation method and our multiple scanpath prediction strategy producing diverse visual scanpaths. Code link: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://ashishverma03.github.io/Diverse-Visual-Scanpath</uri>

Recurrent neural networks-guided vector-valued synchronized key exchange for secure and privacy-preserving communication in Industrial Internet of Things

Artificial Intelligence area has been rapidly advanced around the global companies such as Google, Amazon, IBM and so on. In addition, it is anticipated to facilitate the innovation in a variety of industries in the future. AI provides us with convenience in our lives, on the other hand, the valuable information on the subjects that utilize this has the potential to be exposed at anytime and anywhere. In the next advancement of AI area, the technical developments of the new security are required other than the existing methods. Generation and validation methods of light-weight authentication vector are suggested in this study to be used in many areas as an expanded security function. Upon the results of the capacity analysis, it was verified that efficient and safe security function could be performed using the existing machine learning algorithm. Authentication vector is designed to insert the encrypted data as variable according to the change of time. The security function was performed by comparing coordinate distance values within the authentication vector, and the internal structure was verified to optimize the performance cost required for data reverse search.