Field-programmable true random number generator

True random number generation is crucial in hardware security applications. Proposed is a voltage-controlled true random number generator that is inherently field-programmable. This facilitates increased entropy as a randomness source because there is more than one configuration state which lends itself to more compact and low-power architectures. It is evaluated through electrical characterisation and statistically through industry-standard randomness tests. To the best of the author's knowledge, it is one of the most efficient designs to date with respect to hardware design metrics.

Ultra-lightweight true random number generators

A series of ultra-lightweight digital true random number generators (TRNGs) are presented. These TRNGs are based on the observation that, when a circuit switches from a metastable state to a bi-stable state, the resulting state may be random. Four such circuits with low hardware cost are presented: one uses an XOR gate; one uses a lookup table; one uses a multiplexer and an inverter; and one uses four transistors. The three TRNGs based on the first three circuits are implemented on a field programmable gate array and successfully pass the DIEHARD RNG tests and the National Institute of Standard and Technology (NIST) RNG tests. To the best of the authors' knowledge, the proposed TRNG designs are the most lightweight among existing TRNGs.

Opportunistic spectrum aggregation for cognitive communications under collision constraints

We consider a collision-sensitive secondary system that intends to opportunistically aggregate and utilize spectrum of a primary system to achieve higher data rates. In such opportunistic spectrum access, secondary transmission can collide with primary transmission. When the secondary system aggregates more channels for data transmission, more frequent collisions may occur, limiting the performance obtained by the opportunistic spectrum aggregation. In this context, dynamic spectrum aggregation problem is formulated to maximize the ergodic channel capacity under the constraint of collision tolerable level. To solve the problem, we develop the optimal spectrum aggregation approach, deriving closed-form expressions for the collision probability in terms of primary user traffic load, secondary user transmission interval, and the random number of sub-channels aggregated. Our results show that aggregating only a subset of sub-channels will be a better choice, depending on the ratio of collision sensitivity requirement to the primary user traffic.

Stochastic analysis of seepage under water-retaining structures

This paper investigated the problem of confined flow under dams and water retaining structuresusing stochastic modelling. The approach advocated in the study combined a finite elementsmethod based on the equation governing the dynamics of incompressible fluid flow through aporous medium with a random field generator that generates random hydraulic conductivity basedon lognormal probability distribution. The resulting model was then used to analyse confined flowunder a hydraulic structure. Cases for a structure provided with cutoff wall and when the wall didnot exist were both tested. Various statistical parameters that reflected different degrees ofheterogeneity were examined and the changes in the mean seepage flow, the mean uplift forceand the mean exit gradient observed under the structure were analysed. Results reveal that underheterogeneous conditions, the reduction made by the sheetpile in the uplift force and exit hydraulicgradient may be underestimated when deterministic solutions are used.

Simulation of quantum random walks using the interference of a classical field

We suggest a theoretical scheme for the simulation of quantum random walks on a line using beam splitters, phase shifters, and photodetectors. Our model enables us to simulate a quantum random walk using of the wave nature of classical light fields. Furthermore, the proposed setup allows the analysis of the effects of decoherence. The transition from a pure mean-photon-number distribution to a classical one is studied varying the decoherence parameters.

Genetic variation of the razor clam Ensis siliqua (Jeffreys, 1875) along the European coast based on random amplified polymorphic DNA markers

Ensis siliqua is regarded as an increasingly valuable fishery resource with potential for commercial aquaculture in many European countries. The genetic variation of this razor clam was analysed by randomly amplified polymorphic DNA (RAPD) in six populations from Spain, Portugal and Ireland. Out of the 40 primers tested, five were chosen to assess genetic variation. A total of 61 RAPD loci were developed ranging in size from 400 to 2000 bp. The percentages of polymorphic loci, the allele effective number and the genetic diversity were comparable among populations, and demonstrated a high level of genetic variability. The values of Nei's genetic distance were small among the Spanish and Portuguese populations (0.051-0.065), and high between these and the Irish populations. Cluster and principal coordinate analyses supported these findings. A mantel test performed between geographic and genetic distance matrices showed a significant correlation (r=0.84, P

Hidden Conditional Random Fields for Visual Speech Recognition

In this paper we present the application of Hidden Conditional Random Fields (HCRFs) to modelling speech for visual speech recognition. HCRFs may be easily adapted to model long range dependencies across an observation sequence. As a result visual word recognition performance can be improved as the model is able to take more of a contextual approach to generating state sequences. Results are presented from a speaker-dependent, isolated digit, visual speech recognition task using comparisons with a baseline HMM system. We firstly illustrate that word recognition rates on clean video using HCRFs can be improved by increasing the number of past and future observations being taken into account by each state. Secondly we compare model performances using various levels of video compression on the test set. As far as we are aware this is the first attempted use of HCRFs for visual speech recognition.

Electron number density measurements in magnesium laser produced plumes

Interferometry has been used to investigate the spatio-temporal evolution of electron number density following 248 nm laser ablation of a magnesium target. Fringe shifts were measured as a function of laser power density for a circular spot obtained using a random phase plate. Line averaged electron number densities were obtained at delay times up to ∼100 ns after the laser pulse. Density profiles normal to the target surface were recorded for power densities on target in the range 125–300 MW cm−2.

Random neighbor sandpile model with constrained total energy

We propose as energy-constrained sandpile model with random neighbors. The critical behavior of the model is in the same universality class as the mean-field self-organized criticality sandpile. The critical energy E-c depends on the number of neighbors n of each site, but the various exponents do not. For n = 6, we got that E-c = 0.4545; and a self-similar structure of the energy distribution function with five major peaks is also observed. This is a natural result of system dynamics and the way the system is disturbed.

Universality and self-similarity of an energy-constrained sandpile model with random neighbors

We study an energy-constrained sandpile model with random neighbors. The critical behavior of the model is in the same universality class as the mean-field self-organized criticality sandpile. The critical energy E-c depends on the number of neighbors n for each site, but the various exponents are independent of n. A self-similar structure with n-1 major peaks is developed for the energy distribution p(E) when the system approaches its stationary state. The avalanche dynamics contributes to the major peaks appearing at E-Pk = 2k/(2n - 1) with k = 1,2,...,n-1, while the fine self-similar structure is a natural result of the way the system is disturbed. [S1063-651X(99)10307-6].

Detecting sequence dependent transcriptional pauses from RNA and protein number time series

Background: Evidence suggests that in prokaryotes sequence-dependent transcriptional pauses a?ect the dynamics of transcription and translation, as well as of small genetic circuits. So far, a few pause-prone sequences have been identi?ed from in vitro measurements of transcription elongation kinetics.

Results: Using a stochastic model of gene expression at the nucleotide and codon levels with realistic parameter values, we investigate three di?erent but related questions and present statistical methods for their analysis. First, we show that information from in vivo RNA and protein temporal numbers is su?cient to discriminate between models with and without a pause site in their coding sequence. Second, we demonstrate that it is possible to separate a large variety of models from each other with pauses of various durations and locations in the template by means of a hierarchical clustering and a random forest classi?er. Third, we introduce an approximate likelihood function that allows to estimate the location of a pause site.

Conclusions: This method can aid in detecting unknown pause-prone sequences from temporal measurements of RNA and protein numbers at a genome-wide scale and thus elucidate possible roles that these sequences play in the dynamics of genetic networks and phenotype.

Implementation and testing of Lanczos-based algorithms for Random-Phase Approximation eigenproblems

The treatment of the Random-Phase Approximation Hamiltonians, encountered in different frameworks, like time-dependent density functional theory or Bethe-Salpeter equation, is complicated by their non-Hermicity. Compared to their Hermitian Hamiltonian counterparts, computational methods for the treatment of non-Hermitian Hamiltonians are often less efficient and less stable, sometimes leading to the breakdown of the method. Recently [Gruning et al. Nano Lett. 8 (2009) 28201, we have identified that such Hamiltonians are usually pseudo-Hermitian. Exploiting this property, we have implemented an algorithm of the Lanczos type for Random-Phase Approximation Hamiltonians that benefits from the same stability and computational load as its Hermitian counterpart, and applied it to the study of the optical response of carbon nanotubes. We present here the related theoretical grounds and technical details, and study the performance of the algorithm for the calculation of the optical absorption of a molecule within the Bethe-Salpeter equation framework. (C) 2011 Elsevier B.V. All rights reserved.

Is the number of species on earth increasing or decreasing? Time, chaos and the origin of species

Darwin's On the Origin of Species has led to a theory of evolution with a mass of empirical detail on population genetics below species level, together with heated debate on the details of macroevolutionary patterns above species level. Most of the main principles are clear and generally accepted, notably that life originated once and has evolved over time by descent with modification. Here, I review the fossil and molecular phylogenetic records of the response of life on Earth to Quaternary climatic changes. I suggest that the record can be best understood in terms of the nonlinear dynamics of the relationship between genotype and phenotype, and between climate and environments. 'The origin of species' is essentially unpredictable, but is nevertheless an inevitable consequence of the way that organisms reproduce through time. The process is 'chaotic', but not 'random'. I suggest that biodiversity is best considered as continuously branching systems of lineages, where 'species' are the branch tips. The Earth's biodiversity should thus (1) be in a state of continuous increase and (2) show continuous discrepancies between genetic and morphological data in time and space. © The Palaeontological Association.

Generating Realistic Labelled, Weighted Random Graphs

Generative algorithms for random graphs have yielded insights into the structure and evolution of real-world networks. Most networks exhibit a well-known set of properties, such as heavy-tailed degree distributions, clustering and community formation. Usually, random graph models consider only structural information, but many real-world networks also have labelled vertices and weighted edges. In this paper, we present a generative model for random graphs with discrete vertex labels and numeric edge weights. The weights are represented as a set of Beta Mixture Models (BMMs) with an arbitrary number of mixtures, which are learned from real-world networks. We propose a Bayesian Variational Inference (VI) approach, which yields an accurate estimation while keeping computation times tractable. We compare our approach to state-of-the-art random labelled graph generators and an earlier approach based on Gaussian Mixture Models (GMMs). Our results allow us to draw conclusions about the contribution of vertex labels and edge weights to graph structure.