Scenario generation for stochastic optimization problems via the sparse grid method

DOE PAGES

Chen, Michael; Mehrotra, Sanjay; Papp, David

2015-04-19

We study the use of sparse grids in the scenario generation (or discretization) problem in stochastic programming problems where the uncertainty is modeled using a continuous multivariate distribution. We show that, under a regularity assumption on the random function involved, the sequence of optimal objective function values of the sparse grid approximations converges to the true optimal objective function values as the number of scenarios increases. The rate of convergence is also established. We treat separately the special case when the underlying distribution is an affine transform of a product of univariate distributions, and show how the sparse grid methodmore » can be adapted to the distribution by the use of quadrature formulas tailored to the distribution. We numerically compare the performance of the sparse grid method using different quadrature rules with classic quasi-Monte Carlo (QMC) methods, optimal rank-one lattice rules, and Monte Carlo (MC) scenario generation, using a series of utility maximization problems with up to 160 random variables. The results show that the sparse grid method is very efficient, especially if the integrand is sufficiently smooth. In such problems the sparse grid scenario generation method is found to need several orders of magnitude fewer scenarios than MC and QMC scenario generation to achieve the same accuracy. As a result, it is indicated that the method scales well with the dimension of the distribution--especially when the underlying distribution is an affine transform of a product of univariate distributions, in which case the method appears scalable to thousands of random variables.« less

Generating moment matching scenarios using optimization techniques

DOE PAGES

Mehrotra, Sanjay; Papp, Dávid

2013-05-16

An optimization based method is proposed to generate moment matching scenarios for numerical integration and its use in stochastic programming. The main advantage of the method is its flexibility: it can generate scenarios matching any prescribed set of moments of the underlying distribution rather than matching all moments up to a certain order, and the distribution can be defined over an arbitrary set. This allows for a reduction in the number of scenarios and allows the scenarios to be better tailored to the problem at hand. The method is based on a semi-infinite linear programming formulation of the problem thatmore » is shown to be solvable with polynomial iteration complexity. A practical column generation method is implemented. The column generation subproblems are polynomial optimization problems; however, they need not be solved to optimality. It is found that the columns in the column generation approach can be efficiently generated by random sampling. The number of scenarios generated matches a lower bound of Tchakaloff's. The rate of convergence of the approximation error is established for continuous integrands, and an improved bound is given for smooth integrands. Extensive numerical experiments are presented in which variants of the proposed method are compared to Monte Carlo and quasi-Monte Carlo methods on both numerical integration problems and stochastic optimization problems. The benefits of being able to match any prescribed set of moments, rather than all moments up to a certain order, is also demonstrated using optimization problems with 100-dimensional random vectors. Here, empirical results show that the proposed approach outperforms Monte Carlo and quasi-Monte Carlo based approaches on the tested problems.« less

On grey levels in random CAPTCHA generation

NASA Astrophysics Data System (ADS)

Newton, Fraser; Kouritzin, Michael A.

2011-06-01

A CAPTCHA is an automatically generated test designed to distinguish between humans and computer programs; specifically, they are designed to be easy for humans but difficult for computer programs to pass in order to prevent the abuse of resources by automated bots. They are commonly seen guarding webmail registration forms, online auction sites, and preventing brute force attacks on passwords. In the following, we address the question: How does adding a grey level to random CAPTCHA generation affect the utility of the CAPTCHA? We treat the problem of generating the random CAPTCHA as one of random field simulation: An initial state of background noise is evolved over time using Gibbs sampling and an efficient algorithm for generating correlated random variables. This approach has already been found to yield highly-readable yet difficult-to-crack CAPTCHAs. We detail how the requisite parameters for introducing grey levels are estimated and how we generate the random CAPTCHA. The resulting CAPTCHA will be evaluated in terms of human readability as well as its resistance to automated attacks in the forms of character segmentation and optical character recognition.

Strategic Use of Random Subsample Replication and a Coefficient of Factor Replicability

ERIC Educational Resources Information Center

Katzenmeyer, William G.; Stenner, A. Jackson

1975-01-01

The problem of demonstrating replicability of factor structure across random variables is addressed. Procedures are outlined which combine the use of random subsample replication strategies with the correlations between factor score estimates across replicate pairs to generate a coefficient of replicability and confidence intervals associated with…

Solving and Learning Soft Temporal Constraints: Experimental Setting and Results

NASA Technical Reports Server (NTRS)

Rossi, F.; Sperduti, A.; Venable, K. B.; Khatib, L.; Morris, P.; Morris, R.; Clancy, Daniel (Technical Monitor)

2002-01-01

Soft temporal constraints problems allow to describe in a natural way scenarios where events happen over time and preferences are associated to event distances and durations. However, sometimes such local preferences are difficult to set, and it may be easier instead to associate preferences to some complete solutions of the problem. Machine learning techniques can be useful in this respect. In this paper we describe two solvers (one more general and the other one more efficient) for tractable subclasses of soft temporal problems, and we show some experimental results. The random generator used to build the problems on which tests are performed is also described. We also compare the two solvers highlighting the tradeoff between performance and representational power. Finally, we present a learning module and we show its behavior on randomly-generated examples.

Scope of Various Random Number Generators in ant System Approach for TSP

NASA Technical Reports Server (NTRS)

Sen, S. K.; Shaykhian, Gholam Ali

2007-01-01

Experimented on heuristic, based on an ant system approach for traveling salesman problem, are several quasi- and pseudo-random number generators. This experiment is to explore if any particular generator is most desirable. Such an experiment on large samples has the potential to rank the performance of the generators for the foregoing heuristic. This is mainly to seek an answer to the controversial issue "which generator is the best in terms of quality of the result (accuracy) as well as cost of producing the result (time/computational complexity) in a probabilistic/statistical sense."

A Multilevel, Hierarchical Sampling Technique for Spatially Correlated Random Fields

DOE PAGES

Osborn, Sarah; Vassilevski, Panayot S.; Villa, Umberto

2017-10-26

In this paper, we propose an alternative method to generate samples of a spatially correlated random field with applications to large-scale problems for forward propagation of uncertainty. A classical approach for generating these samples is the Karhunen--Loève (KL) decomposition. However, the KL expansion requires solving a dense eigenvalue problem and is therefore computationally infeasible for large-scale problems. Sampling methods based on stochastic partial differential equations provide a highly scalable way to sample Gaussian fields, but the resulting parametrization is mesh dependent. We propose a multilevel decomposition of the stochastic field to allow for scalable, hierarchical sampling based on solving amore » mixed finite element formulation of a stochastic reaction-diffusion equation with a random, white noise source function. Lastly, numerical experiments are presented to demonstrate the scalability of the sampling method as well as numerical results of multilevel Monte Carlo simulations for a subsurface porous media flow application using the proposed sampling method.« less

A Multilevel, Hierarchical Sampling Technique for Spatially Correlated Random Fields

DOE Office of Scientific and Technical Information (OSTI.GOV)

Osborn, Sarah; Vassilevski, Panayot S.; Villa, Umberto

In this paper, we propose an alternative method to generate samples of a spatially correlated random field with applications to large-scale problems for forward propagation of uncertainty. A classical approach for generating these samples is the Karhunen--Loève (KL) decomposition. However, the KL expansion requires solving a dense eigenvalue problem and is therefore computationally infeasible for large-scale problems. Sampling methods based on stochastic partial differential equations provide a highly scalable way to sample Gaussian fields, but the resulting parametrization is mesh dependent. We propose a multilevel decomposition of the stochastic field to allow for scalable, hierarchical sampling based on solving amore » mixed finite element formulation of a stochastic reaction-diffusion equation with a random, white noise source function. Lastly, numerical experiments are presented to demonstrate the scalability of the sampling method as well as numerical results of multilevel Monte Carlo simulations for a subsurface porous media flow application using the proposed sampling method.« less

Dynamic Loads Generation for Multi-Point Vibration Excitation Problems

NASA Technical Reports Server (NTRS)

Shen, Lawrence

2011-01-01

A random-force method has been developed to predict dynamic loads produced by rocket-engine random vibrations for new rocket-engine designs. The method develops random forces at multiple excitation points based on random vibration environments scaled from accelerometer data obtained during hot-fire tests of existing rocket engines. This random-force method applies random forces to the model and creates expected dynamic response in a manner that simulates the way the operating engine applies self-generated random vibration forces (random pressure acting on an area) with the resulting responses that we measure with accelerometers. This innovation includes the methodology (implementation sequence), the computer code, two methods to generate the random-force vibration spectra, and two methods to reduce some of the inherent conservatism in the dynamic loads. This methodology would be implemented to generate the random-force spectra at excitation nodes without requiring the use of artificial boundary conditions in a finite element model. More accurate random dynamic loads than those predicted by current industry methods can then be generated using the random force spectra. The scaling method used to develop the initial power spectral density (PSD) environments for deriving the random forces for the rocket engine case is based on the Barrett Criteria developed at Marshall Space Flight Center in 1963. This invention approach can be applied in the aerospace, automotive, and other industries to obtain reliable dynamic loads and responses from a finite element model for any structure subject to multipoint random vibration excitations.

Generating equilateral random polygons in confinement

NASA Astrophysics Data System (ADS)

Diao, Y.; Ernst, C.; Montemayor, A.; Ziegler, U.

2011-10-01

One challenging problem in biology is to understand the mechanism of DNA packing in a confined volume such as a cell. It is known that confined circular DNA is often knotted and hence the topology of the extracted (and relaxed) circular DNA can be used as a probe of the DNA packing mechanism. However, in order to properly estimate the topological properties of the confined circular DNA structures using mathematical models, it is necessary to generate large ensembles of simulated closed chains (i.e. polygons) of equal edge lengths that are confined in a volume such as a sphere of certain fixed radius. Finding efficient algorithms that properly sample the space of such confined equilateral random polygons is a difficult problem. In this paper, we propose a method that generates confined equilateral random polygons based on their probability distribution. This method requires the creation of a large database initially. However, once the database has been created, a confined equilateral random polygon of length n can be generated in linear time in terms of n. The errors introduced by the method can be controlled and reduced by the refinement of the database. Furthermore, our numerical simulations indicate that these errors are unbiased and tend to cancel each other in a long polygon.

Near-optimal alternative generation using modified hit-and-run sampling for non-linear, non-convex problems

NASA Astrophysics Data System (ADS)

Rosenberg, D. E.; Alafifi, A.

2016-12-01

Water resources systems analysis often focuses on finding optimal solutions. Yet an optimal solution is optimal only for the modelled issues and managers often seek near-optimal alternatives that address un-modelled objectives, preferences, limits, uncertainties, and other issues. Early on, Modelling to Generate Alternatives (MGA) formalized near-optimal as the region comprising the original problem constraints plus a new constraint that allowed performance within a specified tolerance of the optimal objective function value. MGA identified a few maximally-different alternatives from the near-optimal region. Subsequent work applied Markov Chain Monte Carlo (MCMC) sampling to generate a larger number of alternatives that span the near-optimal region of linear problems or select portions for non-linear problems. We extend the MCMC Hit-And-Run method to generate alternatives that span the full extent of the near-optimal region for non-linear, non-convex problems. First, start at a feasible hit point within the near-optimal region, then run a random distance in a random direction to a new hit point. Next, repeat until generating the desired number of alternatives. The key step at each iterate is to run a random distance along the line in the specified direction to a new hit point. If linear equity constraints exist, we construct an orthogonal basis and use a null space transformation to confine hits and runs to a lower-dimensional space. Linear inequity constraints define the convex bounds on the line that runs through the current hit point in the specified direction. We then use slice sampling to identify a new hit point along the line within bounds defined by the non-linear inequity constraints. This technique is computationally efficient compared to prior near-optimal alternative generation techniques such MGA, MCMC Metropolis-Hastings, evolutionary, or firefly algorithms because search at each iteration is confined to the hit line, the algorithm can move in one step to any point in the near-optimal region, and each iterate generates a new, feasible alternative. We use the method to generate alternatives that span the near-optimal regions of simple and more complicated water management problems and may be preferred to optimal solutions. We also discuss extensions to handle non-linear equity constraints.

Scalable hierarchical PDE sampler for generating spatially correlated random fields using nonmatching meshes: Scalable hierarchical PDE sampler using nonmatching meshes

DOE PAGES

Osborn, Sarah; Zulian, Patrick; Benson, Thomas; ...

2018-01-30

This work describes a domain embedding technique between two nonmatching meshes used for generating realizations of spatially correlated random fields with applications to large-scale sampling-based uncertainty quantification. The goal is to apply the multilevel Monte Carlo (MLMC) method for the quantification of output uncertainties of PDEs with random input coefficients on general and unstructured computational domains. We propose a highly scalable, hierarchical sampling method to generate realizations of a Gaussian random field on a given unstructured mesh by solving a reaction–diffusion PDE with a stochastic right-hand side. The stochastic PDE is discretized using the mixed finite element method on anmore » embedded domain with a structured mesh, and then, the solution is projected onto the unstructured mesh. This work describes implementation details on how to efficiently transfer data from the structured and unstructured meshes at coarse levels, assuming that this can be done efficiently on the finest level. We investigate the efficiency and parallel scalability of the technique for the scalable generation of Gaussian random fields in three dimensions. An application of the MLMC method is presented for quantifying uncertainties of subsurface flow problems. Here, we demonstrate the scalability of the sampling method with nonmatching mesh embedding, coupled with a parallel forward model problem solver, for large-scale 3D MLMC simulations with up to 1.9·109 unknowns.« less

Scalable hierarchical PDE sampler for generating spatially correlated random fields using nonmatching meshes: Scalable hierarchical PDE sampler using nonmatching meshes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Osborn, Sarah; Zulian, Patrick; Benson, Thomas

This work describes a domain embedding technique between two nonmatching meshes used for generating realizations of spatially correlated random fields with applications to large-scale sampling-based uncertainty quantification. The goal is to apply the multilevel Monte Carlo (MLMC) method for the quantification of output uncertainties of PDEs with random input coefficients on general and unstructured computational domains. We propose a highly scalable, hierarchical sampling method to generate realizations of a Gaussian random field on a given unstructured mesh by solving a reaction–diffusion PDE with a stochastic right-hand side. The stochastic PDE is discretized using the mixed finite element method on anmore » embedded domain with a structured mesh, and then, the solution is projected onto the unstructured mesh. This work describes implementation details on how to efficiently transfer data from the structured and unstructured meshes at coarse levels, assuming that this can be done efficiently on the finest level. We investigate the efficiency and parallel scalability of the technique for the scalable generation of Gaussian random fields in three dimensions. An application of the MLMC method is presented for quantifying uncertainties of subsurface flow problems. Here, we demonstrate the scalability of the sampling method with nonmatching mesh embedding, coupled with a parallel forward model problem solver, for large-scale 3D MLMC simulations with up to 1.9·109 unknowns.« less

The Benefits of Computer-Generated Feedback for Mathematics Problem Solving

ERIC Educational Resources Information Center

Fyfe, Emily R.; Rittle-Johnson, Bethany

2016-01-01

The goal of the current research was to better understand when and why feedback has positive effects on learning and to identify features of feedback that may improve its efficacy. In a randomized experiment, second-grade children (N = 75) received instruction on a correct problem-solving strategy and then solved a set of relevant problems.…

Ground States of Random Spanning Trees on a D-Wave 2X

NASA Astrophysics Data System (ADS)

Hall, J. S.; Hobl, L.; Novotny, M. A.; Michielsen, Kristel

The performances of two D-Wave 2 machines (476 and 496 qubits) and of a 1097-qubit D-Wave 2X were investigated. Each chip has a Chimera interaction graph calG . Problem input consists of values for the fields hj and for the two-qubit interactions Ji , j of an Ising spin-glass problem formulated on calG . Output is returned in terms of a spin configuration {sj } , with sj = +/- 1 . We generated random spanning trees (RSTs) uniformly distributed over all spanning trees of calG . On the 476-qubit D-Wave 2, RSTs were generated on the full chip with Ji , j = - 1 and hj = 0 and solved one thousand times. The distribution of solution energies and the average magnetization of each qubit were determined. On both the 476- and 1097-qubit machines, four identical spanning trees were generated on each quadrant of the chip. The statistical independence of these regions was investigated. In another study, on the D-Wave 2X, one hundred RSTs with random Ji , j ∈ { - 1 , 1 } and hj = 0 were generated on the full chip. Each RST problem was solved one hundred times and the number of times the ground state energy was found was recorded. This procedure was repeated for square subgraphs, with dimensions ranging from 7 ×7 to 11 ×11. Supported in part by NSF Grants DGE-0947419 and DMR-1206233. D-Wave time provided by D-Wave Systems and by the USRA Quantum Artificial Intelligence Laboratory Research Opportunity.

Beyond Moore's law: towards competitive quantum devices

NASA Astrophysics Data System (ADS)

Troyer, Matthias

2015-05-01

A century after the invention of quantum theory and fifty years after Bell's inequality we see the first quantum devices emerge as products that aim to be competitive with the best classical computing devices. While a universal quantum computer of non-trivial size is still out of reach there exist a number commercial and experimental devices: quantum random number generators, quantum simulators and quantum annealers. In this colloquium I will present some of these devices and validation tests we performed on them. Quantum random number generators use the inherent randomness in quantum measurements to produce true random numbers, unlike classical pseudorandom number generators which are inherently deterministic. Optical lattice emulators use ultracold atomic gases in optical lattices to mimic typical models of condensed matter physics. In my talk I will focus especially on the devices built by Canadian company D-Wave systems, which are special purpose quantum simulators for solving hard classical optimization problems. I will review the controversy around the quantum nature of these devices and will compare them to state of the art classical algorithms. I will end with an outlook towards universal quantum computing and end with the question: which important problems that are intractable even for post-exa-scale classical computers could we expect to solve once we have a universal quantum computer?

Generating constrained randomized sequences: item frequency matters.

PubMed

French, Robert M; Perruchet, Pierre

2009-11-01

All experimental psychologists understand the importance of randomizing lists of items. However, randomization is generally constrained, and these constraints-in particular, not allowing immediately repeated items-which are designed to eliminate particular biases, frequently engender others. We describe a simple Monte Carlo randomization technique that solves a number of these problems. However, in many experimental settings, we are concerned not only with the number and distribution of items but also with the number and distribution of transitions between items. The algorithm mentioned above provides no control over this. We therefore introduce a simple technique that uses transition tables for generating correctly randomized sequences. We present an analytic method of producing item-pair frequency tables and item-pair transitional probability tables when immediate repetitions are not allowed. We illustrate these difficulties and how to overcome them, with reference to a classic article on word segmentation in infants. Finally, we provide free access to an Excel file that allows users to generate transition tables with up to 10 different item types, as well as to generate appropriately distributed randomized sequences of any length without immediately repeated elements. This file is freely available from http://leadserv.u-bourgogne.fr/IMG/xls/TransitionMatrix.xls.

Automatic Nanodesign Using Evolutionary Techniques

NASA Technical Reports Server (NTRS)

Globus, Al; Saini, Subhash (Technical Monitor)

1998-01-01

Many problems associated with the development of nanotechnology require custom designed molecules. We use genetic graph software, a new development, to automatically evolve molecules of interest when only the requirements are known. Genetic graph software designs molecules, and potentially nanoelectronic circuits, given a fitness function that determines which of two molecules is better. A set of molecules, the first generation, is generated at random then tested with the fitness function, Subsequent generations are created by randomly choosing two parent molecules with a bias towards high scoring molecules, tearing each molecules in two at random, and mating parts from the mother and father to create two children. This procedure is repeated until a satisfactory molecule is found. An atom pair similarity test is currently used as the fitness function to evolve molecules similar to existing pharmaceuticals.

Random Numbers and Monte Carlo Methods

NASA Astrophysics Data System (ADS)

Scherer, Philipp O. J.

Many-body problems often involve the calculation of integrals of very high dimension which cannot be treated by standard methods. For the calculation of thermodynamic averages Monte Carlo methods are very useful which sample the integration volume at randomly chosen points. After summarizing some basic statistics, we discuss algorithms for the generation of pseudo-random numbers with given probability distribution which are essential for all Monte Carlo methods. We show how the efficiency of Monte Carlo integration can be improved by sampling preferentially the important configurations. Finally the famous Metropolis algorithm is applied to classical many-particle systems. Computer experiments visualize the central limit theorem and apply the Metropolis method to the traveling salesman problem.

Assessing Adolescents' Communicative Self-Efficacy to Discuss Controversial Issues: Findings from a Randomized Study of the Word Generation Program

ERIC Educational Resources Information Center

Lin, Alex R.; Lawrence, Joshua F.; Snow, Catherine E.; Taylor, Karen S.

2016-01-01

Communicative self-efficacy serves as an important link between discussing controversial issues and civic engagement because confidence in one's discourse skills is important to managing conflicting perspectives and developing solutions to community-based problems. Freely available to schools, "Word Generation" is a cross-content…

When Gravity Fails: Local Search Topology

NASA Technical Reports Server (NTRS)

Frank, Jeremy; Cheeseman, Peter; Stutz, John; Lau, Sonie (Technical Monitor)

1997-01-01

Local search algorithms for combinatorial search problems frequently encounter a sequence of states in which it is impossible to improve the value of the objective function; moves through these regions, called {\\em plateau moves), dominate the time spent in local search. We analyze and characterize {\\em plateaus) for three different classes of randomly generated Boolean Satisfiability problems. We identify several interesting features of plateaus that impact the performance of local search algorithms. We show that local minima tend to be small but occasionally may be very large. We also show that local minima can be escaped without unsatisfying a large number of clauses, but that systematically searching for an escape route may be computationally expensive if the local minimum is large. We show that plateaus with exits, called benches, tend to be much larger than minima, and that some benches have very few exit states which local search can use to escape. We show that the solutions (i.e. global minima) of randomly generated problem instances form clusters, which behave similarly to local minima. We revisit several enhancements of local search algorithms and explain their performance in light of our results. Finally we discuss strategies for creating the next generation of local search algorithms.

Constraint-based Temporal Reasoning with Preferences

NASA Technical Reports Server (NTRS)

Khatib, Lina; Morris, Paul; Morris, Robert; Rossi, Francesca; Sperduti, Alessandro; Venable, K. Brent

2005-01-01

Often we need to work in scenarios where events happen over time and preferences are associated to event distances and durations. Soft temporal constraints allow one to describe in a natural way problems arising in such scenarios. In general, solving soft temporal problems require exponential time in the worst case, but there are interesting subclasses of problems which are polynomially solvable. In this paper we identify one of such subclasses giving tractability results. Moreover, we describe two solvers for this class of soft temporal problems, and we show some experimental results. The random generator used to build the problems on which tests are performed is also described. We also compare the two solvers highlighting the tradeoff between performance and robustness. Sometimes, however, temporal local preferences are difficult to set, and it may be easier instead to associate preferences to some complete solutions of the problem. To model everything in a uniform way via local preferences only, and also to take advantage of the existing constraint solvers which exploit only local preferences, we show that machine learning techniques can be useful in this respect. In particular, we present a learning module based on a gradient descent technique which induces local temporal preferences from global ones. We also show the behavior of the learning module on randomly-generated examples.

Subjective randomness as statistical inference.

PubMed

Griffiths, Thomas L; Daniels, Dylan; Austerweil, Joseph L; Tenenbaum, Joshua B

2018-06-01

Some events seem more random than others. For example, when tossing a coin, a sequence of eight heads in a row does not seem very random. Where do these intuitions about randomness come from? We argue that subjective randomness can be understood as the result of a statistical inference assessing the evidence that an event provides for having been produced by a random generating process. We show how this account provides a link to previous work relating randomness to algorithmic complexity, in which random events are those that cannot be described by short computer programs. Algorithmic complexity is both incomputable and too general to capture the regularities that people can recognize, but viewing randomness as statistical inference provides two paths to addressing these problems: considering regularities generated by simpler computing machines, and restricting the set of probability distributions that characterize regularity. Building on previous work exploring these different routes to a more restricted notion of randomness, we define strong quantitative models of human randomness judgments that apply not just to binary sequences - which have been the focus of much of the previous work on subjective randomness - but also to binary matrices and spatial clustering. Copyright © 2018 Elsevier Inc. All rights reserved.

An analysis of random projection for changeable and privacy-preserving biometric verification.

PubMed

Wang, Yongjin; Plataniotis, Konstantinos N

2010-10-01

Changeability and privacy protection are important factors for widespread deployment of biometrics-based verification systems. This paper presents a systematic analysis of a random-projection (RP)-based method for addressing these problems. The employed method transforms biometric data using a random matrix with each entry an independent and identically distributed Gaussian random variable. The similarity- and privacy-preserving properties, as well as the changeability of the biometric information in the transformed domain, are analyzed in detail. Specifically, RP on both high-dimensional image vectors and dimensionality-reduced feature vectors is discussed and compared. A vector translation method is proposed to improve the changeability of the generated templates. The feasibility of the introduced solution is well supported by detailed theoretical analyses. Extensive experimentation on a face-based biometric verification problem shows the effectiveness of the proposed method.

DNA based random key generation and management for OTP encryption.

PubMed

Zhang, Yunpeng; Liu, Xin; Sun, Manhui

2017-09-01

One-time pad (OTP) is a principle of key generation applied to the stream ciphering method which offers total privacy. The OTP encryption scheme has proved to be unbreakable in theory, but difficult to realize in practical applications. Because OTP encryption specially requires the absolute randomness of the key, its development has suffered from dense constraints. DNA cryptography is a new and promising technology in the field of information security. DNA chromosomes storing capabilities can be used as one-time pad structures with pseudo-random number generation and indexing in order to encrypt the plaintext messages. In this paper, we present a feasible solution to the OTP symmetric key generation and transmission problem with DNA at the molecular level. Through recombinant DNA technology, by using only sender-receiver known restriction enzymes to combine the secure key represented by DNA sequence and the T vector, we generate the DNA bio-hiding secure key and then place the recombinant plasmid in implanted bacteria for secure key transmission. The designed bio experiments and simulation results show that the security of the transmission of the key is further improved and the environmental requirements of key transmission are reduced. Analysis has demonstrated that the proposed DNA-based random key generation and management solutions are marked by high security and usability. Published by Elsevier B.V.

Bird's-eye view on noise-based logic.

PubMed

Kish, Laszlo B; Granqvist, Claes G; Horvath, Tamas; Klappenecker, Andreas; Wen, He; Bezrukov, Sergey M

2014-01-01

Noise-based logic is a practically deterministic logic scheme inspired by the randomness of neural spikes and uses a system of uncorrelated stochastic processes and their superposition to represent the logic state. We briefly discuss various questions such as ( i ) What does practical determinism mean? ( ii ) Is noise-based logic a Turing machine? ( iii ) Is there hope to beat (the dreams of) quantum computation by a classical physical noise-based processor, and what are the minimum hardware requirements for that? Finally, ( iv ) we address the problem of random number generators and show that the common belief that quantum number generators are superior to classical (thermal) noise-based generators is nothing but a myth.

Bird's-eye view on noise-based logic

NASA Astrophysics Data System (ADS)

Kish, Laszlo B.; Granqvist, Claes G.; Horvath, Tamas; Klappenecker, Andreas; Wen, He; Bezrukov, Sergey M.

2014-09-01

Noise-based logic is a practically deterministic logic scheme inspired by the randomness of neural spikes and uses a system of uncorrelated stochastic processes and their superposition to represent the logic state. We briefly discuss various questions such as (i) What does practical determinism mean? (ii) Is noise-based logic a Turing machine? (iii) Is there hope to beat (the dreams of) quantum computation by a classical physical noise-based processor, and what are the minimum hardware requirements for that? Finally, (iv) we address the problem of random number generators and show that the common belief that quantum number generators are superior to classical (thermal) noise-based generators is nothing but a myth.

Stabilization of Wind Energy Conversion System with Hydrogen Generator by Using EDLC Energy Storage System

NASA Astrophysics Data System (ADS)

Shishido, Seiji; Takahashi, Rion; Murata, Toshiaki; Tamura, Junji; Sugimasa, Masatoshi; Komura, Akiyoshi; Futami, Motoo; Ichinose, Masaya; Ide, Kazumasa

The spread of wind power generation is progressed hugely in recent years from a viewpoint of environmental problems including global warming. Though wind power is considered as a very prospective energy source, wind power fluctuation due to the random fluctuation of wind speed has still created some problems. Therefore, research has been performed how to smooth the wind power fluctuation. This paper proposes Energy Capacitor System (ECS) for the smoothing of wind power which consists of Electric Double-Layer Capacitor (EDLC) and power electronics devices and works as an electric power storage system. Moreover, hydrogen has received much attention in recent years from a viewpoint of exhaustion problem of fossil fuel. Therefore it is also proposed that a hydrogen generator is installed at the wind farm to generate hydrogen. In this paper, the effectiveness of the proposed system is verified by the simulation analyses using PSCAD/EMTDC.

Approximation algorithms for the min-power symmetric connectivity problem

NASA Astrophysics Data System (ADS)

Plotnikov, Roman; Erzin, Adil; Mladenovic, Nenad

2016-10-01

We consider the NP-hard problem of synthesis of optimal spanning communication subgraph in a given arbitrary simple edge-weighted graph. This problem occurs in the wireless networks while minimizing the total transmission power consumptions. We propose several new heuristics based on the variable neighborhood search metaheuristic for the approximation solution of the problem. We have performed a numerical experiment where all proposed algorithms have been executed on the randomly generated test samples. For these instances, on average, our algorithms outperform the previously known heuristics.

A Statistical Method to Distinguish Functional Brain Networks

PubMed Central

Fujita, André; Vidal, Maciel C.; Takahashi, Daniel Y.

2017-01-01

One major problem in neuroscience is the comparison of functional brain networks of different populations, e.g., distinguishing the networks of controls and patients. Traditional algorithms are based on search for isomorphism between networks, assuming that they are deterministic. However, biological networks present randomness that cannot be well modeled by those algorithms. For instance, functional brain networks of distinct subjects of the same population can be different due to individual characteristics. Moreover, networks of subjects from different populations can be generated through the same stochastic process. Thus, a better hypothesis is that networks are generated by random processes. In this case, subjects from the same group are samples from the same random process, whereas subjects from different groups are generated by distinct processes. Using this idea, we developed a statistical test called ANOGVA to test whether two or more populations of graphs are generated by the same random graph model. Our simulations' results demonstrate that we can precisely control the rate of false positives and that the test is powerful to discriminate random graphs generated by different models and parameters. The method also showed to be robust for unbalanced data. As an example, we applied ANOGVA to an fMRI dataset composed of controls and patients diagnosed with autism or Asperger. ANOGVA identified the cerebellar functional sub-network as statistically different between controls and autism (p < 0.001). PMID:28261045

A Statistical Method to Distinguish Functional Brain Networks.

PubMed

Fujita, André; Vidal, Maciel C; Takahashi, Daniel Y

2017-01-01

One major problem in neuroscience is the comparison of functional brain networks of different populations, e.g., distinguishing the networks of controls and patients. Traditional algorithms are based on search for isomorphism between networks, assuming that they are deterministic. However, biological networks present randomness that cannot be well modeled by those algorithms. For instance, functional brain networks of distinct subjects of the same population can be different due to individual characteristics. Moreover, networks of subjects from different populations can be generated through the same stochastic process. Thus, a better hypothesis is that networks are generated by random processes. In this case, subjects from the same group are samples from the same random process, whereas subjects from different groups are generated by distinct processes. Using this idea, we developed a statistical test called ANOGVA to test whether two or more populations of graphs are generated by the same random graph model. Our simulations' results demonstrate that we can precisely control the rate of false positives and that the test is powerful to discriminate random graphs generated by different models and parameters. The method also showed to be robust for unbalanced data. As an example, we applied ANOGVA to an fMRI dataset composed of controls and patients diagnosed with autism or Asperger. ANOGVA identified the cerebellar functional sub-network as statistically different between controls and autism ( p < 0.001).

University Students’ Conceptual Knowledge of Randomness and Probability in the Contexts of Evolution and Mathematics

PubMed Central

Fiedler, Daniela; Tröbst, Steffen; Harms, Ute

2017-01-01

Students of all ages face severe conceptual difficulties regarding key aspects of evolution—the central, unifying, and overarching theme in biology. Aspects strongly related to abstract “threshold” concepts like randomness and probability appear to pose particular difficulties. A further problem is the lack of an appropriate instrument for assessing students’ conceptual knowledge of randomness and probability in the context of evolution. To address this problem, we have developed two instruments, Randomness and Probability Test in the Context of Evolution (RaProEvo) and Randomness and Probability Test in the Context of Mathematics (RaProMath), that include both multiple-choice and free-response items. The instruments were administered to 140 university students in Germany, then the Rasch partial-credit model was applied to assess them. The results indicate that the instruments generate reliable and valid inferences about students’ conceptual knowledge of randomness and probability in the two contexts (which are separable competencies). Furthermore, RaProEvo detected significant differences in knowledge of randomness and probability, as well as evolutionary theory, between biology majors and preservice biology teachers. PMID:28572180

Employing online quantum random number generators for generating truly random quantum states in Mathematica

NASA Astrophysics Data System (ADS)

Miszczak, Jarosław Adam

2013-01-01

The presented package for the Mathematica computing system allows the harnessing of quantum random number generators (QRNG) for investigating the statistical properties of quantum states. The described package implements a number of functions for generating random states. The new version of the package adds the ability to use the on-line quantum random number generator service and implements new functions for retrieving lists of random numbers. Thanks to the introduced improvements, the new version provides faster access to high-quality sources of random numbers and can be used in simulations requiring large amount of random data. New version program summaryProgram title: TRQS Catalogue identifier: AEKA_v2_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEKA_v2_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 18 134 No. of bytes in distributed program, including test data, etc.: 2 520 49 Distribution format: tar.gz Programming language: Mathematica, C. Computer: Any supporting Mathematica in version 7 or higher. Operating system: Any platform supporting Mathematica; tested with GNU/Linux (32 and 64 bit). RAM: Case-dependent Supplementary material: Fig. 1 mentioned below can be downloaded. Classification: 4.15. External routines: Quantis software library (http://www.idquantique.com/support/quantis-trng.html) Catalogue identifier of previous version: AEKA_v1_0 Journal reference of previous version: Comput. Phys. Comm. 183(2012)118 Does the new version supersede the previous version?: Yes Nature of problem: Generation of random density matrices and utilization of high-quality random numbers for the purpose of computer simulation. Solution method: Use of a physical quantum random number generator and an on-line service providing access to the source of true random numbers generated by quantum real number generator. Reasons for new version: Added support for the high-speed on-line quantum random number generator and improved methods for retrieving lists of random numbers. Summary of revisions: The presented version provides two signicant improvements. The first one is the ability to use the on-line Quantum Random Number Generation service developed by PicoQuant GmbH and the Nano-Optics groups at the Department of Physics of Humboldt University. The on-line service supported in the version 2.0 of the TRQS package provides faster access to true randomness sources constructed using the laws of quantum physics. The service is freely available at https://qrng.physik.hu-berlin.de/. The use of this service allows using the presented package with the need of a physical quantum random number generator. The second improvement introduced in this version is the ability to retrieve arrays of random data directly for the used source. This increases the speed of the random number generation, especially in the case of an on-line service, where it reduces the time necessary to establish the connection. Thanks to the speed improvement of the presented version, the package can now be used in simulations requiring larger amounts of random data. Moreover, the functions for generating random numbers provided by the current version of the package more closely follow the pattern of functions for generating pseudo- random numbers provided in Mathematica. Additional comments: Speed comparison: The implementation of the support for the QRNG on-line service provides a noticeable improvement in the speed of random number generation. For the samples of real numbers of size 101; 102,…,107 the times required to generate these samples using Quantis USB device and QRNG service are compared in Fig. 1. The presented results show that the use of the on-line service provides faster access to random numbers. One should note, however, that the speed gain can increase or decrease depending on the connection speed between the computer and the server providing random numbers. Running time: Depends on the used source of randomness and the amount of random data used in the experiment. References: [1] M. Wahl, M. Leifgen, M. Berlin, T. Röhlicke, H.-J. Rahn, O. Benson., An ultrafast quantum random number generator with provably bounded output bias based on photon arrival time measurements, Applied Physics Letters, Vol. 098, 171105 (2011). http://dx.doi.org/10.1063/1.3578456.

Tuning Monotonic Basin Hopping: Improving the Efficiency of Stochastic Search as Applied to Low-Thrust Trajectory Optimization

NASA Technical Reports Server (NTRS)

Englander, Jacob A.; Englander, Arnold C.

2014-01-01

Trajectory optimization methods using monotonic basin hopping (MBH) have become well developed during the past decade [1, 2, 3, 4, 5, 6]. An essential component of MBH is a controlled random search through the multi-dimensional space of possible solutions. Historically, the randomness has been generated by drawing random variable (RV)s from a uniform probability distribution. Here, we investigate the generating the randomness by drawing the RVs from Cauchy and Pareto distributions, chosen because of their characteristic long tails. We demonstrate that using Cauchy distributions (as first suggested by J. Englander [3, 6]) significantly improves monotonic basin hopping (MBH) performance, and that Pareto distributions provide even greater improvements. Improved performance is defined in terms of efficiency and robustness. Efficiency is finding better solutions in less time. Robustness is efficiency that is undiminished by (a) the boundary conditions and internal constraints of the optimization problem being solved, and (b) by variations in the parameters of the probability distribution. Robustness is important for achieving performance improvements that are not problem specific. In this work we show that the performance improvements are the result of how these long-tailed distributions enable MBH to search the solution space faster and more thoroughly. In developing this explanation, we use the concepts of sub-diffusive, normally-diffusive, and super-diffusive random walks (RWs) originally developed in the field of statistical physics.

Perceptual support promotes strategy generation: Evidence from equation solving.

PubMed

Alibali, Martha W; Crooks, Noelle M; McNeil, Nicole M

2017-08-30

Over time, children shift from using less optimal strategies for solving mathematics problems to using better ones. But why do children generate new strategies? We argue that they do so when they begin to encode problems more accurately; therefore, we hypothesized that perceptual support for correct encoding would foster strategy generation. Fourth-grade students solved mathematical equivalence problems (e.g., 3 + 4 + 5 = 3 + __) in a pre-test. They were then randomly assigned to one of three perceptual support conditions or to a Control condition. Participants in all conditions completed three mathematical equivalence problems with feedback about correctness. Participants in the experimental conditions received perceptual support (i.e., highlighting in red ink) for accurately encoding the equal sign, the right side of the equation, or the numbers that could be added to obtain the correct solution. Following this intervention, participants completed a problem-solving post-test. Among participants who solved the problems incorrectly at pre-test, those who received perceptual support for correctly encoding the equal sign were more likely to generate new, correct strategies for solving the problems than were those who received feedback only. Thus, perceptual support for accurate encoding of a key problem feature promoted generation of new, correct strategies. Statement of Contribution What is already known on this subject? With age and experience, children shift to using more effective strategies for solving math problems. Problem encoding also improves with age and experience. What the present study adds? Support for encoding the equal sign led children to generate correct strategies for solving equations. Improvements in problem encoding are one source of new strategies. © 2017 The British Psychological Society.

Automated problem list generation and physicians perspective from a pilot study.

PubMed

Devarakonda, Murthy V; Mehta, Neil; Tsou, Ching-Huei; Liang, Jennifer J; Nowacki, Amy S; Jelovsek, John Eric

2017-09-01

An accurate, comprehensive and up-to-date problem list can help clinicians provide patient-centered care. Unfortunately, problem lists created and maintained in electronic health records by providers tend to be inaccurate, duplicative and out of date. With advances in machine learning and natural language processing, it is possible to automatically generate a problem list from the data in the EHR and keep it current. In this paper, we describe an automated problem list generation method and report on insights from a pilot study of physicians' assessment of the generated problem lists compared to existing providers-curated problem lists in an institution's EHR system. The natural language processing and machine learning-based Watson 1 method models clinical thinking in identifying a patient's problem list using clinical notes and structured data. This pilot study assessed the Watson method and included 15 randomly selected, de-identified patient records from a large healthcare system that were each planned to be reviewed by at least two internal medicine physicians. The physicians created their own problem lists, and then evaluated the overall usefulness of their own problem lists (P), Watson generated problem lists (W), and the existing EHR problem lists (E) on a 10-point scale. The primary outcome was pairwise comparisons of P, W, and E. Six out of the 10 invited physicians completed 27 assessments of P, W, and E, and in process evaluated 732 Watson generated problems and 444 problems in the EHR system. As expected, physicians rated their own lists, P, highest. However, W was rated higher than E. Among 89% of assessments, Watson identified at least one important problem that physicians missed. Cognitive computing systems like this Watson system hold the potential for accurate, problem-list-centered summarization of patient records, potentially leading to increased efficiency, better clinical decision support, and improved quality of patient care. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

An adaptive random search for short term generation scheduling with network constraints.

PubMed

Marmolejo, J A; Velasco, Jonás; Selley, Héctor J

2017-01-01

This paper presents an adaptive random search approach to address a short term generation scheduling with network constraints, which determines the startup and shutdown schedules of thermal units over a given planning horizon. In this model, we consider the transmission network through capacity limits and line losses. The mathematical model is stated in the form of a Mixed Integer Non Linear Problem with binary variables. The proposed heuristic is a population-based method that generates a set of new potential solutions via a random search strategy. The random search is based on the Markov Chain Monte Carlo method. The main key of the proposed method is that the noise level of the random search is adaptively controlled in order to exploring and exploiting the entire search space. In order to improve the solutions, we consider coupling a local search into random search process. Several test systems are presented to evaluate the performance of the proposed heuristic. We use a commercial optimizer to compare the quality of the solutions provided by the proposed method. The solution of the proposed algorithm showed a significant reduction in computational effort with respect to the full-scale outer approximation commercial solver. Numerical results show the potential and robustness of our approach.

A novel artificial fish swarm algorithm for solving large-scale reliability-redundancy application problem.

PubMed

He, Qiang; Hu, Xiangtao; Ren, Hong; Zhang, Hongqi

2015-11-01

A novel artificial fish swarm algorithm (NAFSA) is proposed for solving large-scale reliability-redundancy allocation problem (RAP). In NAFSA, the social behaviors of fish swarm are classified in three ways: foraging behavior, reproductive behavior, and random behavior. The foraging behavior designs two position-updating strategies. And, the selection and crossover operators are applied to define the reproductive ability of an artificial fish. For the random behavior, which is essentially a mutation strategy, the basic cloud generator is used as the mutation operator. Finally, numerical results of four benchmark problems and a large-scale RAP are reported and compared. NAFSA shows good performance in terms of computational accuracy and computational efficiency for large scale RAP. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Monte Carlo, Probability, Algebra, and Pi.

ERIC Educational Resources Information Center

Hinders, Duane C.

1981-01-01

The uses of random number generators are illustrated in three ways: (1) the solution of a probability problem using a coin; (2) the solution of a system of simultaneous linear equations using a die; and (3) the approximation of pi using darts. (MP)

Datasets for supplier selection and order allocation with green criteria, all-unit quantity discounts and varying number of suppliers.

PubMed

Hamdan, Sadeque; Cheaitou, Ali

2017-08-01

This data article provides detailed optimization input and output datasets and optimization code for the published research work titled "Dynamic green supplier selection and order allocation with quantity discounts and varying supplier availability" (Hamdan and Cheaitou, 2017, In press) [1]. Researchers may use these datasets as a baseline for future comparison and extensive analysis of the green supplier selection and order allocation problem with all-unit quantity discount and varying number of suppliers. More particularly, the datasets presented in this article allow researchers to generate the exact optimization outputs obtained by the authors of Hamdan and Cheaitou (2017, In press) [1] using the provided optimization code and then to use them for comparison with the outputs of other techniques or methodologies such as heuristic approaches. Moreover, this article includes the randomly generated optimization input data and the related outputs that are used as input data for the statistical analysis presented in Hamdan and Cheaitou (2017 In press) [1] in which two different approaches for ranking potential suppliers are compared. This article also provides the time analysis data used in (Hamdan and Cheaitou (2017, In press) [1] to study the effect of the problem size on the computation time as well as an additional time analysis dataset. The input data for the time study are generated randomly, in which the problem size is changed, and then are used by the optimization problem to obtain the corresponding optimal outputs as well as the corresponding computation time.

On a numerical solving of random generated hexamatrix games

NASA Astrophysics Data System (ADS)

Orlov, Andrei; Strekalovskiy, Alexander

2016-10-01

In this paper, we develop a global search method for finding a Nash equilibrium in a hexamatrix game (polymatrix game of three players). The method, on the one hand, is based on the equivalence theorem of the problem of finding a Nash equilibrium in the game and a special mathematical optimization problem, and, on the other hand, on the usage of Global Search Theory for solving the latter problem. The efficiency of this approach is demonstrated by the results of computational testing.

Iterative repair for scheduling and rescheduling

NASA Technical Reports Server (NTRS)

Zweben, Monte; Davis, Eugene; Deale, Michael

1991-01-01

An iterative repair search method is described called constraint based simulated annealing. Simulated annealing is a hill climbing search technique capable of escaping local minima. The utility of the constraint based framework is shown by comparing search performance with and without the constraint framework on a suite of randomly generated problems. Results are also shown of applying the technique to the NASA Space Shuttle ground processing problem. These experiments show that the search methods scales to complex, real world problems and reflects interesting anytime behavior.

Neighborhood Deprivation during Early Childhood and Conduct Problems in Middle Childhood: Mediation by Aggressive Response Generation.

PubMed

Galán, Chardée A; Shaw, Daniel S; Dishion, Thomas J; Wilson, Melvin N

2017-07-01

The tremendous negative impact of conduct problems to the individual and society has provided the impetus for identifying risk factors, particularly in early childhood. Exposure to neighborhood deprivation in early childhood is a robust predictor of conduct problems in middle childhood. Efforts to identify and test mediating mechanisms by which neighborhood deprivation confers increased risk for behavioral problems have predominantly focused on peer relationships and community-level social processes. Less attention has been dedicated to potential cognitive mediators of this relationship, such as aggressive response generation, which refers to the tendency to generate aggressive solutions to ambiguous social stimuli with negative outcomes. In this study, we examined aggressive response generation, a salient component of social information processing, as a mediating process linking neighborhood deprivation to later conduct problems at age 10.5. Participants (N = 731; 50.5 % male) were drawn from a multisite randomized prevention trial that includes an ethnically diverse and low-income sample of male and female children and their primary caregivers followed prospectively from toddlerhood to middle childhood. Results indicated that aggressive response generation partially mediated the relationship between neighborhood deprivation and parent- and teacher-report of conduct problems, but not youth-report. Results suggest that the detrimental effects of neighborhood deprivation on youth adjustment may occur by altering the manner in which children process social information.

Neighborhood Deprivation during Early Childhood and Conduct Problems in Middle Childhood: Mediation by Aggressive Response Generation

PubMed Central

Shaw, Daniel S.; Dishion, Thomas J.; Wilson, Melvin N.

2018-01-01

The tremendous negative impact of conduct problems to the individual and society has provided the impetus for identifying risk factors, particularly in early childhood. Exposure to neighborhood deprivation in early childhood is a robust predictor of conduct problems in middle childhood. Efforts to identify and test mediating mechanisms by which neighborhood deprivation confers increased risk for behavioral problems have predominantly focused on peer relationships and community-level social processes. Less attention has been dedicated to potential cognitive mediators of this relationship, such as aggressive response generation, which refers to the tendency to generate aggressive solutions to ambiguous social stimuli with negative outcomes. In this study, we examined aggressive response generation, a salient component of social information processing, as a mediating process linking neighborhood deprivation to later conduct problems at age 10.5. Participants (N = 731; 50.5 % male) were drawn from a multisite randomized prevention trial that includes an ethnically diverse and low-income sample of male and female children and their primary caregivers followed prospectively from toddlerhood to middle childhood. Results indicated that aggressive response generation partially mediated the relationship between neighborhood deprivation and parent- and teacher-report of conduct problems, but not youth-report. Results suggest that the detrimental effects of neighborhood deprivation on youth adjustment may occur by altering the manner in which children process social information. PMID:27696324

Use of Genetic Algorithms to solve Inverse Problems in Relativistic Hydrodynamics

NASA Astrophysics Data System (ADS)

Guzmán, F. S.; González, J. A.

2018-04-01

We present the use of Genetic Algorithms (GAs) as a strategy to solve inverse problems associated with models of relativistic hydrodynamics. The signal we consider to emulate an observation is the density of a relativistic gas, measured at a point where a shock is traveling. This shock is generated numerically out of a Riemann problem with mildly relativistic conditions. The inverse problem we propose is the prediction of the initial conditions of density, velocity and pressure of the Riemann problem that gave origin to that signal. For this we use the density, velocity and pressure of the gas at both sides of the discontinuity, as the six genes of an organism, initially with random values within a tolerance. We then prepare an initial population of N of these organisms and evolve them using methods based on GAs. In the end, the organism with the best fitness of each generation is compared to the signal and the process ends when the set of initial conditions of the organisms of a later generation fit the Signal within a tolerance.

The benefits of computer-generated feedback for mathematics problem solving.

PubMed

Fyfe, Emily R; Rittle-Johnson, Bethany

2016-07-01

The goal of the current research was to better understand when and why feedback has positive effects on learning and to identify features of feedback that may improve its efficacy. In a randomized experiment, second-grade children received instruction on a correct problem-solving strategy and then solved a set of relevant problems. Children were assigned to receive no feedback, immediate feedback, or summative feedback from the computer. On a posttest the following day, feedback resulted in higher scores relative to no feedback for children who started with low prior knowledge. Immediate feedback was particularly effective, facilitating mastery of the material for children with both low and high prior knowledge. Results suggest that minimal computer-generated feedback can be a powerful form of guidance during problem solving. Copyright © 2016 Elsevier Inc. All rights reserved.

Modified truncated randomized singular value decomposition (MTRSVD) algorithms for large scale discrete ill-posed problems with general-form regularization

NASA Astrophysics Data System (ADS)

Jia, Zhongxiao; Yang, Yanfei

2018-05-01

In this paper, we propose new randomization based algorithms for large scale linear discrete ill-posed problems with general-form regularization: subject to , where L is a regularization matrix. Our algorithms are inspired by the modified truncated singular value decomposition (MTSVD) method, which suits only for small to medium scale problems, and randomized SVD (RSVD) algorithms that generate good low rank approximations to A. We use rank-k truncated randomized SVD (TRSVD) approximations to A by truncating the rank- RSVD approximations to A, where q is an oversampling parameter. The resulting algorithms are called modified TRSVD (MTRSVD) methods. At every step, we use the LSQR algorithm to solve the resulting inner least squares problem, which is proved to become better conditioned as k increases so that LSQR converges faster. We present sharp bounds for the approximation accuracy of the RSVDs and TRSVDs for severely, moderately and mildly ill-posed problems, and substantially improve a known basic bound for TRSVD approximations. We prove how to choose the stopping tolerance for LSQR in order to guarantee that the computed and exact best regularized solutions have the same accuracy. Numerical experiments illustrate that the best regularized solutions by MTRSVD are as accurate as the ones by the truncated generalized singular value decomposition (TGSVD) algorithm, and at least as accurate as those by some existing truncated randomized generalized singular value decomposition (TRGSVD) algorithms. This work was supported in part by the National Science Foundation of China (Nos. 11771249 and 11371219).

A Sustainable City Planning Algorithm Based on TLBO and Local Search

NASA Astrophysics Data System (ADS)

Zhang, Ke; Lin, Li; Huang, Xuanxuan; Liu, Yiming; Zhang, Yonggang

2017-09-01

Nowadays, how to design a city with more sustainable features has become a center problem in the field of social development, meanwhile it has provided a broad stage for the application of artificial intelligence theories and methods. Because the design of sustainable city is essentially a constraint optimization problem, the swarm intelligence algorithm of extensive research has become a natural candidate for solving the problem. TLBO (Teaching-Learning-Based Optimization) algorithm is a new swarm intelligence algorithm. Its inspiration comes from the “teaching” and “learning” behavior of teaching class in the life. The evolution of the population is realized by simulating the “teaching” of the teacher and the student “learning” from each other, with features of less parameters, efficient, simple thinking, easy to achieve and so on. It has been successfully applied to scheduling, planning, configuration and other fields, which achieved a good effect and has been paid more and more attention by artificial intelligence researchers. Based on the classical TLBO algorithm, we propose a TLBO_LS algorithm combined with local search. We design and implement the random generation algorithm and evaluation model of urban planning problem. The experiments on the small and medium-sized random generation problem showed that our proposed algorithm has obvious advantages over DE algorithm and classical TLBO algorithm in terms of convergence speed and solution quality.

Evaluation of a new neutron energy spectrum unfolding code based on an Adaptive Neuro-Fuzzy Inference System (ANFIS).

PubMed

Hosseini, Seyed Abolfazl; Esmaili Paeen Afrakoti, Iman

2018-01-17

The purpose of the present study was to reconstruct the energy spectrum of a poly-energetic neutron source using an algorithm developed based on an Adaptive Neuro-Fuzzy Inference System (ANFIS). ANFIS is a kind of artificial neural network based on the Takagi-Sugeno fuzzy inference system. The ANFIS algorithm uses the advantages of both fuzzy inference systems and artificial neural networks to improve the effectiveness of algorithms in various applications such as modeling, control and classification. The neutron pulse height distributions used as input data in the training procedure for the ANFIS algorithm were obtained from the simulations performed by MCNPX-ESUT computational code (MCNPX-Energy engineering of Sharif University of Technology). Taking into account the normalization condition of each energy spectrum, 4300 neutron energy spectra were generated randomly. (The value in each bin was generated randomly, and finally a normalization of each generated energy spectrum was performed). The randomly generated neutron energy spectra were considered as output data of the developed ANFIS computational code in the training step. To calculate the neutron energy spectrum using conventional methods, an inverse problem with an approximately singular response matrix (with the determinant of the matrix close to zero) should be solved. The solution of the inverse problem using the conventional methods unfold neutron energy spectrum with low accuracy. Application of the iterative algorithms in the solution of such a problem, or utilizing the intelligent algorithms (in which there is no need to solve the problem), is usually preferred for unfolding of the energy spectrum. Therefore, the main reason for development of intelligent algorithms like ANFIS for unfolding of neutron energy spectra is to avoid solving the inverse problem. In the present study, the unfolded neutron energy spectra of 252Cf and 241Am-9Be neutron sources using the developed computational code were found to have excellent agreement with the reference data. Also, the unfolded energy spectra of the neutron sources as obtained using ANFIS were more accurate than the results reported from calculations performed using artificial neural networks in previously published papers. © The Author(s) 2018. Published by Oxford University Press on behalf of The Japan Radiation Research Society and Japanese Society for Radiation Oncology.

Global Coverage Measurement Planning Strategies for Mobile Robots Equipped with a Remote Gas Sensor

PubMed Central

Arain, Muhammad Asif; Trincavelli, Marco; Cirillo, Marcello; Schaffernicht, Erik; Lilienthal, Achim J.

2015-01-01

The problem of gas detection is relevant to many real-world applications, such as leak detection in industrial settings and landfill monitoring. In this paper, we address the problem of gas detection in large areas with a mobile robotic platform equipped with a remote gas sensor. We propose an algorithm that leverages a novel method based on convex relaxation for quickly solving sensor placement problems, and for generating an efficient exploration plan for the robot. To demonstrate the applicability of our method to real-world environments, we performed a large number of experimental trials, both on randomly generated maps and on the map of a real environment. Our approach proves to be highly efficient in terms of computational requirements and to provide nearly-optimal solutions. PMID:25803707

Global coverage measurement planning strategies for mobile robots equipped with a remote gas sensor.

PubMed

Arain, Muhammad Asif; Trincavelli, Marco; Cirillo, Marcello; Schaffernicht, Erik; Lilienthal, Achim J

2015-03-20

The problem of gas detection is relevant to many real-world applications, such as leak detection in industrial settings and landfill monitoring. In this paper, we address the problem of gas detection in large areas with a mobile robotic platform equipped with a remote gas sensor. We propose an algorithm that leverages a novel method based on convex relaxation for quickly solving sensor placement problems, and for generating an efficient exploration plan for the robot. To demonstrate the applicability of our method to real-world environments, we performed a large number of experimental trials, both on randomly generated maps and on the map of a real environment. Our approach proves to be highly efficient in terms of computational requirements and to provide nearly-optimal solutions.

Typical performance of approximation algorithms for NP-hard problems

NASA Astrophysics Data System (ADS)

Takabe, Satoshi; Hukushima, Koji

2016-11-01

Typical performance of approximation algorithms is studied for randomized minimum vertex cover problems. A wide class of random graph ensembles characterized by an arbitrary degree distribution is discussed with the presentation of a theoretical framework. Herein, three approximation algorithms are examined: linear-programming relaxation, loopy-belief propagation, and the leaf-removal algorithm. The former two algorithms are analyzed using a statistical-mechanical technique, whereas the average-case analysis of the last one is conducted using the generating function method. These algorithms have a threshold in the typical performance with increasing average degree of the random graph, below which they find true optimal solutions with high probability. Our study reveals that there exist only three cases, determined by the order of the typical performance thresholds. In addition, we provide some conditions for classification of the graph ensembles and demonstrate explicitly some examples for the difference in thresholds.

Some practical problems in implementing randomization.

PubMed

Downs, Matt; Tucker, Kathryn; Christ-Schmidt, Heidi; Wittes, Janet

2010-06-01

While often theoretically simple, implementing randomization to treatment in a masked, but confirmable, fashion can prove difficult in practice. At least three categories of problems occur in randomization: (1) bad judgment in the choice of method, (2) design and programming errors in implementing the method, and (3) human error during the conduct of the trial. This article focuses on these latter two types of errors, dealing operationally with what can go wrong after trial designers have selected the allocation method. We offer several case studies and corresponding recommendations for lessening the frequency of problems in allocating treatment or for mitigating the consequences of errors. Recommendations include: (1) reviewing the randomization schedule before starting a trial, (2) being especially cautious of systems that use on-demand random number generators, (3) drafting unambiguous randomization specifications, (4) performing thorough testing before entering a randomization system into production, (5) maintaining a dataset that captures the values investigators used to randomize participants, thereby allowing the process of treatment allocation to be reproduced and verified, (6) resisting the urge to correct errors that occur in individual treatment assignments, (7) preventing inadvertent unmasking to treatment assignments in kit allocations, and (8) checking a sample of study drug kits to allow detection of errors in drug packaging and labeling. Although we performed a literature search of documented randomization errors, the examples that we provide and the resultant recommendations are based largely on our own experience in industry-sponsored clinical trials. We do not know how representative our experience is or how common errors of the type we have seen occur. Our experience underscores the importance of verifying the integrity of the treatment allocation process before and during a trial. Clinical Trials 2010; 7: 235-245. http://ctj.sagepub.com.

Random analysis of bearing capacity of square footing using the LAS procedure

NASA Astrophysics Data System (ADS)

Kawa, Marek; Puła, Wojciech; Suska, Michał

2016-09-01

In the present paper, a three-dimensional problem of bearing capacity of square footing on random soil medium is analyzed. The random fields of strength parameters c and φ are generated using LAS procedure (Local Average Subdivision, Fenton and Vanmarcke 1990). The procedure used is re-implemented by the authors in Mathematica environment in order to combine it with commercial program. Since the procedure is still tested the random filed has been assumed as one-dimensional: the strength properties of soil are random in vertical direction only. Individual realizations of bearing capacity boundary-problem with strength parameters of medium defined the above procedure are solved using FLAC3D Software. The analysis is performed for two qualitatively different cases, namely for the purely cohesive and cohesive-frictional soils. For the latter case the friction angle and cohesion have been assumed as independent random variables. For these two cases the random square footing bearing capacity results have been obtained for the range of fluctuation scales from 0.5 m to 10 m. Each time 1000 Monte Carlo realizations have been performed. The obtained results allow not only the mean and variance but also the probability density function to be estimated. An example of application of this function for reliability calculation has been presented in the final part of the paper.

Balancing a U-Shaped Assembly Line by Applying Nested Partitions Method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bhagwat, Nikhil V.

2005-01-01

In this study, we applied the Nested Partitions method to a U-line balancing problem and conducted experiments to evaluate the application. From the results, it is quite evident that the Nested Partitions method provided near optimal solutions (optimal in some cases). Besides, the execution time is quite short as compared to the Branch and Bound algorithm. However, for larger data sets, the algorithm took significantly longer times for execution. One of the reasons could be the way in which the random samples are generated. In the present study, a random sample is a solution in itself which requires assignment ofmore » tasks to various stations. The time taken to assign tasks to stations is directly proportional to the number of tasks. Thus, if the number of tasks increases, the time taken to generate random samples for the different regions also increases. The performance index for the Nested Partitions method in the present study was the number of stations in the random solutions (samples) generated. The total idle time for the samples can be used as another performance index. ULINO method is known to have used a combination of bounds to come up with good solutions. This approach of combining different performance indices can be used to evaluate the random samples and obtain even better solutions. Here, we used deterministic time values for the tasks. In industries where majority of tasks are performed manually, the stochastic version of the problem could be of vital importance. Experimenting with different objective functions (No. of stations was used in this study) could be of some significance to some industries where in the cost associated with creation of a new station is not the same. For such industries, the results obtained by using the present approach will not be of much value. Labor costs, task incompletion costs or a combination of those can be effectively used as alternate objective functions.« less

Random Bits Forest: a Strong Classifier/Regressor for Big Data

NASA Astrophysics Data System (ADS)

Wang, Yi; Li, Yi; Pu, Weilin; Wen, Kathryn; Shugart, Yin Yao; Xiong, Momiao; Jin, Li

2016-07-01

Efficiency, memory consumption, and robustness are common problems with many popular methods for data analysis. As a solution, we present Random Bits Forest (RBF), a classification and regression algorithm that integrates neural networks (for depth), boosting (for width), and random forests (for prediction accuracy). Through a gradient boosting scheme, it first generates and selects ~10,000 small, 3-layer random neural networks. These networks are then fed into a modified random forest algorithm to obtain predictions. Testing with datasets from the UCI (University of California, Irvine) Machine Learning Repository shows that RBF outperforms other popular methods in both accuracy and robustness, especially with large datasets (N > 1000). The algorithm also performed highly in testing with an independent data set, a real psoriasis genome-wide association study (GWAS).

Tuning Monotonic Basin Hopping: Improving the Efficiency of Stochastic Search as Applied to Low-Thrust Trajectory Optimization

NASA Technical Reports Server (NTRS)

Englander, Jacob; Englander, Arnold

2014-01-01

Trajectory optimization methods using MBH have become well developed during the past decade. An essential component of MBH is a controlled random search through the multi-dimensional space of possible solutions. Historically, the randomness has been generated by drawing RVs from a uniform probability distribution. Here, we investigate the generating the randomness by drawing the RVs from Cauchy and Pareto distributions, chosen because of their characteristic long tails. We demonstrate that using Cauchy distributions (as first suggested by Englander significantly improves MBH performance, and that Pareto distributions provide even greater improvements. Improved performance is defined in terms of efficiency and robustness, where efficiency is finding better solutions in less time, and robustness is efficiency that is undiminished by (a) the boundary conditions and internal constraints of the optimization problem being solved, and (b) by variations in the parameters of the probability distribution. Robustness is important for achieving performance improvements that are not problem specific. In this work we show that the performance improvements are the result of how these long-tailed distributions enable MBH to search the solution space faster and more thoroughly. In developing this explanation, we use the concepts of sub-diffusive, normally-diffusive, and super-diffusive RWs originally developed in the field of statistical physics.

Generation of Stationary Non-Gaussian Time Histories with a Specified Cross-spectral Density

DOE PAGES

Smallwood, David O.

1997-01-01

The paper reviews several methods for the generation of stationary realizations of sampled time histories with non-Gaussian distributions and introduces a new method which can be used to control the cross-spectral density matrix and the probability density functions (pdfs) of the multiple input problem. Discussed first are two methods for the specialized case of matching the auto (power) spectrum, the skewness, and kurtosis using generalized shot noise and using polynomial functions. It is then shown that the skewness and kurtosis can also be controlled by the phase of a complex frequency domain description of the random process. The general casemore » of matching a target probability density function using a zero memory nonlinear (ZMNL) function is then covered. Next methods for generating vectors of random variables with a specified covariance matrix for a class of spherically invariant random vectors (SIRV) are discussed. Finally the general case of matching the cross-spectral density matrix of a vector of inputs with non-Gaussian marginal distributions is presented.« less

Private randomness expansion with untrusted devices

NASA Astrophysics Data System (ADS)

Colbeck, Roger; Kent, Adrian

2011-03-01

Randomness is an important resource for many applications, from gambling to secure communication. However, guaranteeing that the output from a candidate random source could not have been predicted by an outside party is a challenging task, and many supposedly random sources used today provide no such guarantee. Quantum solutions to this problem exist, for example a device which internally sends a photon through a beamsplitter and observes on which side it emerges, but, presently, such solutions require the user to trust the internal workings of the device. Here, we seek to go beyond this limitation by asking whether randomness can be generated using untrusted devices—even ones created by an adversarial agent—while providing a guarantee that no outside party (including the agent) can predict it. Since this is easily seen to be impossible unless the user has an initially private random string, the task we investigate here is private randomness expansion. We introduce a protocol for private randomness expansion with untrusted devices which is designed to take as input an initially private random string and produce as output a longer private random string. We point out that private randomness expansion protocols are generally vulnerable to attacks that can render the initial string partially insecure, even though that string is used only inside a secure laboratory; our protocol is designed to remove this previously unconsidered vulnerability by privacy amplification. We also discuss extensions of our protocol designed to generate an arbitrarily long random string from a finite initially private random string. The security of these protocols against the most general attacks is left as an open question.

Recursive recovery of Markov transition probabilities from boundary value data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Patch, Sarah Kathyrn

1994-04-01

In an effort to mathematically describe the anisotropic diffusion of infrared radiation in biological tissue Gruenbaum posed an anisotropic diffusion boundary value problem in 1989. In order to accommodate anisotropy, he discretized the temporal as well as the spatial domain. The probabilistic interpretation of the diffusion equation is retained; radiation is assumed to travel according to a random walk (of sorts). In this random walk the probabilities with which photons change direction depend upon their previous as well as present location. The forward problem gives boundary value data as a function of the Markov transition probabilities. The inverse problem requiresmore » finding the transition probabilities from boundary value data. Problems in the plane are studied carefully in this thesis. Consistency conditions amongst the data are derived. These conditions have two effects: they prohibit inversion of the forward map but permit smoothing of noisy data. Next, a recursive algorithm which yields a family of solutions to the inverse problem is detailed. This algorithm takes advantage of all independent data and generates a system of highly nonlinear algebraic equations. Pluecker-Grassmann relations are instrumental in simplifying the equations. The algorithm is used to solve the 4 x 4 problem. Finally, the smallest nontrivial problem in three dimensions, the 2 x 2 x 2 problem, is solved.« less

Validating the performance of one-time decomposition for fMRI analysis using ICA with automatic target generation process.

PubMed

Yao, Shengnan; Zeng, Weiming; Wang, Nizhuan; Chen, Lei

2013-07-01

Independent component analysis (ICA) has been proven to be effective for functional magnetic resonance imaging (fMRI) data analysis. However, ICA decomposition requires to optimize the unmixing matrix iteratively whose initial values are generated randomly. Thus the randomness of the initialization leads to different ICA decomposition results. Therefore, just one-time decomposition for fMRI data analysis is not usually reliable. Under this circumstance, several methods about repeated decompositions with ICA (RDICA) were proposed to reveal the stability of ICA decomposition. Although utilizing RDICA has achieved satisfying results in validating the performance of ICA decomposition, RDICA cost much computing time. To mitigate the problem, in this paper, we propose a method, named ATGP-ICA, to do the fMRI data analysis. This method generates fixed initial values with automatic target generation process (ATGP) instead of being produced randomly. We performed experimental tests on both hybrid data and fMRI data to indicate the effectiveness of the new method and made a performance comparison of the traditional one-time decomposition with ICA (ODICA), RDICA and ATGP-ICA. The proposed method demonstrated that it not only could eliminate the randomness of ICA decomposition, but also could save much computing time compared to RDICA. Furthermore, the ROC (Receiver Operating Characteristic) power analysis also denoted the better signal reconstruction performance of ATGP-ICA than that of RDICA. Copyright © 2013 Elsevier Inc. All rights reserved.

Fuel management optimization using genetic algorithms and code independence

DOE Office of Scientific and Technical Information (OSTI.GOV)

DeChaine, M.D.; Feltus, M.A.

1994-12-31

Fuel management optimization is a hard problem for traditional optimization techniques. Loading pattern optimization is a large combinatorial problem without analytical derivative information. Therefore, methods designed for continuous functions, such as linear programming, do not always work well. Genetic algorithms (GAs) address these problems and, therefore, appear ideal for fuel management optimization. They do not require derivative information and work well with combinatorial. functions. The GAs are a stochastic method based on concepts from biological genetics. They take a group of candidate solutions, called the population, and use selection, crossover, and mutation operators to create the next generation of bettermore » solutions. The selection operator is a {open_quotes}survival-of-the-fittest{close_quotes} operation and chooses the solutions for the next generation. The crossover operator is analogous to biological mating, where children inherit a mixture of traits from their parents, and the mutation operator makes small random changes to the solutions.« less

Investigating Factorial Invariance of Latent Variables Across Populations When Manifest Variables Are Missing Completely

PubMed Central

Widaman, Keith F.; Grimm, Kevin J.; Early, Dawnté R.; Robins, Richard W.; Conger, Rand D.

2013-01-01

Difficulties arise in multiple-group evaluations of factorial invariance if particular manifest variables are missing completely in certain groups. Ad hoc analytic alternatives can be used in such situations (e.g., deleting manifest variables), but some common approaches, such as multiple imputation, are not viable. At least 3 solutions to this problem are viable: analyzing differing sets of variables across groups, using pattern mixture approaches, and a new method using random number generation. The latter solution, proposed in this article, is to generate pseudo-random normal deviates for all observations for manifest variables that are missing completely in a given sample and then to specify multiple-group models in a way that respects the random nature of these values. An empirical example is presented in detail comparing the 3 approaches. The proposed solution can enable quantitative comparisons at the latent variable level between groups using programs that require the same number of manifest variables in each group. PMID:24019738

Computer-Aided Assessment Questions in Engineering Mathematics Using "MapleTA"[R

ERIC Educational Resources Information Center

Jones, I. S.

2008-01-01

The use of "MapleTA"[R] in the assessment of engineering mathematics at Liverpool John Moores University (JMU) is discussed with particular reference to the design of questions. Key aspects in the formulation and coding of questions are considered. Problems associated with the submission of symbolic answers, the use of randomly generated numbers…

Dynamics of Quantum Adiabatic Evolution Algorithm for Number Partitioning

NASA Technical Reports Server (NTRS)

Smelyanskiy, V. N.; Toussaint, U. V.; Timucin, D. A.

2002-01-01

We have developed a general technique to study the dynamics of the quantum adiabatic evolution algorithm applied to random combinatorial optimization problems in the asymptotic limit of large problem size n. We use as an example the NP-complete Number Partitioning problem and map the algorithm dynamics to that of an auxiliary quantum spin glass system with the slowly varying Hamiltonian. We use a Green function method to obtain the adiabatic eigenstates and the minimum excitation gap. g min, = O(n 2(exp -n/2), corresponding to the exponential complexity of the algorithm for Number Partitioning. The key element of the analysis is the conditional energy distribution computed for the set of all spin configurations generated from a given (ancestor) configuration by simultaneous flipping of a fixed number of spins. For the problem in question this distribution is shown to depend on the ancestor spin configuration only via a certain parameter related to 'the energy of the configuration. As the result, the algorithm dynamics can be described in terms of one-dimensional quantum diffusion in the energy space. This effect provides a general limitation of a quantum adiabatic computation in random optimization problems. Analytical results are in agreement with the numerical simulation of the algorithm.

Dynamics of Quantum Adiabatic Evolution Algorithm for Number Partitioning

NASA Technical Reports Server (NTRS)

Smelyanskiy, Vadius; vonToussaint, Udo V.; Timucin, Dogan A.; Clancy, Daniel (Technical Monitor)

2002-01-01

We have developed a general technique to study the dynamics of the quantum adiabatic evolution algorithm applied to random combinatorial optimization problems in the asymptotic limit of large problem size n. We use as an example the NP-complete Number Partitioning problem and map the algorithm dynamics to that of an auxiliary quantum spin glass system with the slowly varying Hamiltonian. We use a Green function method to obtain the adiabatic eigenstates and the minimum exitation gap, gmin = O(n2(sup -n/2)), corresponding to the exponential complexity of the algorithm for Number Partitioning. The key element of the analysis is the conditional energy distribution computed for the set of all spin configurations generated from a given (ancestor) configuration by simultaneous flipping of a fixed number of spins. For the problem in question this distribution is shown to depend on the ancestor spin configuration only via a certain parameter related to the energy of the configuration. As the result, the algorithm dynamics can be described in terms of one-dimensional quantum diffusion in the energy space. This effect provides a general limitation of a quantum adiabatic computation in random optimization problems. Analytical results are in agreement with the numerical simulation of the algorithm.

Meta-RaPS Algorithm for the Aerial Refueling Scheduling Problem

NASA Technical Reports Server (NTRS)

Kaplan, Sezgin; Arin, Arif; Rabadi, Ghaith

2011-01-01

The Aerial Refueling Scheduling Problem (ARSP) can be defined as determining the refueling completion times for each fighter aircraft (job) on multiple tankers (machines). ARSP assumes that jobs have different release times and due dates, The total weighted tardiness is used to evaluate schedule's quality. Therefore, ARSP can be modeled as a parallel machine scheduling with release limes and due dates to minimize the total weighted tardiness. Since ARSP is NP-hard, it will be more appropriate to develop a pproimate or heuristic algorithm to obtain solutions in reasonable computation limes. In this paper, Meta-Raps-ATC algorithm is implemented to create high quality solutions. Meta-RaPS (Meta-heuristic for Randomized Priority Search) is a recent and promising meta heuristic that is applied by introducing randomness to a construction heuristic. The Apparent Tardiness Rule (ATC), which is a good rule for scheduling problems with tardiness objective, is used to construct initial solutions which are improved by an exchanging operation. Results are presented for generated instances.

Improved Results for Route Planning in Stochastic Transportation Networks

NASA Technical Reports Server (NTRS)

Boyan, Justin; Mitzenmacher, Michael

2000-01-01

In the bus network problem, the goal is to generate a plan for getting from point X to point Y within a city using buses in the smallest expected time. Because bus arrival times are not determined by a fixed schedule but instead may be random. the problem requires more than standard shortest path techniques. In recent work, Datar and Ranade provide algorithms in the case where bus arrivals are assumed to be independent and exponentially distributed. We offer solutions to two important generalizations of the problem, answering open questions posed by Datar and Ranade. First, we provide a polynomial time algorithm for a much wider class of arrival distributions, namely those with increasing failure rate. This class includes not only exponential distributions but also uniform, normal, and gamma distributions. Second, in the case where bus arrival times are independent and geometric discrete random variable,. we provide an algorithm for transportation networks of buses and trains, where trains run according to a fixed schedule.

Fuzzy Random λ-Mean SAD Portfolio Selection Problem: An Ant Colony Optimization Approach

NASA Astrophysics Data System (ADS)

Thakur, Gour Sundar Mitra; Bhattacharyya, Rupak; Mitra, Swapan Kumar

2010-10-01

To reach the investment goal, one has to select a combination of securities among different portfolios containing large number of securities. Only the past records of each security do not guarantee the future return. As there are many uncertain factors which directly or indirectly influence the stock market and there are also some newer stock markets which do not have enough historical data, experts' expectation and experience must be combined with the past records to generate an effective portfolio selection model. In this paper the return of security is assumed to be Fuzzy Random Variable Set (FRVS), where returns are set of random numbers which are in turn fuzzy numbers. A new λ-Mean Semi Absolute Deviation (λ-MSAD) portfolio selection model is developed. The subjective opinions of the investors to the rate of returns of each security are taken into consideration by introducing a pessimistic-optimistic parameter vector λ. λ-Mean Semi Absolute Deviation (λ-MSAD) model is preferred as it follows absolute deviation of the rate of returns of a portfolio instead of the variance as the measure of the risk. As this model can be reduced to Linear Programming Problem (LPP) it can be solved much faster than quadratic programming problems. Ant Colony Optimization (ACO) is used for solving the portfolio selection problem. ACO is a paradigm for designing meta-heuristic algorithms for combinatorial optimization problem. Data from BSE is used for illustration.

Genetic algorithms with memory- and elitism-based immigrants in dynamic environments.

PubMed

Yang, Shengxiang

2008-01-01

In recent years the genetic algorithm community has shown a growing interest in studying dynamic optimization problems. Several approaches have been devised. The random immigrants and memory schemes are two major ones. The random immigrants scheme addresses dynamic environments by maintaining the population diversity while the memory scheme aims to adapt genetic algorithms quickly to new environments by reusing historical information. This paper investigates a hybrid memory and random immigrants scheme, called memory-based immigrants, and a hybrid elitism and random immigrants scheme, called elitism-based immigrants, for genetic algorithms in dynamic environments. In these schemes, the best individual from memory or the elite from the previous generation is retrieved as the base to create immigrants into the population by mutation. This way, not only can diversity be maintained but it is done more efficiently to adapt genetic algorithms to the current environment. Based on a series of systematically constructed dynamic problems, experiments are carried out to compare genetic algorithms with the memory-based and elitism-based immigrants schemes against genetic algorithms with traditional memory and random immigrants schemes and a hybrid memory and multi-population scheme. The sensitivity analysis regarding some key parameters is also carried out. Experimental results show that the memory-based and elitism-based immigrants schemes efficiently improve the performance of genetic algorithms in dynamic environments.

Combined rule extraction and feature elimination in supervised classification.

PubMed

Liu, Sheng; Patel, Ronak Y; Daga, Pankaj R; Liu, Haining; Fu, Gang; Doerksen, Robert J; Chen, Yixin; Wilkins, Dawn E

2012-09-01

There are a vast number of biology related research problems involving a combination of multiple sources of data to achieve a better understanding of the underlying problems. It is important to select and interpret the most important information from these sources. Thus it will be beneficial to have a good algorithm to simultaneously extract rules and select features for better interpretation of the predictive model. We propose an efficient algorithm, Combined Rule Extraction and Feature Elimination (CRF), based on 1-norm regularized random forests. CRF simultaneously extracts a small number of rules generated by random forests and selects important features. We applied CRF to several drug activity prediction and microarray data sets. CRF is capable of producing performance comparable with state-of-the-art prediction algorithms using a small number of decision rules. Some of the decision rules are biologically significant.

Statistical analysis of mesoscale rainfall: Dependence of a random cascade generator on large-scale forcing

NASA Technical Reports Server (NTRS)

Over, Thomas, M.; Gupta, Vijay K.

1994-01-01

Under the theory of independent and identically distributed random cascades, the probability distribution of the cascade generator determines the spatial and the ensemble properties of spatial rainfall. Three sets of radar-derived rainfall data in space and time are analyzed to estimate the probability distribution of the generator. A detailed comparison between instantaneous scans of spatial rainfall and simulated cascades using the scaling properties of the marginal moments is carried out. This comparison highlights important similarities and differences between the data and the random cascade theory. Differences are quantified and measured for the three datasets. Evidence is presented to show that the scaling properties of the rainfall can be captured to the first order by a random cascade with a single parameter. The dependence of this parameter on forcing by the large-scale meteorological conditions, as measured by the large-scale spatial average rain rate, is investigated for these three datasets. The data show that this dependence can be captured by a one-to-one function. Since the large-scale average rain rate can be diagnosed from the large-scale dynamics, this relationship demonstrates an important linkage between the large-scale atmospheric dynamics and the statistical cascade theory of mesoscale rainfall. Potential application of this research to parameterization of runoff from the land surface and regional flood frequency analysis is briefly discussed, and open problems for further research are presented.

Essential energy space random walk via energy space metadynamics method to accelerate molecular dynamics simulations

NASA Astrophysics Data System (ADS)

Li, Hongzhi; Min, Donghong; Liu, Yusong; Yang, Wei

2007-09-01

To overcome the possible pseudoergodicity problem, molecular dynamic simulation can be accelerated via the realization of an energy space random walk. To achieve this, a biased free energy function (BFEF) needs to be priori obtained. Although the quality of BFEF is essential for sampling efficiency, its generation is usually tedious and nontrivial. In this work, we present an energy space metadynamics algorithm to efficiently and robustly obtain BFEFs. Moreover, in order to deal with the associated diffusion sampling problem caused by the random walk in the total energy space, the idea in the original umbrella sampling method is generalized to be the random walk in the essential energy space, which only includes the energy terms determining the conformation of a region of interest. This essential energy space generalization allows the realization of efficient localized enhanced sampling and also offers the possibility of further sampling efficiency improvement when high frequency energy terms irrelevant to the target events are free of activation. The energy space metadynamics method and its generalization in the essential energy space for the molecular dynamics acceleration are demonstrated in the simulation of a pentanelike system, the blocked alanine dipeptide model, and the leucine model.

Microgrid energy dispatching for industrial zones with renewable generations and electric vehicles via stochastic optimization and learning

NASA Astrophysics Data System (ADS)

Zhang, Kai; Li, Jingzhi; He, Zhubin; Yan, Wanfeng

2018-07-01

In this paper, a stochastic optimization framework is proposed to address the microgrid energy dispatching problem with random renewable generation and vehicle activity pattern, which is closer to the practical applications. The patterns of energy generation, consumption and storage availability are all random and unknown at the beginning, and the microgrid controller design (MCD) is formulated as a Markov decision process (MDP). Hence, an online learning-based control algorithm is proposed for the microgrid, which could adapt the control policy with increasing knowledge of the system dynamics and converges to the optimal algorithm. We adopt the linear approximation idea to decompose the original value functions as the summation of each per-battery value function. As a consequence, the computational complexity is significantly reduced from exponential growth to linear growth with respect to the size of battery states. Monte Carlo simulation of different scenarios demonstrates the effectiveness and efficiency of our algorithm.

A connectionist model for diagnostic problem solving

NASA Technical Reports Server (NTRS)

Peng, Yun; Reggia, James A.

1989-01-01

A competition-based connectionist model for solving diagnostic problems is described. The problems considered are computationally difficult in that (1) multiple disorders may occur simultaneously and (2) a global optimum in the space exponential to the total number of possible disorders is sought as a solution. The diagnostic problem is treated as a nonlinear optimization problem, and global optimization criteria are decomposed into local criteria governing node activation updating in the connectionist model. Nodes representing disorders compete with each other to account for each individual manifestation, yet complement each other to account for all manifestations through parallel node interactions. When equilibrium is reached, the network settles into a locally optimal state. Three randomly generated examples of diagnostic problems, each of which has 1024 cases, were tested, and the decomposition plus competition plus resettling approach yielded very high accuracy.

A multiobjective hybrid genetic algorithm for the capacitated multipoint network design problem.

PubMed

Lo, C C; Chang, W H

2000-01-01

The capacitated multipoint network design problem (CMNDP) is NP-complete. In this paper, a hybrid genetic algorithm for CMNDP is proposed. The multiobjective hybrid genetic algorithm (MOHGA) differs from other genetic algorithms (GAs) mainly in its selection procedure. The concept of subpopulation is used in MOHGA. Four subpopulations are generated according to the elitism reservation strategy, the shifting Prufer vector, the stochastic universal sampling, and the complete random method, respectively. Mixing these four subpopulations produces the next generation population. The MOHGA can effectively search the feasible solution space due to population diversity. The MOHGA has been applied to CMNDP. By examining computational and analytical results, we notice that the MOHGA can find most nondominated solutions and is much more effective and efficient than other multiobjective GAs.

Developing a Learning Algorithm-Generated Empirical Relaxer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mitchell, Wayne; Kallman, Josh; Toreja, Allen

2016-03-30

One of the main difficulties when running Arbitrary Lagrangian-Eulerian (ALE) simulations is determining how much to relax the mesh during the Eulerian step. This determination is currently made by the user on a simulation-by-simulation basis. We present a Learning Algorithm-Generated Empirical Relaxer (LAGER) which uses a regressive random forest algorithm to automate this decision process. We also demonstrate that LAGER successfully relaxes a variety of test problems, maintains simulation accuracy, and has the potential to significantly decrease both the person-hours and computational hours needed to run a successful ALE simulation.

A new feedback image encryption scheme based on perturbation with dynamical compound chaotic sequence cipher generator

NASA Astrophysics Data System (ADS)

Tong, Xiaojun; Cui, Minggen; Wang, Zhu

2009-07-01

The design of the new compound two-dimensional chaotic function is presented by exploiting two one-dimensional chaotic functions which switch randomly, and the design is used as a chaotic sequence generator which is proved by Devaney's definition proof of chaos. The properties of compound chaotic functions are also proved rigorously. In order to improve the robustness against difference cryptanalysis and produce avalanche effect, a new feedback image encryption scheme is proposed using the new compound chaos by selecting one of the two one-dimensional chaotic functions randomly and a new image pixels method of permutation and substitution is designed in detail by array row and column random controlling based on the compound chaos. The results from entropy analysis, difference analysis, statistical analysis, sequence randomness analysis, cipher sensitivity analysis depending on key and plaintext have proven that the compound chaotic sequence cipher can resist cryptanalytic, statistical and brute-force attacks, and especially it accelerates encryption speed, and achieves higher level of security. By the dynamical compound chaos and perturbation technology, the paper solves the problem of computer low precision of one-dimensional chaotic function.

Measuring, Understanding, and Responding to Covert Social Networks: Passive and Active Tomography

DTIC Science & Technology

2017-11-29

Methods for generating a random sample of networks with desired properties are important tools for the analysis of social , biological, and information...on Theoretical Foundations for Statistical Network Analysis at the Isaac Newton Institute for Mathematical Sciences at Cambridge U. (organized by...Approach SOCIAL SCIENCES STATISTICS EECS Problems span three disciplines Scientific focus is needed at the interfaces

Nonlinear Estimation of Discrete-Time Signals Under Random Observation Delay

DOE Office of Scientific and Technical Information (OSTI.GOV)

Caballero-Aguila, R.; Jimenez-Lopez, J. D.; Hermoso-Carazo, A.

2008-11-06

This paper presents an approximation to the nonlinear least-squares estimation problem of discrete-time stochastic signals using nonlinear observations with additive white noise which can be randomly delayed by one sampling time. The observation delay is modelled by a sequence of independent Bernoulli random variables whose values, zero or one, indicate that the real observation arrives on time or it is delayed and, hence, the available measurement to estimate the signal is not up-to-date. Assuming that the state-space model generating the signal is unknown and only the covariance functions of the processes involved in the observation equation are ready for use,more » a filtering algorithm based on linear approximations of the real observations is proposed.« less

Dairy-cattle health in Gyeongnam, Korea.

PubMed

Jong, S K; Gong, S K; Chung, H K; Dae, S H

2001-12-03

An animal-health monitoring system in the Gyeongnam area was started in 1997 to develop statistically valid data for use in estimating disease frequencies in dairy cattle, and the associated costs. The objectives of this study were to: (1) describe what was done to implement and maintain the system in Gyeongnam; (2) present selected disease frequencies; (3) discuss the epidemiological consideration of what was done and implications for results obtained. Veterinary medical officers (VMOs), comprising professors and graduate students from Gyeongsang National University, faculty of Gyeongnam Livestock Promotion Institute and clinic veterinarians, served as data collectors. After training on current disease and management problems of dairy cattle, interview techniques, sampling methods and data-collection instruments, the VMOs participated in selection of the sample herds and data gathering. Forty (n=40) of 167 dairy herds were selected randomly using a computer-generated list of random numbers and the VMOs visited farms once in a month for 12 months to collect data about management, disease, inventory, production, preventive treatment, financial and other relevant data. Strict data-quality control devices were used. Specific feed-back was developed for the producers and data collectors. The six disorders found most frequently in cows (from the highest to the lowest) were breeding problems, clinical mastitis, birth problems, gastrointestinal problems, metabolic problems and lameness. In young stock, respiratory, multiple system, breeding and gastrointestinal problems were predominant, whereas in calves, gastrointestinal, respiratory and integumental problems predominated.

Microstructural characterization of random packings of cubic particles

PubMed Central

Malmir, Hessam; Sahimi, Muhammad; Tabar, M. Reza Rahimi

2016-01-01

Understanding the properties of random packings of solid objects is of critical importance to a wide variety of fundamental scientific and practical problems. The great majority of the previous works focused, however, on packings of spherical and sphere-like particles. We report the first detailed simulation and characterization of packings of non-overlapping cubic particles. Such packings arise in a variety of problems, ranging from biological materials, to colloids and fabrication of porous scaffolds using salt powders. In addition, packing of cubic salt crystals arise in various problems involving preservation of pavements, paintings, and historical monuments, mineral-fluid interactions, CO2 sequestration in rock, and intrusion of groundwater aquifers by saline water. Not much is known, however, about the structure and statistical descriptors of such packings. We have developed a version of the random sequential addition algorithm to generate such packings, and have computed a variety of microstructural descriptors, including the radial distribution function, two-point probability function, orientational correlation function, specific surface, and mean chord length, and have studied the effect of finite system size and porosity on such characteristics. The results indicate the existence of both spatial and orientational long-range order in the packing, which is more distinctive for higher packing densities. The maximum packing fraction is about 0.57. PMID:27725736

Microstructural characterization of random packings of cubic particles

DOE PAGES

Malmir, Hessam; Sahimi, Muhammad; Tabar, M. Reza Rahimi

2016-10-11

Understanding the properties of random packings of solid objects is of critical importance to a wide variety of fundamental scientific and practical problems. The great majority of the previous works focused, however, on packings of spherical and sphere-like particles. We report the first detailed simulation and characterization of packings of non-overlapping cubic particles. Such packings arise in a variety of problems, ranging from biological materials, to colloids and fabrication of porous scaffolds using salt powders. In addition, packing of cubic salt crystals arise in various problems involving preservation of pavements, paintings, and historical monuments, mineral-fluid interactions, CO 2 sequestration inmore » rock, and intrusion of groundwater aquifers by saline water. Not much is known, however, about the structure and statistical descriptors of such packings. We have developed a version of the random sequential addition algorithm to generate such packings, and have computed a variety of microstructural descriptors, including the radial distribution function, two-point probability function, orientational correlation function, specific surface, and mean chord length, and have studied the effect of finite system size and porosity on such characteristics. Here, the results indicate the existence of both spatial and orientational long-range order in the packing, which is more distinctive for higher packing densities.« less

Analysis of the Space Propulsion System Problem Using RAVEN

DOE Office of Scientific and Technical Information (OSTI.GOV)

diego mandelli; curtis smith; cristian rabiti

This paper presents the solution of the space propulsion problem using a PRA code currently under development at Idaho National Laboratory (INL). RAVEN (Reactor Analysis and Virtual control ENviroment) is a multi-purpose Probabilistic Risk Assessment (PRA) software framework that allows dispatching different functionalities. It is designed to derive and actuate the control logic required to simulate the plant control system and operator actions (guided procedures) and to perform both Monte- Carlo sampling of random distributed events and Event Tree based analysis. In order to facilitate the input/output handling, a Graphical User Interface (GUI) and a post-processing data-mining module are available.more » RAVEN allows also to interface with several numerical codes such as RELAP5 and RELAP-7 and ad-hoc system simulators. For the space propulsion system problem, an ad-hoc simulator has been developed and written in python language and then interfaced to RAVEN. Such simulator fully models both deterministic (e.g., system dynamics and interactions between system components) and stochastic behaviors (i.e., failures of components/systems such as distribution lines and thrusters). Stochastic analysis is performed using random sampling based methodologies (i.e., Monte-Carlo). Such analysis is accomplished to determine both the reliability of the space propulsion system and to propagate the uncertainties associated to a specific set of parameters. As also indicated in the scope of the benchmark problem, the results generated by the stochastic analysis are used to generate risk-informed insights such as conditions under witch different strategy can be followed.« less

The Common Patterns of Nature

PubMed Central

Frank, Steven A.

2010-01-01

We typically observe large-scale outcomes that arise from the interactions of many hidden, small-scale processes. Examples include age of disease onset, rates of amino acid substitutions, and composition of ecological communities. The macroscopic patterns in each problem often vary around a characteristic shape that can be generated by neutral processes. A neutral generative model assumes that each microscopic process follows unbiased or random stochastic fluctuations: random connections of network nodes; amino acid substitutions with no effect on fitness; species that arise or disappear from communities randomly. These neutral generative models often match common patterns of nature. In this paper, I present the theoretical background by which we can understand why these neutral generative models are so successful. I show where the classic patterns come from, such as the Poisson pattern, the normal or Gaussian pattern, and many others. Each classic pattern was often discovered by a simple neutral generative model. The neutral patterns share a special characteristic: they describe the patterns of nature that follow from simple constraints on information. For example, any aggregation of processes that preserves information only about the mean and variance attracts to the Gaussian pattern; any aggregation that preserves information only about the mean attracts to the exponential pattern; any aggregation that preserves information only about the geometric mean attracts to the power law pattern. I present a simple and consistent informational framework of the common patterns of nature based on the method of maximum entropy. This framework shows that each neutral generative model is a special case that helps to discover a particular set of informational constraints; those informational constraints define a much wider domain of non-neutral generative processes that attract to the same neutral pattern. PMID:19538344

Averaging of random walks and shift-invariant measures on a Hilbert space

NASA Astrophysics Data System (ADS)

Sakbaev, V. Zh.

2017-06-01

We study random walks in a Hilbert space H and representations using them of solutions of the Cauchy problem for differential equations whose initial conditions are numerical functions on H. We construct a finitely additive analogue of the Lebesgue measure: a nonnegative finitely additive measure λ that is defined on a minimal subset ring of an infinite-dimensional Hilbert space H containing all infinite-dimensional rectangles with absolutely converging products of the side lengths and is invariant under shifts and rotations in H. We define the Hilbert space H of equivalence classes of complex-valued functions on H that are square integrable with respect to a shift-invariant measure λ. Using averaging of the shift operator in H over random vectors in H with a distribution given by a one-parameter semigroup (with respect to convolution) of Gaussian measures on H, we define a one-parameter semigroup of contracting self-adjoint transformations on H, whose generator is called the diffusion operator. We obtain a representation of solutions of the Cauchy problem for the Schrödinger equation whose Hamiltonian is the diffusion operator.

GASPRNG: GPU accelerated scalable parallel random number generator library

NASA Astrophysics Data System (ADS)

Gao, Shuang; Peterson, Gregory D.

2013-04-01

Graphics processors represent a promising technology for accelerating computational science applications. Many computational science applications require fast and scalable random number generation with good statistical properties, so they use the Scalable Parallel Random Number Generators library (SPRNG). We present the GPU Accelerated SPRNG library (GASPRNG) to accelerate SPRNG in GPU-based high performance computing systems. GASPRNG includes code for a host CPU and CUDA code for execution on NVIDIA graphics processing units (GPUs) along with a programming interface to support various usage models for pseudorandom numbers and computational science applications executing on the CPU, GPU, or both. This paper describes the implementation approach used to produce high performance and also describes how to use the programming interface. The programming interface allows a user to be able to use GASPRNG the same way as SPRNG on traditional serial or parallel computers as well as to develop tightly coupled programs executing primarily on the GPU. We also describe how to install GASPRNG and use it. To help illustrate linking with GASPRNG, various demonstration codes are included for the different usage models. GASPRNG on a single GPU shows up to 280x speedup over SPRNG on a single CPU core and is able to scale for larger systems in the same manner as SPRNG. Because GASPRNG generates identical streams of pseudorandom numbers as SPRNG, users can be confident about the quality of GASPRNG for scalable computational science applications. Catalogue identifier: AEOI_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEOI_v1_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: UTK license. No. of lines in distributed program, including test data, etc.: 167900 No. of bytes in distributed program, including test data, etc.: 1422058 Distribution format: tar.gz Programming language: C and CUDA. Computer: Any PC or workstation with NVIDIA GPU (Tested on Fermi GTX480, Tesla C1060, Tesla M2070). Operating system: Linux with CUDA version 4.0 or later. Should also run on MacOS, Windows, or UNIX. Has the code been vectorized or parallelized?: Yes. Parallelized using MPI directives. RAM: 512 MB˜ 732 MB (main memory on host CPU, depending on the data type of random numbers.) / 512 MB (GPU global memory) Classification: 4.13, 6.5. Nature of problem: Many computational science applications are able to consume large numbers of random numbers. For example, Monte Carlo simulations are able to consume limitless random numbers for the computation as long as resources for the computing are supported. Moreover, parallel computational science applications require independent streams of random numbers to attain statistically significant results. The SPRNG library provides this capability, but at a significant computational cost. The GASPRNG library presented here accelerates the generators of independent streams of random numbers using graphical processing units (GPUs). Solution method: Multiple copies of random number generators in GPUs allow a computational science application to consume large numbers of random numbers from independent, parallel streams. GASPRNG is a random number generators library to allow a computational science application to employ multiple copies of random number generators to boost performance. Users can interface GASPRNG with software code executing on microprocessors and/or GPUs. Running time: The tests provided take a few minutes to run.

CrowdPhase: crowdsourcing the phase problem

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jorda, Julien; Sawaya, Michael R.; Yeates, Todd O., E-mail: yeates@mbi.ucla.edu

The idea of attacking the phase problem by crowdsourcing is introduced. Using an interactive, multi-player, web-based system, participants work simultaneously to select phase sets that correspond to better electron-density maps in order to solve low-resolution phasing problems. The human mind innately excels at some complex tasks that are difficult to solve using computers alone. For complex problems amenable to parallelization, strategies can be developed to exploit human intelligence in a collective form: such approaches are sometimes referred to as ‘crowdsourcing’. Here, a first attempt at a crowdsourced approach for low-resolution ab initio phasing in macromolecular crystallography is proposed. A collaborativemore » online game named CrowdPhase was designed, which relies on a human-powered genetic algorithm, where players control the selection mechanism during the evolutionary process. The algorithm starts from a population of ‘individuals’, each with a random genetic makeup, in this case a map prepared from a random set of phases, and tries to cause the population to evolve towards individuals with better phases based on Darwinian survival of the fittest. Players apply their pattern-recognition capabilities to evaluate the electron-density maps generated from these sets of phases and to select the fittest individuals. A user-friendly interface, a training stage and a competitive scoring system foster a network of well trained players who can guide the genetic algorithm towards better solutions from generation to generation via gameplay. CrowdPhase was applied to two synthetic low-resolution phasing puzzles and it was shown that players could successfully obtain phase sets in the 30° phase error range and corresponding molecular envelopes showing agreement with the low-resolution models. The successful preliminary studies suggest that with further development the crowdsourcing approach could fill a gap in current crystallographic methods by making it possible to extract meaningful information in cases where limited resolution might otherwise prevent initial phasing.« less

Can Targeted Intervention Mitigate Early Emotional and Behavioral Problems?: Generating Robust Evidence within Randomized Controlled Trials

PubMed Central

Doyle, Orla; McGlanaghy, Edel; O’Farrelly, Christine; Tremblay, Richard E.

2016-01-01

This study examined the impact of a targeted Irish early intervention program on children’s emotional and behavioral development using multiple methods to test the robustness of the results. Data on 164 Preparing for Life participants who were randomly assigned into an intervention group, involving home visits from pregnancy onwards, or a control group, was used to test the impact of the intervention on Child Behavior Checklist scores at 24-months. Using inverse probability weighting to account for differential attrition, permutation testing to address small sample size, and quantile regression to characterize the distributional impact of the intervention, we found that the few treatment effects were largely concentrated among boys most at risk of developing emotional and behavioral problems. The average treatment effect identified a 13% reduction in the likelihood of falling into the borderline clinical threshold for Total Problems. The interaction and subgroup analysis found that this main effect was driven by boys. The distributional analysis identified a 10-point reduction in the Externalizing Problems score for boys at the 90th percentile. No effects were observed for girls or for the continuous measures of Total, Internalizing, and Externalizing problems. These findings suggest that the impact of this prenatally commencing home visiting program may be limited to boys experiencing the most difficulties. Further adoption of the statistical methods applied here may help to improve the internal validity of randomized controlled trials and contribute to the field of evaluation science more generally. Trial Registration: ISRCTN Registry ISRCTN04631728 PMID:27253184

Recognition of multiple imbalanced cancer types based on DNA microarray data using ensemble classifiers.

PubMed

Yu, Hualong; Hong, Shufang; Yang, Xibei; Ni, Jun; Dan, Yuanyuan; Qin, Bin

2013-01-01

DNA microarray technology can measure the activities of tens of thousands of genes simultaneously, which provides an efficient way to diagnose cancer at the molecular level. Although this strategy has attracted significant research attention, most studies neglect an important problem, namely, that most DNA microarray datasets are skewed, which causes traditional learning algorithms to produce inaccurate results. Some studies have considered this problem, yet they merely focus on binary-class problem. In this paper, we dealt with multiclass imbalanced classification problem, as encountered in cancer DNA microarray, by using ensemble learning. We utilized one-against-all coding strategy to transform multiclass to multiple binary classes, each of them carrying out feature subspace, which is an evolving version of random subspace that generates multiple diverse training subsets. Next, we introduced one of two different correction technologies, namely, decision threshold adjustment or random undersampling, into each training subset to alleviate the damage of class imbalance. Specifically, support vector machine was used as base classifier, and a novel voting rule called counter voting was presented for making a final decision. Experimental results on eight skewed multiclass cancer microarray datasets indicate that unlike many traditional classification approaches, our methods are insensitive to class imbalance.

Unified heuristics to solve routing problem of reverse logistics in sustainable supply chain

NASA Astrophysics Data System (ADS)

Anbuudayasankar, S. P.; Ganesh, K.; Lenny Koh, S. C.; Mohandas, K.

2010-03-01

A reverse logistics problem, motivated by many real-life applications, is examined where bottles/cans in which products are delivered from a processing depot to customers in one period are available for return to the depot in the following period. The picked-up bottles/cans need to be adjusted in the place of delivery load. This problem is termed as simultaneous delivery and pick-up problem with constrained capacity (SDPC). We develop three unified heuristics based on extended branch and bound heuristic, genetic algorithm and simulated annealing to solve SDPC. These heuristics are also designed to solve standard travelling salesman problem (TSP) and TSP with simultaneous delivery and pick-up (TSDP). We tested the heuristics on standard, derived and randomly generated datasets of TSP, TSDP and SDPC and obtained satisfying results with high convergence in reasonable time.

Generating Adaptive Behaviour within a Memory-Prediction Framework

PubMed Central

Rawlinson, David; Kowadlo, Gideon

2012-01-01

The Memory-Prediction Framework (MPF) and its Hierarchical-Temporal Memory implementation (HTM) have been widely applied to unsupervised learning problems, for both classification and prediction. To date, there has been no attempt to incorporate MPF/HTM in reinforcement learning or other adaptive systems; that is, to use knowledge embodied within the hierarchy to control a system, or to generate behaviour for an agent. This problem is interesting because the human neocortex is believed to play a vital role in the generation of behaviour, and the MPF is a model of the human neocortex. We propose some simple and biologically-plausible enhancements to the Memory-Prediction Framework. These cause it to explore and interact with an external world, while trying to maximize a continuous, time-varying reward function. All behaviour is generated and controlled within the MPF hierarchy. The hierarchy develops from a random initial configuration by interaction with the world and reinforcement learning only. Among other demonstrations, we show that a 2-node hierarchy can learn to successfully play “rocks, paper, scissors” against a predictable opponent. PMID:22272231

Detecting targets hidden in random forests

NASA Astrophysics Data System (ADS)

Kouritzin, Michael A.; Luo, Dandan; Newton, Fraser; Wu, Biao

2009-05-01

Military tanks, cargo or troop carriers, missile carriers or rocket launchers often hide themselves from detection in the forests. This plagues the detection problem of locating these hidden targets. An electro-optic camera mounted on a surveillance aircraft or unmanned aerial vehicle is used to capture the images of the forests with possible hidden targets, e.g., rocket launchers. We consider random forests of longitudinal and latitudinal correlations. Specifically, foliage coverage is encoded with a binary representation (i.e., foliage or no foliage), and is correlated in adjacent regions. We address the detection problem of camouflaged targets hidden in random forests by building memory into the observations. In particular, we propose an efficient algorithm to generate random forests, ground, and camouflage of hidden targets with two dimensional correlations. The observations are a sequence of snapshots consisting of foliage-obscured ground or target. Theoretically, detection is possible because there are subtle differences in the correlations of the ground and camouflage of the rocket launcher. However, these differences are well beyond human perception. To detect the presence of hidden targets automatically, we develop a Markov representation for these sequences and modify the classical filtering equations to allow the Markov chain observation. Particle filters are used to estimate the position of the targets in combination with a novel random weighting technique. Furthermore, we give positive proof-of-concept simulations.

The random fractional matching problem

NASA Astrophysics Data System (ADS)

Lucibello, Carlo; Malatesta, Enrico M.; Parisi, Giorgio; Sicuro, Gabriele

2018-05-01

We consider two formulations of the random-link fractional matching problem, a relaxed version of the more standard random-link (integer) matching problem. In one formulation, we allow each node to be linked to itself in the optimal matching configuration. In the other one, on the contrary, such a link is forbidden. Both problems have the same asymptotic average optimal cost of the random-link matching problem on the complete graph. Using a replica approach and previous results of Wästlund (2010 Acta Mathematica 204 91–150), we analytically derive the finite-size corrections to the asymptotic optimal cost. We compare our results with numerical simulations and we discuss the main differences between random-link fractional matching problems and the random-link matching problem.

Fast optimization algorithms and the cosmological constant

NASA Astrophysics Data System (ADS)

Bao, Ning; Bousso, Raphael; Jordan, Stephen; Lackey, Brad

2017-11-01

Denef and Douglas have observed that in certain landscape models the problem of finding small values of the cosmological constant is a large instance of a problem that is hard for the complexity class NP (Nondeterministic Polynomial-time). The number of elementary operations (quantum gates) needed to solve this problem by brute force search exceeds the estimated computational capacity of the observable Universe. Here we describe a way out of this puzzling circumstance: despite being NP-hard, the problem of finding a small cosmological constant can be attacked by more sophisticated algorithms whose performance vastly exceeds brute force search. In fact, in some parameter regimes the average-case complexity is polynomial. We demonstrate this by explicitly finding a cosmological constant of order 10-120 in a randomly generated 1 09-dimensional Arkani-Hamed-Dimopoulos-Kachru landscape.

Distributed Noise Generation for Density Estimation Based Clustering without Trusted Third Party

NASA Astrophysics Data System (ADS)

Su, Chunhua; Bao, Feng; Zhou, Jianying; Takagi, Tsuyoshi; Sakurai, Kouichi

The rapid growth of the Internet provides people with tremendous opportunities for data collection, knowledge discovery and cooperative computation. However, it also brings the problem of sensitive information leakage. Both individuals and enterprises may suffer from the massive data collection and the information retrieval by distrusted parties. In this paper, we propose a privacy-preserving protocol for the distributed kernel density estimation-based clustering. Our scheme applies random data perturbation (RDP) technique and the verifiable secret sharing to solve the security problem of distributed kernel density estimation in [4] which assumed a mediate party to help in the computation.

Random deflections of a string on an elastic foundation.

NASA Technical Reports Server (NTRS)

Sanders, J. L., Jr.

1972-01-01

The paper is concerned with the problem of a taut string on a random elastic foundation subjected to random loads. The boundary value problem is transformed into an initial value problem by the method of invariant imbedding. Fokker-Planck equations for the random initial value problem are formulated and solved in some special cases. The analysis leads to a complete characterization of the random deflection function.

Semantic segmentation of 3D textured meshes for urban scene analysis

NASA Astrophysics Data System (ADS)

Rouhani, Mohammad; Lafarge, Florent; Alliez, Pierre

2017-01-01

Classifying 3D measurement data has become a core problem in photogrammetry and 3D computer vision, since the rise of modern multiview geometry techniques, combined with affordable range sensors. We introduce a Markov Random Field-based approach for segmenting textured meshes generated via multi-view stereo into urban classes of interest. The input mesh is first partitioned into small clusters, referred to as superfacets, from which geometric and photometric features are computed. A random forest is then trained to predict the class of each superfacet as well as its similarity with the neighboring superfacets. Similarity is used to assign the weights of the Markov Random Field pairwise-potential and to account for contextual information between the classes. The experimental results illustrate the efficacy and accuracy of the proposed framework.

Data-driven probability concentration and sampling on manifold

DOE Office of Scientific and Technical Information (OSTI.GOV)

Soize, C., E-mail: christian.soize@univ-paris-est.fr; Ghanem, R., E-mail: ghanem@usc.edu

2016-09-15

A new methodology is proposed for generating realizations of a random vector with values in a finite-dimensional Euclidean space that are statistically consistent with a dataset of observations of this vector. The probability distribution of this random vector, while a priori not known, is presumed to be concentrated on an unknown subset of the Euclidean space. A random matrix is introduced whose columns are independent copies of the random vector and for which the number of columns is the number of data points in the dataset. The approach is based on the use of (i) the multidimensional kernel-density estimation methodmore » for estimating the probability distribution of the random matrix, (ii) a MCMC method for generating realizations for the random matrix, (iii) the diffusion-maps approach for discovering and characterizing the geometry and the structure of the dataset, and (iv) a reduced-order representation of the random matrix, which is constructed using the diffusion-maps vectors associated with the first eigenvalues of the transition matrix relative to the given dataset. The convergence aspects of the proposed methodology are analyzed and a numerical validation is explored through three applications of increasing complexity. The proposed method is found to be robust to noise levels and data complexity as well as to the intrinsic dimension of data and the size of experimental datasets. Both the methodology and the underlying mathematical framework presented in this paper contribute new capabilities and perspectives at the interface of uncertainty quantification, statistical data analysis, stochastic modeling and associated statistical inverse problems.« less

Student generated learning objectives: extent of congruence with faculty set objectives and factors influencing their generation.

PubMed

Abdul Ghaffar Al-Shaibani, Tarik A; Sachs-Robertson, Annette; Al Shazali, Hafiz O; Sequeira, Reginald P; Hamdy, Hosam; Al-Roomi, Khaldoon

2003-07-01

A problem-based learning strategy is used for curriculum planning and implementation at the Arabian Gulf University, Bahrain. Problems are constructed in a way that faculty-set objectives are expected to be identified by students during tutorials. Students in small groups, along with a tutor functioning as a facilitator, identify learning issues and define their learning objectives. We compared objectives identified by student groups with faculty-set objectives to determine extent of congruence, and identified factors that influenced students' ability at identifying faculty-set objectives. Male and female students were segregated and randomly grouped. A faculty tutor was allocated for each group. This study was based on 13 problems given to entry-level medical students. Pooled objectives of these problems were classified into four categories: structural, functional, clinical and psychosocial. Univariate analysis of variance was used for comparison, and a p > 0.05 was considered significant. The mean of overall objectives generated by the students was 54.2%, for each problem. Students identified psychosocial learning objectives more readily than structural ones. Female students identified more psychosocial objectives, whereas male students identified more of structural objectives. Tutor characteristics such as medical/non-medical background, and the years of teaching were correlated with categories of learning issues identified. Students identify part of the faculty-set learning objectives during tutorials with a faculty tutor acting as a facilitator. Students' gender influences types of learning issues identified. Content expertise of tutors does not influence identification of learning needs by students.

An efficient genetic algorithm for maximum coverage deployment in wireless sensor networks.

PubMed

Yoon, Yourim; Kim, Yong-Hyuk

2013-10-01

Sensor networks have a lot of applications such as battlefield surveillance, environmental monitoring, and industrial diagnostics. Coverage is one of the most important performance metrics for sensor networks since it reflects how well a sensor field is monitored. In this paper, we introduce the maximum coverage deployment problem in wireless sensor networks and analyze the properties of the problem and its solution space. Random deployment is the simplest way to deploy sensor nodes but may cause unbalanced deployment and therefore, we need a more intelligent way for sensor deployment. We found that the phenotype space of the problem is a quotient space of the genotype space in a mathematical view. Based on this property, we propose an efficient genetic algorithm using a novel normalization method. A Monte Carlo method is adopted to design an efficient evaluation function, and its computation time is decreased without loss of solution quality using a method that starts from a small number of random samples and gradually increases the number for subsequent generations. The proposed genetic algorithms could be further improved by combining with a well-designed local search. The performance of the proposed genetic algorithm is shown by a comparative experimental study. When compared with random deployment and existing methods, our genetic algorithm was not only about twice faster, but also showed significant performance improvement in quality.

Low rank approximation methods for MR fingerprinting with large scale dictionaries.

PubMed

Yang, Mingrui; Ma, Dan; Jiang, Yun; Hamilton, Jesse; Seiberlich, Nicole; Griswold, Mark A; McGivney, Debra

2018-04-01

This work proposes new low rank approximation approaches with significant memory savings for large scale MR fingerprinting (MRF) problems. We introduce a compressed MRF with randomized singular value decomposition method to significantly reduce the memory requirement for calculating a low rank approximation of large sized MRF dictionaries. We further relax this requirement by exploiting the structures of MRF dictionaries in the randomized singular value decomposition space and fitting them to low-degree polynomials to generate high resolution MRF parameter maps. In vivo 1.5T and 3T brain scan data are used to validate the approaches. T 1 , T 2 , and off-resonance maps are in good agreement with that of the standard MRF approach. Moreover, the memory savings is up to 1000 times for the MRF-fast imaging with steady-state precession sequence and more than 15 times for the MRF-balanced, steady-state free precession sequence. The proposed compressed MRF with randomized singular value decomposition and dictionary fitting methods are memory efficient low rank approximation methods, which can benefit the usage of MRF in clinical settings. They also have great potentials in large scale MRF problems, such as problems considering multi-component MRF parameters or high resolution in the parameter space. Magn Reson Med 79:2392-2400, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Self-correcting random number generator

DOE Office of Scientific and Technical Information (OSTI.GOV)

Humble, Travis S.; Pooser, Raphael C.

2016-09-06

A system and method for generating random numbers. The system may include a random number generator (RNG), such as a quantum random number generator (QRNG) configured to self-correct or adapt in order to substantially achieve randomness from the output of the RNG. By adapting, the RNG may generate a random number that may be considered random regardless of whether the random number itself is tested as such. As an example, the RNG may include components to monitor one or more characteristics of the RNG during operation, and may use the monitored characteristics as a basis for adapting, or self-correcting, tomore » provide a random number according to one or more performance criteria.« less

Real-time fast physical random number generator with a photonic integrated circuit.

PubMed

Ugajin, Kazusa; Terashima, Yuta; Iwakawa, Kento; Uchida, Atsushi; Harayama, Takahisa; Yoshimura, Kazuyuki; Inubushi, Masanobu

2017-03-20

Random number generators are essential for applications in information security and numerical simulations. Most optical-chaos-based random number generators produce random bit sequences by offline post-processing with large optical components. We demonstrate a real-time hardware implementation of a fast physical random number generator with a photonic integrated circuit and a field programmable gate array (FPGA) electronic board. We generate 1-Tbit random bit sequences and evaluate their statistical randomness using NIST Special Publication 800-22 and TestU01. All of the BigCrush tests in TestU01 are passed using 410-Gbit random bit sequences. A maximum real-time generation rate of 21.1 Gb/s is achieved for random bit sequences in binary format stored in a computer, which can be directly used for applications involving secret keys in cryptography and random seeds in large-scale numerical simulations.

Quantum random number generator

DOEpatents

Pooser, Raphael C.

2016-05-10

A quantum random number generator (QRNG) and a photon generator for a QRNG are provided. The photon generator may be operated in a spontaneous mode below a lasing threshold to emit photons. Photons emitted from the photon generator may have at least one random characteristic, which may be monitored by the QRNG to generate a random number. In one embodiment, the photon generator may include a photon emitter and an amplifier coupled to the photon emitter. The amplifier may enable the photon generator to be used in the QRNG without introducing significant bias in the random number and may enable multiplexing of multiple random numbers. The amplifier may also desensitize the photon generator to fluctuations in power supplied thereto while operating in the spontaneous mode. In one embodiment, the photon emitter and amplifier may be a tapered diode amplifier.

Dual Key Speech Encryption Algorithm Based Underdetermined BSS

PubMed Central

Zhao, Huan; Chen, Zuo; Zhang, Xixiang

2014-01-01

When the number of the mixed signals is less than that of the source signals, the underdetermined blind source separation (BSS) is a significant difficult problem. Due to the fact that the great amount data of speech communications and real-time communication has been required, we utilize the intractability of the underdetermined BSS problem to present a dual key speech encryption method. The original speech is mixed with dual key signals which consist of random key signals (one-time pad) generated by secret seed and chaotic signals generated from chaotic system. In the decryption process, approximate calculation is used to recover the original speech signals. The proposed algorithm for speech signals encryption can resist traditional attacks against the encryption system, and owing to approximate calculation, decryption becomes faster and more accurate. It is demonstrated that the proposed method has high level of security and can recover the original signals quickly and efficiently yet maintaining excellent audio quality. PMID:24955430

Degree-constrained multicast routing for multimedia communications

NASA Astrophysics Data System (ADS)

Wang, Yanlin; Sun, Yugeng; Li, Guidan

2005-02-01

Multicast services have been increasingly used by many multimedia applications. As one of the key techniques to support multimedia applications, the rational and effective multicast routing algorithms are very important to networks performance. When switch nodes in networks have different multicast capability, multicast routing problem is modeled as the degree-constrained Steiner problem. We presented two heuristic algorithms, named BMSTA and BSPTA, for the degree-constrained case in multimedia communications. Both algorithms are used to generate degree-constrained multicast trees with bandwidth and end to end delay bound. Simulations over random networks were carried out to compare the performance of the two proposed algorithms. Experimental results show that the proposed algorithms have advantages in traffic load balancing, which can avoid link blocking and enhance networks performance efficiently. BMSTA has better ability in finding unsaturated links and (or) unsaturated nodes to generate multicast trees than BSPTA. The performance of BMSTA is affected by the variation of degree constraints.

Counting of oligomers in sequences generated by markov chains for DNA motif discovery.

PubMed

Shan, Gao; Zheng, Wei-Mou

2009-02-01

By means of the technique of the imbedded Markov chain, an efficient algorithm is proposed to exactly calculate first, second moments of word counts and the probability for a word to occur at least once in random texts generated by a Markov chain. A generating function is introduced directly from the imbedded Markov chain to derive asymptotic approximations for the problem. Two Z-scores, one based on the number of sequences with hits and the other on the total number of word hits in a set of sequences, are examined for discovery of motifs on a set of promoter sequences extracted from A. thaliana genome. Source code is available at http://www.itp.ac.cn/zheng/oligo.c.

Quantum random number generation

DOE PAGES

Ma, Xiongfeng; Yuan, Xiao; Cao, Zhu; ...

2016-06-28

Quantum physics can be exploited to generate true random numbers, which play important roles in many applications, especially in cryptography. Genuine randomness from the measurement of a quantum system reveals the inherent nature of quantumness -- coherence, an important feature that differentiates quantum mechanics from classical physics. The generation of genuine randomness is generally considered impossible with only classical means. Based on the degree of trustworthiness on devices, quantum random number generators (QRNGs) can be grouped into three categories. The first category, practical QRNG, is built on fully trusted and calibrated devices and typically can generate randomness at a highmore » speed by properly modeling the devices. The second category is self-testing QRNG, where verifiable randomness can be generated without trusting the actual implementation. The third category, semi-self-testing QRNG, is an intermediate category which provides a tradeoff between the trustworthiness on the device and the random number generation speed.« less

Polynomial chaos representation of databases on manifolds

DOE Office of Scientific and Technical Information (OSTI.GOV)

Soize, C., E-mail: christian.soize@univ-paris-est.fr; Ghanem, R., E-mail: ghanem@usc.edu

2017-04-15

Characterizing the polynomial chaos expansion (PCE) of a vector-valued random variable with probability distribution concentrated on a manifold is a relevant problem in data-driven settings. The probability distribution of such random vectors is multimodal in general, leading to potentially very slow convergence of the PCE. In this paper, we build on a recent development for estimating and sampling from probabilities concentrated on a diffusion manifold. The proposed methodology constructs a PCE of the random vector together with an associated generator that samples from the target probability distribution which is estimated from data concentrated in the neighborhood of the manifold. Themore » method is robust and remains efficient for high dimension and large datasets. The resulting polynomial chaos construction on manifolds permits the adaptation of many uncertainty quantification and statistical tools to emerging questions motivated by data-driven queries.« less

Effectiveness of an individual school-based intervention for children with aggressive behaviour: a randomized controlled trial.

PubMed

Stoltz, Sabine; van Londen, Monique; Deković, Maja; de Castro, Bram O; Prinzie, Peter; Lochman, John E

2013-10-01

For elementary school-children with aggressive behaviour problems, there is a strong need for effective preventive interventions to interrupt the developmental trajectory towards more serious behaviour problems. The aim of this RCT-study was to evaluate a school-based individual tailor-made intervention (Stay Cool Kids), designed to reduce aggressive behaviour in selected children by enhancing cognitive behavioural skills. The sample consisted of 48 schools, with 264 fourth-grade children selected by their teachers because of elevated levels of externalizing behaviour (TRF T-score>60), randomly assigned to the intervention or no-intervention control condition. The intervention was found to be effective in reducing reactive and proactive aggressive behaviour as reported by children, mothers, fathers or teachers, with effect sizes ranging from .11 to .32. Clinically relevant changes in teacher-rated externalizing behaviour were found: the intervention reduced behaviour problems to (sub) clinical or normative levels for significantly more children than the control condition. Some aspects of problems in social cognitive functioning were reduced and children showed more positive self-perception. Ethnic background and gender moderated intervention effects on child and teacher reported aggression and child response generation. The results of this study demonstrate the effectiveness on outcome behaviour and child cognitions of an individual tailor-made intervention across informants under real-world conditions.

University Students' Conceptual Knowledge of Randomness and Probability in the Contexts of Evolution and Mathematics.

PubMed

Fiedler, Daniela; Tröbst, Steffen; Harms, Ute

2017-01-01

Students of all ages face severe conceptual difficulties regarding key aspects of evolution-the central, unifying, and overarching theme in biology. Aspects strongly related to abstract "threshold" concepts like randomness and probability appear to pose particular difficulties. A further problem is the lack of an appropriate instrument for assessing students' conceptual knowledge of randomness and probability in the context of evolution. To address this problem, we have developed two instruments, Ra ndomness and Pro bability Test in the Context of Evo lution (RaProEvo) and Ra ndomness and Pro bability Test in the Context of Math ematics (RaProMath), that include both multiple-choice and free-response items. The instruments were administered to 140 university students in Germany, then the Rasch partial-credit model was applied to assess them. The results indicate that the instruments generate reliable and valid inferences about students' conceptual knowledge of randomness and probability in the two contexts (which are separable competencies). Furthermore, RaProEvo detected significant differences in knowledge of randomness and probability, as well as evolutionary theory, between biology majors and preservice biology teachers. © 2017 D. Fiedler et al. CBE—Life Sciences Education © 2017 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

A comparison of mixed-integer linear programming models for workforce scheduling with position-dependent processing times

NASA Astrophysics Data System (ADS)

Moreno-Camacho, Carlos A.; Montoya-Torres, Jairo R.; Vélez-Gallego, Mario C.

2018-06-01

Only a few studies in the available scientific literature address the problem of having a group of workers that do not share identical levels of productivity during the planning horizon. This study considers a workforce scheduling problem in which the actual processing time is a function of the scheduling sequence to represent the decline in workers' performance, evaluating two classical performance measures separately: makespan and maximum tardiness. Several mathematical models are compared with each other to highlight the advantages of each approach. The mathematical models are tested with randomly generated instances available from a public e-library.

Parallelization of a Monte Carlo particle transport simulation code

NASA Astrophysics Data System (ADS)

Hadjidoukas, P.; Bousis, C.; Emfietzoglou, D.

2010-05-01

We have developed a high performance version of the Monte Carlo particle transport simulation code MC4. The original application code, developed in Visual Basic for Applications (VBA) for Microsoft Excel, was first rewritten in the C programming language for improving code portability. Several pseudo-random number generators have been also integrated and studied. The new MC4 version was then parallelized for shared and distributed-memory multiprocessor systems using the Message Passing Interface. Two parallel pseudo-random number generator libraries (SPRNG and DCMT) have been seamlessly integrated. The performance speedup of parallel MC4 has been studied on a variety of parallel computing architectures including an Intel Xeon server with 4 dual-core processors, a Sun cluster consisting of 16 nodes of 2 dual-core AMD Opteron processors and a 200 dual-processor HP cluster. For large problem size, which is limited only by the physical memory of the multiprocessor server, the speedup results are almost linear on all systems. We have validated the parallel implementation against the serial VBA and C implementations using the same random number generator. Our experimental results on the transport and energy loss of electrons in a water medium show that the serial and parallel codes are equivalent in accuracy. The present improvements allow for studying of higher particle energies with the use of more accurate physical models, and improve statistics as more particles tracks can be simulated in low response time.

Adapted random sampling patterns for accelerated MRI.

PubMed

Knoll, Florian; Clason, Christian; Diwoky, Clemens; Stollberger, Rudolf

2011-02-01

Variable density random sampling patterns have recently become increasingly popular for accelerated imaging strategies, as they lead to incoherent aliasing artifacts. However, the design of these sampling patterns is still an open problem. Current strategies use model assumptions like polynomials of different order to generate a probability density function that is then used to generate the sampling pattern. This approach relies on the optimization of design parameters which is very time consuming and therefore impractical for daily clinical use. This work presents a new approach that generates sampling patterns by making use of power spectra of existing reference data sets and hence requires neither parameter tuning nor an a priori mathematical model of the density of sampling points. The approach is validated with downsampling experiments, as well as with accelerated in vivo measurements. The proposed approach is compared with established sampling patterns, and the generalization potential is tested by using a range of reference images. Quantitative evaluation is performed for the downsampling experiments using RMS differences to the original, fully sampled data set. Our results demonstrate that the image quality of the method presented in this paper is comparable to that of an established model-based strategy when optimization of the model parameter is carried out and yields superior results to non-optimized model parameters. However, no random sampling pattern showed superior performance when compared to conventional Cartesian subsampling for the considered reconstruction strategy.

Raster Scan Computer Image Generation (CIG) System Based On Refresh Memory

NASA Astrophysics Data System (ADS)

Dichter, W.; Doris, K.; Conkling, C.

1982-06-01

A full color, Computer Image Generation (CIG) raster visual system has been developed which provides a high level of training sophistication by utilizing advanced semiconductor technology and innovative hardware and firmware techniques. Double buffered refresh memory and efficient algorithms eliminate the problem of conventional raster line ordering by allowing the generated image to be stored in a random fashion. Modular design techniques and simplified architecture provide significant advantages in reduced system cost, standardization of parts, and high reliability. The major system components are a general purpose computer to perform interfacing and data base functions; a geometric processor to define the instantaneous scene image; a display generator to convert the image to a video signal; an illumination control unit which provides final image processing; and a CRT monitor for display of the completed image. Additional optional enhancements include texture generators, increased edge and occultation capability, curved surface shading, and data base extensions.

Exploring the Connection Between Sampling Problems in Bayesian Inference and Statistical Mechanics

NASA Technical Reports Server (NTRS)

Pohorille, Andrew

2006-01-01

The Bayesian and statistical mechanical communities often share the same objective in their work - estimating and integrating probability distribution functions (pdfs) describing stochastic systems, models or processes. Frequently, these pdfs are complex functions of random variables exhibiting multiple, well separated local minima. Conventional strategies for sampling such pdfs are inefficient, sometimes leading to an apparent non-ergodic behavior. Several recently developed techniques for handling this problem have been successfully applied in statistical mechanics. In the multicanonical and Wang-Landau Monte Carlo (MC) methods, the correct pdfs are recovered from uniform sampling of the parameter space by iteratively establishing proper weighting factors connecting these distributions. Trivial generalizations allow for sampling from any chosen pdf. The closely related transition matrix method relies on estimating transition probabilities between different states. All these methods proved to generate estimates of pdfs with high statistical accuracy. In another MC technique, parallel tempering, several random walks, each corresponding to a different value of a parameter (e.g. "temperature"), are generated and occasionally exchanged using the Metropolis criterion. This method can be considered as a statistically correct version of simulated annealing. An alternative approach is to represent the set of independent variables as a Hamiltonian system. Considerab!e progress has been made in understanding how to ensure that the system obeys the equipartition theorem or, equivalently, that coupling between the variables is correctly described. Then a host of techniques developed for dynamical systems can be used. Among them, probably the most powerful is the Adaptive Biasing Force method, in which thermodynamic integration and biased sampling are combined to yield very efficient estimates of pdfs. The third class of methods deals with transitions between states described by rate constants. These problems are isomorphic with chemical kinetics problems. Recently, several efficient techniques for this purpose have been developed based on the approach originally proposed by Gillespie. Although the utility of the techniques mentioned above for Bayesian problems has not been determined, further research along these lines is warranted

Low-Energy Truly Random Number Generation with Superparamagnetic Tunnel Junctions for Unconventional Computing

NASA Astrophysics Data System (ADS)

Vodenicarevic, D.; Locatelli, N.; Mizrahi, A.; Friedman, J. S.; Vincent, A. F.; Romera, M.; Fukushima, A.; Yakushiji, K.; Kubota, H.; Yuasa, S.; Tiwari, S.; Grollier, J.; Querlioz, D.

2017-11-01

Low-energy random number generation is critical for many emerging computing schemes proposed to complement or replace von Neumann architectures. However, current random number generators are always associated with an energy cost that is prohibitive for these computing schemes. We introduce random number bit generation based on specific nanodevices: superparamagnetic tunnel junctions. We experimentally demonstrate high-quality random bit generation that represents an orders-of-magnitude improvement in energy efficiency over current solutions. We show that the random generation speed improves with nanodevice scaling, and we investigate the impact of temperature, magnetic field, and cross talk. Finally, we show how alternative computing schemes can be implemented using superparamagentic tunnel junctions as random number generators. These results open the way for fabricating efficient hardware computing devices leveraging stochasticity, and they highlight an alternative use for emerging nanodevices.

Nonparametric Bayesian models through probit stick-breaking processes

PubMed Central

Rodríguez, Abel; Dunson, David B.

2013-01-01

We describe a novel class of Bayesian nonparametric priors based on stick-breaking constructions where the weights of the process are constructed as probit transformations of normal random variables. We show that these priors are extremely flexible, allowing us to generate a great variety of models while preserving computational simplicity. Particular emphasis is placed on the construction of rich temporal and spatial processes, which are applied to two problems in finance and ecology. PMID:24358072

Nonparametric Bayesian models through probit stick-breaking processes.

PubMed

Rodríguez, Abel; Dunson, David B

2011-03-01

We describe a novel class of Bayesian nonparametric priors based on stick-breaking constructions where the weights of the process are constructed as probit transformations of normal random variables. We show that these priors are extremely flexible, allowing us to generate a great variety of models while preserving computational simplicity. Particular emphasis is placed on the construction of rich temporal and spatial processes, which are applied to two problems in finance and ecology.

Hybrid Self-Adaptive Evolution Strategies Guided by Neighborhood Structures for Combinatorial Optimization Problems.

PubMed

Coelho, V N; Coelho, I M; Souza, M J F; Oliveira, T A; Cota, L P; Haddad, M N; Mladenovic, N; Silva, R C P; Guimarães, F G

2016-01-01

This article presents an Evolution Strategy (ES)--based algorithm, designed to self-adapt its mutation operators, guiding the search into the solution space using a Self-Adaptive Reduced Variable Neighborhood Search procedure. In view of the specific local search operators for each individual, the proposed population-based approach also fits into the context of the Memetic Algorithms. The proposed variant uses the Greedy Randomized Adaptive Search Procedure with different greedy parameters for generating its initial population, providing an interesting exploration-exploitation balance. To validate the proposal, this framework is applied to solve three different [Formula: see text]-Hard combinatorial optimization problems: an Open-Pit-Mining Operational Planning Problem with dynamic allocation of trucks, an Unrelated Parallel Machine Scheduling Problem with Setup Times, and the calibration of a hybrid fuzzy model for Short-Term Load Forecasting. Computational results point out the convergence of the proposed model and highlight its ability in combining the application of move operations from distinct neighborhood structures along the optimization. The results gathered and reported in this article represent a collective evidence of the performance of the method in challenging combinatorial optimization problems from different application domains. The proposed evolution strategy demonstrates an ability of adapting the strength of the mutation disturbance during the generations of its evolution process. The effectiveness of the proposal motivates the application of this novel evolutionary framework for solving other combinatorial optimization problems.

Efficient convex-elastic net algorithm to solve the Euclidean traveling salesman problem.

PubMed

Al-Mulhem, M; Al-Maghrabi, T

1998-01-01

This paper describes a hybrid algorithm that combines an adaptive-type neural network algorithm and a nondeterministic iterative algorithm to solve the Euclidean traveling salesman problem (E-TSP). It begins with a brief introduction to the TSP and the E-TSP. Then, it presents the proposed algorithm with its two major components: the convex-elastic net (CEN) algorithm and the nondeterministic iterative improvement (NII) algorithm. These two algorithms are combined into the efficient convex-elastic net (ECEN) algorithm. The CEN algorithm integrates the convex-hull property and elastic net algorithm to generate an initial tour for the E-TSP. The NII algorithm uses two rearrangement operators to improve the initial tour given by the CEN algorithm. The paper presents simulation results for two instances of E-TSP: randomly generated tours and tours for well-known problems in the literature. Experimental results are given to show that the proposed algorithm ran find the nearly optimal solution for the E-TSP that outperform many similar algorithms reported in the literature. The paper concludes with the advantages of the new algorithm and possible extensions.

Toward a Principled Sampling Theory for Quasi-Orders

PubMed Central

Ünlü, Ali; Schrepp, Martin

2016-01-01

Quasi-orders, that is, reflexive and transitive binary relations, have numerous applications. In educational theories, the dependencies of mastery among the problems of a test can be modeled by quasi-orders. Methods such as item tree or Boolean analysis that mine for quasi-orders in empirical data are sensitive to the underlying quasi-order structure. These data mining techniques have to be compared based on extensive simulation studies, with unbiased samples of randomly generated quasi-orders at their basis. In this paper, we develop techniques that can provide the required quasi-order samples. We introduce a discrete doubly inductive procedure for incrementally constructing the set of all quasi-orders on a finite item set. A randomization of this deterministic procedure allows us to generate representative samples of random quasi-orders. With an outer level inductive algorithm, we consider the uniform random extensions of the trace quasi-orders to higher dimension. This is combined with an inner level inductive algorithm to correct the extensions that violate the transitivity property. The inner level correction step entails sampling biases. We propose three algorithms for bias correction and investigate them in simulation. It is evident that, on even up to 50 items, the new algorithms create close to representative quasi-order samples within acceptable computing time. Hence, the principled approach is a significant improvement to existing methods that are used to draw quasi-orders uniformly at random but cannot cope with reasonably large item sets. PMID:27965601

Toward a Principled Sampling Theory for Quasi-Orders.

PubMed

Ünlü, Ali; Schrepp, Martin

2016-01-01

Quasi-orders, that is, reflexive and transitive binary relations, have numerous applications. In educational theories, the dependencies of mastery among the problems of a test can be modeled by quasi-orders. Methods such as item tree or Boolean analysis that mine for quasi-orders in empirical data are sensitive to the underlying quasi-order structure. These data mining techniques have to be compared based on extensive simulation studies, with unbiased samples of randomly generated quasi-orders at their basis. In this paper, we develop techniques that can provide the required quasi-order samples. We introduce a discrete doubly inductive procedure for incrementally constructing the set of all quasi-orders on a finite item set. A randomization of this deterministic procedure allows us to generate representative samples of random quasi-orders. With an outer level inductive algorithm, we consider the uniform random extensions of the trace quasi-orders to higher dimension. This is combined with an inner level inductive algorithm to correct the extensions that violate the transitivity property. The inner level correction step entails sampling biases. We propose three algorithms for bias correction and investigate them in simulation. It is evident that, on even up to 50 items, the new algorithms create close to representative quasi-order samples within acceptable computing time. Hence, the principled approach is a significant improvement to existing methods that are used to draw quasi-orders uniformly at random but cannot cope with reasonably large item sets.

Learning accurate and interpretable models based on regularized random forests regression

PubMed Central

2014-01-01

Background Many biology related research works combine data from multiple sources in an effort to understand the underlying problems. It is important to find and interpret the most important information from these sources. Thus it will be beneficial to have an effective algorithm that can simultaneously extract decision rules and select critical features for good interpretation while preserving the prediction performance. Methods In this study, we focus on regression problems for biological data where target outcomes are continuous. In general, models constructed from linear regression approaches are relatively easy to interpret. However, many practical biological applications are nonlinear in essence where we can hardly find a direct linear relationship between input and output. Nonlinear regression techniques can reveal nonlinear relationship of data, but are generally hard for human to interpret. We propose a rule based regression algorithm that uses 1-norm regularized random forests. The proposed approach simultaneously extracts a small number of rules from generated random forests and eliminates unimportant features. Results We tested the approach on some biological data sets. The proposed approach is able to construct a significantly smaller set of regression rules using a subset of attributes while achieving prediction performance comparable to that of random forests regression. Conclusion It demonstrates high potential in aiding prediction and interpretation of nonlinear relationships of the subject being studied. PMID:25350120

Pseudo-Random Number Generator Based on Coupled Map Lattices

NASA Astrophysics Data System (ADS)

Lü, Huaping; Wang, Shihong; Hu, Gang

A one-way coupled chaotic map lattice is used for generating pseudo-random numbers. It is shown that with suitable cooperative applications of both chaotic and conventional approaches, the output of the spatiotemporally chaotic system can easily meet the practical requirements of random numbers, i.e., excellent random statistical properties, long periodicity of computer realizations, and fast speed of random number generations. This pseudo-random number generator system can be used as ideal synchronous and self-synchronizing stream cipher systems for secure communications.

Designing management strategies for carbon dioxide storage and utilization under uncertainty using inexact modelling

NASA Astrophysics Data System (ADS)

Wang, Yu; Fan, Jie; Xu, Ye; Sun, Wei; Chen, Dong

2017-06-01

Effective application of carbon capture, utilization and storage (CCUS) systems could help to alleviate the influence of climate change by reducing carbon dioxide (CO2) emissions. The research objective of this study is to develop an equilibrium chance-constrained programming model with bi-random variables (ECCP model) for supporting the CCUS management system under random circumstances. The major advantage of the ECCP model is that it tackles random variables as bi-random variables with a normal distribution, where the mean values follow a normal distribution. This could avoid irrational assumptions and oversimplifications in the process of parameter design and enrich the theory of stochastic optimization. The ECCP model is solved by an equilibrium change-constrained programming algorithm, which provides convenience for decision makers to rank the solution set using the natural order of real numbers. The ECCP model is applied to a CCUS management problem, and the solutions could be useful in helping managers to design and generate rational CO2-allocation patterns under complexities and uncertainties.

Generating random numbers by means of nonlinear dynamic systems

NASA Astrophysics Data System (ADS)

Zang, Jiaqi; Hu, Haojie; Zhong, Juhua; Luo, Duanbin; Fang, Yi

2018-07-01

To introduce the randomness of a physical process to students, a chaotic pendulum experiment was opened in East China University of Science and Technology (ECUST) on the undergraduate level in the physics department. It was shown chaotic motion could be initiated through adjusting the operation of a chaotic pendulum. By using the data of the angular displacements of chaotic motion, random binary numerical arrays can be generated. To check the randomness of generated numerical arrays, the NIST Special Publication 800-20 method was adopted. As a result, it was found that all the random arrays which were generated by the chaotic motion could pass the validity criteria and some of them were even better than the quality of pseudo-random numbers generated by a computer. Through the experiments, it is demonstrated that chaotic pendulum can be used as an efficient mechanical facility in generating random numbers, and can be applied in teaching random motion to the students.

Extracting random numbers from quantum tunnelling through a single diode.

PubMed

Bernardo-Gavito, Ramón; Bagci, Ibrahim Ethem; Roberts, Jonathan; Sexton, James; Astbury, Benjamin; Shokeir, Hamzah; McGrath, Thomas; Noori, Yasir J; Woodhead, Christopher S; Missous, Mohamed; Roedig, Utz; Young, Robert J

2017-12-19

Random number generation is crucial in many aspects of everyday life, as online security and privacy depend ultimately on the quality of random numbers. Many current implementations are based on pseudo-random number generators, but information security requires true random numbers for sensitive applications like key generation in banking, defence or even social media. True random number generators are systems whose outputs cannot be determined, even if their internal structure and response history are known. Sources of quantum noise are thus ideal for this application due to their intrinsic uncertainty. In this work, we propose using resonant tunnelling diodes as practical true random number generators based on a quantum mechanical effect. The output of the proposed devices can be directly used as a random stream of bits or can be further distilled using randomness extraction algorithms, depending on the application.

Utilisation of ISA Reverse Genetics and Large-Scale Random Codon Re-Encoding to Produce Attenuated Strains of Tick-Borne Encephalitis Virus within Days.

PubMed

de Fabritus, Lauriane; Nougairède, Antoine; Aubry, Fabien; Gould, Ernest A; de Lamballerie, Xavier

2016-01-01

Large-scale codon re-encoding is a new method of attenuating RNA viruses. However, the use of infectious clones to generate attenuated viruses has inherent technical problems. We previously developed a bacterium-free reverse genetics protocol, designated ISA, and now combined it with large-scale random codon-re-encoding method to produce attenuated tick-borne encephalitis virus (TBEV), a pathogenic flavivirus which causes febrile illness and encephalitis in humans. We produced wild-type (WT) and two re-encoded TBEVs, containing 273 or 273+284 synonymous mutations in the NS5 and NS5+NS3 coding regions respectively. Both re-encoded viruses were attenuated when compared with WT virus using a laboratory mouse model and the relative level of attenuation increased with the degree of re-encoding. Moreover, all infected animals produced neutralizing antibodies. This novel, rapid and efficient approach to engineering attenuated viruses could potentially expedite the development of safe and effective new-generation live attenuated vaccines.

Randomizer for High Data Rates

NASA Technical Reports Server (NTRS)

Garon, Howard; Sank, Victor J.

2018-01-01

NASA as well as a number of other space agencies now recognize that the current recommended CCSDS randomizer used for telemetry (TM) is too short. When multiple applications of the PN8 Maximal Length Sequence (MLS) are required in order to fully cover a channel access data unit (CADU), spectral problems in the form of elevated spurious discretes (spurs) appear. Originally the randomizer was called a bit transition generator (BTG) precisely because it was thought that its primary value was to insure sufficient bit transitions to allow the bit/symbol synchronizer to lock and remain locked. We, NASA, have shown that the old BTG concept is a limited view of the real value of the randomizer sequence and that the randomizer also aids in signal acquisition as well as minimizing the potential for false decoder lock. Under the guidelines we considered here there are multiple maximal length sequences under GF(2) which appear attractive in this application. Although there may be mitigating reasons why another MLS sequence could be selected, one sequence in particular possesses a combination of desired properties which offsets it from the others.

Quantum random number generation for loophole-free Bell tests

NASA Astrophysics Data System (ADS)

Mitchell, Morgan; Abellan, Carlos; Amaya, Waldimar

2015-05-01

We describe the generation of quantum random numbers at multi-Gbps rates, combined with real-time randomness extraction, to give very high purity random numbers based on quantum events at most tens of ns in the past. The system satisfies the stringent requirements of quantum non-locality tests that aim to close the timing loophole. We describe the generation mechanism using spontaneous-emission-driven phase diffusion in a semiconductor laser, digitization, and extraction by parity calculation using multi-GHz logic chips. We pay special attention to experimental proof of the quality of the random numbers and analysis of the randomness extraction. In contrast to widely-used models of randomness generators in the computer science literature, we argue that randomness generation by spontaneous emission can be extracted from a single source.

Case management services for work related upper extremity disorders. Integrating workplace accommodation and problem solving.

PubMed

Shaw, W S; Feuerstein, M; Lincoln, A E; Miller, V I; Wood, P M

2001-08-01

A case manager's ability to obtain worksite accommodations and engage workers in active problem solving may improve health and return to work outcomes for clients with work related upper extremity disorders (WRUEDs). This study examines the feasibility of a 2 day training seminar to help nurse case managers identify ergonomic risk factors, provide accommodation, and conduct problem solving skills training with workers' compensation claimants recovering from WRUEDs. Eight procedural steps to this case management approach were identified, translated into a training workshop format, and conveyed to 65 randomly selected case managers. Results indicate moderate to high self ratings of confidence to perform ergonomic assessments (mean = 7.5 of 10) and to provide problem solving skills training (mean = 7.2 of 10) after the seminar. This training format was suitable to experienced case managers and generated a moderate to high level of confidence to use this case management approach.

Patch planting of hard spin-glass problems: Getting ready for the next generation of optimization approaches

NASA Astrophysics Data System (ADS)

Wang, Wenlong; Mandrà, Salvatore; Katzgraber, Helmut

We propose a patch planting heuristic that allows us to create arbitrarily-large Ising spin-glass instances on any topology and with any type of disorder, and where the exact ground-state energy of the problem is known by construction. By breaking up the problem into patches that can be treated either with exact or heuristic solvers, we can reconstruct the optimum of the original, considerably larger, problem. The scaling of the computational complexity of these instances with various patch numbers and sizes is investigated and compared with random instances using population annealing Monte Carlo and quantum annealing on the D-Wave 2X quantum annealer. The method can be useful for benchmarking of novel computing technologies and algorithms. NSF-DMR-1208046 and the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via MIT Lincoln Laboratory Air Force Contract No. FA8721-05-C-0002.

Quantum cryptography and applications in the optical fiber network

NASA Astrophysics Data System (ADS)

Luo, Yuhui

2005-09-01

Quantum cryptography, as part of quantum information and communications, can provide absolute security for information transmission because it is established on the fundamental laws of quantum theory, such as the principle of uncertainty, No-cloning theorem and quantum entanglement. In this thesis research, a novel scheme to implement quantum key distribution based on multiphoton entanglement with a new protocol is proposed. Its advantages are: a larger information capacity can be obtained with a longer transmission distance and the detection of multiple photons is easier than that of a single photon. The security and attacks pertaining to such a system are also studied. Next, a quantum key distribution over wavelength division multiplexed (WDM) optical fiber networks is realized. Quantum key distribution in networks is a long-standing problem for practical applications. Here we combine quantum cryptography and WDM to solve this problem because WDM technology is universally deployed in the current and next generation fiber networks. The ultimate target is to deploy quantum key distribution over commercial networks. The problems arising from the networks are also studied in this part. Then quantum key distribution in multi-access networks using wavelength routing technology is investigated in this research. For the first time, quantum cryptography for multiple individually targeted users has been successfully implemented in sharp contrast to that using the indiscriminating broadcasting structure. It overcomes the shortcoming that every user in the network can acquire the quantum key signals intended to be exchanged between only two users. Furthermore, a more efficient scheme of quantum key distribution is adopted, hence resulting in a higher key rate. Lastly, a quantum random number generator based on quantum optics has been experimentally demonstrated. This device is a key component for quantum key distribution as it can create truly random numbers, which is an essential requirement to perform quantum key distribution. This new generator is composed of a single optical fiber coupler with fiber pigtails, which can be easily used in optical fiber communications.

A random forest algorithm for nowcasting of intense precipitation events

NASA Astrophysics Data System (ADS)

Das, Saurabh; Chakraborty, Rohit; Maitra, Animesh

2017-09-01

Automatic nowcasting of convective initiation and thunderstorms has potential applications in several sectors including aviation planning and disaster management. In this paper, random forest based machine learning algorithm is tested for nowcasting of convective rain with a ground based radiometer. Brightness temperatures measured at 14 frequencies (7 frequencies in 22-31 GHz band and 7 frequencies in 51-58 GHz bands) are utilized as the inputs of the model. The lower frequency band is associated to the water vapor absorption whereas the upper frequency band relates to the oxygen absorption and hence, provide information on the temperature and humidity of the atmosphere. Synthetic minority over-sampling technique is used to balance the data set and 10-fold cross validation is used to assess the performance of the model. Results indicate that random forest algorithm with fixed alarm generation time of 30 min and 60 min performs quite well (probability of detection of all types of weather condition ∼90%) with low false alarms. It is, however, also observed that reducing the alarm generation time improves the threat score significantly and also decreases false alarms. The proposed model is found to be very sensitive to the boundary layer instability as indicated by the variable importance measure. The study shows the suitability of a random forest algorithm for nowcasting application utilizing a large number of input parameters from diverse sources and can be utilized in other forecasting problems.

A principle of organization which facilitates broad Lamarckian-like adaptations by improvisation.

PubMed

Soen, Yoav; Knafo, Maor; Elgart, Michael

2015-12-02

During the lifetime of an organism, every individual encounters many combinations of diverse changes in the somatic genome, epigenome and microbiome. This gives rise to many novel combinations of internal failures which are unique to each individual. How any individual can tolerate this high load of new, individual-specific scenarios of failure is not clear. While stress-induced plasticity and hidden variation have been proposed as potential mechanisms of tolerance, the main conceptual problem remains unaddressed, namely: how largely non-beneficial random variation can be rapidly and safely organized into net benefits to every individual. We propose an organizational principle which explains how every individual can alleviate a high load of novel stressful scenarios using many random variations in flexible and inherently less harmful traits. Random changes which happen to reduce stress, benefit the organism and decrease the drive for additional changes. This adaptation (termed 'Adaptive Improvisation') can be further enhanced, propagated, stabilized and memorized when beneficial changes reinforce themselves by auto-regulatory mechanisms. This principle implicates stress not only in driving diverse variations in cells tissues and organs, but also in organizing these variations into adaptive outcomes. Specific (but not exclusive) examples include stress reduction by rapid exchange of mobile genetic elements (or exosomes) in unicellular, and rapid changes in the symbiotic microorganisms of animals. In all cases, adaptive changes can be transmitted across generations, allowing rapid improvement and assimilation in a few generations. We provide testable predictions derived from the hypothesis. The hypothesis raises a critical, but thus far overlooked adaptation problem and explains how random variation can self-organize to confer a wide range of individual-specific adaptations beyond the existing outcomes of natural selection. It portrays gene regulation as an inseparable synergy between natural selection and adaptation by improvisation. The latter provides a basis for Lamarckian adaptation that is not limited to a specific mechanism and readily accounts for the remarkable resistance of tumors to treatment.

PREFACE: The random search problem: trends and perspectives The random search problem: trends and perspectives

NASA Astrophysics Data System (ADS)

da Luz, Marcos G. E.; Grosberg, Alexander; Raposo, Ernesto P.; Viswanathan, Gandhi M.

2009-10-01

`I can't find my keys!' Who hasn't gone through this experience when leaving, in a hurry, to attend to some urgent matter? The keys could be in many different places. Unless one remembers where he or she has left the keys, the only solution is to look around, more or less randomly. Random searches are common because in many cases the locations of the specific targets are not known a priori. Indeed, such problems have been discussed in diverse contexts, attracting the interest of scientists from many fields, for example: the dynamical or stochastic search for a stable minimum in a complex energy landscape, relevant to systems such as glasses, protein (folding), and others; oil recovery from mature reservoirs; proteins searching for their specific target sites on DNA; animal foraging; survival at the edge of extinction due to low availability of energetic resources; automated searches of registers in high-capacity databases, search engine (e.g., `crawlers') that explore the internet; and even pizza delivery in a jammed traffic system of a medium-size town. In this way, the subject is interesting, challenging and has recently become an important scientific area of investigation. Although the applications are diverse, the underlying physical mechanisms are the same which will become clear in this special issue. Moreover, the inherent complexity of the problem, the abundance of ideas and methods found in this growing interdisciplinary field of research is studied in many areas of physics. In particular, the concepts and methods of statistical mechanics are particularly useful to the study of random searches. On one hand, it centres on how to find the global or local maxima of search efficiency functions with incomplete information. This is, of course, related to the long tradition in physics of using different conceptual and mathematical tools, such as variational methods, to extremize relevant quantities, e.g., energy, entropy and action. Such ideas and approaches are very important to solve computationally complex problems (e.g., protein folding), which involve optimizations in very high dimensional energy landscapes. On the other hand, random searches can also be studied from the perspective of diffusion and transport properties which is an important topic in condensed matter and statistical physics. For instance, the features of light scattered in a media, where the scatterers have a power-law distribution of sizes in many aspects, may resemble the patterns generated by a searcher performing Lévy walks. There are many questions related to random searches: how the searcher moves or should move, what are the patterns generated during the locomotion, how do the encounter rates depend on parameters of the search, etc. But perhaps, the most well known issue is how to optimize the search for specific target scenarios. The optimization can be in either continuous or discrete environments, when the information available is limited. The answer to this question determines specific strategies of movement that would maximize some properly defined search efficiency measure. The relevance of the question stems from the fact that the strategy-dynamics represents one of the most important factors that modulate the rate of encounters (e.g., the encounter rate between predator and prey). In the general context, strategy choices can be essential in determining the outcome and thus the success of a given search. For instance, realistic searches—and locomotion in general—require the expenditure of energy. Thus, inefficient search could deplete energy reserves (e.g., fat) and lead to rates of encounters below a minimum acceptable threshold (resulting in extinction of a species, for example). The framework of the random search `game' distinguishes between the two interacting players in a context of pursuit and chance. They are either a `searcher' (e.g., predator, protein, radar, `crawler') or a `target' (e.g., prey, DNA sequence, a missing aircraft, a given web site). Regarding the nature of the searching drive, in certain instances, it can be guided almost entirely by external cues, either by the cognitive (memory) or detective (olfaction, vision, etc) skills of the searcher. However, in many situations the movement is non-oriented, being in essence a stochastic process. Therefore, in such cases (and even when a small deterministic component in the locomotion exists) a random search effectively defines the final rates of encounters. Hence, one reason underlying the richness of the random search problem relates just to the `ignorance' of the locations of the randomly located targets. Contrary to conventional wisdom, the lack of complete information does not necessarily lead to greater complexity. As an illustrative example, let us consider the case of complete information. If the positions of all target sites are known in advance, then the question of what sequential order to visit the sites so to reduce the energy costs of locomotion itself becomes a rather challenging problem: the famous `travelling salesman' optimization query, belonging to the NP-complete class of problems. The ignorance of the target site locations, however, considerably modifies the problem and renders it not amenable to be treated by purely deterministic computational methods. In fact, as expected, the random search problem is not particularly suited to search algorithms that do not use elements of randomness. So, only a statistical approach to the search problem can adequately deal with the element of ignorance. In other words, the incomplete information renders the search under-determined, i.e., it is not possible to find the `best' solution to the problem because all the information is not given. Instead, one must guess and probabilistic or stochastic strategies become unavoidable. Also, the random search problem bears a relation to reaction-diffusion processes, because the search involves a diffusive aspect, movement, as well as a reactive component, e.g., eating, mating, etc. From the comments above, it is clear that the subject can be treated from the perspective of different fields and subfields of physics and mathematics: statistical mechanics, stochastic processes, Lévy walks and flights, complex systems, fractal geometry, and non-linear phenomena. Some important questions in random searches, especially in the case of discrete landscapes, are also associated with graph theory, random lattices, and complex networks. The aim of this special issue is to bring together, in a single publication, all or most of the relevant theoretical concepts-ideas together with discussions of recent findings that are important for understanding the main elements of random searches. In addition, we will address the types of problems which are characteristic of random searching. Thus, we sincerely hope that this collection of works will provide a good overview for anyone interested in this field. Finally, we should thank the editors and staff of Journal of Physics A: Mathematical and Theoretical for opining that random searches are an interesting topic of research deserving a topical publication. Furthermore, we are very grateful to Rebecca Gillan for helping us at all stages of the preparation and organization of this special issue. Finally, we would like to thank the contributing authors who share with the guest editors the enthusiasm and interest for this fascinating field of research.

An On-Demand Optical Quantum Random Number Generator with In-Future Action and Ultra-Fast Response

PubMed Central

Stipčević, Mario; Ursin, Rupert

2015-01-01

Random numbers are essential for our modern information based society e.g. in cryptography. Unlike frequently used pseudo-random generators, physical random number generators do not depend on complex algorithms but rather on a physicsal process to provide true randomness. Quantum random number generators (QRNG) do rely on a process, wich can be described by a probabilistic theory only, even in principle. Here we present a conceptualy simple implementation, which offers a 100% efficiency of producing a random bit upon a request and simultaneously exhibits an ultra low latency. A careful technical and statistical analysis demonstrates its robustness against imperfections of the actual implemented technology and enables to quickly estimate randomness of very long sequences. Generated random numbers pass standard statistical tests without any post-processing. The setup described, as well as the theory presented here, demonstrate the maturity and overall understanding of the technology. PMID:26057576

Security of practical private randomness generation

NASA Astrophysics Data System (ADS)

Pironio, Stefano; Massar, Serge

2013-01-01

Measurements on entangled quantum systems necessarily yield outcomes that are intrinsically unpredictable if they violate a Bell inequality. This property can be used to generate certified randomness in a device-independent way, i.e., without making detailed assumptions about the internal working of the quantum devices used to generate the random numbers. Furthermore these numbers are also private; i.e., they appear random not only to the user but also to any adversary that might possess a perfect description of the devices. Since this process requires a small initial random seed to sample the behavior of the quantum devices and to extract uniform randomness from the raw outputs of the devices, one usually speaks of device-independent randomness expansion. The purpose of this paper is twofold. First, we point out that in most real, practical situations, where the concept of device independence is used as a protection against unintentional flaws or failures of the quantum apparatuses, it is sufficient to show that the generated string is random with respect to an adversary that holds only classical side information; i.e., proving randomness against quantum side information is not necessary. Furthermore, the initial random seed does not need to be private with respect to the adversary, provided that it is generated in a way that is independent from the measured systems. The devices, however, will generate cryptographically secure randomness that cannot be predicted by the adversary, and thus one can, given access to free public randomness, talk about private randomness generation. The theoretical tools to quantify the generated randomness according to these criteria were already introduced in S. Pironio [Nature (London)NATUAS0028-083610.1038/nature09008 464, 1021 (2010)], but the final results were improperly formulated. The second aim of this paper is to correct this inaccurate formulation and therefore lay out a precise theoretical framework for practical device-independent randomness generation.

The RANDOM computer program: A linear congruential random number generator

NASA Technical Reports Server (NTRS)

Miles, R. F., Jr.

1986-01-01

The RANDOM Computer Program is a FORTRAN program for generating random number sequences and testing linear congruential random number generators (LCGs). The linear congruential form of random number generator is discussed, and the selection of parameters of an LCG for a microcomputer described. This document describes the following: (1) The RANDOM Computer Program; (2) RANDOM.MOD, the computer code needed to implement an LCG in a FORTRAN program; and (3) The RANCYCLE and the ARITH Computer Programs that provide computational assistance in the selection of parameters for an LCG. The RANDOM, RANCYCLE, and ARITH Computer Programs are written in Microsoft FORTRAN for the IBM PC microcomputer and its compatibles. With only minor modifications, the RANDOM Computer Program and its LCG can be run on most micromputers or mainframe computers.

A Method for Estimating View Transformations from Image Correspondences Based on the Harmony Search Algorithm.

PubMed

Cuevas, Erik; Díaz, Margarita

2015-01-01

In this paper, a new method for robustly estimating multiple view relations from point correspondences is presented. The approach combines the popular random sampling consensus (RANSAC) algorithm and the evolutionary method harmony search (HS). With this combination, the proposed method adopts a different sampling strategy than RANSAC to generate putative solutions. Under the new mechanism, at each iteration, new candidate solutions are built taking into account the quality of the models generated by previous candidate solutions, rather than purely random as it is the case of RANSAC. The rules for the generation of candidate solutions (samples) are motivated by the improvisation process that occurs when a musician searches for a better state of harmony. As a result, the proposed approach can substantially reduce the number of iterations still preserving the robust capabilities of RANSAC. The method is generic and its use is illustrated by the estimation of homographies, considering synthetic and real images. Additionally, in order to demonstrate the performance of the proposed approach within a real engineering application, it is employed to solve the problem of position estimation in a humanoid robot. Experimental results validate the efficiency of the proposed method in terms of accuracy, speed, and robustness.

Generation of physical random numbers by using homodyne detection

NASA Astrophysics Data System (ADS)

Hirakawa, Kodai; Oya, Shota; Oguri, Yusuke; Ichikawa, Tsubasa; Eto, Yujiro; Hirano, Takuya; Tsurumaru, Toyohiro

2016-10-01

Physical random numbers generated by quantum measurements are, in principle, impossible to predict. We have demonstrated the generation of physical random numbers by using a high-speed balanced photodetector to measure the quadrature amplitudes of vacuum states. Using this method, random numbers were generated at 500 Mbps, which is more than one order of magnitude faster than previously [Gabriel et al:, Nature Photonics 4, 711-715 (2010)]. The Crush test battery of the TestU01 suite consists of 31 tests in 144 variations, and we used them to statistically analyze these numbers. The generated random numbers passed 14 of the 31 tests. To improve the randomness, we performed a hash operation, in which each random number was multiplied by a random Toeplitz matrix; the resulting numbers passed all of the tests in the TestU01 Crush battery.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ma, Xiongfeng; Yuan, Xiao; Cao, Zhu

Quantum physics can be exploited to generate true random numbers, which play important roles in many applications, especially in cryptography. Genuine randomness from the measurement of a quantum system reveals the inherent nature of quantumness -- coherence, an important feature that differentiates quantum mechanics from classical physics. The generation of genuine randomness is generally considered impossible with only classical means. Based on the degree of trustworthiness on devices, quantum random number generators (QRNGs) can be grouped into three categories. The first category, practical QRNG, is built on fully trusted and calibrated devices and typically can generate randomness at a highmore » speed by properly modeling the devices. The second category is self-testing QRNG, where verifiable randomness can be generated without trusting the actual implementation. The third category, semi-self-testing QRNG, is an intermediate category which provides a tradeoff between the trustworthiness on the device and the random number generation speed.« less

An optimal control approach to the design of moving flight simulators

NASA Technical Reports Server (NTRS)

Sivan, R.; Ish-Shalom, J.; Huang, J.-K.

1982-01-01

An abstract flight simulator design problem is formulated in the form of an optimal control problem, which is solved for the linear-quadratic-Gaussian special case using a mathematical model of the vestibular organs. The optimization criterion used is the mean-square difference between the physiological outputs of the vestibular organs of the pilot in the aircraft and the pilot in the simulator. The dynamical equations are linearized, and the output signal is modeled as a random process with rational power spectral density. The method described yields the optimal structure of the simulator's motion generator, or 'washout filter'. A two-degree-of-freedom flight simulator design, including single output simulations, is presented.

A Single Mechanism Can Account for Human Perception of Depth in Mixed Correlation Random Dot Stereograms

PubMed Central

Cumming, Bruce G.

2016-01-01

In order to extract retinal disparity from a visual scene, the brain must match corresponding points in the left and right retinae. This computationally demanding task is known as the stereo correspondence problem. The initial stage of the solution to the correspondence problem is generally thought to consist of a correlation-based computation. However, recent work by Doi et al suggests that human observers can see depth in a class of stimuli where the mean binocular correlation is 0 (half-matched random dot stereograms). Half-matched random dot stereograms are made up of an equal number of correlated and anticorrelated dots, and the binocular energy model—a well-known model of V1 binocular complex cells—fails to signal disparity here. This has led to the proposition that a second, match-based computation must be extracting disparity in these stimuli. Here we show that a straightforward modification to the binocular energy model—adding a point output nonlinearity—is by itself sufficient to produce cells that are disparity-tuned to half-matched random dot stereograms. We then show that a simple decision model using this single mechanism can reproduce psychometric functions generated by human observers, including reduced performance to large disparities and rapidly updating dot patterns. The model makes predictions about how performance should change with dot size in half-matched stereograms and temporal alternation in correlation, which we test in human observers. We conclude that a single correlation-based computation, based directly on already-known properties of V1 neurons, can account for the literature on mixed correlation random dot stereograms. PMID:27196696

Dimer covering and percolation frustration.

PubMed

Haji-Akbari, Amir; Haji-Akbari, Nasim; Ziff, Robert M

2015-09-01

Covering a graph or a lattice with nonoverlapping dimers is a problem that has received considerable interest in areas, such as discrete mathematics, statistical physics, chemistry, and materials science. Yet, the problem of percolation on dimer-covered lattices has received little attention. In particular, percolation on lattices that are fully covered by nonoverlapping dimers has not evidently been considered. Here, we propose a procedure for generating random dimer coverings of a given lattice. We then compute the bond percolation threshold on random and ordered coverings of the square and the triangular lattices on the remaining bonds connecting the dimers. We obtain p_{c}=0.367713(2) and p_{c}=0.235340(1) for random coverings of the square and the triangular lattices, respectively. We observe that the percolation frustration induced as a result of dimer covering is larger in the low-coordination-number square lattice. There is also no relationship between the existence of long-range order in a covering of the square lattice and its percolation threshold. In particular, an ordered covering of the square lattice, denoted by shifted covering in this paper, has an unusually low percolation threshold and is topologically identical to the triangular lattice. This is in contrast to the other ordered dimer coverings considered in this paper, which have higher percolation thresholds than the random covering. In the case of the triangular lattice, the percolation thresholds of the ordered and random coverings are very close, suggesting the lack of sensitivity of the percolation threshold to microscopic details of the covering in highly coordinated networks.

SKIRT: The design of a suite of input models for Monte Carlo radiative transfer simulations

NASA Astrophysics Data System (ADS)

Baes, M.; Camps, P.

2015-09-01

The Monte Carlo method is the most popular technique to perform radiative transfer simulations in a general 3D geometry. The algorithms behind and acceleration techniques for Monte Carlo radiative transfer are discussed extensively in the literature, and many different Monte Carlo codes are publicly available. On the contrary, the design of a suite of components that can be used for the distribution of sources and sinks in radiative transfer codes has received very little attention. The availability of such models, with different degrees of complexity, has many benefits. For example, they can serve as toy models to test new physical ingredients, or as parameterised models for inverse radiative transfer fitting. For 3D Monte Carlo codes, this requires algorithms to efficiently generate random positions from 3D density distributions. We describe the design of a flexible suite of components for the Monte Carlo radiative transfer code SKIRT. The design is based on a combination of basic building blocks (which can be either analytical toy models or numerical models defined on grids or a set of particles) and the extensive use of decorators that combine and alter these building blocks to more complex structures. For a number of decorators, e.g. those that add spiral structure or clumpiness, we provide a detailed description of the algorithms that can be used to generate random positions. Advantages of this decorator-based design include code transparency, the avoidance of code duplication, and an increase in code maintainability. Moreover, since decorators can be chained without problems, very complex models can easily be constructed out of simple building blocks. Finally, based on a number of test simulations, we demonstrate that our design using customised random position generators is superior to a simpler design based on a generic black-box random position generator.

Analysis of Uniform Random Numbers Generated by Randu and Urn Ten Different Seeds.

DTIC Science & Technology

The statistical properties of the numbers generated by two uniform random number generators, RANDU and URN, each using ten different seeds are...The testing is performed on a sequence of 50,000 numbers generated by each uniform random number generator using each of the ten seeds . (Author)

Testing Pairwise Association between Spatially Autocorrelated Variables: A New Approach Using Surrogate Lattice Data

PubMed Central

Deblauwe, Vincent; Kennel, Pol; Couteron, Pierre

2012-01-01

Background Independence between observations is a standard prerequisite of traditional statistical tests of association. This condition is, however, violated when autocorrelation is present within the data. In the case of variables that are regularly sampled in space (i.e. lattice data or images), such as those provided by remote-sensing or geographical databases, this problem is particularly acute. Because analytic derivation of the null probability distribution of the test statistic (e.g. Pearson's r) is not always possible when autocorrelation is present, we propose instead the use of a Monte Carlo simulation with surrogate data. Methodology/Principal Findings The null hypothesis that two observed mapped variables are the result of independent pattern generating processes is tested here by generating sets of random image data while preserving the autocorrelation function of the original images. Surrogates are generated by matching the dual-tree complex wavelet spectra (and hence the autocorrelation functions) of white noise images with the spectra of the original images. The generated images can then be used to build the probability distribution function of any statistic of association under the null hypothesis. We demonstrate the validity of a statistical test of association based on these surrogates with both actual and synthetic data and compare it with a corrected parametric test and three existing methods that generate surrogates (randomization, random rotations and shifts, and iterative amplitude adjusted Fourier transform). Type I error control was excellent, even with strong and long-range autocorrelation, which is not the case for alternative methods. Conclusions/Significance The wavelet-based surrogates are particularly appropriate in cases where autocorrelation appears at all scales or is direction-dependent (anisotropy). We explore the potential of the method for association tests involving a lattice of binary data and discuss its potential for validation of species distribution models. An implementation of the method in Java for the generation of wavelet-based surrogates is available online as supporting material. PMID:23144961

Caustics, counting maps and semi-classical asymptotics

NASA Astrophysics Data System (ADS)

Ercolani, N. M.

2011-02-01

This paper develops a deeper understanding of the structure and combinatorial significance of the partition function for Hermitian random matrices. The coefficients of the large N expansion of the logarithm of this partition function, also known as the genus expansion (and its derivatives), are generating functions for a variety of graphical enumeration problems. The main results are to prove that these generating functions are, in fact, specific rational functions of a distinguished irrational (algebraic) function, z0(t). This distinguished function is itself the generating function for the Catalan numbers (or generalized Catalan numbers, depending on the choice of weight of the parameter t). It is also a solution of the inviscid Burgers equation for certain initial data. The shock formation, or caustic, of the Burgers characteristic solution is directly related to the poles of the rational forms of the generating functions. As an intriguing application, one gains new insights into the relation between certain derivatives of the genus expansion, in a double-scaling limit, and the asymptotic expansion of the first Painlevé transcendent. This provides a precise expression of the Painlevé asymptotic coefficients directly in terms of the coefficients of the partial fractions expansion of the rational form of the generating functions established in this paper. Moreover, these insights point towards a more general program relating the first Painlevé hierarchy to the higher order structure of the double-scaling limit through the specific rational structure of generating functions in the genus expansion. The paper closes with a discussion of the relation of this work to recent developments in understanding the asymptotics of graphical enumeration. As a by-product, these results also yield new information about the asymptotics of recurrence coefficients for orthogonal polynomials with respect to exponential weights, the calculation of correlation functions for certain tied random walks on a 1D lattice, and the large time asymptotics of random matrix partition functions.

Maternal-child dyads of functioning: the intergenerational impact of violence against women on children.

PubMed

McFarlane, Judith; Symes, Lene; Binder, Brenda K; Maddoux, John; Paulson, Rene

2014-11-01

Violence against women is a global epidemic with potential consequences of injury, illness, and death. Children exposed to the violence may also be impacted with functional impairments. Little is known of the inter-generational impact of violence experienced by the mother from an intimate partner on functioning of her children. No dyad analysis was found in the literature. To examine the inter-generational impact of violence against women on the behavioral functioning of children, 300 mothers reporting intimate partner abuse and one randomly chosen child, age 18 months to 16 years of age; were evaluated for borderline and clinical diagnostic levels of problem behaviors. Linear, Logistic, and Ordinal regression models were applied. Mothers' problem behavior scores were significantly related to children's problem behavior scores (internalizing r = 0.611, externalizing r = 0.494, total problems r = 0.662, all ps < 0.001). Mothers who reported clinical and borderline clinical internalized problems (i.e., depression, anxiety) were 7 times more likely to have children with the same problems and mothers with borderline clinical and clinical external problems (i.e., aggression, hostility) were 4.5 times more likely to have children with the same external problems. These dyadic analyses provide evidence of a direct relationship of maternal functioning on child behavioral functioning. Intervention strategies to decrease internalizing maternal behavioral problems, such as depression, anxiety, and post traumatic stress disorder, and/or externalizing problems, such as hostility and aggression, can be expected to have a pass through, secondary impact on the behavioral functioning of children. Awareness of the relationship between intimate partner violence against mothers and child behavioral function can support interventions that decrease the distress experienced by mothers and their children, interrupt intergenerational transmission of abusive behaviors, and promote better maternal child functioning.

Competitive Facility Location with Fuzzy Random Demands

NASA Astrophysics Data System (ADS)

Uno, Takeshi; Katagiri, Hideki; Kato, Kosuke

2010-10-01

This paper proposes a new location problem of competitive facilities, e.g. shops, with uncertainty and vagueness including demands for the facilities in a plane. By representing the demands for facilities as fuzzy random variables, the location problem can be formulated as a fuzzy random programming problem. For solving the fuzzy random programming problem, first the α-level sets for fuzzy numbers are used for transforming it to a stochastic programming problem, and secondly, by using their expectations and variances, it can be reformulated to a deterministic programming problem. After showing that one of their optimal solutions can be found by solving 0-1 programming problems, their solution method is proposed by improving the tabu search algorithm with strategic oscillation. The efficiency of the proposed method is shown by applying it to numerical examples of the facility location problems.

Dynamic Flow Management Problems in Air Transportation

NASA Technical Reports Server (NTRS)

Patterson, Sarah Stock

1997-01-01

In 1995, over six hundred thousand licensed pilots flew nearly thirty-five million flights into over eighteen thousand U.S. airports, logging more than 519 billion passenger miles. Since demand for air travel has increased by more than 50% in the last decade while capacity has stagnated, congestion is a problem of undeniable practical significance. In this thesis, we will develop optimization techniques that reduce the impact of congestion on the national airspace. We start by determining the optimal release times for flights into the airspace and the optimal speed adjustment while airborne taking into account the capacitated airspace. This is called the Air Traffic Flow Management Problem (TFMP). We address the complexity, showing that it is NP-hard. We build an integer programming formulation that is quite strong as some of the proposed inequalities are facet defining for the convex hull of solutions. For practical problems, the solutions of the LP relaxation of the TFMP are very often integral. In essence, we reduce the problem to efficiently solving large scale linear programming problems. Thus, the computation times are reasonably small for large scale, practical problems involving thousands of flights. Next, we address the problem of determining how to reroute aircraft in the airspace system when faced with dynamically changing weather conditions. This is called the Air Traffic Flow Management Rerouting Problem (TFMRP) We present an integrated mathematical programming approach for the TFMRP, which utilizes several methodologies, in order to minimize delay costs. In order to address the high dimensionality, we present an aggregate model, in which we formulate the TFMRP as a multicommodity, integer, dynamic network flow problem with certain side constraints. Using Lagrangian relaxation, we generate aggregate flows that are decomposed into a collection of flight paths using a randomized rounding heuristic. This collection of paths is used in a packing integer programming formulation, the solution of which generates feasible and near-optimal routes for individual flights. The algorithm, termed the Lagrangian Generation Algorithm, is used to solve practical problems in the southwestern portion of United States in which the solutions are within 1% of the corresponding lower bounds.

PUFKEY: A High-Security and High-Throughput Hardware True Random Number Generator for Sensor Networks

PubMed Central

Li, Dongfang; Lu, Zhaojun; Zou, Xuecheng; Liu, Zhenglin

2015-01-01

Random number generators (RNG) play an important role in many sensor network systems and applications, such as those requiring secure and robust communications. In this paper, we develop a high-security and high-throughput hardware true random number generator, called PUFKEY, which consists of two kinds of physical unclonable function (PUF) elements. Combined with a conditioning algorithm, true random seeds are extracted from the noise on the start-up pattern of SRAM memories. These true random seeds contain full entropy. Then, the true random seeds are used as the input for a non-deterministic hardware RNG to generate a stream of true random bits with a throughput as high as 803 Mbps. The experimental results show that the bitstream generated by the proposed PUFKEY can pass all standard national institute of standards and technology (NIST) randomness tests and is resilient to a wide range of security attacks. PMID:26501283

PUFKEY: a high-security and high-throughput hardware true random number generator for sensor networks.

PubMed

Li, Dongfang; Lu, Zhaojun; Zou, Xuecheng; Liu, Zhenglin

2015-10-16

Random number generators (RNG) play an important role in many sensor network systems and applications, such as those requiring secure and robust communications. In this paper, we develop a high-security and high-throughput hardware true random number generator, called PUFKEY, which consists of two kinds of physical unclonable function (PUF) elements. Combined with a conditioning algorithm, true random seeds are extracted from the noise on the start-up pattern of SRAM memories. These true random seeds contain full entropy. Then, the true random seeds are used as the input for a non-deterministic hardware RNG to generate a stream of true random bits with a throughput as high as 803 Mbps. The experimental results show that the bitstream generated by the proposed PUFKEY can pass all standard national institute of standards and technology (NIST) randomness tests and is resilient to a wide range of security attacks.

A New Framework for Analysis of Coevolutionary Systems-Directed Graph Representation and Random Walks.

PubMed

Chong, Siang Yew; Tiňo, Peter; He, Jun; Yao, Xin

2017-11-20

Studying coevolutionary systems in the context of simplified models (i.e., games with pairwise interactions between coevolving solutions modeled as self plays) remains an open challenge since the rich underlying structures associated with pairwise-comparison-based fitness measures are often not taken fully into account. Although cyclic dynamics have been demonstrated in several contexts (such as intransitivity in coevolutionary problems), there is no complete characterization of cycle structures and their effects on coevolutionary search. We develop a new framework to address this issue. At the core of our approach is the directed graph (digraph) representation of coevolutionary problems that fully captures structures in the relations between candidate solutions. Coevolutionary processes are modeled as a specific type of Markov chains-random walks on digraphs. Using this framework, we show that coevolutionary problems admit a qualitative characterization: a coevolutionary problem is either solvable (there is a subset of solutions that dominates the remaining candidate solutions) or not. This has an implication on coevolutionary search. We further develop our framework that provides the means to construct quantitative tools for analysis of coevolutionary processes and demonstrate their applications through case studies. We show that coevolution of solvable problems corresponds to an absorbing Markov chain for which we can compute the expected hitting time of the absorbing class. Otherwise, coevolution will cycle indefinitely and the quantity of interest will be the limiting invariant distribution of the Markov chain. We also provide an index for characterizing complexity in coevolutionary problems and show how they can be generated in a controlled manner.

Early vision and focal attention

NASA Astrophysics Data System (ADS)

Julesz, Bela

1991-07-01

At the thirty-year anniversary of the introduction of the technique of computer-generated random-dot stereograms and random-dot cinematograms into psychology, the impact of the technique on brain research and on the study of artificial intelligence is reviewed. The main finding-that stereoscopic depth perception (stereopsis), motion perception, and preattentive texture discrimination are basically bottom-up processes, which occur without the help of the top-down processes of cognition and semantic memory-greatly simplifies the study of these processes of early vision and permits the linking of human perception with monkey neurophysiology. Particularly interesting are the unexpected findings that stereopsis (assumed to be local) is a global process, while texture discrimination (assumed to be a global process, governed by statistics) is local, based on some conspicuous local features (textons). It is shown that the top-down process of "shape (depth) from shading" does not affect stereopsis, and some of the models of machine vision are evaluated. The asymmetry effect of human texture discrimination is discussed, together with recent nonlinear spatial filter models and a novel extension of the texton theory that can cope with the asymmetry problem. This didactic review attempts to introduce the physicist to the field of psychobiology and its problems-including metascientific problems of brain research, problems of scientific creativity, the state of artificial intelligence research (including connectionist neural networks) aimed at modeling brain activity, and the fundamental role of focal attention in mental events.

An evolutionary strategy based on partial imitation for solving optimization problems

NASA Astrophysics Data System (ADS)

Javarone, Marco Alberto

2016-12-01

In this work we introduce an evolutionary strategy to solve combinatorial optimization tasks, i.e. problems characterized by a discrete search space. In particular, we focus on the Traveling Salesman Problem (TSP), i.e. a famous problem whose search space grows exponentially, increasing the number of cities, up to becoming NP-hard. The solutions of the TSP can be codified by arrays of cities, and can be evaluated by fitness, computed according to a cost function (e.g. the length of a path). Our method is based on the evolution of an agent population by means of an imitative mechanism, we define 'partial imitation'. In particular, agents receive a random solution and then, interacting among themselves, may imitate the solutions of agents with a higher fitness. Since the imitation mechanism is only partial, agents copy only one entry (randomly chosen) of another array (i.e. solution). In doing so, the population converges towards a shared solution, behaving like a spin system undergoing a cooling process, i.e. driven towards an ordered phase. We highlight that the adopted 'partial imitation' mechanism allows the population to generate solutions over time, before reaching the final equilibrium. Results of numerical simulations show that our method is able to find, in a finite time, both optimal and suboptimal solutions, depending on the size of the considered search space.

A rule-based software test data generator

NASA Technical Reports Server (NTRS)

Deason, William H.; Brown, David B.; Chang, Kai-Hsiung; Cross, James H., II

1991-01-01

Rule-based software test data generation is proposed as an alternative to either path/predicate analysis or random data generation. A prototype rule-based test data generator for Ada programs is constructed and compared to a random test data generator. Four Ada procedures are used in the comparison. Approximately 2000 rule-based test cases and 100,000 randomly generated test cases are automatically generated and executed. The success of the two methods is compared using standard coverage metrics. Simple statistical tests showing that even the primitive rule-based test data generation prototype is significantly better than random data generation are performed. This result demonstrates that rule-based test data generation is feasible and shows great promise in assisting test engineers, especially when the rule base is developed further.

Optimal Strategy for Integrated Dynamic Inventory Control and Supplier Selection in Unknown Environment via Stochastic Dynamic Programming

NASA Astrophysics Data System (ADS)

Sutrisno; Widowati; Solikhin

2016-06-01

In this paper, we propose a mathematical model in stochastic dynamic optimization form to determine the optimal strategy for an integrated single product inventory control problem and supplier selection problem where the demand and purchasing cost parameters are random. For each time period, by using the proposed model, we decide the optimal supplier and calculate the optimal product volume purchased from the optimal supplier so that the inventory level will be located at some point as close as possible to the reference point with minimal cost. We use stochastic dynamic programming to solve this problem and give several numerical experiments to evaluate the model. From the results, for each time period, the proposed model was generated the optimal supplier and the inventory level was tracked the reference point well.

Maximizing the nurses' preferences in nurse scheduling problem: mathematical modeling and a meta-heuristic algorithm

NASA Astrophysics Data System (ADS)

Jafari, Hamed; Salmasi, Nasser

2015-09-01

The nurse scheduling problem (NSP) has received a great amount of attention in recent years. In the NSP, the goal is to assign shifts to the nurses in order to satisfy the hospital's demand during the planning horizon by considering different objective functions. In this research, we focus on maximizing the nurses' preferences for working shifts and weekends off by considering several important factors such as hospital's policies, labor laws, governmental regulations, and the status of nurses at the end of the previous planning horizon in one of the largest hospitals in Iran i.e., Milad Hospital. Due to the shortage of available nurses, at first, the minimum total number of required nurses is determined. Then, a mathematical programming model is proposed to solve the problem optimally. Since the proposed research problem is NP-hard, a meta-heuristic algorithm based on simulated annealing (SA) is applied to heuristically solve the problem in a reasonable time. An initial feasible solution generator and several novel neighborhood structures are applied to enhance performance of the SA algorithm. Inspired from our observations in Milad hospital, random test problems are generated to evaluate the performance of the SA algorithm. The results of computational experiments indicate that the applied SA algorithm provides solutions with average percentage gap of 5.49 % compared to the upper bounds obtained from the mathematical model. Moreover, the applied SA algorithm provides significantly better solutions in a reasonable time than the schedules provided by the head nurses.

Unification theory of optimal life histories and linear demographic models in internal stochasticity.

PubMed

Oizumi, Ryo

2014-01-01

Life history of organisms is exposed to uncertainty generated by internal and external stochasticities. Internal stochasticity is generated by the randomness in each individual life history, such as randomness in food intake, genetic character and size growth rate, whereas external stochasticity is due to the environment. For instance, it is known that the external stochasticity tends to affect population growth rate negatively. It has been shown in a recent theoretical study using path-integral formulation in structured linear demographic models that internal stochasticity can affect population growth rate positively or negatively. However, internal stochasticity has not been the main subject of researches. Taking account of effect of internal stochasticity on the population growth rate, the fittest organism has the optimal control of life history affected by the stochasticity in the habitat. The study of this control is known as the optimal life schedule problems. In order to analyze the optimal control under internal stochasticity, we need to make use of "Stochastic Control Theory" in the optimal life schedule problem. There is, however, no such kind of theory unifying optimal life history and internal stochasticity. This study focuses on an extension of optimal life schedule problems to unify control theory of internal stochasticity into linear demographic models. First, we show the relationship between the general age-states linear demographic models and the stochastic control theory via several mathematical formulations, such as path-integral, integral equation, and transition matrix. Secondly, we apply our theory to a two-resource utilization model for two different breeding systems: semelparity and iteroparity. Finally, we show that the diversity of resources is important for species in a case. Our study shows that this unification theory can address risk hedges of life history in general age-states linear demographic models.

Unification Theory of Optimal Life Histories and Linear Demographic Models in Internal Stochasticity

PubMed Central

Oizumi, Ryo

2014-01-01

Life history of organisms is exposed to uncertainty generated by internal and external stochasticities. Internal stochasticity is generated by the randomness in each individual life history, such as randomness in food intake, genetic character and size growth rate, whereas external stochasticity is due to the environment. For instance, it is known that the external stochasticity tends to affect population growth rate negatively. It has been shown in a recent theoretical study using path-integral formulation in structured linear demographic models that internal stochasticity can affect population growth rate positively or negatively. However, internal stochasticity has not been the main subject of researches. Taking account of effect of internal stochasticity on the population growth rate, the fittest organism has the optimal control of life history affected by the stochasticity in the habitat. The study of this control is known as the optimal life schedule problems. In order to analyze the optimal control under internal stochasticity, we need to make use of “Stochastic Control Theory” in the optimal life schedule problem. There is, however, no such kind of theory unifying optimal life history and internal stochasticity. This study focuses on an extension of optimal life schedule problems to unify control theory of internal stochasticity into linear demographic models. First, we show the relationship between the general age-states linear demographic models and the stochastic control theory via several mathematical formulations, such as path–integral, integral equation, and transition matrix. Secondly, we apply our theory to a two-resource utilization model for two different breeding systems: semelparity and iteroparity. Finally, we show that the diversity of resources is important for species in a case. Our study shows that this unification theory can address risk hedges of life history in general age-states linear demographic models. PMID:24945258

Lymphedema following treatment for breast cancer: a new approach to an old problem.

PubMed

O'Toole, Jean; Jammallo, Lauren S; Skolny, Melissa N; Miller, Cynthia L; Elliott, Krista; Specht, Michelle C; Taghian, Alphonse G

2013-11-01

Lymphedema following treatment for breast cancer can be an irreversible condition with a profound negative impact on quality of life. The lack of consensus regarding standard definitions of clinically significant lymphedema and optimal methods of measurement and quantification are unresolved problems. Inconsistencies persist regarding the appropriate timing of intervention and what forms of treatment should be the standard of care. There are reports that early detection and intervention can prevent progression, however,the Level 1 evidence to support this hypothesis has yet to be generated. To assess these controversies, we propose the implementation of a screening program to detect early lymphedema in conjunction with a randomized, prospective trial designed to generate Level 1 evidence regarding the efficacy of early intervention and appropriate treatment strategies. Collaboration among institutions that manage breast cancer patients is essential to establish a standardized approach to lymphedema and to establish guidelines for best practice. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Modeling missing data in knowledge space theory.

PubMed

de Chiusole, Debora; Stefanutti, Luca; Anselmi, Pasquale; Robusto, Egidio

2015-12-01

Missing data are a well known issue in statistical inference, because some responses may be missing, even when data are collected carefully. The problem that arises in these cases is how to deal with missing data. In this article, the missingness is analyzed in knowledge space theory, and in particular when the basic local independence model (BLIM) is applied to the data. Two extensions of the BLIM to missing data are proposed: The former, called ignorable missing BLIM (IMBLIM), assumes that missing data are missing completely at random; the latter, called missing BLIM (MissBLIM), introduces specific dependencies of the missing data on the knowledge states, thus assuming that the missing data are missing not at random. The IMBLIM and the MissBLIM modeled the missingness in a satisfactory way, in both a simulation study and an empirical application, depending on the process that generates the missingness: If the missing data-generating process is of type missing completely at random, then either IMBLIM or MissBLIM provide adequate fit to the data. However, if the pattern of missingness is functionally dependent upon unobservable features of the data (e.g., missing answers are more likely to be wrong), then only a correctly specified model of the missingness distribution provides an adequate fit to the data. (c) 2015 APA, all rights reserved).

Realization of a Quantum Random Generator Certified with the Kochen-Specker Theorem

NASA Astrophysics Data System (ADS)

Kulikov, Anatoly; Jerger, Markus; Potočnik, Anton; Wallraff, Andreas; Fedorov, Arkady

2017-12-01

Random numbers are required for a variety of applications from secure communications to Monte Carlo simulation. Yet randomness is an asymptotic property, and no output string generated by a physical device can be strictly proven to be random. We report an experimental realization of a quantum random number generator (QRNG) with randomness certified by quantum contextuality and the Kochen-Specker theorem. The certification is not performed in a device-independent way but through a rigorous theoretical proof of each outcome being value indefinite even in the presence of experimental imperfections. The analysis of the generated data confirms the incomputable nature of our QRNG.

Realization of a Quantum Random Generator Certified with the Kochen-Specker Theorem.

PubMed

Kulikov, Anatoly; Jerger, Markus; Potočnik, Anton; Wallraff, Andreas; Fedorov, Arkady

2017-12-15

Random numbers are required for a variety of applications from secure communications to Monte Carlo simulation. Yet randomness is an asymptotic property, and no output string generated by a physical device can be strictly proven to be random. We report an experimental realization of a quantum random number generator (QRNG) with randomness certified by quantum contextuality and the Kochen-Specker theorem. The certification is not performed in a device-independent way but through a rigorous theoretical proof of each outcome being value indefinite even in the presence of experimental imperfections. The analysis of the generated data confirms the incomputable nature of our QRNG.

CrowdPhase: crowdsourcing the phase problem

PubMed Central

Jorda, Julien; Sawaya, Michael R.; Yeates, Todd O.

2014-01-01

The human mind innately excels at some complex tasks that are difficult to solve using computers alone. For complex problems amenable to parallelization, strategies can be developed to exploit human intelligence in a collective form: such approaches are sometimes referred to as ‘crowdsourcing’. Here, a first attempt at a crowdsourced approach for low-resolution ab initio phasing in macromolecular crystallography is proposed. A collaborative online game named CrowdPhase was designed, which relies on a human-powered genetic algorithm, where players control the selection mechanism during the evolutionary process. The algorithm starts from a population of ‘individuals’, each with a random genetic makeup, in this case a map prepared from a random set of phases, and tries to cause the population to evolve towards individuals with better phases based on Darwinian survival of the fittest. Players apply their pattern-recognition capabilities to evaluate the electron-density maps generated from these sets of phases and to select the fittest individuals. A user-friendly interface, a training stage and a competitive scoring system foster a network of well trained players who can guide the genetic algorithm towards better solutions from generation to generation via gameplay. CrowdPhase was applied to two synthetic low-resolution phasing puzzles and it was shown that players could successfully obtain phase sets in the 30° phase error range and corresponding molecular envelopes showing agreement with the low-resolution models. The successful preliminary studies suggest that with further development the crowdsourcing approach could fill a gap in current crystallographic methods by making it possible to extract meaningful information in cases where limited resolution might otherwise prevent initial phasing. PMID:24914965

Source-Independent Quantum Random Number Generation

NASA Astrophysics Data System (ADS)

Cao, Zhu; Zhou, Hongyi; Yuan, Xiao; Ma, Xiongfeng

2016-01-01

Quantum random number generators can provide genuine randomness by appealing to the fundamental principles of quantum mechanics. In general, a physical generator contains two parts—a randomness source and its readout. The source is essential to the quality of the resulting random numbers; hence, it needs to be carefully calibrated and modeled to achieve information-theoretical provable randomness. However, in practice, the source is a complicated physical system, such as a light source or an atomic ensemble, and any deviations in the real-life implementation from the theoretical model may affect the randomness of the output. To close this gap, we propose a source-independent scheme for quantum random number generation in which output randomness can be certified, even when the source is uncharacterized and untrusted. In our randomness analysis, we make no assumptions about the dimension of the source. For instance, multiphoton emissions are allowed in optical implementations. Our analysis takes into account the finite-key effect with the composable security definition. In the limit of large data size, the length of the input random seed is exponentially small compared to that of the output random bit. In addition, by modifying a quantum key distribution system, we experimentally demonstrate our scheme and achieve a randomness generation rate of over 5 ×103 bit /s .

Quantum Random Number Generation Using a Quanta Image Sensor

PubMed Central

Amri, Emna; Felk, Yacine; Stucki, Damien; Ma, Jiaju; Fossum, Eric R.

2016-01-01

A new quantum random number generation method is proposed. The method is based on the randomness of the photon emission process and the single photon counting capability of the Quanta Image Sensor (QIS). It has the potential to generate high-quality random numbers with remarkable data output rate. In this paper, the principle of photon statistics and theory of entropy are discussed. Sample data were collected with QIS jot device, and its randomness quality was analyzed. The randomness assessment method and results are discussed. PMID:27367698

Identification of mutated driver pathways in cancer using a multi-objective optimization model.

PubMed

Zheng, Chun-Hou; Yang, Wu; Chong, Yan-Wen; Xia, Jun-Feng

2016-05-01

New-generation high-throughput technologies, including next-generation sequencing technology, have been extensively applied to solve biological problems. As a result, large cancer genomics projects such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium are producing large amount of rich and diverse data in multiple cancer types. The identification of mutated driver genes and driver pathways from these data is a significant challenge. Genome aberrations in cancer cells can be divided into two types: random 'passenger mutation' and functional 'driver mutation'. In this paper, we introduced a Multi-objective Optimization model based on a Genetic Algorithm (MOGA) to solve the maximum weight submatrix problem, which can be employed to identify driver genes and driver pathways promoting cancer proliferation. The maximum weight submatrix problem defined to find mutated driver pathways is based on two specific properties, i.e., high coverage and high exclusivity. The multi-objective optimization model can adjust the trade-off between high coverage and high exclusivity. We proposed an integrative model by combining gene expression data and mutation data to improve the performance of the MOGA algorithm in a biological context. Copyright © 2016 Elsevier Ltd. All rights reserved.

Image encryption using random sequence generated from generalized information domain

NASA Astrophysics Data System (ADS)

Xia-Yan, Zhang; Guo-Ji, Zhang; Xuan, Li; Ya-Zhou, Ren; Jie-Hua, Wu

2016-05-01

A novel image encryption method based on the random sequence generated from the generalized information domain and permutation-diffusion architecture is proposed. The random sequence is generated by reconstruction from the generalized information file and discrete trajectory extraction from the data stream. The trajectory address sequence is used to generate a P-box to shuffle the plain image while random sequences are treated as keystreams. A new factor called drift factor is employed to accelerate and enhance the performance of the random sequence generator. An initial value is introduced to make the encryption method an approximately one-time pad. Experimental results show that the random sequences pass the NIST statistical test with a high ratio and extensive analysis demonstrates that the new encryption scheme has superior security.

Extended fault inversion with random slipmaps: A resolution test for the 2012 Mw 7.6 Nicoya, Costa Rica earthquake from a Popperian inversion strategy.

NASA Astrophysics Data System (ADS)

Ángel López Comino, José; Stich, Daniel; Ferreira, Ana M. G.; Morales Soto, José

2015-04-01

The inversion of seismic data for extended fault slip distributions provides us detailed models of earthquake sources. The validity of the solutions depends on the fit between observed and synthetic seismograms generated with the source model. However, there may exist more than one model that fit the data in a similar way, leading to a multiplicity of solutions. This underdetermined problem has been analyzed and studied by several authors, who agree that inverting for a single best model may become overly dependent on the details of the procedure. We have addressed this resolution problem by using a global search that scans the solutions domain using random slipmaps, applying a Popperian inversion strategy that involves the generation of a representative set of slip distributions. The proposed technique solves the forward problem for a large set of models calculating their corresponding synthetic seismograms. Then, we propose to perform extended fault inversion through falsification, that is, falsify inappropriate trial models that do not reproduce the data within a reasonable level of mismodelling. The remainder of surviving trial models forms our set of coequal solutions. Thereby the ambiguities that might exist can be detected by taking a look at the solutions, allowing for an efficient assessment of the resolution. The solution set may contain only members with similar slip distributions, or else uncover some fundamental ambiguity like, for example, different patterns of main slip patches or different patterns of rupture propagation. For a feasibility study, the proposed resolution test has been evaluated using teleseismic body wave recordings from the September 5th 2012 Nicoya, Costa Rica earthquake. Note that the inversion strategy can be applied to any type of seismic, geodetic or tsunami data for which we can handle the forward problem. A 2D von Karman distribution is used to describe the spectrum of heterogeneity in slipmaps, and we generate possible models by spectral synthesis for random phase, keeping the rake angle, rupture velocity and slip velocity function fixed. The 2012 Nicoya earthquake turns out to be relatively well constrained from 50 teleseismic waveforms. The solution set contains 252 out of 10.000 trial models with normalized L1-fit within 5 percent from the global minimum. The set includes only similar solutions -a single centred slip patch- with minor differences. Uncertainties are related to the details of the slip maximum, including the amount of peak slip (2m to 3.5m), as well as the characteristics of peripheral slip below 1 m. Synthetic tests suggest that slip patterns like Nicoya may be a fortunate case, while it may be more difficult to unambiguously reconstruct more distributed slip from teleseismic data.

A generator for unique quantum random numbers based on vacuum states

NASA Astrophysics Data System (ADS)

Gabriel, Christian; Wittmann, Christoffer; Sych, Denis; Dong, Ruifang; Mauerer, Wolfgang; Andersen, Ulrik L.; Marquardt, Christoph; Leuchs, Gerd

2010-10-01

Random numbers are a valuable component in diverse applications that range from simulations over gambling to cryptography. The quest for true randomness in these applications has engendered a large variety of different proposals for producing random numbers based on the foundational unpredictability of quantum mechanics. However, most approaches do not consider that a potential adversary could have knowledge about the generated numbers, so the numbers are not verifiably random and unique. Here we present a simple experimental setup based on homodyne measurements that uses the purity of a continuous-variable quantum vacuum state to generate unique random numbers. We use the intrinsic randomness in measuring the quadratures of a mode in the lowest energy vacuum state, which cannot be correlated to any other state. The simplicity of our source, combined with its verifiably unique randomness, are important attributes for achieving high-reliability, high-speed and low-cost quantum random number generators.

Scaling of peak flows with constant flow velocity in random self-similar networks

USGS Publications Warehouse

Troutman, Brent M.; Mantilla, Ricardo; Gupta, Vijay K.

2011-01-01

A methodology is presented to understand the role of the statistical self-similar topology of real river networks on scaling, or power law, in peak flows for rainfall-runoff events. We created Monte Carlo generated sets of ensembles of 1000 random self-similar networks (RSNs) with geometrically distributed interior and exterior generators having parameters pi and pe, respectively. The parameter values were chosen to replicate the observed topology of real river networks. We calculated flow hydrographs in each of these networks by numerically solving the link-based mass and momentum conservation equation under the assumption of constant flow velocity. From these simulated RSNs and hydrographs, the scaling exponents β and φ characterizing power laws with respect to drainage area, and corresponding to the width functions and flow hydrographs respectively, were estimated. We found that, in general, φ > β, which supports a similar finding first reported for simulations in the river network of the Walnut Gulch basin, Arizona. Theoretical estimation of β and φ in RSNs is a complex open problem. Therefore, using results for a simpler problem associated with the expected width function and expected hydrograph for an ensemble of RSNs, we give heuristic arguments for theoretical derivations of the scaling exponents β(E) and φ(E) that depend on the Horton ratios for stream lengths and areas. These ratios in turn have a known dependence on the parameters of the geometric distributions of RSN generators. Good agreement was found between the analytically conjectured values of β(E) and φ(E) and the values estimated by the simulated ensembles of RSNs and hydrographs. The independence of the scaling exponents φ(E) and φ with respect to the value of flow velocity and runoff intensity implies an interesting connection between unit hydrograph theory and flow dynamics. Our results provide a reference framework to study scaling exponents under more complex scenarios of flow dynamics and runoff generation processes using ensembles of RSNs.

Multi-dimensional Fokker-Planck equation analysis using the modified finite element method

NASA Astrophysics Data System (ADS)

Náprstek, J.; Král, R.

2016-09-01

The Fokker-Planck equation (FPE) is a frequently used tool for the solution of cross probability density function (PDF) of a dynamic system response excited by a vector of random processes. FEM represents a very effective solution possibility, particularly when transition processes are investigated or a more detailed solution is needed. Actual papers deal with single degree of freedom (SDOF) systems only. So the respective FPE includes two independent space variables only. Stepping over this limit into MDOF systems a number of specific problems related to a true multi-dimensionality must be overcome. Unlike earlier studies, multi-dimensional simplex elements in any arbitrary dimension should be deployed and rectangular (multi-brick) elements abandoned. Simple closed formulae of integration in multi-dimension domain have been derived. Another specific problem represents the generation of multi-dimensional finite element mesh. Assembling of system global matrices should be subjected to newly composed algorithms due to multi-dimensionality. The system matrices are quite full and no advantages following from their sparse character can be profited from, as is commonly used in conventional FEM applications in 2D/3D problems. After verification of partial algorithms, an illustrative example dealing with a 2DOF non-linear aeroelastic system in combination with random and deterministic excitations is discussed.

Generating subtour elimination constraints for the TSP from pure integer solutions.

PubMed

Pferschy, Ulrich; Staněk, Rostislav

2017-01-01

The traveling salesman problem ( TSP ) is one of the most prominent combinatorial optimization problems. Given a complete graph [Formula: see text] and non-negative distances d for every edge, the TSP asks for a shortest tour through all vertices with respect to the distances d. The method of choice for solving the TSP to optimality is a branch and cut approach . Usually the integrality constraints are relaxed first and all separation processes to identify violated inequalities are done on fractional solutions . In our approach we try to exploit the impressive performance of current ILP-solvers and work only with integer solutions without ever interfering with fractional solutions. We stick to a very simple ILP-model and relax the subtour elimination constraints only. The resulting problem is solved to integer optimality, violated constraints (which are trivial to find) are added and the process is repeated until a feasible solution is found. In order to speed up the algorithm we pursue several attempts to find as many relevant subtours as possible. These attempts are based on the clustering of vertices with additional insights gained from empirical observations and random graph theory. Computational results are performed on test instances taken from the TSPLIB95 and on random Euclidean graphs .

Realizations of highly heterogeneous collagen networks via stochastic reconstruction for micromechanical analysis of tumor cell invasion

NASA Astrophysics Data System (ADS)

Nan, Hanqing; Liang, Long; Chen, Guo; Liu, Liyu; Liu, Ruchuan; Jiao, Yang

2018-03-01

Three-dimensional (3D) collective cell migration in a collagen-based extracellular matrix (ECM) is among one of the most significant topics in developmental biology, cancer progression, tissue regeneration, and immune response. Recent studies have suggested that collagen-fiber mediated force transmission in cellularized ECM plays an important role in stress homeostasis and regulation of collective cellular behaviors. Motivated by the recent in vitro observation that oriented collagen can significantly enhance the penetration of migrating breast cancer cells into dense Matrigel which mimics the intravasation process in vivo [Han et al. Proc. Natl. Acad. Sci. USA 113, 11208 (2016), 10.1073/pnas.1610347113], we devise a procedure for generating realizations of highly heterogeneous 3D collagen networks with prescribed microstructural statistics via stochastic optimization. Specifically, a collagen network is represented via the graph (node-bond) model and the microstructural statistics considered include the cross-link (node) density, valence distribution, fiber (bond) length distribution, as well as fiber orientation distribution. An optimization problem is formulated in which the objective function is defined as the squared difference between a set of target microstructural statistics and the corresponding statistics for the simulated network. Simulated annealing is employed to solve the optimization problem by evolving an initial network via random perturbations to generate realizations of homogeneous networks with randomly oriented fibers, homogeneous networks with aligned fibers, heterogeneous networks with a continuous variation of fiber orientation along a prescribed direction, as well as a binary system containing a collagen region with aligned fibers and a dense Matrigel region with randomly oriented fibers. The generation and propagation of active forces in the simulated networks due to polarized contraction of an embedded ellipsoidal cell and a small group of cells are analyzed by considering a nonlinear fiber model incorporating strain hardening upon large stretching and buckling upon compression. Our analysis shows that oriented fibers can significantly enhance long-range force transmission in the network. Moreover, in the oriented-collagen-Matrigel system, the forces generated by a polarized cell in collagen can penetrate deeply into the Matrigel region. The stressed Matrigel fibers could provide contact guidance for the migrating cell cells, and thus enhance their penetration into Matrigel. This suggests a possible mechanism for the observed enhanced intravasation by oriented collagen.

Anomalous transport in turbulent plasmas and continuous time random walks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Balescu, R.

1995-05-01

The possibility of a model of anomalous transport problems in a turbulent plasma by a purely stochastic process is investigated. The theory of continuous time random walks (CTRW`s) is briefly reviewed. It is shown that a particular class, called the standard long tail CTRW`s is of special interest for the description of subdiffusive transport. Its evolution is described by a non-Markovian diffusion equation that is constructed in such a way as to yield exact values for all the moments of the density profile. The concept of a CTRW model is compared to an exact solution of a simple test problem:more » transport of charged particles in a fluctuating magnetic field in the limit of infinite perpendicular correlation length. Although the well-known behavior of the mean square displacement proportional to {ital t}{sup 1/2} is easily recovered, the exact density profile cannot be modeled by a CTRW. However, the quasilinear approximation of the kinetic equation has the form of a non-Markovian diffusion equation and can thus be generated by a CTRW.« less

Probabilistic inference in discrete spaces can be implemented into networks of LIF neurons.

PubMed

Probst, Dimitri; Petrovici, Mihai A; Bytschok, Ilja; Bill, Johannes; Pecevski, Dejan; Schemmel, Johannes; Meier, Karlheinz

2015-01-01

The means by which cortical neural networks are able to efficiently solve inference problems remains an open question in computational neuroscience. Recently, abstract models of Bayesian computation in neural circuits have been proposed, but they lack a mechanistic interpretation at the single-cell level. In this article, we describe a complete theoretical framework for building networks of leaky integrate-and-fire neurons that can sample from arbitrary probability distributions over binary random variables. We test our framework for a model inference task based on a psychophysical phenomenon (the Knill-Kersten optical illusion) and further assess its performance when applied to randomly generated distributions. As the local computations performed by the network strongly depend on the interaction between neurons, we compare several types of couplings mediated by either single synapses or interneuron chains. Due to its robustness to substrate imperfections such as parameter noise and background noise correlations, our model is particularly interesting for implementation on novel, neuro-inspired computing architectures, which can thereby serve as a fast, low-power substrate for solving real-world inference problems.

Probabilistic inference in discrete spaces can be implemented into networks of LIF neurons

PubMed Central

Probst, Dimitri; Petrovici, Mihai A.; Bytschok, Ilja; Bill, Johannes; Pecevski, Dejan; Schemmel, Johannes; Meier, Karlheinz

2015-01-01

The means by which cortical neural networks are able to efficiently solve inference problems remains an open question in computational neuroscience. Recently, abstract models of Bayesian computation in neural circuits have been proposed, but they lack a mechanistic interpretation at the single-cell level. In this article, we describe a complete theoretical framework for building networks of leaky integrate-and-fire neurons that can sample from arbitrary probability distributions over binary random variables. We test our framework for a model inference task based on a psychophysical phenomenon (the Knill-Kersten optical illusion) and further assess its performance when applied to randomly generated distributions. As the local computations performed by the network strongly depend on the interaction between neurons, we compare several types of couplings mediated by either single synapses or interneuron chains. Due to its robustness to substrate imperfections such as parameter noise and background noise correlations, our model is particularly interesting for implementation on novel, neuro-inspired computing architectures, which can thereby serve as a fast, low-power substrate for solving real-world inference problems. PMID:25729361

Random ambience using high fidelity images

NASA Astrophysics Data System (ADS)

Abu, Nur Azman; Sahib, Shahrin

2011-06-01

Most of the secure communication nowadays mandates true random keys as an input. These operations are mostly designed and taken care of by the developers of the cryptosystem. Due to the nature of confidential crypto development today, pseudorandom keys are typically designed and still preferred by the developers of the cryptosystem. However, these pseudorandom keys are predictable, periodic and repeatable, hence they carry minimal entropy. True random keys are believed to be generated only via hardware random number generators. Careful statistical analysis is still required to have any confidence the process and apparatus generates numbers that are sufficiently random to suit the cryptographic use. In this underlying research, each moment in life is considered unique in itself. The random key is unique for the given moment generated by the user whenever he or she needs the random keys in practical secure communication. An ambience of high fidelity digital image shall be tested for its randomness according to the NIST Statistical Test Suite. Recommendation on generating a simple 4 megabits per second random cryptographic keys live shall be reported.

Superparamagnetic perpendicular magnetic tunnel junctions for true random number generators

NASA Astrophysics Data System (ADS)

Parks, Bradley; Bapna, Mukund; Igbokwe, Julianne; Almasi, Hamid; Wang, Weigang; Majetich, Sara A.

2018-05-01

Superparamagnetic perpendicular magnetic tunnel junctions are fabricated and analyzed for use in random number generators. Time-resolved resistance measurements are used as streams of bits in statistical tests for randomness. Voltage control of the thermal stability enables tuning the average speed of random bit generation up to 70 kHz in a 60 nm diameter device. In its most efficient operating mode, the device generates random bits at an energy cost of 600 fJ/bit. A narrow range of magnetic field tunes the probability of a given state from 0 to 1, offering a means of probabilistic computing.

Brownian motion properties of optoelectronic random bit generators based on laser chaos.

PubMed

Li, Pu; Yi, Xiaogang; Liu, Xianglian; Wang, Yuncai; Wang, Yongge

2016-07-11

The nondeterministic property of the optoelectronic random bit generator (RBG) based on laser chaos are experimentally analyzed from two aspects of the central limit theorem and law of iterated logarithm. The random bits are extracted from an optical feedback chaotic laser diode using a multi-bit extraction technique in the electrical domain. Our experimental results demonstrate that the generated random bits have no statistical distance from the Brownian motion, besides that they can pass the state-of-the-art industry-benchmark statistical test suite (NIST SP800-22). All of them give a mathematically provable evidence that the ultrafast random bit generator based on laser chaos can be used as a nondeterministic random bit source.

Towards a high-speed quantum random number generator

NASA Astrophysics Data System (ADS)

Stucki, Damien; Burri, Samuel; Charbon, Edoardo; Chunnilall, Christopher; Meneghetti, Alessio; Regazzoni, Francesco

2013-10-01

Randomness is of fundamental importance in various fields, such as cryptography, numerical simulations, or the gaming industry. Quantum physics, which is fundamentally probabilistic, is the best option for a physical random number generator. In this article, we will present the work carried out in various projects in the context of the development of a commercial and certified high speed random number generator.

Epidemics in networks: a master equation approach

NASA Astrophysics Data System (ADS)

Cotacallapa, M.; Hase, M. O.

2016-02-01

A problem closely related to epidemiology, where a subgraph of ‘infected’ links is defined inside a larger network, is investigated. This subgraph is generated from the underlying network by a random variable, which decides whether a link is able to propagate a disease/information. The relaxation timescale of this random variable is examined in both annealed and quenched limits, and the effectiveness of propagation of disease/information is analyzed. The dynamics of the model is governed by a master equation and two types of underlying network are considered: one is scale-free and the other has exponential degree distribution. We have shown that the relaxation timescale of the contagion variable has a major influence on the topology of the subgraph of infected links, which determines the efficiency of spreading of disease/information over the network.

Simulation using computer-piloted point excitations of vibrations induced on a structure by an acoustic environment

NASA Astrophysics Data System (ADS)

Monteil, P.

1981-11-01

Computation of the overall levels and spectral densities of the responses measured on a launcher skin, the fairing for instance, merged into a random acoustic environment during take off, was studied. The analysis of transmission of these vibrations to the payload required the simulation of these responses by a shaker control system, using a small number of distributed shakers. Results show that this closed loop computerized digital system allows the acquisition of auto and cross spectral densities equal to those of the responses previously computed. However, wider application is sought, e.g., road and runway profiles. The problems of multiple input-output system identification, multiple true random signal generation, and real time programming are evoked. The system should allow for the control of four shakers.

Robot Vision

NASA Technical Reports Server (NTRS)

Sutro, L. L.; Lerman, J. B.

1973-01-01

The operation of a system is described that is built both to model the vision of primate animals, including man, and serve as a pre-prototype of possible object recognition system. It was employed in a series of experiments to determine the practicability of matching left and right images of a scene to determine the range and form of objects. The experiments started with computer generated random-dot stereograms as inputs and progressed through random square stereograms to a real scene. The major problems were the elimination of spurious matches, between the left and right views, and the interpretation of ambiguous regions, on the left side of an object that can be viewed only by the left camera, and on the right side of an object that can be viewed only by the right camera.

Error free physically unclonable function with programmed resistive random access memory using reliable resistance states by specific identification-generation method

NASA Astrophysics Data System (ADS)

Tseng, Po-Hao; Hsu, Kai-Chieh; Lin, Yu-Yu; Lee, Feng-Min; Lee, Ming-Hsiu; Lung, Hsiang-Lan; Hsieh, Kuang-Yeu; Chung Wang, Keh; Lu, Chih-Yuan

2018-04-01

A high performance physically unclonable function (PUF) implemented with WO3 resistive random access memory (ReRAM) is presented in this paper. This robust ReRAM-PUF can eliminated bit flipping problem at very high temperature (up to 250 °C) due to plentiful read margin by using initial resistance state and set resistance state. It is also promised 10 years retention at the temperature range of 210 °C. These two stable resistance states enable stable operation at automotive environments from -40 to 125 °C without need of temperature compensation circuit. The high uniqueness of PUF can be achieved by implementing a proposed identification (ID)-generation method. Optimized forming condition can move 50% of the cells to low resistance state and the remaining 50% remain at initial high resistance state. The inter- and intra-PUF evaluations with unlimited separation of hamming distance (HD) are successfully demonstrated even under the corner condition. The number of reproduction was measured to exceed 107 times with 0% bit error rate (BER) at read voltage from 0.4 to 0.7 V.

Generating variable and random schedules of reinforcement using Microsoft Excel macros.

PubMed

Bancroft, Stacie L; Bourret, Jason C

2008-01-01

Variable reinforcement schedules are used to arrange the availability of reinforcement following varying response ratios or intervals of time. Random reinforcement schedules are subtypes of variable reinforcement schedules that can be used to arrange the availability of reinforcement at a constant probability across number of responses or time. Generating schedule values for variable and random reinforcement schedules can be difficult. The present article describes the steps necessary to write macros in Microsoft Excel that will generate variable-ratio, variable-interval, variable-time, random-ratio, random-interval, and random-time reinforcement schedule values.

Self-consistent determination of the spike-train power spectrum in a neural network with sparse connectivity.

PubMed

Dummer, Benjamin; Wieland, Stefan; Lindner, Benjamin

2014-01-01

A major source of random variability in cortical networks is the quasi-random arrival of presynaptic action potentials from many other cells. In network studies as well as in the study of the response properties of single cells embedded in a network, synaptic background input is often approximated by Poissonian spike trains. However, the output statistics of the cells is in most cases far from being Poisson. This is inconsistent with the assumption of similar spike-train statistics for pre- and postsynaptic cells in a recurrent network. Here we tackle this problem for the popular class of integrate-and-fire neurons and study a self-consistent statistics of input and output spectra of neural spike trains. Instead of actually using a large network, we use an iterative scheme, in which we simulate a single neuron over several generations. In each of these generations, the neuron is stimulated with surrogate stochastic input that has a similar statistics as the output of the previous generation. For the surrogate input, we employ two distinct approximations: (i) a superposition of renewal spike trains with the same interspike interval density as observed in the previous generation and (ii) a Gaussian current with a power spectrum proportional to that observed in the previous generation. For input parameters that correspond to balanced input in the network, both the renewal and the Gaussian iteration procedure converge quickly and yield comparable results for the self-consistent spike-train power spectrum. We compare our results to large-scale simulations of a random sparsely connected network of leaky integrate-and-fire neurons (Brunel, 2000) and show that in the asynchronous regime close to a state of balanced synaptic input from the network, our iterative schemes provide an excellent approximations to the autocorrelation of spike trains in the recurrent network.

Bootstrapping on Undirected Binary Networks Via Statistical Mechanics

NASA Astrophysics Data System (ADS)

Fushing, Hsieh; Chen, Chen; Liu, Shan-Yu; Koehl, Patrice

2014-09-01

We propose a new method inspired from statistical mechanics for extracting geometric information from undirected binary networks and generating random networks that conform to this geometry. In this method an undirected binary network is perceived as a thermodynamic system with a collection of permuted adjacency matrices as its states. The task of extracting information from the network is then reformulated as a discrete combinatorial optimization problem of searching for its ground state. To solve this problem, we apply multiple ensembles of temperature regulated Markov chains to establish an ultrametric geometry on the network. This geometry is equipped with a tree hierarchy that captures the multiscale community structure of the network. We translate this geometry into a Parisi adjacency matrix, which has a relative low energy level and is in the vicinity of the ground state. The Parisi adjacency matrix is then further optimized by making block permutations subject to the ultrametric geometry. The optimal matrix corresponds to the macrostate of the original network. An ensemble of random networks is then generated such that each of these networks conforms to this macrostate; the corresponding algorithm also provides an estimate of the size of this ensemble. By repeating this procedure at different scales of the ultrametric geometry of the network, it is possible to compute its evolution entropy, i.e. to estimate the evolution of its complexity as we move from a coarse to a fine description of its geometric structure. We demonstrate the performance of this method on simulated as well as real data networks.

Parameter identification using a creeping-random-search algorithm

NASA Technical Reports Server (NTRS)

Parrish, R. V.

1971-01-01

A creeping-random-search algorithm is applied to different types of problems in the field of parameter identification. The studies are intended to demonstrate that a random-search algorithm can be applied successfully to these various problems, which often cannot be handled by conventional deterministic methods, and, also, to introduce methods that speed convergence to an extremal of the problem under investigation. Six two-parameter identification problems with analytic solutions are solved, and two application problems are discussed in some detail. Results of the study show that a modified version of the basic creeping-random-search algorithm chosen does speed convergence in comparison with the unmodified version. The results also show that the algorithm can successfully solve problems that contain limits on state or control variables, inequality constraints (both independent and dependent, and linear and nonlinear), or stochastic models.

On the inherent competition between valid and spurious inductive inferences in Boolean data

NASA Astrophysics Data System (ADS)

Andrecut, M.

Inductive inference is the process of extracting general rules from specific observations. This problem also arises in the analysis of biological networks, such as genetic regulatory networks, where the interactions are complex and the observations are incomplete. A typical task in these problems is to extract general interaction rules as combinations of Boolean covariates, that explain a measured response variable. The inductive inference process can be considered as an incompletely specified Boolean function synthesis problem. This incompleteness of the problem will also generate spurious inferences, which are a serious threat to valid inductive inference rules. Using random Boolean data as a null model, here we attempt to measure the competition between valid and spurious inductive inference rules from a given data set. We formulate two greedy search algorithms, which synthesize a given Boolean response variable in a sparse disjunct normal form, and respectively a sparse generalized algebraic normal form of the variables from the observation data, and we evaluate numerically their performance.

Resource-constrained scheduling with hard due windows and rejection penalties

NASA Astrophysics Data System (ADS)

Garcia, Christopher

2016-09-01

This work studies a scheduling problem where each job must be either accepted and scheduled to complete within its specified due window, or rejected altogether. Each job has a certain processing time and contributes a certain profit if accepted or penalty cost if rejected. There is a set of renewable resources, and no resource limit can be exceeded at any time. Each job requires a certain amount of each resource when processed, and the objective is to maximize total profit. A mixed-integer programming formulation and three approximation algorithms are presented: a priority rule heuristic, an algorithm based on the metaheuristic for randomized priority search and an evolutionary algorithm. Computational experiments comparing these four solution methods were performed on a set of generated benchmark problems covering a wide range of problem characteristics. The evolutionary algorithm outperformed the other methods in most cases, often significantly, and never significantly underperformed any method.

Creating targeted initial populations for genetic product searches in heterogeneous markets

NASA Astrophysics Data System (ADS)

Foster, Garrett; Turner, Callaway; Ferguson, Scott; Donndelinger, Joseph

2014-12-01

Genetic searches often use randomly generated initial populations to maximize diversity and enable a thorough sampling of the design space. While many of these initial configurations perform poorly, the trade-off between population diversity and solution quality is typically acceptable for small-scale problems. Navigating complex design spaces, however, often requires computationally intelligent approaches that improve solution quality. This article draws on research advances in market-based product design and heuristic optimization to strategically construct 'targeted' initial populations. Targeted initial designs are created using respondent-level part-worths estimated from discrete choice models. These designs are then integrated into a traditional genetic search. Two case study problems of differing complexity are presented to illustrate the benefits of this approach. In both problems, targeted populations lead to computational savings and product configurations with improved market share of preferences. Future research efforts to tailor this approach and extend it towards multiple objectives are also discussed.

A Method for Estimating View Transformations from Image Correspondences Based on the Harmony Search Algorithm

PubMed Central

Cuevas, Erik; Díaz, Margarita

2015-01-01

In this paper, a new method for robustly estimating multiple view relations from point correspondences is presented. The approach combines the popular random sampling consensus (RANSAC) algorithm and the evolutionary method harmony search (HS). With this combination, the proposed method adopts a different sampling strategy than RANSAC to generate putative solutions. Under the new mechanism, at each iteration, new candidate solutions are built taking into account the quality of the models generated by previous candidate solutions, rather than purely random as it is the case of RANSAC. The rules for the generation of candidate solutions (samples) are motivated by the improvisation process that occurs when a musician searches for a better state of harmony. As a result, the proposed approach can substantially reduce the number of iterations still preserving the robust capabilities of RANSAC. The method is generic and its use is illustrated by the estimation of homographies, considering synthetic and real images. Additionally, in order to demonstrate the performance of the proposed approach within a real engineering application, it is employed to solve the problem of position estimation in a humanoid robot. Experimental results validate the efficiency of the proposed method in terms of accuracy, speed, and robustness. PMID:26339228

An intrinsic algorithm for parallel Poisson disk sampling on arbitrary surfaces.

PubMed

Ying, Xiang; Xin, Shi-Qing; Sun, Qian; He, Ying

2013-09-01

Poisson disk sampling has excellent spatial and spectral properties, and plays an important role in a variety of visual computing. Although many promising algorithms have been proposed for multidimensional sampling in euclidean space, very few studies have been reported with regard to the problem of generating Poisson disks on surfaces due to the complicated nature of the surface. This paper presents an intrinsic algorithm for parallel Poisson disk sampling on arbitrary surfaces. In sharp contrast to the conventional parallel approaches, our method neither partitions the given surface into small patches nor uses any spatial data structure to maintain the voids in the sampling domain. Instead, our approach assigns each sample candidate a random and unique priority that is unbiased with regard to the distribution. Hence, multiple threads can process the candidates simultaneously and resolve conflicts by checking the given priority values. Our algorithm guarantees that the generated Poisson disks are uniformly and randomly distributed without bias. It is worth noting that our method is intrinsic and independent of the embedding space. This intrinsic feature allows us to generate Poisson disk patterns on arbitrary surfaces in IR(n). To our knowledge, this is the first intrinsic, parallel, and accurate algorithm for surface Poisson disk sampling. Furthermore, by manipulating the spatially varying density function, we can obtain adaptive sampling easily.

A complexity theory model in science education problem solving: random walks for working memory and mental capacity.

PubMed

Stamovlasis, Dimitrios; Tsaparlis, Georgios

2003-07-01

The present study examines the role of limited human channel capacity from a science education perspective. A model of science problem solving has been previously validated by applying concepts and tools of complexity theory (the working memory, random walk method). The method correlated the subjects' rank-order achievement scores in organic-synthesis chemistry problems with the subjects' working memory capacity. In this work, we apply the same nonlinear approach to a different data set, taken from chemical-equilibrium problem solving. In contrast to the organic-synthesis problems, these problems are algorithmic, require numerical calculations, and have a complex logical structure. As a result, these problems cause deviations from the model, and affect the pattern observed with the nonlinear method. In addition to Baddeley's working memory capacity, the Pascual-Leone's mental (M-) capacity is examined by the same random-walk method. As the complexity of the problem increases, the fractal dimension of the working memory random walk demonstrates a sudden drop, while the fractal dimension of the M-capacity random walk decreases in a linear fashion. A review of the basic features of the two capacities and their relation is included. The method and findings have consequences for problem solving not only in chemistry and science education, but also in other disciplines.

A new neural network model for solving random interval linear programming problems.

PubMed

Arjmandzadeh, Ziba; Safi, Mohammadreza; Nazemi, Alireza

2017-05-01

This paper presents a neural network model for solving random interval linear programming problems. The original problem involving random interval variable coefficients is first transformed into an equivalent convex second order cone programming problem. A neural network model is then constructed for solving the obtained convex second order cone problem. Employing Lyapunov function approach, it is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact satisfactory solution of the original problem. Several illustrative examples are solved in support of this technique. Copyright © 2017 Elsevier Ltd. All rights reserved.

Numerical study for heat generation/absorption in flow of nanofluid by a rotating disk

NASA Astrophysics Data System (ADS)

Aziz, Arsalan; Alsaedi, Ahmed; Muhammad, Taseer; Hayat, Tasawar

2018-03-01

Here MHD three-dimensional flow of viscous nanoliquid by a rotating disk with heat generation/absorption and slip effects is addressed. Thermophoresis and random motion features are also incorporated. Velocity, temperature and concentration slip conditions are imposed at boundary. Applied magnetic field is utilized. Low magnetic Reynolds number and boundary layer approximations have been employed in the problem formulation. Suitable transformations lead to strong nonlinear ordinary differential system. The obtained nonlinear system is solved numerically through NDSolve technique. Graphs have been sketched in order to analyze that how the velocity, temperature and concentration fields are affected by various pertinent variables. Moreover the numerical values for rates of heat and mass transfer have been tabulated and discussed.

Efficient 3D porous microstructure reconstruction via Gaussian random field and hybrid optimization.

PubMed

Jiang, Z; Chen, W; Burkhart, C

2013-11-01

Obtaining an accurate three-dimensional (3D) structure of a porous microstructure is important for assessing the material properties based on finite element analysis. Whereas directly obtaining 3D images of the microstructure is impractical under many circumstances, two sets of methods have been developed in literature to generate (reconstruct) 3D microstructure from its 2D images: one characterizes the microstructure based on certain statistical descriptors, typically two-point correlation function and cluster correlation function, and then performs an optimization process to build a 3D structure that matches those statistical descriptors; the other method models the microstructure using stochastic models like a Gaussian random field and generates a 3D structure directly from the function. The former obtains a relatively accurate 3D microstructure, but computationally the optimization process can be very intensive, especially for problems with large image size; the latter generates a 3D microstructure quickly but sacrifices the accuracy due to issues in numerical implementations. A hybrid optimization approach of modelling the 3D porous microstructure of random isotropic two-phase materials is proposed in this paper, which combines the two sets of methods and hence maintains the accuracy of the correlation-based method with improved efficiency. The proposed technique is verified for 3D reconstructions based on silica polymer composite images with different volume fractions. A comparison of the reconstructed microstructures and the optimization histories for both the original correlation-based method and our hybrid approach demonstrates the improved efficiency of the approach. © 2013 The Authors Journal of Microscopy © 2013 Royal Microscopical Society.

Secure uniform random-number extraction via incoherent strategies

NASA Astrophysics Data System (ADS)

Hayashi, Masahito; Zhu, Huangjun

2018-01-01

To guarantee the security of uniform random numbers generated by a quantum random-number generator, we study secure extraction of uniform random numbers when the environment of a given quantum state is controlled by the third party, the eavesdropper. Here we restrict our operations to incoherent strategies that are composed of the measurement on the computational basis and incoherent operations (or incoherence-preserving operations). We show that the maximum secure extraction rate is equal to the relative entropy of coherence. By contrast, the coherence of formation gives the extraction rate when a certain constraint is imposed on the eavesdropper's operations. The condition under which the two extraction rates coincide is then determined. Furthermore, we find that the exponential decreasing rate of the leaked information is characterized by Rényi relative entropies of coherence. These results clarify the power of incoherent strategies in random-number generation, and can be applied to guarantee the quality of random numbers generated by a quantum random-number generator.

Certified randomness in quantum physics.

PubMed

Acín, Antonio; Masanes, Lluis

2016-12-07

The concept of randomness plays an important part in many disciplines. On the one hand, the question of whether random processes exist is fundamental for our understanding of nature. On the other, randomness is a resource for cryptography, algorithms and simulations. Standard methods for generating randomness rely on assumptions about the devices that are often not valid in practice. However, quantum technologies enable new methods for generating certified randomness, based on the violation of Bell inequalities. These methods are referred to as device-independent because they do not rely on any modelling of the devices. Here we review efforts to design device-independent randomness generators and the associated challenges.

Estimating rare events in biochemical systems using conditional sampling.

PubMed

Sundar, V S

2017-01-28

The paper focuses on development of variance reduction strategies to estimate rare events in biochemical systems. Obtaining this probability using brute force Monte Carlo simulations in conjunction with the stochastic simulation algorithm (Gillespie's method) is computationally prohibitive. To circumvent this, important sampling tools such as the weighted stochastic simulation algorithm and the doubly weighted stochastic simulation algorithm have been proposed. However, these strategies require an additional step of determining the important region to sample from, which is not straightforward for most of the problems. In this paper, we apply the subset simulation method, developed as a variance reduction tool in the context of structural engineering, to the problem of rare event estimation in biochemical systems. The main idea is that the rare event probability is expressed as a product of more frequent conditional probabilities. These conditional probabilities are estimated with high accuracy using Monte Carlo simulations, specifically the Markov chain Monte Carlo method with the modified Metropolis-Hastings algorithm. Generating sample realizations of the state vector using the stochastic simulation algorithm is viewed as mapping the discrete-state continuous-time random process to the standard normal random variable vector. This viewpoint opens up the possibility of applying more sophisticated and efficient sampling schemes developed elsewhere to problems in stochastic chemical kinetics. The results obtained using the subset simulation method are compared with existing variance reduction strategies for a few benchmark problems, and a satisfactory improvement in computational time is demonstrated.

Recommendations and illustrations for the evaluation of photonic random number generators

NASA Astrophysics Data System (ADS)

Hart, Joseph D.; Terashima, Yuta; Uchida, Atsushi; Baumgartner, Gerald B.; Murphy, Thomas E.; Roy, Rajarshi

2017-09-01

The never-ending quest to improve the security of digital information combined with recent improvements in hardware technology has caused the field of random number generation to undergo a fundamental shift from relying solely on pseudo-random algorithms to employing optical entropy sources. Despite these significant advances on the hardware side, commonly used statistical measures and evaluation practices remain ill-suited to understand or quantify the optical entropy that underlies physical random number generation. We review the state of the art in the evaluation of optical random number generation and recommend a new paradigm: quantifying entropy generation and understanding the physical limits of the optical sources of randomness. In order to do this, we advocate for the separation of the physical entropy source from deterministic post-processing in the evaluation of random number generators and for the explicit consideration of the impact of the measurement and digitization process on the rate of entropy production. We present the Cohen-Procaccia estimate of the entropy rate h (𝜖 ,τ ) as one way to do this. In order to provide an illustration of our recommendations, we apply the Cohen-Procaccia estimate as well as the entropy estimates from the new NIST draft standards for physical random number generators to evaluate and compare three common optical entropy sources: single photon time-of-arrival detection, chaotic lasers, and amplified spontaneous emission.

Amoeba-Inspired Heuristic Search Dynamics for Exploring Chemical Reaction Paths.

PubMed

Aono, Masashi; Wakabayashi, Masamitsu

2015-09-01

We propose a nature-inspired model for simulating chemical reactions in a computationally resource-saving manner. The model was developed by extending our previously proposed heuristic search algorithm, called "AmoebaSAT [Aono et al. 2013]," which was inspired by the spatiotemporal dynamics of a single-celled amoeboid organism that exhibits sophisticated computing capabilities in adapting to its environment efficiently [Zhu et al. 2013]. AmoebaSAT is used for solving an NP-complete combinatorial optimization problem [Garey and Johnson 1979], "the satisfiability problem," and finds a constraint-satisfying solution at a speed that is dramatically faster than one of the conventionally known fastest stochastic local search methods [Iwama and Tamaki 2004] for a class of randomly generated problem instances [ http://www.cs.ubc.ca/~hoos/5/benchm.html ]. In cases where the problem has more than one solution, AmoebaSAT exhibits dynamic transition behavior among a variety of the solutions. Inheriting these features of AmoebaSAT, we formulate "AmoebaChem," which explores a variety of metastable molecules in which several constraints determined by input atoms are satisfied and generates dynamic transition processes among the metastable molecules. AmoebaChem and its developed forms will be applied to the study of the origins of life, to discover reaction paths for which expected or unexpected organic compounds may be formed via unknown unstable intermediates and to estimate the likelihood of each of the discovered paths.

Development of Solution Algorithm and Sensitivity Analysis for Random Fuzzy Portfolio Selection Model

NASA Astrophysics Data System (ADS)

Hasuike, Takashi; Katagiri, Hideki

2010-10-01

This paper focuses on the proposition of a portfolio selection problem considering an investor's subjectivity and the sensitivity analysis for the change of subjectivity. Since this proposed problem is formulated as a random fuzzy programming problem due to both randomness and subjectivity presented by fuzzy numbers, it is not well-defined. Therefore, introducing Sharpe ratio which is one of important performance measures of portfolio models, the main problem is transformed into the standard fuzzy programming problem. Furthermore, using the sensitivity analysis for fuzziness, the analytical optimal portfolio with the sensitivity factor is obtained.

A Bankruptcy Problem Approach to Load-shedding in Multiagent-based Microgrid Operation

PubMed Central

Kim, Hak-Man; Kinoshita, Tetsuo; Lim, Yujin; Kim, Tai-Hoon

2010-01-01

A microgrid is composed of distributed power generation systems (DGs), distributed energy storage devices (DSs), and loads. To maintain a specific frequency in the islanded mode as an important requirement, the control of DGs’ output and charge action of DSs are used in supply surplus conditions and load-shedding and discharge action of DSs are used in supply shortage conditions. Recently, multiagent systems for autonomous microgrid operation have been studied. Especially, load-shedding, which is intentional reduction of electricity use, is a critical problem in islanded microgrid operation based on the multiagent system. Therefore, effective schemes for load-shedding are required. Meanwhile, the bankruptcy problem deals with dividing short resources among multiple agents. In order to solve the bankruptcy problem, division rules, such as the constrained equal awards rule (CEA), the constrained equal losses rule (CEL), and the random arrival rule (RA), have been used. In this paper, we approach load-shedding as a bankruptcy problem. We compare load-shedding results by above-mentioned rules in islanded microgrid operation based on wireless sensor network (WSN) as the communication link for an agent’s interactions. PMID:22163386

A bankruptcy problem approach to load-shedding in multiagent-based microgrid operation.

PubMed

Kim, Hak-Man; Kinoshita, Tetsuo; Lim, Yujin; Kim, Tai-Hoon

2010-01-01

A microgrid is composed of distributed power generation systems (DGs), distributed energy storage devices (DSs), and loads. To maintain a specific frequency in the islanded mode as an important requirement, the control of DGs' output and charge action of DSs are used in supply surplus conditions and load-shedding and discharge action of DSs are used in supply shortage conditions. Recently, multiagent systems for autonomous microgrid operation have been studied. Especially, load-shedding, which is intentional reduction of electricity use, is a critical problem in islanded microgrid operation based on the multiagent system. Therefore, effective schemes for load-shedding are required. Meanwhile, the bankruptcy problem deals with dividing short resources among multiple agents. In order to solve the bankruptcy problem, division rules, such as the constrained equal awards rule (CEA), the constrained equal losses rule (CEL), and the random arrival rule (RA), have been used. In this paper, we approach load-shedding as a bankruptcy problem. We compare load-shedding results by above-mentioned rules in islanded microgrid operation based on wireless sensor network (WSN) as the communication link for an agent's interactions.

The Prevention Program for Externalizing Problem Behavior (PEP) Improves Child Behavior by Reducing Negative Parenting: Analysis of Mediating Processes in a Randomized Controlled Trial

ERIC Educational Resources Information Center

Hanisch, Charlotte; Hautmann, Christopher; Plück, Julia; Eichelberger, Ilka; Döpfner, Manfred

2014-01-01

Background: Our indicated Prevention program for preschool children with Externalizing Problem behavior (PEP) demonstrated improved parenting and child problem behavior in a randomized controlled efficacy trial and in a study with an effectiveness design. The aim of the present analysis of data from the randomized controlled trial was to identify…

Generating Variable and Random Schedules of Reinforcement Using Microsoft Excel Macros

PubMed Central

Bancroft, Stacie L; Bourret, Jason C

2008-01-01

Variable reinforcement schedules are used to arrange the availability of reinforcement following varying response ratios or intervals of time. Random reinforcement schedules are subtypes of variable reinforcement schedules that can be used to arrange the availability of reinforcement at a constant probability across number of responses or time. Generating schedule values for variable and random reinforcement schedules can be difficult. The present article describes the steps necessary to write macros in Microsoft Excel that will generate variable-ratio, variable-interval, variable-time, random-ratio, random-interval, and random-time reinforcement schedule values. PMID:18595286

A hybrid heuristic for the multiple choice multidimensional knapsack problem

NASA Astrophysics Data System (ADS)

Mansi, Raïd; Alves, Cláudio; Valério de Carvalho, J. M.; Hanafi, Saïd

2013-08-01

In this article, a new solution approach for the multiple choice multidimensional knapsack problem is described. The problem is a variant of the multidimensional knapsack problem where items are divided into classes, and exactly one item per class has to be chosen. Both problems are NP-hard. However, the multiple choice multidimensional knapsack problem appears to be more difficult to solve in part because of its choice constraints. Many real applications lead to very large scale multiple choice multidimensional knapsack problems that can hardly be addressed using exact algorithms. A new hybrid heuristic is proposed that embeds several new procedures for this problem. The approach is based on the resolution of linear programming relaxations of the problem and reduced problems that are obtained by fixing some variables of the problem. The solutions of these problems are used to update the global lower and upper bounds for the optimal solution value. A new strategy for defining the reduced problems is explored, together with a new family of cuts and a reformulation procedure that is used at each iteration to improve the performance of the heuristic. An extensive set of computational experiments is reported for benchmark instances from the literature and for a large set of hard instances generated randomly. The results show that the approach outperforms other state-of-the-art methods described so far, providing the best known solution for a significant number of benchmark instances.

Stochastic reduced order models for inverse problems under uncertainty

PubMed Central

Warner, James E.; Aquino, Wilkins; Grigoriu, Mircea D.

2014-01-01

This work presents a novel methodology for solving inverse problems under uncertainty using stochastic reduced order models (SROMs). Given statistical information about an observed state variable in a system, unknown parameters are estimated probabilistically through the solution of a model-constrained, stochastic optimization problem. The point of departure and crux of the proposed framework is the representation of a random quantity using a SROM - a low dimensional, discrete approximation to a continuous random element that permits e cient and non-intrusive stochastic computations. Characterizing the uncertainties with SROMs transforms the stochastic optimization problem into a deterministic one. The non-intrusive nature of SROMs facilitates e cient gradient computations for random vector unknowns and relies entirely on calls to existing deterministic solvers. Furthermore, the method is naturally extended to handle multiple sources of uncertainty in cases where state variable data, system parameters, and boundary conditions are all considered random. The new and widely-applicable SROM framework is formulated for a general stochastic optimization problem in terms of an abstract objective function and constraining model. For demonstration purposes, however, we study its performance in the specific case of inverse identification of random material parameters in elastodynamics. We demonstrate the ability to efficiently recover random shear moduli given material displacement statistics as input data. We also show that the approach remains effective for the case where the loading in the problem is random as well. PMID:25558115

Programmable random interval generator

NASA Technical Reports Server (NTRS)

Lindsey, R. S., Jr.

1973-01-01

Random pulse generator can supply constant-amplitude randomly distributed pulses with average rate ranging from a few counts per second to more than one million counts per second. Generator requires no high-voltage power supply or any special thermal cooling apparatus. Device is uniquely versatile and provides wide dynamic range of operation.

Hypotheses generation as supervised link discovery with automated class labeling on large-scale biomedical concept networks

PubMed Central

2012-01-01

Computational approaches to generate hypotheses from biomedical literature have been studied intensively in recent years. Nevertheless, it still remains a challenge to automatically discover novel, cross-silo biomedical hypotheses from large-scale literature repositories. In order to address this challenge, we first model a biomedical literature repository as a comprehensive network of biomedical concepts and formulate hypotheses generation as a process of link discovery on the concept network. We extract the relevant information from the biomedical literature corpus and generate a concept network and concept-author map on a cluster using Map-Reduce frame-work. We extract a set of heterogeneous features such as random walk based features, neighborhood features and common author features. The potential number of links to consider for the possibility of link discovery is large in our concept network and to address the scalability problem, the features from a concept network are extracted using a cluster with Map-Reduce framework. We further model link discovery as a classification problem carried out on a training data set automatically extracted from two network snapshots taken in two consecutive time duration. A set of heterogeneous features, which cover both topological and semantic features derived from the concept network, have been studied with respect to their impacts on the accuracy of the proposed supervised link discovery process. A case study of hypotheses generation based on the proposed method has been presented in the paper. PMID:22759614

Observation of quantum criticality with ultracold atoms in optical lattices

NASA Astrophysics Data System (ADS)

Zhang, Xibo

As biological problems are becoming more complex and data growing at a rate much faster than that of computer hardware, new and faster algorithms are required. This dissertation investigates computational problems arising in two of the fields: comparative genomics and epigenomics, and employs a variety of computational techniques to address the problems. One fundamental question in the studies of chromosome evolution is whether the rearrangement breakpoints are happening at random positions or along certain hotspots. We investigate the breakpoint reuse phenomenon, and show the analyses that support the more recently proposed fragile breakage model as opposed to the conventional random breakage models for chromosome evolution. The identification of syntenic regions between chromosomes forms the basis for studies of genome architectures, comparative genomics, and evolutionary genomics. The previous synteny block reconstruction algorithms could not be scaled to a large number of mammalian genomes being sequenced; neither did they address the issue of generating non-overlapping synteny blocks suitable for analyzing rearrangements and evolutionary history of large-scale duplications prevalent in plant genomes. We present a new unified synteny block generation algorithm based on A-Bruijn graph framework that overcomes these shortcomings. In the epigenome sequencing, a sample may contain a mixture of epigenomes and there is a need to resolve the distinct methylation patterns from the mixture. Many sequencing applications, such as haplotype inference for diploid or polyploid genomes, and metagenomic sequencing, share the similar objective: to infer a set of distinct assemblies from reads that are sequenced from a heterogeneous sample and subsequently aligned to a reference genome. We model the problem from both a combinatorial and a statistical angles. First, we describe a theoretical framework. A linear-time algorithm is then given to resolve a minimum number of assemblies that are consistent with all reads, substantially improving on previous algorithms. An efficient algorithm is also described to determine a set of assemblies that is consistent with a maximum subset of the reads, a previously untreated problem. We then prove that allowing nested reads or permitting mismatches between reads and their assemblies renders these problems NP-hard. Second, we describe a mixture model-based approach, and applied the model for the detection of allele-specific methylations.

On the predictivity of pore-scale simulations: Estimating uncertainties with multilevel Monte Carlo

NASA Astrophysics Data System (ADS)

Icardi, Matteo; Boccardo, Gianluca; Tempone, Raúl

2016-09-01

A fast method with tunable accuracy is proposed to estimate errors and uncertainties in pore-scale and Digital Rock Physics (DRP) problems. The overall predictivity of these studies can be, in fact, hindered by many factors including sample heterogeneity, computational and imaging limitations, model inadequacy and not perfectly known physical parameters. The typical objective of pore-scale studies is the estimation of macroscopic effective parameters such as permeability, effective diffusivity and hydrodynamic dispersion. However, these are often non-deterministic quantities (i.e., results obtained for specific pore-scale sample and setup are not totally reproducible by another ;equivalent; sample and setup). The stochastic nature can arise due to the multi-scale heterogeneity, the computational and experimental limitations in considering large samples, and the complexity of the physical models. These approximations, in fact, introduce an error that, being dependent on a large number of complex factors, can be modeled as random. We propose a general simulation tool, based on multilevel Monte Carlo, that can reduce drastically the computational cost needed for computing accurate statistics of effective parameters and other quantities of interest, under any of these random errors. This is, to our knowledge, the first attempt to include Uncertainty Quantification (UQ) in pore-scale physics and simulation. The method can also provide estimates of the discretization error and it is tested on three-dimensional transport problems in heterogeneous materials, where the sampling procedure is done by generation algorithms able to reproduce realistic consolidated and unconsolidated random sphere and ellipsoid packings and arrangements. A totally automatic workflow is developed in an open-source code [1], that include rigid body physics and random packing algorithms, unstructured mesh discretization, finite volume solvers, extrapolation and post-processing techniques. The proposed method can be efficiently used in many porous media applications for problems such as stochastic homogenization/upscaling, propagation of uncertainty from microscopic fluid and rock properties to macro-scale parameters, robust estimation of Representative Elementary Volume size for arbitrary physics.

Experimentally generated randomness certified by the impossibility of superluminal signals.

PubMed

Bierhorst, Peter; Knill, Emanuel; Glancy, Scott; Zhang, Yanbao; Mink, Alan; Jordan, Stephen; Rommal, Andrea; Liu, Yi-Kai; Christensen, Bradley; Nam, Sae Woo; Stevens, Martin J; Shalm, Lynden K

2018-04-01

From dice to modern electronic circuits, there have been many attempts to build better devices to generate random numbers. Randomness is fundamental to security and cryptographic systems and to safeguarding privacy. A key challenge with random-number generators is that it is hard to ensure that their outputs are unpredictable 1-3 . For a random-number generator based on a physical process, such as a noisy classical system or an elementary quantum measurement, a detailed model that describes the underlying physics is necessary to assert unpredictability. Imperfections in the model compromise the integrity of the device. However, it is possible to exploit the phenomenon of quantum non-locality with a loophole-free Bell test to build a random-number generator that can produce output that is unpredictable to any adversary that is limited only by general physical principles, such as special relativity 1-11 . With recent technological developments, it is now possible to carry out such a loophole-free Bell test 12-14,22 . Here we present certified randomness obtained from a photonic Bell experiment and extract 1,024 random bits that are uniformly distributed to within 10 -12 . These random bits could not have been predicted according to any physical theory that prohibits faster-than-light (superluminal) signalling and that allows independent measurement choices. To certify and quantify the randomness, we describe a protocol that is optimized for devices that are characterized by a low per-trial violation of Bell inequalities. Future random-number generators based on loophole-free Bell tests may have a role in increasing the security and trust of our cryptographic systems and infrastructure.

Portfolio optimization and the random magnet problem

NASA Astrophysics Data System (ADS)

Rosenow, B.; Plerou, V.; Gopikrishnan, P.; Stanley, H. E.

2002-08-01

Diversification of an investment into independently fluctuating assets reduces its risk. In reality, movements of assets are mutually correlated and therefore knowledge of cross-correlations among asset price movements are of great importance. Our results support the possibility that the problem of finding an investment in stocks which exposes invested funds to a minimum level of risk is analogous to the problem of finding the magnetization of a random magnet. The interactions for this "random magnet problem" are given by the cross-correlation matrix C of stock returns. We find that random matrix theory allows us to make an estimate for C which outperforms the standard estimate in terms of constructing an investment which carries a minimum level of risk.

Pseudo-random number generator for the Sigma 5 computer

NASA Technical Reports Server (NTRS)

Carroll, S. N.

1983-01-01

A technique is presented for developing a pseudo-random number generator based on the linear congruential form. The two numbers used for the generator are a prime number and a corresponding primitive root, where the prime is the largest prime number that can be accurately represented on a particular computer. The primitive root is selected by applying Marsaglia's lattice test. The technique presented was applied to write a random number program for the Sigma 5 computer. The new program, named S:RANDOM1, is judged to be superior to the older program named S:RANDOM. For applications requiring several independent random number generators, a table is included showing several acceptable primitive roots. The technique and programs described can be applied to any computer having word length different from that of the Sigma 5.

Implementation of a quantum random number generator based on the optimal clustering of photocounts

NASA Astrophysics Data System (ADS)

Balygin, K. A.; Zaitsev, V. I.; Klimov, A. N.; Kulik, S. P.; Molotkov, S. N.

2017-10-01

To implement quantum random number generators, it is fundamentally important to have a mathematically provable and experimentally testable process of measurements of a system from which an initial random sequence is generated. This makes sure that randomness indeed has a quantum nature. A quantum random number generator has been implemented with the use of the detection of quasi-single-photon radiation by a silicon photomultiplier (SiPM) matrix, which makes it possible to reliably reach the Poisson statistics of photocounts. The choice and use of the optimal clustering of photocounts for the initial sequence of photodetection events and a method of extraction of a random sequence of 0's and 1's, which is polynomial in the length of the sequence, have made it possible to reach a yield rate of 64 Mbit/s of the output certainly random sequence.

Anisotropic spectra of acoustic type turbulence

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kuznetsov, E.; P.N. Lebedev Physical Institute, 53 Leninsky Ave., 119991 Moscow; Krasnoselskikh, V.

2008-06-15

The problem of spectra for acoustic type of turbulence generated by shocks being randomly distributed in space is considered. It is shown that for turbulence with a weak anisotropy, such spectra have the same dependence in k-space as the Kadomtsev-Petviashvili spectrum: E(k){approx}k{sup -2}. However, the frequency spectrum has always the falling {approx}{omega}{sup -2}, independent of anisotropy. In the strong anisotropic case the energy distribution relative to wave vectors takes anisotropic dependence, forming in the large-k region spectra of the jet type.

On Tree-Based Phylogenetic Networks.

PubMed

Zhang, Louxin

2016-07-01

A large class of phylogenetic networks can be obtained from trees by the addition of horizontal edges between the tree edges. These networks are called tree-based networks. We present a simple necessary and sufficient condition for tree-based networks and prove that a universal tree-based network exists for any number of taxa that contains as its base every phylogenetic tree on the same set of taxa. This answers two problems posted by Francis and Steel recently. A byproduct is a computer program for generating random binary phylogenetic networks under the uniform distribution model.

Robustly Aligning a Shape Model and Its Application to Car Alignment of Unknown Pose.

PubMed

Li, Yan; Gu, Leon; Kanade, Takeo

2011-09-01

Precisely localizing in an image a set of feature points that form a shape of an object, such as car or face, is called alignment. Previous shape alignment methods attempted to fit a whole shape model to the observed data, based on the assumption of Gaussian observation noise and the associated regularization process. However, such an approach, though able to deal with Gaussian noise in feature detection, turns out not to be robust or precise because it is vulnerable to gross feature detection errors or outliers resulting from partial occlusions or spurious features from the background or neighboring objects. We address this problem by adopting a randomized hypothesis-and-test approach. First, a Bayesian inference algorithm is developed to generate a shape-and-pose hypothesis of the object from a partial shape or a subset of feature points. For alignment, a large number of hypotheses are generated by randomly sampling subsets of feature points, and then evaluated to find the one that minimizes the shape prediction error. This method of randomized subset-based matching can effectively handle outliers and recover the correct object shape. We apply this approach on a challenging data set of over 5,000 different-posed car images, spanning a wide variety of car types, lighting, background scenes, and partial occlusions. Experimental results demonstrate favorable improvements over previous methods on both accuracy and robustness.

Study of Randomness in AES Ciphertexts Produced by Randomly Generated S-Boxes and S-Boxes with Various Modulus and Additive Constant Polynomials

NASA Astrophysics Data System (ADS)

Das, Suman; Sadique Uz Zaman, J. K. M.; Ghosh, Ranjan

2016-06-01

In Advanced Encryption Standard (AES), the standard S-Box is conventionally generated by using a particular irreducible polynomial {11B} in GF(28) as the modulus and a particular additive constant polynomial {63} in GF(2), though it can be generated by many other polynomials. In this paper, it has been shown that it is possible to generate secured AES S-Boxes by using some other selected modulus and additive polynomials and also can be generated randomly, using a PRNG like BBS. A comparative study has been made on the randomness of corresponding AES ciphertexts generated, using these S-Boxes, by the NIST Test Suite coded for this paper. It has been found that besides using the standard one, other moduli and additive constants are also able to generate equally or better random ciphertexts; the same is true for random S-Boxes also. As these new types of S-Boxes are user-defined, hence unknown, they are able to prevent linear and differential cryptanalysis. Moreover, they act as additional key-inputs to AES, thus increasing the key-space.

Harvesting Entropy for Random Number Generation for Internet of Things Constrained Devices Using On-Board Sensors

PubMed Central

Pawlowski, Marcin Piotr; Jara, Antonio; Ogorzalek, Maciej

2015-01-01

Entropy in computer security is associated with the unpredictability of a source of randomness. The random source with high entropy tends to achieve a uniform distribution of random values. Random number generators are one of the most important building blocks of cryptosystems. In constrained devices of the Internet of Things ecosystem, high entropy random number generators are hard to achieve due to hardware limitations. For the purpose of the random number generation in constrained devices, this work proposes a solution based on the least-significant bits concatenation entropy harvesting method. As a potential source of entropy, on-board integrated sensors (i.e., temperature, humidity and two different light sensors) have been analyzed. Additionally, the costs (i.e., time and memory consumption) of the presented approach have been measured. The results obtained from the proposed method with statistical fine tuning achieved a Shannon entropy of around 7.9 bits per byte of data for temperature and humidity sensors. The results showed that sensor-based random number generators are a valuable source of entropy with very small RAM and Flash memory requirements for constrained devices of the Internet of Things. PMID:26506357

Harvesting entropy for random number generation for internet of things constrained devices using on-board sensors.

PubMed

Pawlowski, Marcin Piotr; Jara, Antonio; Ogorzalek, Maciej

2015-10-22

Entropy in computer security is associated with the unpredictability of a source of randomness. The random source with high entropy tends to achieve a uniform distribution of random values. Random number generators are one of the most important building blocks of cryptosystems. In constrained devices of the Internet of Things ecosystem, high entropy random number generators are hard to achieve due to hardware limitations. For the purpose of the random number generation in constrained devices, this work proposes a solution based on the least-significant bits concatenation entropy harvesting method. As a potential source of entropy, on-board integrated sensors (i.e., temperature, humidity and two different light sensors) have been analyzed. Additionally, the costs (i.e., time and memory consumption) of the presented approach have been measured. The results obtained from the proposed method with statistical fine tuning achieved a Shannon entropy of around 7.9 bits per byte of data for temperature and humidity sensors. The results showed that sensor-based random number generators are a valuable source of entropy with very small RAM and Flash memory requirements for constrained devices of the Internet of Things.

Demand side management in recycling and electricity retail pricing

NASA Astrophysics Data System (ADS)

Kazan, Osman

This dissertation addresses several problems from the recycling industry and electricity retail market. The first paper addresses a real-life scheduling problem faced by a national industrial recycling company. Based on their practices, a scheduling problem is defined, modeled, analyzed, and a solution is approximated efficiently. The recommended application is tested on the real-life data and randomly generated data. The scheduling improvements and the financial benefits are presented. The second problem is from electricity retail market. There are well-known patterns in daily usage in hours. These patterns change in shape and magnitude by seasons and days of the week. Generation costs are multiple times higher during the peak hours of the day. Yet most consumers purchase electricity at flat rates. This work explores analytic pricing tools to reduce peak load electricity demand for retailers. For that purpose, a nonlinear model that determines optimal hourly prices is established based on two major components: unit generation costs and consumers' utility. Both are analyzed and estimated empirically in the third paper. A pricing model is introduced to maximize the electric retailer's profit. As a result, a closed-form expression for the optimal price vector is obtained. Possible scenarios are evaluated for consumers' utility distribution. For the general case, we provide a numerical solution methodology to obtain the optimal pricing scheme. The models recommended are tested under various scenarios that consider consumer segmentation and multiple pricing policies. The recommended model reduces the peak load significantly in most cases. Several utility companies offer hourly pricing to their customers. They determine prices using historical data of unit electricity cost over time. In this dissertation we develop a nonlinear model that determines optimal hourly prices with parameter estimation. The last paper includes a regression analysis of the unit generation cost function obtained from Independent Service Operators. A consumer experiment is established to replicate the peak load behavior. As a result, consumers' utility function is estimated and optimal retail electricity prices are computed.

Test Method for the Fatigue Life of Layered TiB/Ti Functionally Graded Beams Subjected to Fully Reversed Bending

NASA Astrophysics Data System (ADS)

Byrd, Larry; Rickerd, Greg; Wyen, Travis; Cooley, Glenn; Quast, Jeff

2008-02-01

Sonic fatigue of aircraft is characterized by fully reversed bending of components subjected to acoustic excitation. This problem is compounded in high temperature environments because solutions for acoustics which tend to result in stiff structures make thermal problems worse. Conversely solutions to the thermal problem which allow expansion often fail in the presence of high acoustic levels. Errors in fatigue life prediction in the combined environment often range from a factor of 4 to 10. This results in either heavy, overly stiff structure or premature failure. This work will test the hypothesis that the fatigue life of a layered functionally graded material (FGM) will be dominated by the failure of the stiffest outer layer. This is based on the observation that for isotropic materials the life is approximately 90% crack initiation and only 10% crack growth before failure. Four sets of cantilever specimens will be tested using an electro-mechanical shaker for base excitation. The excitation will be narrow band random around the fundamental frequency. Two sets of specimens are of uniform composition consisting of 85%TiB/Ti and two are graded specimens consisting of layers that vary from commercially pure titanium to 85%TiB/Ti. Strain vs number of cycles to failure curves will be generated with both constant amplitude sine and narrow band random around the fundamental frequency excitation. The results will be examined to compare life of the uniform material to the functionally graded material. Also to be studied will be the use of Miner's rule to predict the fatigue life of the randomly excited specimens.

A Comparison of Two Types of Social Support for Mothers of Mentally Ill Children

PubMed Central

Scharer, Kathleen; Colon, Eileen; Moneyham, Linda; Hussey, Jim; Tavakoli, Abbas; Shugart, Margaret

2009-01-01

PROBLEM The purpose of this analysis was to compare social support offered by two telehealth nursing interventions for mothers of children with serious mental illnesses. METHODS A randomized, controlled, quantitative investigation is underway to test two support interventions, using the telephone (TSS) or Internet (WEB). Qualitative description was used to analyze data generated during telehealth interventions. FINDINGS The behaviors and attitudes of children were challenging for the mothers to manage. Mothers’ emotional reactions included fear, frustration, concern, and guilt. They sought to be advocates for their children. The nurses provided emotional, informational, and appraisal support. TSS mothers were passive recipients, while WEB mothers had to choose to participate. CONCLUSIONS Mothers in both interventions shared similar concerns and sought support related to their child’s problems. PMID:19490279

Independent tasks scheduling in cloud computing via improved estimation of distribution algorithm

NASA Astrophysics Data System (ADS)

Sun, Haisheng; Xu, Rui; Chen, Huaping

2018-04-01

To minimize makespan for scheduling independent tasks in cloud computing, an improved estimation of distribution algorithm (IEDA) is proposed to tackle the investigated problem in this paper. Considering that the problem is concerned with multi-dimensional discrete problems, an improved population-based incremental learning (PBIL) algorithm is applied, which the parameter for each component is independent with other components in PBIL. In order to improve the performance of PBIL, on the one hand, the integer encoding scheme is used and the method of probability calculation of PBIL is improved by using the task average processing time; on the other hand, an effective adaptive learning rate function that related to the number of iterations is constructed to trade off the exploration and exploitation of IEDA. In addition, both enhanced Max-Min and Min-Min algorithms are properly introduced to form two initial individuals. In the proposed IEDA, an improved genetic algorithm (IGA) is applied to generate partial initial population by evolving two initial individuals and the rest of initial individuals are generated at random. Finally, the sampling process is divided into two parts including sampling by probabilistic model and IGA respectively. The experiment results show that the proposed IEDA not only gets better solution, but also has faster convergence speed.

DNA-based random number generation in security circuitry.

PubMed

Gearheart, Christy M; Arazi, Benjamin; Rouchka, Eric C

2010-06-01

DNA-based circuit design is an area of research in which traditional silicon-based technologies are replaced by naturally occurring phenomena taken from biochemistry and molecular biology. This research focuses on further developing DNA-based methodologies to mimic digital data manipulation. While exhibiting fundamental principles, this work was done in conjunction with the vision that DNA-based circuitry, when the technology matures, will form the basis for a tamper-proof security module, revolutionizing the meaning and concept of tamper-proofing and possibly preventing it altogether based on accurate scientific observations. A paramount part of such a solution would be self-generation of random numbers. A novel prototype schema employs solid phase synthesis of oligonucleotides for random construction of DNA sequences; temporary storage and retrieval is achieved through plasmid vectors. A discussion of how to evaluate sequence randomness is included, as well as how these techniques are applied to a simulation of the random number generation circuitry. Simulation results show generated sequences successfully pass three selected NIST random number generation tests specified for security applications.

Fast physical-random number generation using laser diode's frequency noise: influence of frequency discriminator

NASA Astrophysics Data System (ADS)

Matsumoto, Kouhei; Kasuya, Yuki; Yumoto, Mitsuki; Arai, Hideaki; Sato, Takashi; Sakamoto, Shuichi; Ohkawa, Masashi; Ohdaira, Yasuo

2018-02-01

Not so long ago, pseudo random numbers generated by numerical formulae were considered to be adequate for encrypting important data-files, because of the time needed to decode them. With today's ultra high-speed processors, however, this is no longer true. So, in order to thwart ever-more advanced attempts to breach our system's protections, cryptologists have devised a method that is considered to be virtually impossible to decode, and uses what is a limitless number of physical random numbers. This research describes a method, whereby laser diode's frequency noise generate a large quantities of physical random numbers. Using two types of photo detectors (APD and PIN-PD), we tested the abilities of two types of lasers (FP-LD and VCSEL) to generate random numbers. In all instances, an etalon served as frequency discriminator, the examination pass rates were determined using NIST FIPS140-2 test at each bit, and the Random Number Generation (RNG) speed was noted.

Generation of pseudo-random numbers

NASA Technical Reports Server (NTRS)

Howell, L. W.; Rheinfurth, M. H.

1982-01-01

Practical methods for generating acceptable random numbers from a variety of probability distributions which are frequently encountered in engineering applications are described. The speed, accuracy, and guarantee of statistical randomness of the various methods are discussed.

Ultra-fast quantum randomness generation by accelerated phase diffusion in a pulsed laser diode.

PubMed

Abellán, C; Amaya, W; Jofre, M; Curty, M; Acín, A; Capmany, J; Pruneri, V; Mitchell, M W

2014-01-27

We demonstrate a high bit-rate quantum random number generator by interferometric detection of phase diffusion in a gain-switched DFB laser diode. Gain switching at few-GHz frequencies produces a train of bright pulses with nearly equal amplitudes and random phases. An unbalanced Mach-Zehnder interferometer is used to interfere subsequent pulses and thereby generate strong random-amplitude pulses, which are detected and digitized to produce a high-rate random bit string. Using established models of semiconductor laser field dynamics, we predict a regime of high visibility interference and nearly complete vacuum-fluctuation-induced phase diffusion between pulses. These are confirmed by measurement of pulse power statistics at the output of the interferometer. Using a 5.825 GHz excitation rate and 14-bit digitization, we observe 43 Gbps quantum randomness generation.

Self-balanced real-time photonic scheme for ultrafast random number generation

NASA Astrophysics Data System (ADS)

Li, Pu; Guo, Ya; Guo, Yanqiang; Fan, Yuanlong; Guo, Xiaomin; Liu, Xianglian; Shore, K. Alan; Dubrova, Elena; Xu, Bingjie; Wang, Yuncai; Wang, Anbang

2018-06-01

We propose a real-time self-balanced photonic method for extracting ultrafast random numbers from broadband randomness sources. In place of electronic analog-to-digital converters (ADCs), the balanced photo-detection technology is used to directly quantize optically sampled chaotic pulses into a continuous random number stream. Benefitting from ultrafast photo-detection, our method can efficiently eliminate the generation rate bottleneck from electronic ADCs which are required in nearly all the available fast physical random number generators. A proof-of-principle experiment demonstrates that using our approach 10 Gb/s real-time and statistically unbiased random numbers are successfully extracted from a bandwidth-enhanced chaotic source. The generation rate achieved experimentally here is being limited by the bandwidth of the chaotic source. The method described has the potential to attain a real-time rate of 100 Gb/s.

Probing for quantum speedup in spin-glass problems with planted solutions

NASA Astrophysics Data System (ADS)

Hen, Itay; Job, Joshua; Albash, Tameem; Rønnow, Troels F.; Troyer, Matthias; Lidar, Daniel A.

2015-10-01

The availability of quantum annealing devices with hundreds of qubits has made the experimental demonstration of a quantum speedup for optimization problems a coveted, albeit elusive goal. Going beyond earlier studies of random Ising problems, here we introduce a method to construct a set of frustrated Ising-model optimization problems with tunable hardness. We study the performance of a D-Wave Two device (DW2) with up to 503 qubits on these problems and compare it to a suite of classical algorithms, including a highly optimized algorithm designed to compete directly with the DW2. The problems are generated around predetermined ground-state configurations, called planted solutions, which makes them particularly suitable for benchmarking purposes. The problem set exhibits properties familiar from constraint satisfaction (SAT) problems, such as a peak in the typical hardness of the problems, determined by a tunable clause density parameter. We bound the hardness regime where the DW2 device either does not or might exhibit a quantum speedup for our problem set. While we do not find evidence for a speedup for the hardest and most frustrated problems in our problem set, we cannot rule out that a speedup might exist for some of the easier, less frustrated problems. Our empirical findings pertain to the specific D-Wave processor and problem set we studied and leave open the possibility that future processors might exhibit a quantum speedup on the same problem set.

Random Matrix Approach for Primal-Dual Portfolio Optimization Problems

NASA Astrophysics Data System (ADS)

Tada, Daichi; Yamamoto, Hisashi; Shinzato, Takashi

2017-12-01

In this paper, we revisit the portfolio optimization problems of the minimization/maximization of investment risk under constraints of budget and investment concentration (primal problem) and the maximization/minimization of investment concentration under constraints of budget and investment risk (dual problem) for the case that the variances of the return rates of the assets are identical. We analyze both optimization problems by the Lagrange multiplier method and the random matrix approach. Thereafter, we compare the results obtained from our proposed approach with the results obtained in previous work. Moreover, we use numerical experiments to validate the results obtained from the replica approach and the random matrix approach as methods for analyzing both the primal and dual portfolio optimization problems.

Using landscape topology to compare continuous metaheuristics: a framework and case study on EDAs and ridge structure.

PubMed

Morgan, R; Gallagher, M

2012-01-01

In this paper we extend a previously proposed randomized landscape generator in combination with a comparative experimental methodology to study the behavior of continuous metaheuristic optimization algorithms. In particular, we generate two-dimensional landscapes with parameterized, linear ridge structure, and perform pairwise comparisons of algorithms to gain insight into what kind of problems are easy and difficult for one algorithm instance relative to another. We apply this methodology to investigate the specific issue of explicit dependency modeling in simple continuous estimation of distribution algorithms. Experimental results reveal specific examples of landscapes (with certain identifiable features) where dependency modeling is useful, harmful, or has little impact on mean algorithm performance. Heat maps are used to compare algorithm performance over a large number of landscape instances and algorithm trials. Finally, we perform a meta-search in the landscape parameter space to find landscapes which maximize the performance between algorithms. The results are related to some previous intuition about the behavior of these algorithms, but at the same time lead to new insights into the relationship between dependency modeling in EDAs and the structure of the problem landscape. The landscape generator and overall methodology are quite general and extendable and can be used to examine specific features of other algorithms.

A novel approach for incremental uncertainty rule generation from databases with missing values handling: application to dynamic medical databases.

PubMed

Konias, Sokratis; Chouvarda, Ioanna; Vlahavas, Ioannis; Maglaveras, Nicos

2005-09-01

Current approaches for mining association rules usually assume that the mining is performed in a static database, where the problem of missing attribute values does not practically exist. However, these assumptions are not preserved in some medical databases, like in a home care system. In this paper, a novel uncertainty rule algorithm is illustrated, namely URG-2 (Uncertainty Rule Generator), which addresses the problem of mining dynamic databases containing missing values. This algorithm requires only one pass from the initial dataset in order to generate the item set, while new metrics corresponding to the notion of Support and Confidence are used. URG-2 was evaluated over two medical databases, introducing randomly multiple missing values for each record's attribute (rate: 5-20% by 5% increments) in the initial dataset. Compared with the classical approach (records with missing values are ignored), the proposed algorithm was more robust in mining rules from datasets containing missing values. In all cases, the difference in preserving the initial rules ranged between 30% and 60% in favour of URG-2. Moreover, due to its incremental nature, URG-2 saved over 90% of the time required for thorough re-mining. Thus, the proposed algorithm can offer a preferable solution for mining in dynamic relational databases.

High-capacity quantum key distribution via hyperentangled degrees of freedom

NASA Astrophysics Data System (ADS)

Simon, David S.; Sergienko, Alexander V.

2014-06-01

Quantum key distribution (QKD) has long been a promising area for the application of quantum effects in solving real-world problems. However, two major obstacles have stood in the way of its widespread application: low secure key generation rates and short achievable operating distances. In this paper, a new physical mechanism for dealing with the first of these problems is proposed: the interplay between different degrees of freedom in a hyperentangled system (parametric down-conversion) is used to increase the Hilbert space dimension available for key generation while maintaining security. Polarization-based Bell tests provide security checking, while orbital angular momentum (OAM) and total angular momentum (TAM) provide a higher key generation rate. Whether to measure TAM or OAM is decided randomly in each trial. The concurrent noncommutativity of TAM with OAM and polarization provides the physical basis for quantum security. TAM measurements link polarization to OAM, so that if the legitimate participants measure OAM while the eavesdropper measures TAM (or vice-versa), then polarization entanglement is lost, revealing the eavesdropper. In contrast to other OAM-based QKD methods, complex active switching between OAM bases is not required; instead, passive switching by beam splitters combined with much simpler active switching between polarization bases makes implementation at high OAM more practical.

Electric Grid Expansion Planning with High Levels of Variable Generation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hadley, Stanton W.; You, Shutang; Shankar, Mallikarjun

2016-02-01

Renewables are taking a large proportion of generation capacity in U.S. power grids. As their randomness has increasing influence on power system operation, it is necessary to consider their impact on system expansion planning. To this end, this project studies the generation and transmission expansion co-optimization problem of the US Eastern Interconnection (EI) power grid with a high wind power penetration rate. In this project, the generation and transmission expansion problem for the EI system is modeled as a mixed-integer programming (MIP) problem. This study analyzed a time series creation method to capture the diversity of load and wind powermore » across balancing regions in the EI system. The obtained time series can be easily introduced into the MIP co-optimization problem and then solved robustly through available MIP solvers. Simulation results show that the proposed time series generation method and the expansion co-optimization model and can improve the expansion result significantly after considering the diversity of wind and load across EI regions. The improved expansion plan that combines generation and transmission will aid system planners and policy makers to maximize the social welfare. This study shows that modelling load and wind variations and diversities across balancing regions will produce significantly different expansion result compared with former studies. For example, if wind is modeled in more details (by increasing the number of wind output levels) so that more wind blocks are considered in expansion planning, transmission expansion will be larger and the expansion timing will be earlier. Regarding generation expansion, more wind scenarios will slightly reduce wind generation expansion in the EI system and increase the expansion of other generation such as gas. Also, adopting detailed wind scenarios will reveal that it may be uneconomic to expand transmission networks for transmitting a large amount of wind power through a long distance in the EI system. Incorporating more details of renewables in expansion planning will inevitably increase the computational burden. Therefore, high performance computing (HPC) techniques are urgently needed for power system operation and planning optimization. As a scoping study task, this project tested some preliminary parallel computation techniques such as breaking down the simulation task into several sub-tasks based on chronology splitting or sample splitting, and then assigning these sub-tasks to different cores. Testing results show significant time reduction when a simulation task is split into several sub-tasks for parallel execution.« less

Note: Fully integrated 3.2 Gbps quantum random number generator with real-time extraction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Xiao-Guang; Nie, You-Qi; Liang, Hao

2016-07-15

We present a real-time and fully integrated quantum random number generator (QRNG) by measuring laser phase fluctuations. The QRNG scheme based on laser phase fluctuations is featured for its capability of generating ultra-high-speed random numbers. However, the speed bottleneck of a practical QRNG lies on the limited speed of randomness extraction. To close the gap between the fast randomness generation and the slow post-processing, we propose a pipeline extraction algorithm based on Toeplitz matrix hashing and implement it in a high-speed field-programmable gate array. Further, all the QRNG components are integrated into a module, including a compact and actively stabilizedmore » interferometer, high-speed data acquisition, and real-time data post-processing and transmission. The final generation rate of the QRNG module with real-time extraction can reach 3.2 Gbps.« less

Experimental nonlocality-based randomness generation with nonprojective measurements

NASA Astrophysics Data System (ADS)

Gómez, S.; Mattar, A.; Gómez, E. S.; Cavalcanti, D.; Farías, O. Jiménez; Acín, A.; Lima, G.

2018-04-01

We report on an optical setup generating more than one bit of randomness from one entangled bit (i.e., a maximally entangled state of two qubits). The amount of randomness is certified through the observation of Bell nonlocal correlations. To attain this result we implemented a high-purity entanglement source and a nonprojective three-outcome measurement. Our implementation achieves a gain of 27% of randomness as compared with the standard methods using projective measurements. Additionally, we estimate the amount of randomness certified in a one-sided device-independent scenario, through the observation of Einstein-Podolsky-Rosen steering. Our results prove that nonprojective quantum measurements allow extending the limits for nonlocality-based certified randomness generation using current technology.

The cosmic microwave background radiation power spectrum as a random bit generator for symmetric- and asymmetric-key cryptography.

PubMed

Lee, Jeffrey S; Cleaver, Gerald B

2017-10-01

In this note, the Cosmic Microwave Background (CMB) Radiation is shown to be capable of functioning as a Random Bit Generator, and constitutes an effectively infinite supply of truly random one-time pad values of arbitrary length. It is further argued that the CMB power spectrum potentially conforms to the FIPS 140-2 standard. Additionally, its applicability to the generation of a (n × n) random key matrix for a Vernam cipher is established.

A new simple technique for improving the random properties of chaos-based cryptosystems

NASA Astrophysics Data System (ADS)

Garcia-Bosque, M.; Pérez-Resa, A.; Sánchez-Azqueta, C.; Celma, S.

2018-03-01

A new technique for improving the security of chaos-based stream ciphers has been proposed and tested experimentally. This technique manages to improve the randomness properties of the generated keystream by preventing the system to fall into short period cycles due to digitation. In order to test this technique, a stream cipher based on a Skew Tent Map algorithm has been implemented on a Virtex 7 FPGA. The randomness of the keystream generated by this system has been compared to the randomness of the keystream generated by the same system with the proposed randomness-enhancement technique. By subjecting both keystreams to the National Institute of Standards and Technology (NIST) tests, we have proved that our method can considerably improve the randomness of the generated keystreams. In order to incorporate our randomness-enhancement technique, only 41 extra slices have been needed, proving that, apart from effective, this method is also efficient in terms of area and hardware resources.

Double-blind comparison of first- and second-generation antipsychotics in early-onset schizophrenia and schizo-affective disorder: findings from the treatment of early-onset schizophrenia spectrum disorders (TEOSS) study.

PubMed

Sikich, Linmarie; Frazier, Jean A; McClellan, Jon; Findling, Robert L; Vitiello, Benedetto; Ritz, Louise; Ambler, Denisse; Puglia, Madeline; Maloney, Ann E; Michael, Emily; De Jong, Sandra; Slifka, Karen; Noyes, Nancy; Hlastala, Stefanie; Pierson, Leslie; McNamara, Nora K; Delporto-Bedoya, Denise; Anderson, Robert; Hamer, Robert M; Lieberman, Jeffrey A

2008-11-01

Atypical (second-generation) antipsychotics are considered standard treatment for children and adolescents with early-onset schizophrenia and schizoaffective disorder. However, the superiority of second-generation antipsychotics over first-generation antipsychotics has not been demonstrated. This study compared the efficacy and safety of two second-generation antipsychotics (olanzapine and risperidone) with a first-generation antipsychotic (molindone) in the treatment of early-onset schizophrenia and schizoaffective disorder. This double-blind multisite trial randomly assigned pediatric patients with early-onset schizophrenia and schizoaffective disorder to treatment with either olanzapine (2.5-20 mg/day), risperidone (0.5-6 mg/day), or molindone (10-140 mg/day, plus 1 mg/day of benztropine) for 8 weeks. The primary outcome was response to treatment, defined as a Clinical Global Impression (CGI) improvement score of 1 or 2 and >or=20% reduction in Positive and Negative Syndrome Scale (PANSS) total score after 8 weeks of treatment. In total, 119 youth were randomly assigned to treatment. Of these subjects, 116 received at least one dose of treatment and thus were available for analysis. No significant differences were found among treatment groups in response rates (molindone: 50%; olanzapine: 34%; risperidone: 46%) or magnitude of symptom reduction. Olanzapine and risperidone were associated with significantly greater weight gain. Olanzapine showed the greatest risk of weight gain and significant increases in fasting cholesterol, low density lipoprotein, insulin, and liver transaminase levels. Molindone led to more self-reports of akathisia. Risperidone and olanzapine did not demonstrate superior efficacy over molindone for treating early-onset schizophrenia and schizoaffective disorder. Adverse effects were frequent but differed among medications. The results question the nearly exclusive use of second-generation antipsychotics to treat early-onset schizophrenia and schizoaffective disorder. The safety findings related to weight gain and metabolic problems raise important public health concerns, given the widespread use of second-generation antipsychotics in youth for nonpsychotic disorders.

Doing better by getting worse: posthypnotic amnesia improves random number generation.

PubMed

Terhune, Devin Blair; Brugger, Peter

2011-01-01

Although forgetting is often regarded as a deficit that we need to control to optimize cognitive functioning, it can have beneficial effects in a number of contexts. We examined whether disrupting memory for previous numerical responses would attenuate repetition avoidance (the tendency to avoid repeating the same number) during random number generation and thereby improve the randomness of responses. Low suggestible and low dissociative and high dissociative highly suggestible individuals completed a random number generation task in a control condition, following a posthypnotic amnesia suggestion to forget previous numerical responses, and in a second control condition following the cancellation of the suggestion. High dissociative highly suggestible participants displayed a selective increase in repetitions during posthypnotic amnesia, with equivalent repetition frequency to a random system, whereas the other two groups exhibited repetition avoidance across conditions. Our results demonstrate that temporarily disrupting memory for previous numerical responses improves random number generation.

Doing Better by Getting Worse: Posthypnotic Amnesia Improves Random Number Generation

PubMed Central

Terhune, Devin Blair; Brugger, Peter

2011-01-01

Although forgetting is often regarded as a deficit that we need to control to optimize cognitive functioning, it can have beneficial effects in a number of contexts. We examined whether disrupting memory for previous numerical responses would attenuate repetition avoidance (the tendency to avoid repeating the same number) during random number generation and thereby improve the randomness of responses. Low suggestible and low dissociative and high dissociative highly suggestible individuals completed a random number generation task in a control condition, following a posthypnotic amnesia suggestion to forget previous numerical responses, and in a second control condition following the cancellation of the suggestion. High dissociative highly suggestible participants displayed a selective increase in repetitions during posthypnotic amnesia, with equivalent repetition frequency to a random system, whereas the other two groups exhibited repetition avoidance across conditions. Our results demonstrate that temporarily disrupting memory for previous numerical responses improves random number generation. PMID:22195022

Method and apparatus for in-situ characterization of energy storage and energy conversion devices

DOEpatents

Christophersen, Jon P [Idaho Falls, ID; Motloch, Chester G [Idaho Falls, ID; Morrison, John L [Butte, MT; Albrecht, Weston [Layton, UT

2010-03-09

Disclosed are methods and apparatuses for determining an impedance of an energy-output device using a random noise stimulus applied to the energy-output device. A random noise signal is generated and converted to a random noise stimulus as a current source correlated to the random noise signal. A bias-reduced response of the energy-output device to the random noise stimulus is generated by comparing a voltage at the energy-output device terminal to an average voltage signal. The random noise stimulus and bias-reduced response may be periodically sampled to generate a time-varying current stimulus and a time-varying voltage response, which may be correlated to generate an autocorrelated stimulus, an autocorrelated response, and a cross-correlated response. Finally, the autocorrelated stimulus, the autocorrelated response, and the cross-correlated response may be combined to determine at least one of impedance amplitude, impedance phase, and complex impedance.

Random numbers certified by Bell's theorem.

PubMed

Pironio, S; Acín, A; Massar, S; de la Giroday, A Boyer; Matsukevich, D N; Maunz, P; Olmschenk, S; Hayes, D; Luo, L; Manning, T A; Monroe, C

2010-04-15

Randomness is a fundamental feature of nature and a valuable resource for applications ranging from cryptography and gambling to numerical simulation of physical and biological systems. Random numbers, however, are difficult to characterize mathematically, and their generation must rely on an unpredictable physical process. Inaccuracies in the theoretical modelling of such processes or failures of the devices, possibly due to adversarial attacks, limit the reliability of random number generators in ways that are difficult to control and detect. Here, inspired by earlier work on non-locality-based and device-independent quantum information processing, we show that the non-local correlations of entangled quantum particles can be used to certify the presence of genuine randomness. It is thereby possible to design a cryptographically secure random number generator that does not require any assumption about the internal working of the device. Such a strong form of randomness generation is impossible classically and possible in quantum systems only if certified by a Bell inequality violation. We carry out a proof-of-concept demonstration of this proposal in a system of two entangled atoms separated by approximately one metre. The observed Bell inequality violation, featuring near perfect detection efficiency, guarantees that 42 new random numbers are generated with 99 per cent confidence. Our results lay the groundwork for future device-independent quantum information experiments and for addressing fundamental issues raised by the intrinsic randomness of quantum theory.

Social Problem Solving and Depressive Symptoms over Time: A Randomized Clinical Trial of Cognitive-Behavioral Analysis System of Psychotherapy, Brief Supportive Psychotherapy, and Pharmacotherapy

ERIC Educational Resources Information Center

Klein, Daniel N.; Leon, Andrew C.; Li, Chunshan; D'Zurilla, Thomas J.; Black, Sarah R.; Vivian, Dina; Dowling, Frank; Arnow, Bruce A.; Manber, Rachel; Markowitz, John C.; Kocsis, James H.

2011-01-01

Objective: Depression is associated with poor social problem solving, and psychotherapies that focus on problem-solving skills are efficacious in treating depression. We examined the associations between treatment, social problem solving, and depression in a randomized clinical trial testing the efficacy of psychotherapy augmentation for…

Social Noise: Generating Random Numbers from Twitter Streams

NASA Astrophysics Data System (ADS)

Fernández, Norberto; Quintas, Fernando; Sánchez, Luis; Arias, Jesús

2015-12-01

Due to the multiple applications of random numbers in computer systems (cryptography, online gambling, computer simulation, etc.) it is important to have mechanisms to generate these numbers. True Random Number Generators (TRNGs) are commonly used for this purpose. TRNGs rely on non-deterministic sources to generate randomness. Physical processes (like noise in semiconductors, quantum phenomenon, etc.) play this role in state of the art TRNGs. In this paper, we depart from previous work and explore the possibility of defining social TRNGs using the stream of public messages of the microblogging service Twitter as randomness source. Thus, we define two TRNGs based on Twitter stream information and evaluate them using the National Institute of Standards and Technology (NIST) statistical test suite. The results of the evaluation confirm the feasibility of the proposed approach.

Fast generation of sparse random kernel graphs

DOE PAGES

Hagberg, Aric; Lemons, Nathan; Du, Wen -Bo

2015-09-10

The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in timemore » at most ο(n(logn)²). As an example, we show how to generate samples of power-law degree distribution graphs with tunable assortativity.« less

Compact Quantum Random Number Generator with Silicon Nanocrystals Light Emitting Device Coupled to a Silicon Photomultiplier

NASA Astrophysics Data System (ADS)

Bisadi, Zahra; Acerbi, Fabio; Fontana, Giorgio; Zorzi, Nicola; Piemonte, Claudio; Pucker, Georg; Pavesi, Lorenzo

2018-02-01

A small-sized photonic quantum random number generator, easy to be implemented in small electronic devices for secure data encryption and other applications, is highly demanding nowadays. Here, we propose a compact configuration with Silicon nanocrystals large area light emitting device (LED) coupled to a Silicon photomultiplier to generate random numbers. The random number generation methodology is based on the photon arrival time and is robust against the non-idealities of the detector and the source of quantum entropy. The raw data show high quality of randomness and pass all the statistical tests in national institute of standards and technology tests (NIST) suite without a post-processing algorithm. The highest bit rate is 0.5 Mbps with the efficiency of 4 bits per detected photon.

An effective hybrid self-adapting differential evolution algorithm for the joint replenishment and location-inventory problem in a three-level supply chain.

PubMed

Wang, Lin; Qu, Hui; Chen, Tao; Yan, Fang-Ping

2013-01-01

The integration with different decisions in the supply chain is a trend, since it can avoid the suboptimal decisions. In this paper, we provide an effective intelligent algorithm for a modified joint replenishment and location-inventory problem (JR-LIP). The problem of the JR-LIP is to determine the reasonable number and location of distribution centers (DCs), the assignment policy of customers, and the replenishment policy of DCs such that the overall cost is minimized. However, due to the JR-LIP's difficult mathematical properties, simple and effective solutions for this NP-hard problem have eluded researchers. To find an effective approach for the JR-LIP, a hybrid self-adapting differential evolution algorithm (HSDE) is designed. To verify the effectiveness of the HSDE, two intelligent algorithms that have been proven to be effective algorithms for the similar problems named genetic algorithm (GA) and hybrid DE (HDE) are chosen to compare with it. Comparative results of benchmark functions and randomly generated JR-LIPs show that HSDE outperforms GA and HDE. Moreover, a sensitive analysis of cost parameters reveals the useful managerial insight. All comparative results show that HSDE is more stable and robust in handling this complex problem especially for the large-scale problem.

An Effective Hybrid Self-Adapting Differential Evolution Algorithm for the Joint Replenishment and Location-Inventory Problem in a Three-Level Supply Chain

PubMed Central

Chen, Tao; Yan, Fang-Ping

2013-01-01

The integration with different decisions in the supply chain is a trend, since it can avoid the suboptimal decisions. In this paper, we provide an effective intelligent algorithm for a modified joint replenishment and location-inventory problem (JR-LIP). The problem of the JR-LIP is to determine the reasonable number and location of distribution centers (DCs), the assignment policy of customers, and the replenishment policy of DCs such that the overall cost is minimized. However, due to the JR-LIP's difficult mathematical properties, simple and effective solutions for this NP-hard problem have eluded researchers. To find an effective approach for the JR-LIP, a hybrid self-adapting differential evolution algorithm (HSDE) is designed. To verify the effectiveness of the HSDE, two intelligent algorithms that have been proven to be effective algorithms for the similar problems named genetic algorithm (GA) and hybrid DE (HDE) are chosen to compare with it. Comparative results of benchmark functions and randomly generated JR-LIPs show that HSDE outperforms GA and HDE. Moreover, a sensitive analysis of cost parameters reveals the useful managerial insight. All comparative results show that HSDE is more stable and robust in handling this complex problem especially for the large-scale problem. PMID:24453822

Chance vs. necessity in living systems: a false antinomy.

PubMed

Buiatti, Marcello; Buiatti, Marco

2008-01-01

The concepts of order and randomness are crucial to understand 'living systems' structural and dynamical rules. In the history of biology, they lay behind the everlasting debate on the relative roles of chance and determinism in evolution. Jacques Monod [1970] built a theory where chance (randomness) and determinism (order) were considered as two complementary aspects of life. In the present paper, we will give an up to date version of the problem going beyond the dichotomy between chance and determinism. To this end, we will first see how the view on living systems has evolved from the mechanistic one of the 19th century to the one stemming from the most recent literature, where they emerge as complex systems continuously evolving through multiple interactions among their components and with the surrounding environment. We will then report on the ever increasing evidence of "friendly" co-existence in living beings between a number of "variability generators", fixed by evolution, and the "spontaneous order" derived from interactions between components. We will propose that the "disorder" generated is "benevolent" because it allows living systems to rapidly adapt to changes in the environment by continuously changing, while keeping their internal harmony.

Implementation of the DPM Monte Carlo code on a parallel architecture for treatment planning applications.

PubMed

Tyagi, Neelam; Bose, Abhijit; Chetty, Indrin J

2004-09-01

We have parallelized the Dose Planning Method (DPM), a Monte Carlo code optimized for radiotherapy class problems, on distributed-memory processor architectures using the Message Passing Interface (MPI). Parallelization has been investigated on a variety of parallel computing architectures at the University of Michigan-Center for Advanced Computing, with respect to efficiency and speedup as a function of the number of processors. We have integrated the parallel pseudo random number generator from the Scalable Parallel Pseudo-Random Number Generator (SPRNG) library to run with the parallel DPM. The Intel cluster consisting of 800 MHz Intel Pentium III processor shows an almost linear speedup up to 32 processors for simulating 1 x 10(8) or more particles. The speedup results are nearly linear on an Athlon cluster (up to 24 processors based on availability) which consists of 1.8 GHz+ Advanced Micro Devices (AMD) Athlon processors on increasing the problem size up to 8 x 10(8) histories. For a smaller number of histories (1 x 10(8)) the reduction of efficiency with the Athlon cluster (down to 83.9% with 24 processors) occurs because the processing time required to simulate 1 x 10(8) histories is less than the time associated with interprocessor communication. A similar trend was seen with the Opteron Cluster (consisting of 1400 MHz, 64-bit AMD Opteron processors) on increasing the problem size. Because of the 64-bit architecture Opteron processors are capable of storing and processing instructions at a faster rate and hence are faster as compared to the 32-bit Athlon processors. We have validated our implementation with an in-phantom dose calculation study using a parallel pencil monoenergetic electron beam of 20 MeV energy. The phantom consists of layers of water, lung, bone, aluminum, and titanium. The agreement in the central axis depth dose curves and profiles at different depths shows that the serial and parallel codes are equivalent in accuracy.

Improving Language Comprehension in Preschool Children with Language Difficulties: A Cluster Randomized Trial

ERIC Educational Resources Information Center

Hagen, Åste M.; Melby-Lervåg, Monica; Lervåg, Arne

2017-01-01

Background: Children with language comprehension difficulties are at risk of educational and social problems, which in turn impede employment prospects in adulthood. However, few randomized trials have examined how such problems can be ameliorated during the preschool years. Methods: We conducted a cluster randomized trial in 148 preschool…

Steinhaus’ Geometric Location Problem for Random Samples in the Plane.

DTIC Science & Technology

1982-05-11

NAL 411R A1, ’I 7 - I STEINHAUS ’ GEOMETRIC LOCATION PROBLEM FOR RANDOM SAMPLES IN THE PLANE By Dorit Hochbaum and J. Michael Steele TECHNICAL REPORT...DEPARTMENT OF STATISTICS -Dltrib’ytion/ STANFORD UNIVERSITY A-I.abilty Codes STANFORD, CALIFORNIA Dist Spciat ecial Steinhaus ’ Geometric Location Problem for...Random Samples in the Plane By Dorit Hochbaum and J. Michael Steele I. Introduction. The work of H. Steinhaus U wf94 as apparently the first explicit

Quantum Optimization of Fully Connected Spin Glasses

NASA Astrophysics Data System (ADS)

Venturelli, Davide; Mandrà, Salvatore; Knysh, Sergey; O'Gorman, Bryan; Biswas, Rupak; Smelyanskiy, Vadim

2015-07-01

Many NP-hard problems can be seen as the task of finding a ground state of a disordered highly connected Ising spin glass. If solutions are sought by means of quantum annealing, it is often necessary to represent those graphs in the annealer's hardware by means of the graph-minor embedding technique, generating a final Hamiltonian consisting of coupled chains of ferromagnetically bound spins, whose binding energy is a free parameter. In order to investigate the effect of embedding on problems of interest, the fully connected Sherrington-Kirkpatrick model with random ±1 couplings is programmed on the D-Wave TwoTM annealer using up to 270 qubits interacting on a Chimera-type graph. We present the best embedding prescriptions for encoding the Sherrington-Kirkpatrick problem in the Chimera graph. The results indicate that the optimal choice of embedding parameters could be associated with the emergence of the spin-glass phase of the embedded problem, whose presence was previously uncertain. This optimal parameter setting allows the performance of the quantum annealer to compete with (and potentially outperform, in the absence of analog control errors) optimized simulated annealing algorithms.

A deterministic Lagrangian particle separation-based method for advective-diffusion problems

NASA Astrophysics Data System (ADS)

Wong, Ken T. M.; Lee, Joseph H. W.; Choi, K. W.

2008-12-01

A simple and robust Lagrangian particle scheme is proposed to solve the advective-diffusion transport problem. The scheme is based on relative diffusion concepts and simulates diffusion by regulating particle separation. This new approach generates a deterministic result and requires far less number of particles than the random walk method. For the advection process, particles are simply moved according to their velocity. The general scheme is mass conservative and is free from numerical diffusion. It can be applied to a wide variety of advective-diffusion problems, but is particularly suited for ecological and water quality modelling when definition of particle attributes (e.g., cell status for modelling algal blooms or red tides) is a necessity. The basic derivation, numerical stability and practical implementation of the NEighborhood Separation Technique (NEST) are presented. The accuracy of the method is demonstrated through a series of test cases which embrace realistic features of coastal environmental transport problems. Two field application examples on the tidal flushing of a fish farm and the dynamics of vertically migrating marine algae are also presented.

Easi-CRISPR: a robust method for one-step generation of mice carrying conditional and insertion alleles using long ssDNA donors and CRISPR ribonucleoproteins.

PubMed

Quadros, Rolen M; Miura, Hiromi; Harms, Donald W; Akatsuka, Hisako; Sato, Takehito; Aida, Tomomi; Redder, Ronald; Richardson, Guy P; Inagaki, Yutaka; Sakai, Daisuke; Buckley, Shannon M; Seshacharyulu, Parthasarathy; Batra, Surinder K; Behlke, Mark A; Zeiner, Sarah A; Jacobi, Ashley M; Izu, Yayoi; Thoreson, Wallace B; Urness, Lisa D; Mansour, Suzanne L; Ohtsuka, Masato; Gurumurthy, Channabasavaiah B

2017-05-17

Conditional knockout mice and transgenic mice expressing recombinases, reporters, and inducible transcriptional activators are key for many genetic studies and comprise over 90% of mouse models created. Conditional knockout mice are generated using labor-intensive methods of homologous recombination in embryonic stem cells and are available for only ~25% of all mouse genes. Transgenic mice generated by random genomic insertion approaches pose problems of unreliable expression, and thus there is a need for targeted-insertion models. Although CRISPR-based strategies were reported to create conditional and targeted-insertion alleles via one-step delivery of targeting components directly to zygotes, these strategies are quite inefficient. Here we describe Easi-CRISPR (Efficient additions with ssDNA inserts-CRISPR), a targeting strategy in which long single-stranded DNA donors are injected with pre-assembled crRNA + tracrRNA + Cas9 ribonucleoprotein (ctRNP) complexes into mouse zygotes. We show for over a dozen loci that Easi-CRISPR generates correctly targeted conditional and insertion alleles in 8.5-100% of the resulting live offspring. Easi-CRISPR solves the major problem of animal genome engineering, namely the inefficiency of targeted DNA cassette insertion. The approach is robust, succeeding for all tested loci. It is versatile, generating both conditional and targeted insertion alleles. Finally, it is highly efficient, as treating an average of only 50 zygotes is sufficient to produce a correctly targeted allele in up to 100% of live offspring. Thus, Easi-CRISPR offers a comprehensive means of building large-scale Cre-LoxP animal resources.

Response Rates in Random-Digit-Dialed Telephone Surveys: Estimation vs. Measurement.

ERIC Educational Resources Information Center

Franz, Jennifer D.

The efficacy of the random digit dialing method in telephone surveys was examined. Random digit dialing (RDD) generates a pure random sample and provides the advantage of including unlisted phone numbers, as well as numbers which are too new to be listed. Its disadvantage is that it generates a major proportion of nonworking and business…

Energy mechanics of rock and snow avalanches and the role of fragmentation (invited)

NASA Astrophysics Data System (ADS)

Bartelt, Perry; Buser, Othmar; Glover, James

2014-05-01

The energy mechanics of rock and snow avalanches are traditionally described using a two-step transformation: potential energy is first converted into kinetic energy; kinetic energy is dissipated to heat by frictional processes. If the frictional processes are known, the energy fluxes of avalanches can be calculated completely. The break-up of the released mass, however, introduces several new energy fluxes into the avalanche problem. The first energy is associated with the fragmentation, which generates random particle motions. This is true kinetic energy. Inter-particle interactions (collisions, abrasion, fracture) cause the energy of the random particle motion to dissipate to heat. A constraint on the random motions is the basal boundary. It is at this interface that the dispersive pressure is created by vertical particle motions that are directed upwards into the flow. The integral of the upward particle motions can induce a change in avalanche flow volume and density, depending on the relationship between the weight of the flow and the dispersive pressure. Interestingly, normal pressures will only diverge from hydrostatic when there are changes in flow density. We are therefore confronted with the problem of calculating not only the vertical acceleration of the dispersive pressure, but also the change in vertical acceleration. In this contribution we discuss a method to calculate random particle motions, dispersive pressure and changes in avalanche flow density. These are dependent not only on the absolute mass, but also on the material properties of the disintegrating mass. This becomes particularly interesting when considering the motion of snow and rock avalanches as it allows the prediction of flow regime changes and therefore extreme avalanche run-out potential.

Revisiting sample size: are big trials the answer?

PubMed

Lurati Buse, Giovanna A L; Botto, Fernando; Devereaux, P J

2012-07-18

The superiority of the evidence generated in randomized controlled trials over observational data is not only conditional to randomization. Randomized controlled trials require proper design and implementation to provide a reliable effect estimate. Adequate random sequence generation, allocation implementation, analyses based on the intention-to-treat principle, and sufficient power are crucial to the quality of a randomized controlled trial. Power, or the probability of the trial to detect a difference when a real difference between treatments exists, strongly depends on sample size. The quality of orthopaedic randomized controlled trials is frequently threatened by a limited sample size. This paper reviews basic concepts and pitfalls in sample-size estimation and focuses on the importance of large trials in the generation of valid evidence.

Heterogeneous Suppression of Sequential Effects in Random Sequence Generation, but Not in Operant Learning.

PubMed

Shteingart, Hanan; Loewenstein, Yonatan

2016-01-01

There is a long history of experiments in which participants are instructed to generate a long sequence of binary random numbers. The scope of this line of research has shifted over the years from identifying the basic psychological principles and/or the heuristics that lead to deviations from randomness, to one of predicting future choices. In this paper, we used generalized linear regression and the framework of Reinforcement Learning in order to address both points. In particular, we used logistic regression analysis in order to characterize the temporal sequence of participants' choices. Surprisingly, a population analysis indicated that the contribution of the most recent trial has only a weak effect on behavior, compared to more preceding trials, a result that seems irreconcilable with standard sequential effects that decay monotonously with the delay. However, when considering each participant separately, we found that the magnitudes of the sequential effect are a monotonous decreasing function of the delay, yet these individual sequential effects are largely averaged out in a population analysis because of heterogeneity. The substantial behavioral heterogeneity in this task is further demonstrated quantitatively by considering the predictive power of the model. We show that a heterogeneous model of sequential dependencies captures the structure available in random sequence generation. Finally, we show that the results of the logistic regression analysis can be interpreted in the framework of reinforcement learning, allowing us to compare the sequential effects in the random sequence generation task to those in an operant learning task. We show that in contrast to the random sequence generation task, sequential effects in operant learning are far more homogenous across the population. These results suggest that in the random sequence generation task, different participants adopt different cognitive strategies to suppress sequential dependencies when generating the "random" sequences.

How does routinely delivered cognitive-behavioural therapy for gambling disorder compare to "gold standard" clinical trial?

PubMed

Smith, David P; Fairweather-Schmidt, A Kate; Harvey, Peter W; Battersby, Malcolm W

2018-03-01

Currently, it is unknown whether treatment outcomes derived from randomized controlled trials (RCTs) of cognitive-behavioural therapy (CBT) for problem gamblers still hold when applied to patients seen in routine practice. Thus, data from an RCT of cognitive therapy versus exposure therapy for problem gambling versus patients of a gambling help service were compared. Assessments of problem gambling severity, psychosocial impairment, and alcohol use were undertaken at baseline and post-treatment and evaluated within a counterfactual framework. Findings showed that the contrast between routine CBT for pokies and horse betting had a significant effect, indicative of a 62% lower gambling urge score if routine CBT recipients had all been horse/track betters opposed to gambling with "pokies." However, the majority of contrasts indicated therapeutic outcomes achieved in routine CBT treatments were of equivalent robustness relative to RCT conditions. The present findings infer routine practice treatment outcomes are as efficacious as those generated in RCT contexts. Copyright © 2017 John Wiley & Sons, Ltd.

Permutation coding technique for image recognition systems.

PubMed

Kussul, Ernst M; Baidyk, Tatiana N; Wunsch, Donald C; Makeyev, Oleksandr; Martín, Anabel

2006-11-01

A feature extractor and neural classifier for image recognition systems are proposed. The proposed feature extractor is based on the concept of random local descriptors (RLDs). It is followed by the encoder that is based on the permutation coding technique that allows to take into account not only detected features but also the position of each feature on the image and to make the recognition process invariant to small displacements. The combination of RLDs and permutation coding permits us to obtain a sufficiently general description of the image to be recognized. The code generated by the encoder is used as an input data for the neural classifier. Different types of images were used to test the proposed image recognition system. It was tested in the handwritten digit recognition problem, the face recognition problem, and the microobject shape recognition problem. The results of testing are very promising. The error rate for the Modified National Institute of Standards and Technology (MNIST) database is 0.44% and for the Olivetti Research Laboratory (ORL) database it is 0.1%.

Asymmetrically dominated choice problems, the isolation hypothesis and random incentive mechanisms.

PubMed

Cox, James C; Sadiraj, Vjollca; Schmidt, Ulrich

2014-01-01

This paper presents an experimental study of the random incentive mechanisms which are a standard procedure in economic and psychological experiments. Random incentive mechanisms have several advantages but are incentive-compatible only if responses to the single tasks are independent. This is true if either the independence axiom of expected utility theory or the isolation hypothesis of prospect theory holds. We present a simple test of this in the context of choice under risk. In the baseline (one task) treatment we observe risk behavior in a given choice problem. We show that by integrating a second, asymmetrically dominated choice problem in a random incentive mechanism risk behavior can be manipulated systematically. This implies that the isolation hypothesis is violated and the random incentive mechanism does not elicit true preferences in our example.

Solution-Processed Carbon Nanotube True Random Number Generator.

PubMed

Gaviria Rojas, William A; McMorrow, Julian J; Geier, Michael L; Tang, Qianying; Kim, Chris H; Marks, Tobin J; Hersam, Mark C

2017-08-09

With the growing adoption of interconnected electronic devices in consumer and industrial applications, there is an increasing demand for robust security protocols when transmitting and receiving sensitive data. Toward this end, hardware true random number generators (TRNGs), commonly used to create encryption keys, offer significant advantages over software pseudorandom number generators. However, the vast network of devices and sensors envisioned for the "Internet of Things" will require small, low-cost, and mechanically flexible TRNGs with low computational complexity. These rigorous constraints position solution-processed semiconducting single-walled carbon nanotubes (SWCNTs) as leading candidates for next-generation security devices. Here, we demonstrate the first TRNG using static random access memory (SRAM) cells based on solution-processed SWCNTs that digitize thermal noise to generate random bits. This bit generation strategy can be readily implemented in hardware with minimal transistor and computational overhead, resulting in an output stream that passes standardized statistical tests for randomness. By using solution-processed semiconducting SWCNTs in a low-power, complementary architecture to achieve TRNG, we demonstrate a promising approach for improving the security of printable and flexible electronics.

Conic Sampling: An Efficient Method for Solving Linear and Quadratic Programming by Randomly Linking Constraints within the Interior

PubMed Central

Serang, Oliver

2012-01-01

Linear programming (LP) problems are commonly used in analysis and resource allocation, frequently surfacing as approximations to more difficult problems. Existing approaches to LP have been dominated by a small group of methods, and randomized algorithms have not enjoyed popularity in practice. This paper introduces a novel randomized method of solving LP problems by moving along the facets and within the interior of the polytope along rays randomly sampled from the polyhedral cones defined by the bounding constraints. This conic sampling method is then applied to randomly sampled LPs, and its runtime performance is shown to compare favorably to the simplex and primal affine-scaling algorithms, especially on polytopes with certain characteristics. The conic sampling method is then adapted and applied to solve a certain quadratic program, which compute a projection onto a polytope; the proposed method is shown to outperform the proprietary software Mathematica on large, sparse QP problems constructed from mass spectometry-based proteomics. PMID:22952741

Experimental Matching of Instances to Heuristics for Constraint Satisfaction Problems.

PubMed

Moreno-Scott, Jorge Humberto; Ortiz-Bayliss, José Carlos; Terashima-Marín, Hugo; Conant-Pablos, Santiago Enrique

2016-01-01

Constraint satisfaction problems are of special interest for the artificial intelligence and operations research community due to their many applications. Although heuristics involved in solving these problems have largely been studied in the past, little is known about the relation between instances and the respective performance of the heuristics used to solve them. This paper focuses on both the exploration of the instance space to identify relations between instances and good performing heuristics and how to use such relations to improve the search. Firstly, the document describes a methodology to explore the instance space of constraint satisfaction problems and evaluate the corresponding performance of six variable ordering heuristics for such instances in order to find regions on the instance space where some heuristics outperform the others. Analyzing such regions favors the understanding of how these heuristics work and contribute to their improvement. Secondly, we use the information gathered from the first stage to predict the most suitable heuristic to use according to the features of the instance currently being solved. This approach proved to be competitive when compared against the heuristics applied in isolation on both randomly generated and structured instances of constraint satisfaction problems.

Experimental Matching of Instances to Heuristics for Constraint Satisfaction Problems

PubMed Central

Moreno-Scott, Jorge Humberto; Ortiz-Bayliss, José Carlos; Terashima-Marín, Hugo; Conant-Pablos, Santiago Enrique

2016-01-01

Constraint satisfaction problems are of special interest for the artificial intelligence and operations research community due to their many applications. Although heuristics involved in solving these problems have largely been studied in the past, little is known about the relation between instances and the respective performance of the heuristics used to solve them. This paper focuses on both the exploration of the instance space to identify relations between instances and good performing heuristics and how to use such relations to improve the search. Firstly, the document describes a methodology to explore the instance space of constraint satisfaction problems and evaluate the corresponding performance of six variable ordering heuristics for such instances in order to find regions on the instance space where some heuristics outperform the others. Analyzing such regions favors the understanding of how these heuristics work and contribute to their improvement. Secondly, we use the information gathered from the first stage to predict the most suitable heuristic to use according to the features of the instance currently being solved. This approach proved to be competitive when compared against the heuristics applied in isolation on both randomly generated and structured instances of constraint satisfaction problems. PMID:26949383

A Genetic Algorithm Approach for the TV Self-Promotion Assignment Problem

NASA Astrophysics Data System (ADS)

Pereira, Paulo A.; Fontes, Fernando A. C. C.; Fontes, Dalila B. M. M.

2009-09-01

We report on the development of a Genetic Algorithm (GA), which has been integrated into a Decision Support System to plan the best assignment of the weekly self-promotion space for a TV station. The problem addressed consists on deciding which shows to advertise and when such that the number of viewers, of an intended group or target, is maximized. The GA proposed incorporates a greedy heuristic to find good initial solutions. These solutions, as well as the solutions later obtained through the use of the GA, go then through a repair procedure. This is used with two objectives, which are addressed in turn. Firstly, it checks the solution feasibility and if unfeasible it is fixed by removing some shows. Secondly, it tries to improve the solution by adding some extra shows. Since the problem faced by the commercial TV station is too big and has too many features it cannot be solved exactly. Therefore, in order to test the quality of the solutions provided by the proposed GA we have randomly generated some smaller problem instances. For these problems we have obtained solutions on average within 1% of the optimal solution value.

Effectiveness of a Web-based Intervention for Problem Drinkers and Reasons for Dropout: Randomized Controlled Trial

PubMed Central

de Haan, Hein A; ter Huurne, Elke D; Becker, Eni S; de Jong, Cor AJ

2010-01-01

Background Online self-help interventions for problem drinkers show promising results, but the effectiveness of online therapy with active involvement of a therapist via the Internet only has not been examined. Objective The objective of our study was to evaluate an e-therapy program with active therapeutic involvement for problem drinkers, with the hypotheses that e-therapy would (1) reduce weekly alcohol consumption, and (2) improve health status. Reasons for dropout were also systematically investigated. Method In an open randomized controlled trial, Dutch-speaking problem drinkers in the general population were randomly assigned (in blocks of 8, according to a computer-generated random list) to the 3-month e-therapy program (n = 78) or the waiting list control group (n = 78). The e-therapy program consisted of a structured 2-part online treatment program in which the participant and the therapist communicated asynchronously, via the Internet only. Participants in the waiting list control group received “no-reply” email messages once every 2 weeks. The primary outcome measures were (1) the difference in the score on weekly alcohol consumption, and (2) the proportion of participants drinking under the problem drinking limit. Intention-to-treat analyses were performed using multiple imputations to deal with loss to follow-up. A dropout questionnaire was sent to anyone who did not complete the 3-month assessment. Reasons for dropout were independently assessed by the first and third author. Results Of the 156 individuals who were randomly assigned, 102 (65%) completed assessment at 3 months. In the intention-to-treat analyses, the e-therapy group (n = 78) showed a significantly greater decrease in alcohol consumption than those in the control group (n = 78) at 3 months. The e-therapy group decreased their mean weekly alcohol consumption by 28.8 units compared with 3.1 units in the control group, a difference in means of 25.6 units on a weekly basis (95% confidence interval 15.69-35.80, P < .001). The between-group effect size (pooled SD) was large (d = 1.21). The results also showed that 68% (53/78) of the e-therapy group was drinking less than 15 (females) or 22 (males) units a week, compared with 15% (12/78) in the control group (OR 12.0, number needed to treat 1.9, P < .001). Dropout analysis showed that the main reasons for dropouts (n = 54) were personal reasons unrelated to the e-therapy program, discomfort with the treatment protocol, and satisfaction with the positive results achieved. Conclusions E-therapy for problem drinking is an effective intervention that can be delivered to a large population who otherwise do not seek help for their drinking problem. Insight into reasons for dropout can help improve e-therapy programs to decrease the number of dropouts. Additional research is needed to directly compare the effectiveness of the e-therapy program with a face-to-face treatment program. Trial registration ISRCTN39104853; http://controlled-trials.com/ISRCTN39104853/ISRCTN39104853 (Archived by WebCite at http://www.webcitation.org/5uX1R5xfW) PMID:21163776

XMDS2: Fast, scalable simulation of coupled stochastic partial differential equations

NASA Astrophysics Data System (ADS)

Dennis, Graham R.; Hope, Joseph J.; Johnsson, Mattias T.

2013-01-01

XMDS2 is a cross-platform, GPL-licensed, open source package for numerically integrating initial value problems that range from a single ordinary differential equation up to systems of coupled stochastic partial differential equations. The equations are described in a high-level XML-based script, and the package generates low-level optionally parallelised C++ code for the efficient solution of those equations. It combines the advantages of high-level simulations, namely fast and low-error development, with the speed, portability and scalability of hand-written code. XMDS2 is a complete redesign of the XMDS package, and features support for a much wider problem space while also producing faster code. Program summaryProgram title: XMDS2 Catalogue identifier: AENK_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AENK_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: GNU General Public License, version 2 No. of lines in distributed program, including test data, etc.: 872490 No. of bytes in distributed program, including test data, etc.: 45522370 Distribution format: tar.gz Programming language: Python and C++. Computer: Any computer with a Unix-like system, a C++ compiler and Python. Operating system: Any Unix-like system; developed under Mac OS X and GNU/Linux. RAM: Problem dependent (roughly 50 bytes per grid point) Classification: 4.3, 6.5. External routines: The external libraries required are problem-dependent. Uses FFTW3 Fourier transforms (used only for FFT-based spectral methods), dSFMT random number generation (used only for stochastic problems), MPI message-passing interface (used only for distributed problems), HDF5, GNU Scientific Library (used only for Bessel-based spectral methods) and a BLAS implementation (used only for non-FFT-based spectral methods). Nature of problem: General coupled initial-value stochastic partial differential equations. Solution method: Spectral method with method-of-lines integration Running time: Determined by the size of the problem

Randomized interpolative decomposition of separated representations

NASA Astrophysics Data System (ADS)

Biagioni, David J.; Beylkin, Daniel; Beylkin, Gregory

2015-01-01

We introduce an algorithm to compute tensor interpolative decomposition (dubbed CTD-ID) for the reduction of the separation rank of Canonical Tensor Decompositions (CTDs). Tensor ID selects, for a user-defined accuracy ɛ, a near optimal subset of terms of a CTD to represent the remaining terms via a linear combination of the selected terms. CTD-ID can be used as an alternative to or in combination with the Alternating Least Squares (ALS) algorithm. We present examples of its use within a convergent iteration to compute inverse operators in high dimensions. We also briefly discuss the spectral norm as a computational alternative to the Frobenius norm in estimating approximation errors of tensor ID. We reduce the problem of finding tensor IDs to that of constructing interpolative decompositions of certain matrices. These matrices are generated via randomized projection of the terms of the given tensor. We provide cost estimates and several examples of the new approach to the reduction of separation rank.

640-Gbit/s fast physical random number generation using a broadband chaotic semiconductor laser

NASA Astrophysics Data System (ADS)

Zhang, Limeng; Pan, Biwei; Chen, Guangcan; Guo, Lu; Lu, Dan; Zhao, Lingjuan; Wang, Wei

2017-04-01

An ultra-fast physical random number generator is demonstrated utilizing a photonic integrated device based broadband chaotic source with a simple post data processing method. The compact chaotic source is implemented by using a monolithic integrated dual-mode amplified feedback laser (AFL) with self-injection, where a robust chaotic signal with RF frequency coverage of above 50 GHz and flatness of ±3.6 dB is generated. By using 4-least significant bits (LSBs) retaining from the 8-bit digitization of the chaotic waveform, random sequences with a bit-rate up to 640 Gbit/s (160 GS/s × 4 bits) are realized. The generated random bits have passed each of the fifteen NIST statistics tests (NIST SP800-22), indicating its randomness for practical applications.

Physical Unclonable Function with Multiplexing Units and its Evaluation

NASA Astrophysics Data System (ADS)

Yoshikawa, Masaya; Asai, Toshiya; Shiozaki, Mitsuru; Fujino, Takeshi

Recently, semiconductor counterfeiting has become an increasingly serious problem. Therefore, techniques to prevent the counterfeit by using random characteristic patterns that are difficult to control artificially have attracted attention. The physical unclonable function (PUF) is one of the techniques. It is a method to derive ID information peculiar to a device by detecting random physical features that cannot be controlled during the device's manufacture. Because information such as the ID information is difficult to replicate, PUF is used as a technique to prevent counterfeiting. Several studies have been reported on PUF. Arbiter PUF, which utilizes the difference in signal propagation delay between selectors, is the typical method of composing PUF using delay characteristics. This paper proposed a new PUF which is based on the arbiter PUF. The proposed PUF introduces new multiplexing selector units. It attempts to generate an effective response using the orders of three signal arrivals. Experiments using FPGAs verify the validity of the proposed PUF. Although Uniqueness is deteriorated, Correctness, Steadiness, Randomness and Resistance against the machine learning attacks are improved in comparison with conventional one.

SLMRACE: a noise-free RACE implementation with reduced computational time

NASA Astrophysics Data System (ADS)

Chauvin, Juliet; Provenzi, Edoardo

2017-05-01

We present a faster and noise-free implementation of the RACE algorithm. RACE has mixed characteristics between the famous Retinex model of Land and McCann and the automatic color equalization (ACE) color-correction algorithm. The original random spray-based RACE implementation suffers from two main problems: its computational time and the presence of noise. Here, we will show that it is possible to adapt two techniques recently proposed by Banić et al. to the RACE framework in order to drastically decrease the computational time and noise generation. The implementation will be called smart-light-memory-RACE (SLMRACE).

Inverse random source scattering for the Helmholtz equation in inhomogeneous media

NASA Astrophysics Data System (ADS)

Li, Ming; Chen, Chuchu; Li, Peijun

2018-01-01

This paper is concerned with an inverse random source scattering problem in an inhomogeneous background medium. The wave propagation is modeled by the stochastic Helmholtz equation with the source driven by additive white noise. The goal is to reconstruct the statistical properties of the random source such as the mean and variance from the boundary measurement of the radiated random wave field at multiple frequencies. Both the direct and inverse problems are considered. We show that the direct problem has a unique mild solution by a constructive proof. For the inverse problem, we derive Fredholm integral equations, which connect the boundary measurement of the radiated wave field with the unknown source function. A regularized block Kaczmarz method is developed to solve the ill-posed integral equations. Numerical experiments are included to demonstrate the effectiveness of the proposed method.

Evolving prosocial and sustainable neighborhoods and communities.

PubMed

Biglan, Anthony; Hinds, Erika

2009-01-01

In this review, we examine randomized controlled trials of community interventions to affect health. The evidence supports the efficacy of community interventions for preventing tobacco, alcohol, and other drug use; several recent trials have shown the benefits of community interventions for preventing multiple problems of young people, including antisocial behavior. However, the next generation of community intervention research needs to reflect more fully the fact that most psychological and behavioral problems of humans are interrelated and result from the same environmental conditions. The evidence supports testing new comprehensive community interventions that focus on increasing nurturance in communities. Nurturing communities will be ones in which families, schools, neighborhoods, and workplaces (a) minimize biologically and socially toxic events, (b) richly reinforce prosocial behavior, and (c) foster psychological acceptance. Such interventions also have the potential to make neighborhoods more sustainable.

Evolving Prosocial and Sustainable Neighborhoods and Communities

PubMed Central

Biglan, Anthony; Hinds, Erika

2008-01-01

In this chapter, we review randomized controlled trials of community interventions to affect health. The evidence supports the efficacy of community interventions for preventing tobacco, alcohol, and other drug use; several recent trials have shown the benefits of community interventions for preventing multiple problems of young people, including antisocial behavior. However, the next generation of community intervention research needs to reflect more fully the fact that most psychological and behavioral problems of humans are inter-related and result from the same environmental conditions. The evidence supports testing a new set of comprehensive community interventions that focus on increasing nurturance in communities. Nurturing communities will be ones in which families, schools, neighborhoods, and workplaces (a) minimize biologically and socially toxic events, (b) richly reinforce prosocial behavior, and (c) foster psychological acceptance. Such interventions also have the potential to make neighborhoods more sustainable. PMID:19327029

What Does a Random Line Look Like: An Experimental Study

ERIC Educational Resources Information Center

Turner, Nigel E.; Liu, Eleanor; Toneatto, Tony

2011-01-01

The study examined the perception of random lines by people with gambling problems compared to people without gambling problems. The sample consisted of 67 probable pathological gamblers and 46 people without gambling problems. Participants completed a number of questionnaires about their gambling and were then presented with a series of random…

A Comparison of One Time Pad Random Key Generation using Linear Congruential Generator and Quadratic Congruential Generator

NASA Astrophysics Data System (ADS)

Apdilah, D.; Harahap, M. K.; Khairina, N.; Husein, A. M.; Harahap, M.

2018-04-01

One Time Pad algorithm always requires a pairing of the key for plaintext. If the length of keys less than a length of the plaintext, the key will be repeated until the length of the plaintext same with the length of the key. In this research, we use Linear Congruential Generator and Quadratic Congruential Generator for generating a random number. One Time Pad use a random number as a key for encryption and decryption process. Key will generate the first letter from the plaintext, we compare these two algorithms in terms of time speed encryption, and the result is a combination of OTP with LCG faster than the combination of OTP with QCG.

Competitive Facility Location with Random Demands

NASA Astrophysics Data System (ADS)

Uno, Takeshi; Katagiri, Hideki; Kato, Kosuke

2009-10-01

This paper proposes a new location problem of competitive facilities, e.g. shops and stores, with uncertain demands in the plane. By representing the demands for facilities as random variables, the location problem is formulated to a stochastic programming problem, and for finding its solution, three deterministic programming problems: expectation maximizing problem, probability maximizing problem, and satisfying level maximizing problem are considered. After showing that one of their optimal solutions can be found by solving 0-1 programming problems, their solution method is proposed by improving the tabu search algorithm with strategic vibration. Efficiency of the solution method is shown by applying to numerical examples of the facility location problems.

Early stage hot spot analysis through standard cell base random pattern generation

NASA Astrophysics Data System (ADS)

Jeon, Joong-Won; Song, Jaewan; Kim, Jeong-Lim; Park, Seongyul; Yang, Seung-Hune; Lee, Sooryong; Kang, Hokyu; Madkour, Kareem; ElManhawy, Wael; Lee, SeungJo; Kwan, Joe

2017-04-01

Due to limited availability of DRC clean patterns during the process and RET recipe development, OPC recipes are not tested with high pattern coverage. Various kinds of pattern can help OPC engineer to detect sensitive patterns to lithographic effects. Random pattern generation is needed to secure robust OPC recipe. However, simple random patterns without considering real product layout style can't cover patterning hotspot in production levels. It is not effective to use them for OPC optimization thus it is important to generate random patterns similar to real product patterns. This paper presents a strategy for generating random patterns based on design architecture information and preventing hotspot in early process development stage through a tool called Layout Schema Generator (LSG). Using LSG, we generate standard cell based on random patterns reflecting real design cell structure - fin pitch, gate pitch and cell height. The output standard cells from LSG are applied to an analysis methodology to assess their hotspot severity by assigning a score according to their optical image parameters - NILS, MEEF, %PV band and thus potential hotspots can be defined by determining their ranking. This flow is demonstrated on Samsung 7nm technology optimizing OPC recipe and early enough in the process avoiding using problematic patterns.

Device-independent randomness generation from several Bell estimators

NASA Astrophysics Data System (ADS)

Nieto-Silleras, Olmo; Bamps, Cédric; Silman, Jonathan; Pironio, Stefano

2018-02-01

Device-independent randomness generation and quantum key distribution protocols rely on a fundamental relation between the non-locality of quantum theory and its random character. This relation is usually expressed in terms of a trade-off between the probability of guessing correctly the outcomes of measurements performed on quantum systems and the amount of violation of a given Bell inequality. However, a more accurate assessment of the randomness produced in Bell experiments can be obtained if the value of several Bell expressions is simultaneously taken into account, or if the full set of probabilities characterizing the behavior of the device is considered. We introduce protocols for device-independent randomness generation secure against classical side information, that rely on the estimation of an arbitrary number of Bell expressions or even directly on the experimental frequencies of measurement outcomes. Asymptotically, this results in an optimal generation of randomness from experimental data (as measured by the min-entropy), without having to assume beforehand that the devices violate a specific Bell inequality.

A novel image encryption algorithm based on synchronized random bit generated in cascade-coupled chaotic semiconductor ring lasers

NASA Astrophysics Data System (ADS)

Li, Jiafu; Xiang, Shuiying; Wang, Haoning; Gong, Junkai; Wen, Aijun

2018-03-01

In this paper, a novel image encryption algorithm based on synchronization of physical random bit generated in a cascade-coupled semiconductor ring lasers (CCSRL) system is proposed, and the security analysis is performed. In both transmitter and receiver parts, the CCSRL system is a master-slave configuration consisting of a master semiconductor ring laser (M-SRL) with cross-feedback and a solitary SRL (S-SRL). The proposed image encryption algorithm includes image preprocessing based on conventional chaotic maps, pixel confusion based on control matrix extracted from physical random bit, and pixel diffusion based on random bit stream extracted from physical random bit. Firstly, the preprocessing method is used to eliminate the correlation between adjacent pixels. Secondly, physical random bit with verified randomness is generated based on chaos in the CCSRL system, and is used to simultaneously generate the control matrix and random bit stream. Finally, the control matrix and random bit stream are used for the encryption algorithm in order to change the position and the values of pixels, respectively. Simulation results and security analysis demonstrate that the proposed algorithm is effective and able to resist various typical attacks, and thus is an excellent candidate for secure image communication application.

Randomization in clinical trials in orthodontics: its significance in research design and methods to achieve it.

PubMed

Pandis, Nikolaos; Polychronopoulou, Argy; Eliades, Theodore

2011-12-01

Randomization is a key step in reducing selection bias during the treatment allocation phase in randomized clinical trials. The process of randomization follows specific steps, which include generation of the randomization list, allocation concealment, and implementation of randomization. The phenomenon in the dental and orthodontic literature of characterizing treatment allocation as random is frequent; however, often the randomization procedures followed are not appropriate. Randomization methods assign, at random, treatment to the trial arms without foreknowledge of allocation by either the participants or the investigators thus reducing selection bias. Randomization entails generation of random allocation, allocation concealment, and the actual methodology of implementing treatment allocation randomly and unpredictably. Most popular randomization methods include some form of restricted and/or stratified randomization. This article introduces the reasons, which make randomization an integral part of solid clinical trial methodology, and presents the main randomization schemes applicable to clinical trials in orthodontics.

Sequential Reactions of Surface-Tethered Glycolytic Enzymes

PubMed Central

Mukai, Chinatsu; Bergkvist, Magnus; Nelson, Jacquelyn L.; Travis, Alexander J.

2014-01-01

SUMMARY The development of complex hybrid organic-inorganic devices faces several challenges, including how they can generate energy. Cells face similar challenges regarding local energy production. Mammalian sperm solve this problem by generating ATP down the flagellar principal piece by means of glycolytic enzymes, several of which are tethered to a cytoskeletal support via germ cell-specific targeting domains. Inspired by this design, we have produced recombinant hexokinase type 1 and glucose-6-phosphate isomerase capable of oriented immobilization on a nickel-nitrilotriacetic acid modified surface. Specific activities of enzymes tethered via this strategy were substantially higher than when randomly adsorbed. Furthermore, these enzymes showed sequential activities when tethered onto the same surface. This is the first demonstration of surface-tethered pathway components showing sequential enzymatic activities, and it provides a first step toward reconstitution of glycolysis on engineered hybrid devices. PMID:19778729

Stochastic Optimization For Water Resources Allocation

NASA Astrophysics Data System (ADS)

Yamout, G.; Hatfield, K.

2003-12-01

For more than 40 years, water resources allocation problems have been addressed using deterministic mathematical optimization. When data uncertainties exist, these methods could lead to solutions that are sub-optimal or even infeasible. While optimization models have been proposed for water resources decision-making under uncertainty, no attempts have been made to address the uncertainties in water allocation problems in an integrated approach. This paper presents an Integrated Dynamic, Multi-stage, Feedback-controlled, Linear, Stochastic, and Distributed parameter optimization approach to solve a problem of water resources allocation. It attempts to capture (1) the conflict caused by competing objectives, (2) environmental degradation produced by resource consumption, and finally (3) the uncertainty and risk generated by the inherently random nature of state and decision parameters involved in such a problem. A theoretical system is defined throughout its different elements. These elements consisting mainly of water resource components and end-users are described in terms of quantity, quality, and present and future associated risks and uncertainties. Models are identified, modified, and interfaced together to constitute an integrated water allocation optimization framework. This effort is a novel approach to confront the water allocation optimization problem while accounting for uncertainties associated with all its elements; thus resulting in a solution that correctly reflects the physical problem in hand.

A hybrid-type quantum random number generator

NASA Astrophysics Data System (ADS)

Hai-Qiang, Ma; Wu, Zhu; Ke-Jin, Wei; Rui-Xue, Li; Hong-Wei, Liu

2016-05-01

This paper proposes a well-performing hybrid-type truly quantum random number generator based on the time interval between two independent single-photon detection signals, which is practical and intuitive, and generates the initial random number sources from a combination of multiple existing random number sources. A time-to-amplitude converter and multichannel analyzer are used for qualitative analysis to demonstrate that each and every step is random. Furthermore, a carefully designed data acquisition system is used to obtain a high-quality random sequence. Our scheme is simple and proves that the random number bit rate can be dramatically increased to satisfy practical requirements. Project supported by the National Natural Science Foundation of China (Grant Nos. 61178010 and 11374042), the Fund of State Key Laboratory of Information Photonics and Optical Communications (Beijing University of Posts and Telecommunications), China, and the Fundamental Research Funds for the Central Universities of China (Grant No. bupt2014TS01).

High-speed true random number generation based on paired memristors for security electronics

NASA Astrophysics Data System (ADS)

Zhang, Teng; Yin, Minghui; Xu, Changmin; Lu, Xiayan; Sun, Xinhao; Yang, Yuchao; Huang, Ru

2017-11-01

True random number generator (TRNG) is a critical component in hardware security that is increasingly important in the era of mobile computing and internet of things. Here we demonstrate a TRNG using intrinsic variation of memristors as a natural source of entropy that is otherwise undesirable in most applications. The random bits were produced by cyclically switching a pair of tantalum oxide based memristors and comparing their resistance values in the off state, taking advantage of the more pronounced resistance variation compared with that in the on state. Using an alternating read scheme in the designed TRNG circuit, the unbiasedness of the random numbers was significantly improved, and the bitstream passed standard randomness tests. The Pt/TaO x /Ta memristors fabricated in this work have fast programming/erasing speeds of ˜30 ns, suggesting a high random number throughput. The approach proposed here thus holds great promise for physically-implemented random number generation.

High-speed true random number generation based on paired memristors for security electronics.

PubMed

Zhang, Teng; Yin, Minghui; Xu, Changmin; Lu, Xiayan; Sun, Xinhao; Yang, Yuchao; Huang, Ru

2017-11-10

True random number generator (TRNG) is a critical component in hardware security that is increasingly important in the era of mobile computing and internet of things. Here we demonstrate a TRNG using intrinsic variation of memristors as a natural source of entropy that is otherwise undesirable in most applications. The random bits were produced by cyclically switching a pair of tantalum oxide based memristors and comparing their resistance values in the off state, taking advantage of the more pronounced resistance variation compared with that in the on state. Using an alternating read scheme in the designed TRNG circuit, the unbiasedness of the random numbers was significantly improved, and the bitstream passed standard randomness tests. The Pt/TaO x /Ta memristors fabricated in this work have fast programming/erasing speeds of ∼30 ns, suggesting a high random number throughput. The approach proposed here thus holds great promise for physically-implemented random number generation.

On the design of henon and logistic map-based random number generator

NASA Astrophysics Data System (ADS)

Magfirawaty; Suryadi, M. T.; Ramli, Kalamullah

2017-10-01

The key sequence is one of the main elements in the cryptosystem. True Random Number Generators (TRNG) method is one of the approaches to generating the key sequence. The randomness source of the TRNG divided into three main groups, i.e. electrical noise based, jitter based and chaos based. The chaos based utilizes a non-linear dynamic system (continuous time or discrete time) as an entropy source. In this study, a new design of TRNG based on discrete time chaotic system is proposed, which is then simulated in LabVIEW. The principle of the design consists of combining 2D and 1D chaotic systems. A mathematical model is implemented for numerical simulations. We used comparator process as a harvester method to obtain the series of random bits. Without any post processing, the proposed design generated random bit sequence with high entropy value and passed all NIST 800.22 statistical tests.

A Randomized Trial of Brief Interventions for Problem and Pathological Gamblers

ERIC Educational Resources Information Center

Petry, Nancy M.; Weinstock, Jeremiah; Ledgerwood, David M.; Morasco, Benjamin

2008-01-01

Limited research exists regarding methods for reducing problem gambling. Problem gamblers (N = 180) were randomly assigned to assessment only control, 10 min of brief advice, 1 session of motivational enhancement therapy (MET), or 1 session of MET plus 3 sessions of cognitive-behavioral therapy. Gambling was assessed at baseline, at 6 weeks, and…

A Model for Predicting Behavioural Sleep Problems in a Random Sample of Australian Pre-Schoolers

ERIC Educational Resources Information Center

Hall, Wendy A.; Zubrick, Stephen R.; Silburn, Sven R.; Parsons, Deborah E.; Kurinczuk, Jennifer J.

2007-01-01

Behavioural sleep problems (childhood insomnias) can cause distress for both parents and children. This paper reports a model describing predictors of high sleep problem scores in a representative population-based random sample survey of non-Aboriginal singleton children born in 1995 and 1996 (1085 girls and 1129 boys) in Western Australia.…

Random Walk Method for Potential Problems

NASA Technical Reports Server (NTRS)

Krishnamurthy, T.; Raju, I. S.

2002-01-01

A local Random Walk Method (RWM) for potential problems governed by Lapalace's and Paragon's equations is developed for two- and three-dimensional problems. The RWM is implemented and demonstrated in a multiprocessor parallel environment on a Beowulf cluster of computers. A speed gain of 16 is achieved as the number of processors is increased from 1 to 23.

LTE-advanced random access mechanism for M2M communication: A review

NASA Astrophysics Data System (ADS)

Mustafa, Rashid; Sarowa, Sandeep; Jaglan, Reena Rathee; Khan, Mohammad Junaid; Agrawal, Sunil

2016-03-01

Machine Type Communications (MTC) enables one or more self-sufficient machines to communicate directly with one another without human interference. MTC applications include smart grid, security, e-Health and intelligent automation system. To support huge numbers of MTC devices, one of the challenging issues is to provide a competent way for numerous access in the network and to minimize network overload. In this article, the different control mechanisms for overload random access are reviewed to avoid congestion caused by random access channel (RACH) of MTC devices. However, past and present wireless technologies have been engineered for Human-to-Human (H2H) communications, in particular, for transmission of voice. Consequently the Long Term Evolution (LTE) -Advanced is expected to play a central role in communicating Machine to Machine (M2M) and are very optimistic about H2H communications. Distinct and unique characteristics of M2M communications create new challenges from those in H2H communications. In this article, we investigate the impact of massive M2M terminals attempting random access to LTE-Advanced all at once. We discuss and review the solutions to alleviate the overload problem by Third Generation Partnership Project (3GPP). As a result, we evaluate and compare these solutions that can effectively eliminate the congestion on the random access channel for M2M communications without affecting H2H communications.

Pseudo-random bit generator based on lag time series

NASA Astrophysics Data System (ADS)

García-Martínez, M.; Campos-Cantón, E.

2014-12-01

In this paper, we present a pseudo-random bit generator (PRBG) based on two lag time series of the logistic map using positive and negative values in the bifurcation parameter. In order to hidden the map used to build the pseudo-random series we have used a delay in the generation of time series. These new series when they are mapped xn against xn+1 present a cloud of points unrelated to the logistic map. Finally, the pseudo-random sequences have been tested with the suite of NIST giving satisfactory results for use in stream ciphers.

Exact parallel algorithms for some members of the traveling salesman problem family

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pekny, J.F.

1989-01-01

The traveling salesman problem and its many generalizations comprise one of the best known combinatorial optimization problem families. Most members of the family are NP-complete problems so that exact algorithms require an unpredictable and sometimes large computational effort. Parallel computers offer hope for providing the power required to meet these demands. A major barrier to applying parallel computers is the lack of parallel algorithms. The contributions presented in this thesis center around new exact parallel algorithms for the asymmetric traveling salesman problem (ATSP), prize collecting traveling salesman problem (PCTSP), and resource constrained traveling salesman problem (RCTSP). The RCTSP is amore » particularly difficult member of the family since finding a feasible solution is an NP-complete problem. An exact sequential algorithm is also presented for the directed hamiltonian cycle problem (DHCP). The DHCP algorithm is superior to current heuristic approaches and represents the first exact method applicable to large graphs. Computational results presented for each of the algorithms demonstrates the effectiveness of combining efficient algorithms with parallel computing methods. Performance statistics are reported for randomly generated ATSPs with 7,500 cities, PCTSPs with 200 cities, RCTSPs with 200 cities, DHCPs with 3,500 vertices, and assignment problems of size 10,000. Sequential results were collected on a Sun 4/260 engineering workstation, while parallel results were collected using a 14 and 100 processor BBN Butterfly Plus computer. The computational results represent the largest instances ever solved to optimality on any type of computer.« less

Life skills, mathematical reasoning and critical thinking: a curriculum for the prevention of problem gambling.

PubMed

Turner, Nigel E; Macdonald, John; Somerset, Matthew

2008-09-01

Previous studies have shown that youth are two to three times more likely than adults to report gambling related problems. This paper reports on the development and pilot evaluation of a school-based problem gambling prevention curriculum. The prevention program focused on problem gambling awareness and self-monitoring skills, coping skills, and knowledge of the nature of random events. The results of a controlled experiment evaluating the students learning from the program are reported. We found significant improvement in the students' knowledge of random events, knowledge of problem gambling awareness and self-monitoring, and knowledge of coping skills. The results suggest that knowledge based material on random events, problem gambling awareness and self-monitoring skills, and coping skills can be taught. Future development of the curriculum will focus on content to expand the students' coping skill options.

Multiple ECG Fiducial Points-Based Random Binary Sequence Generation for Securing Wireless Body Area Networks.

PubMed

Zheng, Guanglou; Fang, Gengfa; Shankaran, Rajan; Orgun, Mehmet A; Zhou, Jie; Qiao, Li; Saleem, Kashif

2017-05-01

Generating random binary sequences (BSes) is a fundamental requirement in cryptography. A BS is a sequence of N bits, and each bit has a value of 0 or 1. For securing sensors within wireless body area networks (WBANs), electrocardiogram (ECG)-based BS generation methods have been widely investigated in which interpulse intervals (IPIs) from each heartbeat cycle are processed to produce BSes. Using these IPI-based methods to generate a 128-bit BS in real time normally takes around half a minute. In order to improve the time efficiency of such methods, this paper presents an ECG multiple fiducial-points based binary sequence generation (MFBSG) algorithm. The technique of discrete wavelet transforms is employed to detect arrival time of these fiducial points, such as P, Q, R, S, and T peaks. Time intervals between them, including RR, RQ, RS, RP, and RT intervals, are then calculated based on this arrival time, and are used as ECG features to generate random BSes with low latency. According to our analysis on real ECG data, these ECG feature values exhibit the property of randomness and, thus, can be utilized to generate random BSes. Compared with the schemes that solely rely on IPIs to generate BSes, this MFBSG algorithm uses five feature values from one heart beat cycle, and can be up to five times faster than the solely IPI-based methods. So, it achieves a design goal of low latency. According to our analysis, the complexity of the algorithm is comparable to that of fast Fourier transforms. These randomly generated ECG BSes can be used as security keys for encryption or authentication in a WBAN system.

Random Item Generation Is Affected by Age

ERIC Educational Resources Information Center

Multani, Namita; Rudzicz, Frank; Wong, Wing Yiu Stephanie; Namasivayam, Aravind Kumar; van Lieshout, Pascal

2016-01-01

Purpose: Random item generation (RIG) involves central executive functioning. Measuring aspects of random sequences can therefore provide a simple method to complement other tools for cognitive assessment. We examine the extent to which RIG relates to specific measures of cognitive function, and whether those measures can be estimated using RIG…

On the limiting characteristics of quantum random number generators at various clusterings of photocounts

NASA Astrophysics Data System (ADS)

Molotkov, S. N.

2017-03-01

Various methods for the clustering of photocounts constituting a sequence of random numbers are considered. It is shown that the clustering of photocounts resulting in the Fermi-Dirac distribution makes it possible to achieve the theoretical limit of the random number generation rate.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Eads, Damian Ryan; Rosten, Edward; Helmbold, David

The authors present BEAMER: a new spatially exploitative approach to learning object detectors which shows excellent results when applied to the task of detecting objects in greyscale aerial imagery in the presence of ambiguous and noisy data. There are four main contributions used to produce these results. First, they introduce a grammar-guided feature extraction system, enabling the exploration of a richer feature space while constraining the features to a useful subset. This is specified with a rule-based generative grammer crafted by a human expert. Second, they learn a classifier on this data using a newly proposed variant of AdaBoost whichmore » takes into account the spatially correlated nature of the data. Third, they perform another round of training to optimize the method of converting the pixel classifications generated by boosting into a high quality set of (x,y) locations. lastly, they carefully define three common problems in object detection and define two evaluation criteria that are tightly matched to these problems. Major strengths of this approach are: (1) a way of randomly searching a broad feature space, (2) its performance when evaluated on well-matched evaluation criteria, and (3) its use of the location prediction domain to learn object detectors as well as to generate detections that perform well on several tasks: object counting, tracking, and target detection. They demonstrate the efficacy of BEAMER with a comprehensive experimental evaluation on a challenging data set.« less

Hiding the Source Based on Limited Flooding for Sensor Networks.

PubMed

Chen, Juan; Lin, Zhengkui; Hu, Ying; Wang, Bailing

2015-11-17

Wireless sensor networks are widely used to monitor valuable objects such as rare animals or armies. Once an object is detected, the source, i.e., the sensor nearest to the object, generates and periodically sends a packet about the object to the base station. Since attackers can capture the object by localizing the source, many protocols have been proposed to protect source location. Instead of transmitting the packet to the base station directly, typical source location protection protocols first transmit packets randomly for a few hops to a phantom location, and then forward the packets to the base station. The problem with these protocols is that the generated phantom locations are usually not only near the true source but also close to each other. As a result, attackers can easily trace a route back to the source from the phantom locations. To address the above problem, we propose a new protocol for source location protection based on limited flooding, named SLP. Compared with existing protocols, SLP can generate phantom locations that are not only far away from the source, but also widely distributed. It improves source location security significantly with low communication cost. We further propose a protocol, namely SLP-E, to protect source location against more powerful attackers with wider fields of vision. The performance of our SLP and SLP-E are validated by both theoretical analysis and simulation results.

The correlation structure of several popular pseudorandom number generators

NASA Technical Reports Server (NTRS)

Neuman, F.; Merrick, R.; Martin, C. F.

1973-01-01

One of the desirable properties of a pseudorandom number generator is that the sequence of numbers it generates should have very low autocorrelation for all shifts except for zero shift and those that are multiples of its cycle length. Due to the simple methods of constructing random numbers, the ideal is often not quite fulfilled. A simple method of examining any random generator for previously unsuspected regularities is discussed. Once they are discovered it is often easy to derive the mathematical relationships, which describe the mathematical relationships, which describe the regular behavior. As examples, it is shown that high correlation exists in mixed and multiplicative congruential random number generators and prime moduli Lehmer generators for shifts a fraction of their cycle lengths.

Charting epilepsy by searching for intelligence in network space with the help of evolving autonomous agents.

PubMed

Ohayon, Elan L; Kalitzin, Stiliyan; Suffczynski, Piotr; Jin, Frank Y; Tsang, Paul W; Borrett, Donald S; Burnham, W McIntyre; Kwan, Hon C

2004-01-01

The problem of demarcating neural network space is formidable. A simple fully connected recurrent network of five units (binary activations, synaptic weight resolution of 10) has 3.2 *10(26) possible initial states. The problem increases drastically with scaling. Here we consider three complementary approaches to help direct the exploration to distinguish epileptic from healthy networks. [1] First, we perform a gross mapping of the space of five-unit continuous recurrent networks using randomized weights and initial activations. The majority of weight patterns (>70%) were found to result in neural assemblies exhibiting periodic limit-cycle oscillatory behavior. [2] Next we examine the activation space of non-periodic networks demonstrating that the emergence of paroxysmal activity does not require changes in connectivity. [3] The next challenge is to focus the search of network space to identify networks with more complex dynamics. Here we rely on a major available indicator critical to clinical assessment but largely ignored by epilepsy modelers, namely: behavioral states. To this end, we connected the above network layout to an external robot in which interactive states were evolved. The first random generation showed a distribution in line with approach [1]. That is, the predominate phenotypes were fixed-point or oscillatory with seizure-like motor output. As evolution progressed the profile changed markedly. Within 20 generations the entire population was able to navigate a simple environment with all individuals exhibiting multiply-stable behaviors with no cases of default locked limit-cycle oscillatory motor behavior. The resultant population may thus afford us a view of the architectural principles demarcating healthy biological networks from the pathological. The approach has an advantage over other epilepsy modeling techniques in providing a way to clarify whether observed dynamics or suggested therapies are pointing to computational viability or dead space.

Randomized Trial of the Effect of Four Second-Generation Antipsychotics and One First-Generation Antipsychotic on Cigarette Smoking, Alcohol, and Drug Use in Chronic Schizophrenia.

PubMed

Mohamed, Somaia; Rosenheck, Robert A; Lin, Haiqun; Swartz, Marvin; McEvoy, Joseph; Stroup, Scott

2015-07-01

No large-scale randomized trial has compared the effect of different second-generation antipsychotic drugs and any first-generation drug on alcohol, drug and nicotine use in patients with schizophrenia. The Clinical Antipsychotic Trial of Intervention Effectiveness study randomly assigned 1432 patients formally diagnosed with schizophrenia to four second-generation antipsychotic drugs (olanzapine, risperidone quetiapine, and ziprasidone) and one first-generation antipsychotic (perphenazine) and followed them for up to 18 months. Secondary outcome data documented cigarettes smoked in the past week and alcohol and drug use severity ratings. At baseline, 61% of patients smoked, 35% used alcohol, and 23% used illicit drugs. Although there were significant effects of time showing reduction in substance use over the 18 months (all p < 0.0001), this study found no evidence that any antipsychotic was robustly superior to any other in a secondary analysis of data on substance use outcomes from a large 18-month randomized schizophrenia trial.

High-Speed Device-Independent Quantum Random Number Generation without a Detection Loophole

NASA Astrophysics Data System (ADS)

Liu, Yang; Yuan, Xiao; Li, Ming-Han; Zhang, Weijun; Zhao, Qi; Zhong, Jiaqiang; Cao, Yuan; Li, Yu-Huai; Chen, Luo-Kan; Li, Hao; Peng, Tianyi; Chen, Yu-Ao; Peng, Cheng-Zhi; Shi, Sheng-Cai; Wang, Zhen; You, Lixing; Ma, Xiongfeng; Fan, Jingyun; Zhang, Qiang; Pan, Jian-Wei

2018-01-01

Quantum mechanics provides the means of generating genuine randomness that is impossible with deterministic classical processes. Remarkably, the unpredictability of randomness can be certified in a manner that is independent of implementation devices. Here, we present an experimental study of device-independent quantum random number generation based on a detection-loophole-free Bell test with entangled photons. In the randomness analysis, without the independent identical distribution assumption, we consider the worst case scenario that the adversary launches the most powerful attacks against the quantum adversary. After considering statistical fluctuations and applying an 80 Gb ×45.6 Mb Toeplitz matrix hashing, we achieve a final random bit rate of 114 bits /s , with a failure probability less than 10-5. This marks a critical step towards realistic applications in cryptography and fundamental physics tests.

KMCLib 1.1: Extended random number support and technical updates to the KMCLib general framework for kinetic Monte-Carlo simulations

NASA Astrophysics Data System (ADS)

Leetmaa, Mikael; Skorodumova, Natalia V.

2015-11-01

We here present a revised version, v1.1, of the KMCLib general framework for kinetic Monte-Carlo (KMC) simulations. The generation of random numbers in KMCLib now relies on the C++11 standard library implementation, and support has been added for the user to choose from a set of C++11 implemented random number generators. The Mersenne-twister, the 24 and 48 bit RANLUX and a 'minimal-standard' PRNG are supported. We have also included the possibility to use true random numbers via the C++11 std::random_device generator. This release also includes technical updates to support the use of an extended range of operating systems and compilers.

Minimalist design of a robust real-time quantum random number generator

NASA Astrophysics Data System (ADS)

Kravtsov, K. S.; Radchenko, I. V.; Kulik, S. P.; Molotkov, S. N.

2015-08-01

We present a simple and robust construction of a real-time quantum random number generator (QRNG). Our minimalist approach ensures stable operation of the device as well as its simple and straightforward hardware implementation as a stand-alone module. As a source of randomness the device uses measurements of time intervals between clicks of a single-photon detector. The obtained raw sequence is then filtered and processed by a deterministic randomness extractor, which is realized as a look-up table. This enables high speed on-the-fly processing without the need of extensive computations. The overall performance of the device is around 1 random bit per detector click, resulting in 1.2 Mbit/s generation rate in our implementation.

Local polynomial chaos expansion for linear differential equations with high dimensional random inputs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Yi; Jakeman, John; Gittelson, Claude

2015-01-08

In this paper we present a localized polynomial chaos expansion for partial differential equations (PDE) with random inputs. In particular, we focus on time independent linear stochastic problems with high dimensional random inputs, where the traditional polynomial chaos methods, and most of the existing methods, incur prohibitively high simulation cost. Furthermore, the local polynomial chaos method employs a domain decomposition technique to approximate the stochastic solution locally. In each subdomain, a subdomain problem is solved independently and, more importantly, in a much lower dimensional random space. In a postprocesing stage, accurate samples of the original stochastic problems are obtained frommore » the samples of the local solutions by enforcing the correct stochastic structure of the random inputs and the coupling conditions at the interfaces of the subdomains. Overall, the method is able to solve stochastic PDEs in very large dimensions by solving a collection of low dimensional local problems and can be highly efficient. In our paper we present the general mathematical framework of the methodology and use numerical examples to demonstrate the properties of the method.« less

Spline methods for approximating quantile functions and generating random samples

NASA Technical Reports Server (NTRS)

Schiess, J. R.; Matthews, C. G.

1985-01-01

Two cubic spline formulations are presented for representing the quantile function (inverse cumulative distribution function) of a random sample of data. Both B-spline and rational spline approximations are compared with analytic representations of the quantile function. It is also shown how these representations can be used to generate random samples for use in simulation studies. Comparisons are made on samples generated from known distributions and a sample of experimental data. The spline representations are more accurate for multimodal and skewed samples and to require much less time to generate samples than the analytic representation.

Randomized Controlled Trial of Problem-Solving Therapy for Minor Depression in Home Care

ERIC Educational Resources Information Center

Gellis, Zvi D.; McGinty, Jean; Tierney, Lynda; Jordan, Cindy; Burton, Jean; Misener, Elizabeth

2008-01-01

Objective: Data are presented from a pilot research program initiated to develop, refine, and test the outcomes of problem-solving therapy that targets the needs of older adults with minor depression in home care settings. Method: A pilot randomized clinical trial compares the impact of problem-solving therapy for home care to treatment as usual…

Reducing Conduct Problems among Children Exposed to Intimate Partner Violence: A Randomized Clinical Trial Examining Effects of Project Support

ERIC Educational Resources Information Center

Jouriles, Ernest N.; McDonald, Renee; Rosenfield, David; Stephens, Nanette; Corbitt-Shindler, Deborah; Miller, Pamela C.

2009-01-01

This study was a randomized clinical trial of Project Support, an intervention designed to reduce conduct problems among children exposed to intimate partner violence. Participants were 66 families (mothers and children) with at least 1 child exhibiting clinical levels of conduct problems. Families were recruited from domestic violence shelters.…

Network problem threshold

NASA Technical Reports Server (NTRS)

Gejji, Raghvendra, R.

1992-01-01

Network transmission errors such as collisions, CRC errors, misalignment, etc. are statistical in nature. Although errors can vary randomly, a high level of errors does indicate specific network problems, e.g. equipment failure. In this project, we have studied the random nature of collisions theoretically as well as by gathering statistics, and established a numerical threshold above which a network problem is indicated with high probability.

Randomized Trial of Treatment for Children with Sexual Behavior Problems: Ten-Year Follow-Up

ERIC Educational Resources Information Center

Carpentier, Melissa Y.; Silovsky, Jane F.; Chaffin, Mark

2006-01-01

This study prospectively follows 135 children 5-12 years of age with sexual behavior problems from a randomized trial comparing a 12-session group cognitive-behavioral therapy (CBT) with group play therapy and follows 156 general clinic children with nonsexual behavior problems. Ten-year follow-up data on future juvenile and adult arrests and…

Random Number Generation and Executive Functions in Parkinson's Disease: An Event-Related Brain Potential Study.

PubMed

Münte, Thomas F; Joppich, Gregor; Däuper, Jan; Schrader, Christoph; Dengler, Reinhard; Heldmann, Marcus

2015-01-01

The generation of random sequences is considered to tax executive functions and has been reported to be impaired in Parkinson's disease (PD) previously. To assess the neurophysiological markers of random number generation in PD. Event-related potentials (ERP) were recorded in 12 PD patients and 12 age-matched normal controls (NC) while either engaging in random number generation (RNG) by pressing the number keys on a computer keyboard in a random sequence or in ordered number generation (ONG) necessitating key presses in the canonical order. Key presses were paced by an external auditory stimulus at a rate of 1 tone every 1800 ms. As a secondary task subjects had to monitor the tone-sequence for a particular target tone to which the number "0" key had to be pressed. This target tone occurred randomly and infrequently, thus creating a secondary oddball task. Behaviorally, PD patients showed an increased tendency to count in steps of one as well as a tendency towards repetition avoidance. Electrophysiologically, the amplitude of the P3 component of the ERP to the target tone of the secondary task was reduced during RNG in PD but not in NC. The behavioral findings indicate less random behavior in PD while the ERP findings suggest that this impairment comes about, because attentional resources are depleted in PD.

Anderson localization for radial tree-like random quantum graphs

NASA Astrophysics Data System (ADS)

Hislop, Peter D.; Post, Olaf

We prove that certain random models associated with radial, tree-like, rooted quantum graphs exhibit Anderson localization at all energies. The two main examples are the random length model (RLM) and the random Kirchhoff model (RKM). In the RLM, the lengths of each generation of edges form a family of independent, identically distributed random variables (iid). For the RKM, the iid random variables are associated with each generation of vertices and moderate the current flow through the vertex. We consider extensions to various families of decorated graphs and prove stability of localization with respect to decoration. In particular, we prove Anderson localization for the random necklace model.

The Reliability of Randomly Generated Math Curriculum-Based Measurements

ERIC Educational Resources Information Center

Strait, Gerald G.; Smith, Bradley H.; Pender, Carolyn; Malone, Patrick S.; Roberts, Jarod; Hall, John D.

2015-01-01

"Curriculum-Based Measurement" (CBM) is a direct method of academic assessment used to screen and evaluate students' skills and monitor their responses to academic instruction and intervention. Interventioncentral.org offers a math worksheet generator at no cost that creates randomly generated "math curriculum-based measures"…

Recourse-based facility-location problems in hybrid uncertain environment.

PubMed

Wang, Shuming; Watada, Junzo; Pedrycz, Witold

2010-08-01

The objective of this paper is to study facility-location problems in the presence of a hybrid uncertain environment involving both randomness and fuzziness. A two-stage fuzzy-random facility-location model with recourse (FR-FLMR) is developed in which both the demands and costs are assumed to be fuzzy-random variables. The bounds of the optimal objective value of the two-stage FR-FLMR are derived. As, in general, the fuzzy-random parameters of the FR-FLMR can be regarded as continuous fuzzy-random variables with an infinite number of realizations, the computation of the recourse requires solving infinite second-stage programming problems. Owing to this requirement, the recourse function cannot be determined analytically, and, hence, the model cannot benefit from the use of techniques of classical mathematical programming. In order to solve the location problems of this nature, we first develop a technique of fuzzy-random simulation to compute the recourse function. The convergence of such simulation scenarios is discussed. In the sequel, we propose a hybrid mutation-based binary ant-colony optimization (MBACO) approach to the two-stage FR-FLMR, which comprises the fuzzy-random simulation and the simplex algorithm. A numerical experiment illustrates the application of the hybrid MBACO algorithm. The comparison shows that the hybrid MBACO finds better solutions than the one using other discrete metaheuristic algorithms, such as binary particle-swarm optimization, genetic algorithm, and tabu search.

A random utility based estimation framework for the household activity pattern problem.

DOT National Transportation Integrated Search

2016-06-01

This paper develops a random utility based estimation framework for the Household Activity : Pattern Problem (HAPP). Based on the realization that output of complex activity-travel decisions : form a continuous pattern in space-time dimension, the es...

Quantum random number generator based on quantum nature of vacuum fluctuations

NASA Astrophysics Data System (ADS)

Ivanova, A. E.; Chivilikhin, S. A.; Gleim, A. V.

2017-11-01

Quantum random number generator (QRNG) allows obtaining true random bit sequences. In QRNG based on quantum nature of vacuum, optical beam splitter with two inputs and two outputs is normally used. We compare mathematical descriptions of spatial beam splitter and fiber Y-splitter in the quantum model for QRNG, based on homodyne detection. These descriptions were identical, that allows to use fiber Y-splitters in practical QRNG schemes, simplifying the setup. Also we receive relations between the input radiation and the resulting differential current in homodyne detector. We experimentally demonstrate possibility of true random bits generation by using QRNG based on homodyne detection with Y-splitter.

Sensitivity to imputation models and assumptions in receiver operating characteristic analysis with incomplete data

PubMed Central

Karakaya, Jale; Karabulut, Erdem; Yucel, Recai M.

2015-01-01

Modern statistical methods using incomplete data have been increasingly applied in a wide variety of substantive problems. Similarly, receiver operating characteristic (ROC) analysis, a method used in evaluating diagnostic tests or biomarkers in medical research, has also been increasingly popular problem in both its development and application. While missing-data methods have been applied in ROC analysis, the impact of model mis-specification and/or assumptions (e.g. missing at random) underlying the missing data has not been thoroughly studied. In this work, we study the performance of multiple imputation (MI) inference in ROC analysis. Particularly, we investigate parametric and non-parametric techniques for MI inference under common missingness mechanisms. Depending on the coherency of the imputation model with the underlying data generation mechanism, our results show that MI generally leads to well-calibrated inferences under ignorable missingness mechanisms. PMID:26379316

Self-reported Stress Problems among Teachers in Hong Kong

NASA Astrophysics Data System (ADS)

Chan, Alan H. S.; Chen, K.; Chong, Elaine Y. L.

2010-10-01

The present study was developed to comprehensively investigate the occupational health problems among teachers of primary and secondary schools in Hong Kong. A random sample of 1,710 respondents was generated from the database of Hong Kong Professional Teachers' Union (HKPTU) members. A self-administrated questionnaire was designed and sent by mail to the teachers of primary and secondary schools in HK. The results indicated that comparing with one year and five years ago, 91.6% and 97.3% of the responding teachers reported an increase of perceived stress level, respectively. Heavy workload and time pressure, education reforms, external school review, pursuing further education, and managing students' behaviour and learning were the most frequently reported sources of work stress. The four most frequently reported stress management activities were sleeping, talking to neighbors and friends, self-relaxing, and watching television, while the least frequently reported activity was doing more exercises or sports.

A Multi-Start Evolutionary Local Search for the Two-Echelon Location Routing Problem

NASA Astrophysics Data System (ADS)

Nguyen, Viet-Phuong; Prins, Christian; Prodhon, Caroline

This paper presents a new hybrid metaheuristic between a greedy randomized adaptive search procedure (GRASP) and an evolutionary/iterated local search (ELS/ILS), using Tabu list to solve the two-echelon location routing problem (LRP-2E). The GRASP uses in turn three constructive heuristics followed by local search to generate the initial solutions. From a solution of GRASP, an intensification strategy is carried out by a dynamic alternation between ELS and ILS. In this phase, each child is obtained by mutation and evaluated through a splitting procedure of giant tour followed by a local search. The tabu list, defined by two characteristics of solution (total cost and number of trips), is used to avoid searching a space already explored. The results show that our metaheuristic clearly outperforms all previously published methods on LRP-2E benchmark instances. Furthermore, it is competitive with the best meta-heuristic published for the single-echelon LRP.

Mixture Model and MDSDCA for Textual Data

NASA Astrophysics Data System (ADS)

Allouti, Faryel; Nadif, Mohamed; Hoai An, Le Thi; Otjacques, Benoît

E-mailing has become an essential component of cooperation in business. Consequently, the large number of messages manually produced or automatically generated can rapidly cause information overflow for users. Many research projects have examined this issue but surprisingly few have tackled the problem of the files attached to e-mails that, in many cases, contain a substantial part of the semantics of the message. This paper considers this specific topic and focuses on the problem of clustering and visualization of attached files. Relying on the multinomial mixture model, we used the Classification EM algorithm (CEM) to cluster the set of files, and MDSDCA to visualize the obtained classes of documents. Like the Multidimensional Scaling method, the aim of the MDSDCA algorithm based on the Difference of Convex functions is to optimize the stress criterion. As MDSDCA is iterative, we propose an initialization approach to avoid starting with random values. Experiments are investigated using simulations and textual data.

Experimental loss-tolerant quantum coin flipping

PubMed Central

Berlín, Guido; Brassard, Gilles; Bussières, Félix; Godbout, Nicolas; Slater, Joshua A.; Tittel, Wolfgang

2011-01-01

Coin flipping is a cryptographic primitive in which two distrustful parties wish to generate a random bit to choose between two alternatives. This task is impossible to realize when it relies solely on the asynchronous exchange of classical bits: one dishonest player has complete control over the final outcome. It is only when coin flipping is supplemented with quantum communication that this problem can be alleviated, although partial bias remains. Unfortunately, practical systems are subject to loss of quantum data, which allows a cheater to force a bias that is complete or arbitrarily close to complete in all previous protocols and implementations. Here we report on the first experimental demonstration of a quantum coin-flipping protocol for which loss cannot be exploited to cheat better. By eliminating the problem of loss, which is unavoidable in any realistic setting, quantum coin flipping takes a significant step towards real-world applications of quantum communication. PMID:22127057

A revision of the subtract-with-borrow random number generators

NASA Astrophysics Data System (ADS)

Sibidanov, Alexei

2017-12-01

The most popular and widely used subtract-with-borrow generator, also known as RANLUX, is reimplemented as a linear congruential generator using large integer arithmetic with the modulus size of 576 bits. Modern computers, as well as the specific structure of the modulus inferred from RANLUX, allow for the development of a fast modular multiplication - the core of the procedure. This was previously believed to be slow and have too high cost in terms of computing resources. Our tests show a significant gain in generation speed which is comparable with other fast, high quality random number generators. An additional feature is the fast skipping of generator states leading to a seeding scheme which guarantees the uniqueness of random number sequences. Licensing provisions: GPLv3 Programming language: C++, C, Assembler

Graphene based widely-tunable and singly-polarized pulse generation with random fiber lasers

PubMed Central

Yao, B. C.; Rao, Y. J.; Wang, Z. N.; Wu, Y.; Zhou, J. H.; Wu, H.; Fan, M. Q.; Cao, X. L.; Zhang, W. L.; Chen, Y. F.; Li, Y. R.; Churkin, D.; Turitsyn, S.; Wong, C. W.

2015-01-01

Pulse generation often requires a stabilized cavity and its corresponding mode structure for initial phase-locking. Contrastingly, modeless cavity-free random lasers provide new possibilities for high quantum efficiency lasing that could potentially be widely tunable spectrally and temporally. Pulse generation in random lasers, however, has remained elusive since the discovery of modeless gain lasing. Here we report coherent pulse generation with modeless random lasers based on the unique polarization selectivity and broadband saturable absorption of monolayer graphene. Simultaneous temporal compression of cavity-free pulses are observed with such a polarization modulation, along with a broadly-tunable pulsewidth across two orders of magnitude down to 900 ps, a broadly-tunable repetition rate across three orders of magnitude up to 3 MHz, and a singly-polarized pulse train at 41 dB extinction ratio, about an order of magnitude larger than conventional pulsed fiber lasers. Moreover, our graphene-based pulse formation also demonstrates robust pulse-to-pulse stability and wide-wavelength operation due to the cavity-less feature. Such a graphene-based architecture not only provides a tunable pulsed random laser for fiber-optic sensing, speckle-free imaging, and laser-material processing, but also a new way for the non-random CW fiber lasers to generate widely tunable and singly-polarized pulses. PMID:26687730

Graphene based widely-tunable and singly-polarized pulse generation with random fiber lasers.

PubMed

Yao, B C; Rao, Y J; Wang, Z N; Wu, Y; Zhou, J H; Wu, H; Fan, M Q; Cao, X L; Zhang, W L; Chen, Y F; Li, Y R; Churkin, D; Turitsyn, S; Wong, C W

2015-12-21

Pulse generation often requires a stabilized cavity and its corresponding mode structure for initial phase-locking. Contrastingly, modeless cavity-free random lasers provide new possibilities for high quantum efficiency lasing that could potentially be widely tunable spectrally and temporally. Pulse generation in random lasers, however, has remained elusive since the discovery of modeless gain lasing. Here we report coherent pulse generation with modeless random lasers based on the unique polarization selectivity and broadband saturable absorption of monolayer graphene. Simultaneous temporal compression of cavity-free pulses are observed with such a polarization modulation, along with a broadly-tunable pulsewidth across two orders of magnitude down to 900 ps, a broadly-tunable repetition rate across three orders of magnitude up to 3 MHz, and a singly-polarized pulse train at 41 dB extinction ratio, about an order of magnitude larger than conventional pulsed fiber lasers. Moreover, our graphene-based pulse formation also demonstrates robust pulse-to-pulse stability and wide-wavelength operation due to the cavity-less feature. Such a graphene-based architecture not only provides a tunable pulsed random laser for fiber-optic sensing, speckle-free imaging, and laser-material processing, but also a new way for the non-random CW fiber lasers to generate widely tunable and singly-polarized pulses.

High-Speed Device-Independent Quantum Random Number Generation without a Detection Loophole.

PubMed

Liu, Yang; Yuan, Xiao; Li, Ming-Han; Zhang, Weijun; Zhao, Qi; Zhong, Jiaqiang; Cao, Yuan; Li, Yu-Huai; Chen, Luo-Kan; Li, Hao; Peng, Tianyi; Chen, Yu-Ao; Peng, Cheng-Zhi; Shi, Sheng-Cai; Wang, Zhen; You, Lixing; Ma, Xiongfeng; Fan, Jingyun; Zhang, Qiang; Pan, Jian-Wei

2018-01-05

Quantum mechanics provides the means of generating genuine randomness that is impossible with deterministic classical processes. Remarkably, the unpredictability of randomness can be certified in a manner that is independent of implementation devices. Here, we present an experimental study of device-independent quantum random number generation based on a detection-loophole-free Bell test with entangled photons. In the randomness analysis, without the independent identical distribution assumption, we consider the worst case scenario that the adversary launches the most powerful attacks against the quantum adversary. After considering statistical fluctuations and applying an 80  Gb×45.6  Mb Toeplitz matrix hashing, we achieve a final random bit rate of 114  bits/s, with a failure probability less than 10^{-5}. This marks a critical step towards realistic applications in cryptography and fundamental physics tests.

First-passage problems: A probabilistic dynamic analysis for degraded structures

NASA Technical Reports Server (NTRS)

Shiao, Michael C.; Chamis, Christos C.

1990-01-01

Structures subjected to random excitations with uncertain system parameters degraded by surrounding environments (a random time history) are studied. Methods are developed to determine the statistics of dynamic responses, such as the time-varying mean, the standard deviation, the autocorrelation functions, and the joint probability density function of any response and its derivative. Moreover, the first-passage problems with deterministic and stationary/evolutionary random barriers are evaluated. The time-varying (joint) mean crossing rate and the probability density function of the first-passage time for various random barriers are derived.

Can Early Intervention Improve Maternal Well-Being? Evidence from a Randomized Controlled Trial

PubMed Central

Doyle, Orla; Delaney, Liam; O’Farrelly, Christine; Fitzpatrick, Nick; Daly, Michael

2017-01-01

Objective This study estimates the effect of a targeted early childhood intervention program on global and experienced measures of maternal well-being utilizing a randomized controlled trial design. The primary aim of the intervention is to improve children’s school readiness skills by working directly with parents to improve their knowledge of child development and parenting behavior. One potential externality of the program is well-being benefits for parents given its direct focus on improving parental coping, self-efficacy, and problem solving skills, as well as generating an indirect effect on parental well-being by targeting child developmental problems. Methods Participants from a socio-economically disadvantaged community are randomly assigned during pregnancy to an intensive 5-year home visiting parenting program or a control group. We estimate and compare treatment effects on multiple measures of global and experienced well-being using permutation testing to account for small sample size and a stepdown procedure to account for multiple testing. Results The intervention has no impact on global well-being as measured by life satisfaction and parenting stress or experienced negative affect using episodic reports derived from the Day Reconstruction Method (DRM). Treatment effects are observed on measures of experienced positive affect derived from the DRM and a measure of mood yesterday. Conclusion The limited treatment effects suggest that early intervention programs may produce some improvements in experienced positive well-being, but no effects on negative aspects of well-being. Different findings across measures may result as experienced measures of well-being avoid the cognitive biases that impinge upon global assessments. PMID:28095505

Mapping disease at an approximated individual level using aggregate data: a case study of mapping New Hampshire birth defects.

PubMed

Shi, Xun; Miller, Stephanie; Mwenda, Kevin; Onda, Akikazu; Reese, Judy; Onega, Tracy; Gui, Jiang; Karagas, Margret; Demidenko, Eugene; Moeschler, John

2013-09-06

Limited by data availability, most disease maps in the literature are for relatively large and subjectively-defined areal units, which are subject to problems associated with polygon maps. High resolution maps based on objective spatial units are needed to more precisely detect associations between disease and environmental factors. We propose to use a Restricted and Controlled Monte Carlo (RCMC) process to disaggregate polygon-level location data to achieve mapping aggregate data at an approximated individual level. RCMC assigns a random point location to a polygon-level location, in which the randomization is restricted by the polygon and controlled by the background (e.g., population at risk). RCMC allows analytical processes designed for individual data to be applied, and generates high-resolution raster maps. We applied RCMC to the town-level birth defect data for New Hampshire and generated raster maps at the resolution of 100 m. Besides the map of significance of birth defect risk represented by p-value, the output also includes a map of spatial uncertainty and a map of hot spots. RCMC is an effective method to disaggregate aggregate data. An RCMC-based disease mapping maximizes the use of available spatial information, and explicitly estimates the spatial uncertainty resulting from aggregation.

Fault Diagnosis Strategies for SOFC-Based Power Generation Plants

PubMed Central

Costamagna, Paola; De Giorgi, Andrea; Gotelli, Alberto; Magistri, Loredana; Moser, Gabriele; Sciaccaluga, Emanuele; Trucco, Andrea

2016-01-01

The success of distributed power generation by plants based on solid oxide fuel cells (SOFCs) is hindered by reliability problems that can be mitigated through an effective fault detection and isolation (FDI) system. However, the numerous operating conditions under which such plants can operate and the random size of the possible faults make identifying damaged plant components starting from the physical variables measured in the plant very difficult. In this context, we assess two classical FDI strategies (model-based with fault signature matrix and data-driven with statistical classification) and the combination of them. For this assessment, a quantitative model of the SOFC-based plant, which is able to simulate regular and faulty conditions, is used. Moreover, a hybrid approach based on the random forest (RF) classification method is introduced to address the discrimination of regular and faulty situations due to its practical advantages. Working with a common dataset, the FDI performances obtained using the aforementioned strategies, with different sets of monitored variables, are observed and compared. We conclude that the hybrid FDI strategy, realized by combining a model-based scheme with a statistical classifier, outperforms the other strategies. In addition, the inclusion of two physical variables that should be measured inside the SOFCs can significantly improve the FDI performance, despite the actual difficulty in performing such measurements. PMID:27556472

Progress developing the JAXA next generation satellite data repository (G-Portal).

NASA Astrophysics Data System (ADS)

Ikehata, Y.

2016-12-01

JAXA has been operating the "G-Portal" as a repository for search and access data of Earth observation satellite related JAXA since February 2013. The G-Portal handles ten satellites data; GPM, TRMM, Aqua, ADEOS-II, ALOS (search only), ALOS-2 (search only), MOS-1, MOS-1b, ERS-1 and JERS-1. G-Portal plans to import future satellites GCOM-C and EarthCARE. Except for ALOS and ALOS-2, all of these data are open and free. The G-Portal supports web search, catalogue search (CSW and OpenSearch) and direct download by SFTP for data access. However, the G-Portal has some problems about performance and usability. For example, about performance, the G-Portal is based on 10Gbps network and uses scale out architecture. (Conceptual design was reported in AGU fall meeting 2015. (IN23D-1748)) In order to improve those problems, JAXA is developing the next generation repository since February 2016. This paper describes usability problems improvements and challenges towards the next generation system. The improvements and challenges include the following points. Current web interface uses "step by step" design and URL is generated randomly. For that reason, users must see the Web page and click many times to get desired satellite data. So, Web design will be changed completely from "step by step" to "1 page" and URL will be based on REST (REpresentational State Transfer). Regarding direct download, the current method(SFTP) is very hard to use because of anomaly port assign and key-authentication. So, we will support FTP protocol. Additionally, the next G-Portal improve catalogue service. Currently catalogue search is available only to limited users including NASA, ESA and CEOS due to performance and reliability issue, but we will remove this limitation. Furthermore, catalogue search client function will be implemented to take in other agencies satellites catalogue. Users will be able to search satellite data across agencies.

Neutron monitor generated data distributions in quantum variational Monte Carlo

NASA Astrophysics Data System (ADS)

Kussainov, A. S.; Pya, N.

2016-08-01

We have assessed the potential applications of the neutron monitor hardware as random number generator for normal and uniform distributions. The data tables from the acquisition channels with no extreme changes in the signal level were chosen as the retrospective model. The stochastic component was extracted by fitting the raw data with splines and then subtracting the fit. Scaling the extracted data to zero mean and variance of one is sufficient to obtain a stable standard normal random variate. Distributions under consideration pass all available normality tests. Inverse transform sampling is suggested to use as a source of the uniform random numbers. Variational Monte Carlo method for quantum harmonic oscillator was used to test the quality of our random numbers. If the data delivery rate is of importance and the conventional one minute resolution neutron count is insufficient, we could always settle for an efficient seed generator to feed into the faster algorithmic random number generator or create a buffer.

Heterogeneous Hardware Parallelism Review of the IN2P3 2016 Computing School

NASA Astrophysics Data System (ADS)

Lafage, Vincent

2017-11-01

Parallel and hybrid Monte Carlo computation. The Monte Carlo method is the main workhorse for computation of particle physics observables. This paper provides an overview of various HPC technologies that can be used today: multicore (OpenMP, HPX), manycore (OpenCL). The rewrite of a twenty years old Fortran 77 Monte Carlo will illustrate the various programming paradigms in use beyond language implementation. The problem of parallel random number generator will be addressed. We will give a short report of the one week school dedicated to these recent approaches, that took place in École Polytechnique in May 2016.

Quantum cryptography: a view from classical cryptography

NASA Astrophysics Data System (ADS)

Buchmann, Johannes; Braun, Johannes; Demirel, Denise; Geihs, Matthias

2017-06-01

Much of digital data requires long-term protection of confidentiality, for example, medical health records. Cryptography provides such protection. However, currently used cryptographic techniques such as Diffe-Hellman key exchange may not provide long-term security. Such techniques rely on certain computational assumptions, such as the hardness of the discrete logarithm problem that may turn out to be incorrect. On the other hand, quantum cryptography---in particular quantum random number generation and quantum key distribution---offers information theoretic protection. In this paper, we explore the challenge of providing long-term confidentiality and we argue that a combination of quantum cryptography and classical cryptography can provide such protection.

Unsupervised Learning Through Randomized Algorithms for High-Volume High-Velocity Data (ULTRA-HV).

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pinar, Ali; Kolda, Tamara G.; Carlberg, Kevin Thomas

Through long-term investments in computing, algorithms, facilities, and instrumentation, DOE is an established leader in massive-scale, high-fidelity simulations, as well as science-leading experimentation. In both cases, DOE is generating more data than it can analyze and the problem is intensifying quickly. The need for advanced algorithms that can automatically convert the abundance of data into a wealth of useful information by discovering hidden structures is well recognized. Such efforts however, are hindered by the massive volume of the data and its high velocity. Here, the challenge is developing unsupervised learning methods to discover hidden structure in high-volume, high-velocity data.

Robustness of networks formed from interdependent correlated networks under intentional attacks

NASA Astrophysics Data System (ADS)

Liu, Long; Meng, Ke; Dong, Zhaoyang

2018-02-01

We study the problem of intentional attacks targeting to interdependent networks generated with known degree distribution (in-degree oriented model) or distribution of interlinks (out-degree oriented model). In both models, each node's degree is correlated with the number of its links that connect to the other network. For both models, varying the correlation coefficient has a significant effect on the robustness of a system undergoing random attacks or attacks targeting nodes with low degree. For a system with an assortative relationship between in-degree and out-degree, reducing the broadness of networks' degree distributions can increase the resistance of systems against intentional attacks.

JacksonBot - Design, Simulation and Optimal Control of an Action Painting Robot

NASA Astrophysics Data System (ADS)

Raschke, Michael; Mombaur, Katja; Schubert, Alexander

We present the robotics platform JacksonBot which is capable to produce paintings inspired by the Action Painting style of Jackson Pollock. A dynamically moving robot arm splashes color from a container at the end effector on the canvas. The paintings produced by this platform rely on a combination of the algorithmic generation of robot arm motions with random effects of the splashing color. The robot can be considered as a complex and powerful tool to generate art works programmed by a user. Desired end effector motions can be prescribed either by mathematical functions, by point sequences or by data glove motions. We have evaluated the effect of different shapes of input motions on the resulting painting. In order to compute the robot joint trajectories necessary to move along a desired end effector path, we use an optimal control based approach to solve the inverse kinematics problem.

Random bits, true and unbiased, from atmospheric turbulence

PubMed Central

Marangon, Davide G.; Vallone, Giuseppe; Villoresi, Paolo

2014-01-01

Random numbers represent a fundamental ingredient for secure communications and numerical simulation as well as to games and in general to Information Science. Physical processes with intrinsic unpredictability may be exploited to generate genuine random numbers. The optical propagation in strong atmospheric turbulence is here taken to this purpose, by observing a laser beam after a 143 km free-space path. In addition, we developed an algorithm to extract the randomness of the beam images at the receiver without post-processing. The numbers passed very selective randomness tests for qualification as genuine random numbers. The extracting algorithm can be easily generalized to random images generated by different physical processes. PMID:24976499

A random generation approach to pattern library creation for full chip lithographic simulation

NASA Astrophysics Data System (ADS)

Zou, Elain; Hong, Sid; Liu, Limei; Huang, Lucas; Yang, Legender; Kabeel, Aliaa; Madkour, Kareem; ElManhawy, Wael; Kwan, Joe; Du, Chunshan; Hu, Xinyi; Wan, Qijian; Zhang, Recoo

2017-04-01

As technology advances, the need for running lithographic (litho) checking for early detection of hotspots before tapeout has become essential. This process is important at all levels—from designing standard cells and small blocks to large intellectual property (IP) and full chip layouts. Litho simulation provides high accuracy for detecting printability issues due to problematic geometries, but it has the disadvantage of slow performance on large designs and blocks [1]. Foundries have found a good compromise solution for running litho simulation on full chips by filtering out potential candidate hotspot patterns using pattern matching (PM), and then performing simulation on the matched locations. The challenge has always been how to easily create a PM library of candidate patterns that provides both comprehensive coverage for litho problems and fast runtime performance. This paper presents a new strategy for generating candidate real design patterns through a random generation approach using a layout schema generator (LSG) utility. The output patterns from the LSG are simulated, and then classified by a scoring mechanism that categorizes patterns according to the severity of the hotspots, probability of their presence in the design, and the likelihood of the pattern causing a hotspot. The scoring output helps to filter out the yield problematic patterns that should be removed from any standard cell design, and also to define potential problematic patterns that must be simulated within a bigger context to decide whether or not they represent an actual hotspot. This flow is demonstrated on SMIC 14nm technology, creating a candidate hotspot pattern library that can be used in full chip simulation with very high coverage and robust performance.

True randomness from an incoherent source

NASA Astrophysics Data System (ADS)

Qi, Bing

2017-11-01

Quantum random number generators (QRNGs) harness the intrinsic randomness in measurement processes: the measurement outputs are truly random, given the input state is a superposition of the eigenstates of the measurement operators. In the case of trusted devices, true randomness could be generated from a mixed state ρ so long as the system entangled with ρ is well protected. We propose a random number generation scheme based on measuring the quadrature fluctuations of a single mode thermal state using an optical homodyne detector. By mixing the output of a broadband amplified spontaneous emission (ASE) source with a single mode local oscillator (LO) at a beam splitter and performing differential photo-detection, we can selectively detect the quadrature fluctuation of a single mode output of the ASE source, thanks to the filtering function of the LO. Experimentally, a quadrature variance about three orders of magnitude larger than the vacuum noise has been observed, suggesting this scheme can tolerate much higher detector noise in comparison with QRNGs based on measuring the vacuum noise. The high quality of this entropy source is evidenced by the small correlation coefficients of the acquired data. A Toeplitz-hashing extractor is applied to generate unbiased random bits from the Gaussian distributed raw data, achieving an efficiency of 5.12 bits per sample. The output of the Toeplitz extractor successfully passes all the NIST statistical tests for random numbers.

Ensembles of physical states and random quantum circuits on graphs

NASA Astrophysics Data System (ADS)

Hamma, Alioscia; Santra, Siddhartha; Zanardi, Paolo

2012-11-01

In this paper we continue and extend the investigations of the ensembles of random physical states introduced in Hamma [Phys. Rev. Lett.PRLTAO0031-900710.1103/PhysRevLett.109.040502 109, 040502 (2012)]. These ensembles are constructed by finite-length random quantum circuits (RQC) acting on the (hyper)edges of an underlying (hyper)graph structure. The latter encodes for the locality structure associated with finite-time quantum evolutions generated by physical, i.e., local, Hamiltonians. Our goal is to analyze physical properties of typical states in these ensembles; in particular here we focus on proxies of quantum entanglement as purity and α-Renyi entropies. The problem is formulated in terms of matrix elements of superoperators which depend on the graph structure, choice of probability measure over the local unitaries, and circuit length. In the α=2 case these superoperators act on a restricted multiqubit space generated by permutation operators associated to the subsets of vertices of the graph. For permutationally invariant interactions the dynamics can be further restricted to an exponentially smaller subspace. We consider different families of RQCs and study their typical entanglement properties for finite time as well as their asymptotic behavior. We find that area law holds in average and that the volume law is a typical property (that is, it holds in average and the fluctuations around the average are vanishing for the large system) of physical states. The area law arises when the evolution time is O(1) with respect to the size L of the system, while the volume law arises as is typical when the evolution time scales like O(L).

Photogrammetric DSM denoising

NASA Astrophysics Data System (ADS)

Nex, F.; Gerke, M.

2014-08-01

Image matching techniques can nowadays provide very dense point clouds and they are often considered a valid alternative to LiDAR point cloud. However, photogrammetric point clouds are often characterized by a higher level of random noise compared to LiDAR data and by the presence of large outliers. These problems constitute a limitation in the practical use of photogrammetric data for many applications but an effective way to enhance the generated point cloud has still to be found. In this paper we concentrate on the restoration of Digital Surface Models (DSM), computed from dense image matching point clouds. A photogrammetric DSM, i.e. a 2.5D representation of the surface is still one of the major products derived from point clouds. Four different algorithms devoted to DSM denoising are presented: a standard median filter approach, a bilateral filter, a variational approach (TGV: Total Generalized Variation), as well as a newly developed algorithm, which is embedded into a Markov Random Field (MRF) framework and optimized through graph-cuts. The ability of each algorithm to recover the original DSM has been quantitatively evaluated. To do that, a synthetic DSM has been generated and different typologies of noise have been added to mimic the typical errors of photogrammetric DSMs. The evaluation reveals that standard filters like median and edge preserving smoothing through a bilateral filter approach cannot sufficiently remove typical errors occurring in a photogrammetric DSM. The TGV-based approach much better removes random noise, but large areas with outliers still remain. Our own method which explicitly models the degradation properties of those DSM outperforms the others in all aspects.

Analysis of entropy extraction efficiencies in random number generation systems

NASA Astrophysics Data System (ADS)

Wang, Chao; Wang, Shuang; Chen, Wei; Yin, Zhen-Qiang; Han, Zheng-Fu

2016-05-01

Random numbers (RNs) have applications in many areas: lottery games, gambling, computer simulation, and, most importantly, cryptography [N. Gisin et al., Rev. Mod. Phys. 74 (2002) 145]. In cryptography theory, the theoretical security of the system calls for high quality RNs. Therefore, developing methods for producing unpredictable RNs with adequate speed is an attractive topic. Early on, despite the lack of theoretical support, pseudo RNs generated by algorithmic methods performed well and satisfied reasonable statistical requirements. However, as implemented, those pseudorandom sequences were completely determined by mathematical formulas and initial seeds, which cannot introduce extra entropy or information. In these cases, “random” bits are generated that are not at all random. Physical random number generators (RNGs), which, in contrast to algorithmic methods, are based on unpredictable physical random phenomena, have attracted considerable research interest. However, the way that we extract random bits from those physical entropy sources has a large influence on the efficiency and performance of the system. In this manuscript, we will review and discuss several randomness extraction schemes that are based on radiation or photon arrival times. We analyze the robustness, post-processing requirements and, in particular, the extraction efficiency of those methods to aid in the construction of efficient, compact and robust physical RNG systems.

Quantum random bit generation using energy fluctuations in stimulated Raman scattering.

PubMed

Bustard, Philip J; England, Duncan G; Nunn, Josh; Moffatt, Doug; Spanner, Michael; Lausten, Rune; Sussman, Benjamin J

2013-12-02

Random number sequences are a critical resource in modern information processing systems, with applications in cryptography, numerical simulation, and data sampling. We introduce a quantum random number generator based on the measurement of pulse energy quantum fluctuations in Stokes light generated by spontaneously-initiated stimulated Raman scattering. Bright Stokes pulse energy fluctuations up to five times the mean energy are measured with fast photodiodes and converted to unbiased random binary strings. Since the pulse energy is a continuous variable, multiple bits can be extracted from a single measurement. Our approach can be generalized to a wide range of Raman active materials; here we demonstrate a prototype using the optical phonon line in bulk diamond.

Experimental study of a quantum random-number generator based on two independent lasers

NASA Astrophysics Data System (ADS)

Sun, Shi-Hai; Xu, Feihu

2017-12-01

A quantum random-number generator (QRNG) can produce true randomness by utilizing the inherent probabilistic nature of quantum mechanics. Recently, the spontaneous-emission quantum phase noise of the laser has been widely deployed for quantum random-number generation, due to its high rate, its low cost, and the feasibility of chip-scale integration. Here, we perform a comprehensive experimental study of a phase-noise-based QRNG with two independent lasers, each of which operates in either continuous-wave (CW) or pulsed mode. We implement the QRNG by operating the two lasers in three configurations, namely, CW + CW, CW + pulsed, and pulsed + pulsed, and demonstrate their trade-offs, strengths, and weaknesses.

Pattern formations and optimal packing.

PubMed

Mityushev, Vladimir

2016-04-01

Patterns of different symmetries may arise after solution to reaction-diffusion equations. Hexagonal arrays, layers and their perturbations are observed in different models after numerical solution to the corresponding initial-boundary value problems. We demonstrate an intimate connection between pattern formations and optimal random packing on the plane. The main study is based on the following two points. First, the diffusive flux in reaction-diffusion systems is approximated by piecewise linear functions in the framework of structural approximations. This leads to a discrete network approximation of the considered continuous problem. Second, the discrete energy minimization yields optimal random packing of the domains (disks) in the representative cell. Therefore, the general problem of pattern formations based on the reaction-diffusion equations is reduced to the geometric problem of random packing. It is demonstrated that all random packings can be divided onto classes associated with classes of isomorphic graphs obtained from the Delaunay triangulation. The unique optimal solution is constructed in each class of the random packings. If the number of disks per representative cell is finite, the number of classes of isomorphic graphs, hence, the number of optimal packings is also finite. Copyright © 2016 Elsevier Inc. All rights reserved.

Minimal-post-processing 320-Gbps true random bit generation using physical white chaos.

PubMed

Wang, Anbang; Wang, Longsheng; Li, Pu; Wang, Yuncai

2017-02-20

Chaotic external-cavity semiconductor laser (ECL) is a promising entropy source for generation of high-speed physical random bits or digital keys. The rate and randomness is unfortunately limited by laser relaxation oscillation and external-cavity resonance, and is usually improved by complicated post processing. Here, we propose using a physical broadband white chaos generated by optical heterodyning of two ECLs as entropy source to construct high-speed random bit generation (RBG) with minimal post processing. The optical heterodyne chaos not only has a white spectrum without signature of relaxation oscillation and external-cavity resonance but also has a symmetric amplitude distribution. Thus, after quantization with a multi-bit analog-digital-convertor (ADC), random bits can be obtained by extracting several least significant bits (LSBs) without any other processing. In experiments, a white chaos with a 3-dB bandwidth of 16.7 GHz is generated. Its entropy rate is estimated as 16 Gbps by single-bit quantization which means a spectrum efficiency of 96%. With quantization using an 8-bit ADC, 320-Gbps physical RBG is achieved by directly extracting 4 LSBs at 80-GHz sampling rate.

[Computer-assisted education in problem-solving in neurology; a randomized educational study].

PubMed

Weverling, G J; Stam, J; ten Cate, T J; van Crevel, H

1996-02-24

To determine the effect of computer-based medical teaching (CBMT) as a supplementary method to teach clinical problem-solving during the clerkship in neurology. Randomized controlled blinded study. Academic Medical Centre, Amsterdam, the Netherlands. 103 Students were assigned at random to a group with access to CBMT and a control group. CBMT consisted of 20 computer-simulated patients with neurological diseases, and was permanently available during five weeks to students in the CBMT group. The ability to recognize and solve neurological problems was assessed with two free-response tests, scored by two blinded observers. The CBMT students scored significantly better on the test related to the CBMT cases (mean score 7.5 on a zero to 10 point scale; control group 6.2; p < 0.001). There was no significant difference on the control test not related to the problems practised with CBMT. CBMT can be an effective method for teaching clinical problem-solving, when used as a supplementary teaching facility during a clinical clerkship. The increased ability to solve problems learned by CBMT had no demonstrable effect on the performance with other neurological problems.

Total variation regularization of the 3-D gravity inverse problem using a randomized generalized singular value decomposition

NASA Astrophysics Data System (ADS)

Vatankhah, Saeed; Renaut, Rosemary A.; Ardestani, Vahid E.

2018-04-01

We present a fast algorithm for the total variation regularization of the 3-D gravity inverse problem. Through imposition of the total variation regularization, subsurface structures presenting with sharp discontinuities are preserved better than when using a conventional minimum-structure inversion. The associated problem formulation for the regularization is nonlinear but can be solved using an iteratively reweighted least-squares algorithm. For small-scale problems the regularized least-squares problem at each iteration can be solved using the generalized singular value decomposition. This is not feasible for large-scale, or even moderate-scale, problems. Instead we introduce the use of a randomized generalized singular value decomposition in order to reduce the dimensions of the problem and provide an effective and efficient solution technique. For further efficiency an alternating direction algorithm is used to implement the total variation weighting operator within the iteratively reweighted least-squares algorithm. Presented results for synthetic examples demonstrate that the novel randomized decomposition provides good accuracy for reduced computational and memory demands as compared to use of classical approaches.

The preliminary effect of a parenting program for Korean American mothers: a randomized controlled experimental study.

PubMed

Kim, Eunjung; Cain, Kevin C; Webster-Stratton, Carolyn

2008-09-01

Traditional Korean American discipline is characterized by a lack of expression of affection and use of harsh discipline. The purpose of this study was to pilot test the effect of the Incredible Years Parenting Program among Korean American mothers. A randomized controlled experimental study design was used; 29 first-generation Korean American mothers of young children (3-8 years old) were randomly assigned to intervention (n=20) and control (n=9) groups. Intervention group mothers received a 12-week parenting program. Control group mothers did not receive the intervention. Mothers reported on discipline styles (positive, appropriate, and harsh), level of acculturation, and their child's outcomes (behavioral problems and social competence) at pre-, post-, and 1-year follow-up intervals. After completing the program, intervention group mothers significantly increased use of positive discipline as compared to control group mothers. Among intervention group mothers, high-acculturated mothers significantly increased appropriate discipline whereas low-acculturated mothers significantly decreased harsh discipline. In the 1-year follow-up, intervention group mothers maintained the significant effect for positive discipline. Providing this program appears to be a promising way of promoting positive discipline among Korean American mothers.

Using Self-Generated Drawings to Solve Arithmetic Word Problems.

ERIC Educational Resources Information Center

Van Essen, Gerard; Hamaker, Christiaan

1990-01-01

Results are presented from two intervention studies which investigate whether encouraging elementary students to generate drawings of arithmetic word problems facilitates problem-solving performance. Findings indicate that fifth graders (N=50) generated many drawings of word problems and improved problem solutions after the intervention, whereas…

A DAG Scheduling Scheme on Heterogeneous Computing Systems Using Tuple-Based Chemical Reaction Optimization

PubMed Central

Jiang, Yuyi; Shao, Zhiqing; Guo, Yi

2014-01-01

A complex computing problem can be solved efficiently on a system with multiple computing nodes by dividing its implementation code into several parallel processing modules or tasks that can be formulated as directed acyclic graph (DAG) problems. The DAG jobs may be mapped to and scheduled on the computing nodes to minimize the total execution time. Searching an optimal DAG scheduling solution is considered to be NP-complete. This paper proposed a tuple molecular structure-based chemical reaction optimization (TMSCRO) method for DAG scheduling on heterogeneous computing systems, based on a very recently proposed metaheuristic method, chemical reaction optimization (CRO). Comparing with other CRO-based algorithms for DAG scheduling, the design of tuple reaction molecular structure and four elementary reaction operators of TMSCRO is more reasonable. TMSCRO also applies the concept of constrained critical paths (CCPs), constrained-critical-path directed acyclic graph (CCPDAG) and super molecule for accelerating convergence. In this paper, we have also conducted simulation experiments to verify the effectiveness and efficiency of TMSCRO upon a large set of randomly generated graphs and the graphs for real world problems. PMID:25143977

A DAG scheduling scheme on heterogeneous computing systems using tuple-based chemical reaction optimization.

PubMed

Jiang, Yuyi; Shao, Zhiqing; Guo, Yi

2014-01-01

A complex computing problem can be solved efficiently on a system with multiple computing nodes by dividing its implementation code into several parallel processing modules or tasks that can be formulated as directed acyclic graph (DAG) problems. The DAG jobs may be mapped to and scheduled on the computing nodes to minimize the total execution time. Searching an optimal DAG scheduling solution is considered to be NP-complete. This paper proposed a tuple molecular structure-based chemical reaction optimization (TMSCRO) method for DAG scheduling on heterogeneous computing systems, based on a very recently proposed metaheuristic method, chemical reaction optimization (CRO). Comparing with other CRO-based algorithms for DAG scheduling, the design of tuple reaction molecular structure and four elementary reaction operators of TMSCRO is more reasonable. TMSCRO also applies the concept of constrained critical paths (CCPs), constrained-critical-path directed acyclic graph (CCPDAG) and super molecule for accelerating convergence. In this paper, we have also conducted simulation experiments to verify the effectiveness and efficiency of TMSCRO upon a large set of randomly generated graphs and the graphs for real world problems.

Superpixel-based graph cuts for accurate stereo matching

NASA Astrophysics Data System (ADS)

Feng, Liting; Qin, Kaihuai

2017-06-01

Estimating the surface normal vector and disparity of a pixel simultaneously, also known as three-dimensional label method, has been widely used in recent continuous stereo matching problem to achieve sub-pixel accuracy. However, due to the infinite label space, it’s extremely hard to assign each pixel an appropriate label. In this paper, we present an accurate and efficient algorithm, integrating patchmatch with graph cuts, to approach this critical computational problem. Besides, to get robust and precise matching cost, we use a convolutional neural network to learn a similarity measure on small image patches. Compared with other MRF related methods, our method has several advantages: its sub-modular property ensures a sub-problem optimality which is easy to perform in parallel; graph cuts can simultaneously update multiple pixels, avoiding local minima caused by sequential optimizers like belief propagation; it uses segmentation results for better local expansion move; local propagation and randomization can easily generate the initial solution without using external methods. Middlebury experiments show that our method can get higher accuracy than other MRF-based algorithms.

Feasibility of inverse problem solution for determination of city emission function from night sky radiance measurements

NASA Astrophysics Data System (ADS)

Petržala, Jaromír

2018-07-01

The knowledge of the emission function of a city is crucial for simulation of sky glow in its vicinity. The indirect methods to achieve this function from radiances measured over a part of the sky have been recently developed. In principle, such methods represent an ill-posed inverse problem. This paper deals with the theoretical feasibility study of various approaches to solving of given inverse problem. Particularly, it means testing of fitness of various stabilizing functionals within the Tikhonov's regularization. Further, the L-curve and generalized cross validation methods were investigated as indicators of an optimal regularization parameter. At first, we created the theoretical model for calculation of a sky spectral radiance in the form of a functional of an emission spectral radiance. Consequently, all the mentioned approaches were examined in numerical experiments with synthetical data generated for the fictitious city and loaded by random errors. The results demonstrate that the second order Tikhonov's regularization method together with regularization parameter choice by the L-curve maximum curvature criterion provide solutions which are in good agreement with the supposed model emission functions.

Empirical Analysis and Refinement of Expert System Knowledge Bases

DTIC Science & Technology

1988-08-31

refinement. Both a simulated case generation program, and a random rule basher were developed to enhance rule refinement experimentation. *Substantial...the second fiscal year 88 objective was fully met. Rule Refinement System Simulated Rule Basher Case Generator Stored Cases Expert System Knowledge...generated until the rule is satisfied. Cases may be randomly generated for a given rule or hypothesis. Rule Basher Given that one has a correct

Reliability Overhaul Model

DTIC Science & Technology

1989-08-01

Random variables for the conditional exponential distribution are generated using the inverse transform method. C1) Generate U - UCO,i) (2) Set s - A ln...e - [(x+s - 7)/ n] 0 + [Cx-T)/n]0 c. Random variables from the conditional weibull distribution are generated using the inverse transform method. C1...using a standard normal transformation and the inverse transform method. B - 3 APPENDIX 3 DISTRIBUTIONS SUPPORTED BY THE MODEL (1) Generate Y - PCX S

Practical quantum random number generator based on measuring the shot noise of vacuum states

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shen Yong; Zou Hongxin; Tian Liang

2010-06-15

The shot noise of vacuum states is a kind of quantum noise and is totally random. In this paper a nondeterministic random number generation scheme based on measuring the shot noise of vacuum states is presented and experimentally demonstrated. We use a homodyne detector to measure the shot noise of vacuum states. Considering that the frequency bandwidth of our detector is limited, we derive the optimal sampling rate so that sampling points have the least correlation with each other. We also choose a method to extract random numbers from sampling values, and prove that the influence of classical noise canmore » be avoided with this method so that the detector does not have to be shot-noise limited. The random numbers generated with this scheme have passed ent and diehard tests.« less

Compact quantum random number generator based on superluminescent light-emitting diodes

NASA Astrophysics Data System (ADS)

Wei, Shihai; Yang, Jie; Fan, Fan; Huang, Wei; Li, Dashuang; Xu, Bingjie

2017-12-01

By measuring the amplified spontaneous emission (ASE) noise of the superluminescent light emitting diodes, we propose and realize a quantum random number generator (QRNG) featured with practicability. In the QRNG, after the detection and amplification of the ASE noise, the data acquisition and randomness extraction which is integrated in a field programmable gate array (FPGA) are both implemented in real-time, and the final random bit sequences are delivered to a host computer with a real-time generation rate of 1.2 Gbps. Further, to achieve compactness, all the components of the QRNG are integrated on three independent printed circuit boards with a compact design, and the QRNG is packed in a small enclosure sized 140 mm × 120 mm × 25 mm. The final random bit sequences can pass all the NIST-STS and DIEHARD tests.

Pseudo-random properties of a linear congruential generator investigated by b-adic diaphony

NASA Astrophysics Data System (ADS)

Stoev, Peter; Stoilova, Stanislava

2017-12-01

In the proposed paper we continue the study of the diaphony, defined in b-adic number system, and we extend it in different directions. We investigate this diaphony as a tool for estimation of the pseudorandom properties of some of the most used random number generators. This is done by evaluating the distribution of specially constructed two-dimensional nets on the base of the obtained random numbers. The aim is to see how the generated numbers are suitable for calculations in some numerical methods (Monte Carlo etc.).

A random spatial network model based on elementary postulates

USGS Publications Warehouse

Karlinger, Michael R.; Troutman, Brent M.

1989-01-01

A model for generating random spatial networks that is based on elementary postulates comparable to those of the random topology model is proposed. In contrast to the random topology model, this model ascribes a unique spatial specification to generated drainage networks, a distinguishing property of some network growth models. The simplicity of the postulates creates an opportunity for potential analytic investigations of the probabilistic structure of the drainage networks, while the spatial specification enables analyses of spatially dependent network properties. In the random topology model all drainage networks, conditioned on magnitude (number of first-order streams), are equally likely, whereas in this model all spanning trees of a grid, conditioned on area and drainage density, are equally likely. As a result, link lengths in the generated networks are not independent, as usually assumed in the random topology model. For a preliminary model evaluation, scale-dependent network characteristics, such as geometric diameter and link length properties, and topologic characteristics, such as bifurcation ratio, are computed for sets of drainage networks generated on square and rectangular grids. Statistics of the bifurcation and length ratios fall within the range of values reported for natural drainage networks, but geometric diameters tend to be relatively longer than those for natural networks.

Using empirical Bayes predictors from generalized linear mixed models to test and visualize associations among longitudinal outcomes.

PubMed

Mikulich-Gilbertson, Susan K; Wagner, Brandie D; Grunwald, Gary K; Riggs, Paula D; Zerbe, Gary O

2018-01-01

Medical research is often designed to investigate changes in a collection of response variables that are measured repeatedly on the same subjects. The multivariate generalized linear mixed model (MGLMM) can be used to evaluate random coefficient associations (e.g. simple correlations, partial regression coefficients) among outcomes that may be non-normal and differently distributed by specifying a multivariate normal distribution for their random effects and then evaluating the latent relationship between them. Empirical Bayes predictors are readily available for each subject from any mixed model and are observable and hence, plotable. Here, we evaluate whether second-stage association analyses of empirical Bayes predictors from a MGLMM, provide a good approximation and visual representation of these latent association analyses using medical examples and simulations. Additionally, we compare these results with association analyses of empirical Bayes predictors generated from separate mixed models for each outcome, a procedure that could circumvent computational problems that arise when the dimension of the joint covariance matrix of random effects is large and prohibits estimation of latent associations. As has been shown in other analytic contexts, the p-values for all second-stage coefficients that were determined by naively assuming normality of empirical Bayes predictors provide a good approximation to p-values determined via permutation analysis. Analyzing outcomes that are interrelated with separate models in the first stage and then associating the resulting empirical Bayes predictors in a second stage results in different mean and covariance parameter estimates from the maximum likelihood estimates generated by a MGLMM. The potential for erroneous inference from using results from these separate models increases as the magnitude of the association among the outcomes increases. Thus if computable, scatterplots of the conditionally independent empirical Bayes predictors from a MGLMM are always preferable to scatterplots of empirical Bayes predictors generated by separate models, unless the true association between outcomes is zero.

Method and apparatus for determining position using global positioning satellites

NASA Technical Reports Server (NTRS)

Ward, John (Inventor); Ward, William S. (Inventor)

1998-01-01

A global positioning satellite receiver having an antenna for receiving a L1 signal from a satellite. The L1 signal is processed by a preamplifier stage including a band pass filter and a low noise amplifier and output as a radio frequency (RF) signal. A mixer receives and de-spreads the RF signal in response to a pseudo-random noise code, i.e., Gold code, generated by an internal pseudo-random noise code generator. A microprocessor enters a code tracking loop, such that during the code tracking loop, it addresses the pseudo-random code generator to cause the pseudo-random code generator to sequentially output pseudo-random codes corresponding to satellite codes used to spread the L1 signal, until correlation occurs. When an output of the mixer is indicative of the occurrence of correlation between the RF signal and the generated pseudo-random codes, the microprocessor enters an operational state which slows the receiver code sequence to stay locked with the satellite code sequence. The output of the mixer is provided to a detector which, in turn, controls certain routines of the microprocessor. The microprocessor will output pseudo range information according to an interrupt routine in response detection of correlation. The pseudo range information is to be telemetered to a ground station which determines the position of the global positioning satellite receiver.

Narrow-band generation in random distributed feedback fiber laser.

PubMed

Sugavanam, Srikanth; Tarasov, Nikita; Shu, Xuewen; Churkin, Dmitry V

2013-07-15

Narrow-band emission of spectral width down to ~0.05 nm line-width is achieved in the random distributed feedback fiber laser employing narrow-band fiber Bragg grating or fiber Fabry-Perot interferometer filters. The observed line-width is ~10 times less than line-width of other demonstrated up to date random distributed feedback fiber lasers. The random DFB laser with Fabry-Perot interferometer filter provides simultaneously multi-wavelength and narrow-band (within each line) generation with possibility of further wavelength tuning.

Online interventions for problem gamblers with and without co-occurring problem drinking: study protocol of a randomized controlled trial.

PubMed

Cunningham, John A; Hodgins, David C; Keough, Matthew; Hendershot, Christian S; Bennett, Kylie; Bennett, Anthony; Godinho, Alexandra

2018-05-25

The current randomized controlled trial seeks to evaluate whether providing access to an Internet intervention for problem drinking in addition to an Internet intervention for problem gambling is beneficial for participants with gambling problems who do or do not have co-occurring problem drinking. Potential participants will be recruited online via a comprehensive advertisement strategy, if they meet the criteria for problem gambling. As part of the baseline measures, problem drinking will also be assessed. Eligible participants (N = 280) who agree to partake in the study and to be followed up for 6 months will be randomized into one of two versions of an Internet intervention for gamblers: an intervention that targets only gambling issues (G-only) and one that combines a gambling intervention with an intervention for problem drinking (G + A). For problem gamblers who exhibit co-occurring problem drinking, it is predicted that participants who are provided access to the G + A intervention will demonstrate a significantly greater level of reduction in gambling outcomes at 6 months compared to those provided access to the G-only intervention. This trial will expand upon the current research on Internet interventions for addictions and inform the development of treatments for those with co-occurring problem drinking and gambling. ClinicalTrials.gov, NCT03323606 . Registered on 24 October 2017.

The fast algorithm of spark in compressive sensing

NASA Astrophysics Data System (ADS)

Xie, Meihua; Yan, Fengxia

2017-01-01

Compressed Sensing (CS) is an advanced theory on signal sampling and reconstruction. In CS theory, the reconstruction condition of signal is an important theory problem, and spark is a good index to study this problem. But the computation of spark is NP hard. In this paper, we study the problem of computing spark. For some special matrixes, for example, the Gaussian random matrix and 0-1 random matrix, we obtain some conclusions. Furthermore, for Gaussian random matrix with fewer rows than columns, we prove that its spark equals to the number of its rows plus one with probability 1. For general matrix, two methods are given to compute its spark. One is the method of directly searching and the other is the method of dual-tree searching. By simulating 24 Gaussian random matrixes and 18 0-1 random matrixes, we tested the computation time of these two methods. Numerical results showed that the dual-tree searching method had higher efficiency than directly searching, especially for those matrixes which has as much as rows and columns.

Programmable quantum random number generator without postprocessing.

PubMed

Nguyen, Lac; Rehain, Patrick; Sua, Yong Meng; Huang, Yu-Ping

2018-02-15

We demonstrate a viable source of unbiased quantum random numbers whose statistical properties can be arbitrarily programmed without the need for any postprocessing such as randomness distillation or distribution transformation. It is based on measuring the arrival time of single photons in shaped temporal modes that are tailored with an electro-optical modulator. We show that quantum random numbers can be created directly in customized probability distributions and pass all randomness tests of the NIST and Dieharder test suites without any randomness extraction. The min-entropies of such generated random numbers are measured close to the theoretical limits, indicating their near-ideal statistics and ultrahigh purity. Easy to implement and arbitrarily programmable, this technique can find versatile uses in a multitude of data analysis areas.

Optimal Operation of Energy Storage in Power Transmission and Distribution

NASA Astrophysics Data System (ADS)

Akhavan Hejazi, Seyed Hossein

In this thesis, we investigate optimal operation of energy storage units in power transmission and distribution grids. At transmission level, we investigate the problem where an investor-owned independently-operated energy storage system seeks to offer energy and ancillary services in the day-ahead and real-time markets. We specifically consider the case where a significant portion of the power generated in the grid is from renewable energy resources and there exists significant uncertainty in system operation. In this regard, we formulate a stochastic programming framework to choose optimal energy and reserve bids for the storage units that takes into account the fluctuating nature of the market prices due to the randomness in the renewable power generation availability. At distribution level, we develop a comprehensive data set to model various stochastic factors on power distribution networks, with focus on networks that have high penetration of electric vehicle charging load and distributed renewable generation. Furthermore, we develop a data-driven stochastic model for energy storage operation at distribution level, where the distribution of nodal voltage and line power flow are modelled as stochastic functions of the energy storage unit's charge and discharge schedules. In particular, we develop new closed-form stochastic models for such key operational parameters in the system. Our approach is analytical and allows formulating tractable optimization problems. Yet, it does not involve any restricting assumption on the distribution of random parameters, hence, it results in accurate modeling of uncertainties. By considering the specific characteristics of random variables, such as their statistical dependencies and often irregularly-shaped probability distributions, we propose a non-parametric chance-constrained optimization approach to operate and plan energy storage units in power distribution girds. In the proposed stochastic optimization, we consider uncertainty from various elements, such as solar photovoltaic , electric vehicle chargers, and residential baseloads, in the form of discrete probability functions. In the last part of this thesis we address some other resources and concepts for enhancing the operation of power distribution and transmission systems. In particular, we proposed a new framework to determine the best sites, sizes, and optimal payment incentives under special contracts for committed-type DG projects to offset distribution network investment costs. In this framework, the aim is to allocate DGs such that the profit gained by the distribution company is maximized while each DG unit's individual profit is also taken into account to assure that private DG investment remains economical.

The role of ferroelectric domain structure in second harmonic generation in random quadratic media.

PubMed

Roppo, Vito; Wang, W; Kalinowski, K; Kong, Y; Cojocaru, C; Trull, J; Vilaseca, R; Scalora, M; Krolikowski, W; Kivshar, Yu

2010-03-01

We study theoretically and numerically the second harmonic generation in a nonlinear crystal with random distribution of ferroelectric domains. We show that the specific features of disordered domain structure greatly affect the emission pattern of the generated harmonics. This phenomena can be used to characterize the degree of disorder in nonlinear photonic structures.

Killing (absorption) versus survival in random motion

NASA Astrophysics Data System (ADS)

Garbaczewski, Piotr

2017-09-01

We address diffusion processes in a bounded domain, while focusing on somewhat unexplored affinities between the presence of absorbing and/or inaccessible boundaries. For the Brownian motion (Lévy-stable cases are briefly mentioned) model-independent features are established of the dynamical law that underlies the short-time behavior of these random paths, whose overall lifetime is predefined to be long. As a by-product, the limiting regime of a permanent trapping in a domain is obtained. We demonstrate that the adopted conditioning method, involving the so-called Bernstein transition function, works properly also in an unbounded domain, for stochastic processes with killing (Feynman-Kac kernels play the role of transition densities), provided the spectrum of the related semigroup operator is discrete. The method is shown to be useful in the case, when the spectrum of the generator goes down to zero and no isolated minimal (ground state) eigenvalue is in existence, like in the problem of the long-term survival on a half-line with a sink at origin.

Enhancing Multimedia Imbalanced Concept Detection Using VIMP in Random Forests.

PubMed

Sadiq, Saad; Yan, Yilin; Shyu, Mei-Ling; Chen, Shu-Ching; Ishwaran, Hemant

2016-07-01

Recent developments in social media and cloud storage lead to an exponential growth in the amount of multimedia data, which increases the complexity of managing, storing, indexing, and retrieving information from such big data. Many current content-based concept detection approaches lag from successfully bridging the semantic gap. To solve this problem, a multi-stage random forest framework is proposed to generate predictor variables based on multivariate regressions using variable importance (VIMP). By fine tuning the forests and significantly reducing the predictor variables, the concept detection scores are evaluated when the concept of interest is rare and imbalanced, i.e., having little collaboration with other high level concepts. Using classical multivariate statistics, estimating the value of one coordinate using other coordinates standardizes the covariates and it depends upon the variance of the correlations instead of the mean. Thus, conditional dependence on the data being normally distributed is eliminated. Experimental results demonstrate that the proposed framework outperforms those approaches in the comparison in terms of the Mean Average Precision (MAP) values.

Automatic Recognition of Indoor Navigation Elements from Kinect Point Clouds

NASA Astrophysics Data System (ADS)

Zeng, L.; Kang, Z.

2017-09-01

This paper realizes automatically the navigating elements defined by indoorGML data standard - door, stairway and wall. The data used is indoor 3D point cloud collected by Kinect v2 launched in 2011 through the means of ORB-SLAM. By contrast, it is cheaper and more convenient than lidar, but the point clouds also have the problem of noise, registration error and large data volume. Hence, we adopt a shape descriptor - histogram of distances between two randomly chosen points, proposed by Osada and merges with other descriptor - in conjunction with random forest classifier to recognize the navigation elements (door, stairway and wall) from Kinect point clouds. This research acquires navigation elements and their 3-d location information from each single data frame through segmentation of point clouds, boundary extraction, feature calculation and classification. Finally, this paper utilizes the acquired navigation elements and their information to generate the state data of the indoor navigation module automatically. The experimental results demonstrate a high recognition accuracy of the proposed method.

Bagging Voronoi classifiers for clustering spatial functional data

NASA Astrophysics Data System (ADS)

Secchi, Piercesare; Vantini, Simone; Vitelli, Valeria

2013-06-01

We propose a bagging strategy based on random Voronoi tessellations for the exploration of geo-referenced functional data, suitable for different purposes (e.g., classification, regression, dimensional reduction, …). Urged by an application to environmental data contained in the Surface Solar Energy database, we focus in particular on the problem of clustering functional data indexed by the sites of a spatial finite lattice. We thus illustrate our strategy by implementing a specific algorithm whose rationale is to (i) replace the original data set with a reduced one, composed by local representatives of neighborhoods covering the entire investigated area; (ii) analyze the local representatives; (iii) repeat the previous analysis many times for different reduced data sets associated to randomly generated different sets of neighborhoods, thus obtaining many different weak formulations of the analysis; (iv) finally, bag together the weak analyses to obtain a conclusive strong analysis. Through an extensive simulation study, we show that this new procedure - which does not require an explicit model for spatial dependence - is statistically and computationally efficient.

Childhood consequences of maternal obesity and excessive weight gain during pregnancy.

PubMed

Gaillard, Romy; Felix, Janine F; Duijts, Liesbeth; Jaddoe, Vincent W V

2014-11-01

Obesity is a major public health concern. In western countries, the prevalence of obesity in pregnant women has strongly increased, with reported prevalence rates reaching 30%. Also, up to 40% of women gain an excessive amount of weight during pregnancy. Recent observational studies and meta-analyses strongly suggest long-term impact of maternal obesity and excessive weight gain during pregnancy on adiposity, cardiovascular and respiratory related health outcomes in their children. These observations suggest that maternal adiposity during pregnancy may program common health problems in the offspring. Currently, it remains unclear whether the observed associations are causal, or just reflect confounding by family-based sociodemographic or lifestyle-related factors. Parent-offspring studies, sibling comparison studies, Mendelian randomization studies and randomized trials can help to explore the causality and underlying mechanisms. Also, the potential for prevention of common diseases in future generations by reducing maternal obesity and excessive weight gain during pregnancy needs to be explored. © 2014 Nordic Federation of Societies of Obstetrics and Gynecology.

Layout decomposition of self-aligned double patterning for 2D random logic patterning

NASA Astrophysics Data System (ADS)

Ban, Yongchan; Miloslavsky, Alex; Lucas, Kevin; Choi, Soo-Han; Park, Chul-Hong; Pan, David Z.

2011-04-01

Self-aligned double pattering (SADP) has been adapted as a promising solution for sub-30nm technology nodes due to its lower overlay problem and better process tolerance. SADP is in production use for 1D dense patterns with good pitch control such as NAND Flash memory applications, but it is still challenging to apply SADP to 2D random logic patterns. The favored type of SADP for complex logic interconnects is a two mask approach using a core mask and a trim mask. In this paper, we first describe layout decomposition methods of spacer-type double patterning lithography, then report a type of SADP compliant layouts, and finally report SADP applications on Samsung 22nm SRAM layout. For SADP decomposition, we propose several SADP-aware layout coloring algorithms and a method of generating lithography-friendly core mask patterns. Experimental results on 22nm node designs show that our proposed layout decomposition for SADP effectively decomposes any given layouts.

The Effects of Schema-Broadening Instruction on Second Graders’ Word-Problem Performance and Their Ability to Represent Word Problems with Algebraic Equations: A Randomized Control Study

PubMed Central

Fuchs, Lynn S.; Zumeta, Rebecca O.; Schumacher, Robin Finelli; Powell, Sarah R.; Seethaler, Pamela M.; Hamlett, Carol L.; Fuchs, Douglas

2010-01-01

The purpose of this study was to assess the effects of schema-broadening instruction (SBI) on second graders’ word-problem-solving skills and their ability to represent the structure of word problems using algebraic equations. Teachers (n = 18) were randomly assigned to conventional word-problem instruction or SBI word-problem instruction, which taught students to represent the structural, defining features of word problems with overarching equations. Intervention lasted 16 weeks. We pretested and posttested 270 students on measures of word-problem skill; analyses that accounted for the nested structure of the data indicated superior word-problem learning for SBI students. Descriptive analyses of students’ word-problem work indicated that SBI helped students represent the structure of word problems with algebraic equations, suggesting that SBI promoted this aspect of students’ emerging algebraic reasoning. PMID:20539822

Problem decomposition by mutual information and force-based clustering

NASA Astrophysics Data System (ADS)

Otero, Richard Edward

The scale of engineering problems has sharply increased over the last twenty years. Larger coupled systems, increasing complexity, and limited resources create a need for methods that automatically decompose problems into manageable sub-problems by discovering and leveraging problem structure. The ability to learn the coupling (inter-dependence) structure and reorganize the original problem could lead to large reductions in the time to analyze complex problems. Such decomposition methods could also provide engineering insight on the fundamental physics driving problem solution. This work forwards the current state of the art in engineering decomposition through the application of techniques originally developed within computer science and information theory. The work describes the current state of automatic problem decomposition in engineering and utilizes several promising ideas to advance the state of the practice. Mutual information is a novel metric for data dependence and works on both continuous and discrete data. Mutual information can measure both the linear and non-linear dependence between variables without the limitations of linear dependence measured through covariance. Mutual information is also able to handle data that does not have derivative information, unlike other metrics that require it. The value of mutual information to engineering design work is demonstrated on a planetary entry problem. This study utilizes a novel tool developed in this work for planetary entry system synthesis. A graphical method, force-based clustering, is used to discover related sub-graph structure as a function of problem structure and links ranked by their mutual information. This method does not require the stochastic use of neural networks and could be used with any link ranking method currently utilized in the field. Application of this method is demonstrated on a large, coupled low-thrust trajectory problem. Mutual information also serves as the basis for an alternative global optimizer, called MIMIC, which is unrelated to Genetic Algorithms. Advancement to the current practice demonstrates the use of MIMIC as a global method that explicitly models problem structure with mutual information, providing an alternate method for globally searching multi-modal domains. By leveraging discovered problem inter- dependencies, MIMIC may be appropriate for highly coupled problems or those with large function evaluation cost. This work introduces a useful addition to the MIMIC algorithm that enables its use on continuous input variables. By leveraging automatic decision tree generation methods from Machine Learning and a set of randomly generated test problems, decision trees for which method to apply are also created, quantifying decomposition performance over a large region of the design space.

Advanced Numerical Methods for Computing Statistical Quantities of Interest from Solutions of SPDES

DTIC Science & Technology

2012-01-19

and related optimization problems; developing numerical methods for option pricing problems in the presence of random arbitrage return. 1. Novel...equations (BSDEs) are connected to nonlinear partial differen- tial equations and non-linear semigroups, to the theory of hedging and pricing of contingent...the presence of random arbitrage return [3] We consider option pricing problems when we relax the condition of no arbitrage in the Black- Scholes

A Micro-Computer Model for Army Air Defense Training.

DTIC Science & Technology

1985-03-01

generator. The period is 32763 numbers generated before a repetitive sequence is encountered on the development system. Chi-Squared tests for frequency...C’ Tests CPeriodicity. The period is 32763 numbers generated C’before a repetitive sequence is encountered on the development system. This was...positions in the test array. This was done with several different random number seeds. In each case 32763 p random numbers were generated before a

Experimentally Generated Random Numbers Certified by the Impossibility of Superluminal Signaling

NASA Astrophysics Data System (ADS)

Bierhorst, Peter; Shalm, Lynden K.; Mink, Alan; Jordan, Stephen; Liu, Yi-Kai; Rommal, Andrea; Glancy, Scott; Christensen, Bradley; Nam, Sae Woo; Knill, Emanuel

Random numbers are an important resource for applications such as numerical simulation and secure communication. However, it is difficult to certify whether a physical random number generator is truly unpredictable. Here, we exploit the phenomenon of quantum nonlocality in a loophole-free photonic Bell test experiment to obtain data containing randomness that cannot be predicted by any theory that does not also allow the sending of signals faster than the speed of light. To certify and quantify the randomness, we develop a new protocol that performs well in an experimental regime characterized by low violation of Bell inequalities. Applying an extractor function to our data, we obtain 256 new random bits, uniform to within 10- 3 .

Random pulse generator

NASA Technical Reports Server (NTRS)

Lindsey, R. S., Jr. (Inventor)

1975-01-01

An exemplary embodiment of the present invention provides a source of random width and random spaced rectangular voltage pulses whose mean or average frequency of operation is controllable within prescribed limits of about 10 hertz to 1 megahertz. A pair of thin-film metal resistors are used to provide a differential white noise voltage pulse source. Pulse shaping and amplification circuitry provide relatively short duration pulses of constant amplitude which are applied to anti-bounce logic circuitry to prevent ringing effects. The pulse outputs from the anti-bounce circuits are then used to control two one-shot multivibrators whose output comprises the random length and random spaced rectangular pulses. Means are provided for monitoring, calibrating and evaluating the relative randomness of the generator.

Development of a Probabilistic Dynamic Synthesis Method for the Analysis of Nondeterministic Structures

NASA Technical Reports Server (NTRS)

Brown, A. M.

1998-01-01

Accounting for the statistical geometric and material variability of structures in analysis has been a topic of considerable research for the last 30 years. The determination of quantifiable measures of statistical probability of a desired response variable, such as natural frequency, maximum displacement, or stress, to replace experience-based "safety factors" has been a primary goal of these studies. There are, however, several problems associated with their satisfactory application to realistic structures, such as bladed disks in turbomachinery. These include the accurate definition of the input random variables (rv's), the large size of the finite element models frequently used to simulate these structures, which makes even a single deterministic analysis expensive, and accurate generation of the cumulative distribution function (CDF) necessary to obtain the probability of the desired response variables. The research presented here applies a methodology called probabilistic dynamic synthesis (PDS) to solve these problems. The PDS method uses dynamic characteristics of substructures measured from modal test as the input rv's, rather than "primitive" rv's such as material or geometric uncertainties. These dynamic characteristics, which are the free-free eigenvalues, eigenvectors, and residual flexibility (RF), are readily measured and for many substructures, a reasonable sample set of these measurements can be obtained. The statistics for these rv's accurately account for the entire random character of the substructure. Using the RF method of component mode synthesis, these dynamic characteristics are used to generate reduced-size sample models of the substructures, which are then coupled to form system models. These sample models are used to obtain the CDF of the response variable by either applying Monte Carlo simulation or by generating data points for use in the response surface reliability method, which can perform the probabilistic analysis with an order of magnitude less computational effort. Both free- and forced-response analyses have been performed, and the results indicate that, while there is considerable room for improvement, the method produces usable and more representative solutions for the design of realistic structures with a substantial savings in computer time.

True Randomness from Big Data.

PubMed

Papakonstantinou, Periklis A; Woodruff, David P; Yang, Guang

2016-09-26

Generating random bits is a difficult task, which is important for physical systems simulation, cryptography, and many applications that rely on high-quality random bits. Our contribution is to show how to generate provably random bits from uncertain events whose outcomes are routinely recorded in the form of massive data sets. These include scientific data sets, such as in astronomics, genomics, as well as data produced by individuals, such as internet search logs, sensor networks, and social network feeds. We view the generation of such data as the sampling process from a big source, which is a random variable of size at least a few gigabytes. Our view initiates the study of big sources in the randomness extraction literature. Previous approaches for big sources rely on statistical assumptions about the samples. We introduce a general method that provably extracts almost-uniform random bits from big sources and extensively validate it empirically on real data sets. The experimental findings indicate that our method is efficient enough to handle large enough sources, while previous extractor constructions are not efficient enough to be practical. Quality-wise, our method at least matches quantum randomness expanders and classical world empirical extractors as measured by standardized tests.

True Randomness from Big Data

NASA Astrophysics Data System (ADS)

Papakonstantinou, Periklis A.; Woodruff, David P.; Yang, Guang

2016-09-01

Generating random bits is a difficult task, which is important for physical systems simulation, cryptography, and many applications that rely on high-quality random bits. Our contribution is to show how to generate provably random bits from uncertain events whose outcomes are routinely recorded in the form of massive data sets. These include scientific data sets, such as in astronomics, genomics, as well as data produced by individuals, such as internet search logs, sensor networks, and social network feeds. We view the generation of such data as the sampling process from a big source, which is a random variable of size at least a few gigabytes. Our view initiates the study of big sources in the randomness extraction literature. Previous approaches for big sources rely on statistical assumptions about the samples. We introduce a general method that provably extracts almost-uniform random bits from big sources and extensively validate it empirically on real data sets. The experimental findings indicate that our method is efficient enough to handle large enough sources, while previous extractor constructions are not efficient enough to be practical. Quality-wise, our method at least matches quantum randomness expanders and classical world empirical extractors as measured by standardized tests.

True Randomness from Big Data

PubMed Central

Papakonstantinou, Periklis A.; Woodruff, David P.; Yang, Guang

2016-01-01

Generating random bits is a difficult task, which is important for physical systems simulation, cryptography, and many applications that rely on high-quality random bits. Our contribution is to show how to generate provably random bits from uncertain events whose outcomes are routinely recorded in the form of massive data sets. These include scientific data sets, such as in astronomics, genomics, as well as data produced by individuals, such as internet search logs, sensor networks, and social network feeds. We view the generation of such data as the sampling process from a big source, which is a random variable of size at least a few gigabytes. Our view initiates the study of big sources in the randomness extraction literature. Previous approaches for big sources rely on statistical assumptions about the samples. We introduce a general method that provably extracts almost-uniform random bits from big sources and extensively validate it empirically on real data sets. The experimental findings indicate that our method is efficient enough to handle large enough sources, while previous extractor constructions are not efficient enough to be practical. Quality-wise, our method at least matches quantum randomness expanders and classical world empirical extractors as measured by standardized tests. PMID:27666514

Source-Device-Independent Ultrafast Quantum Random Number Generation.

PubMed

Marangon, Davide G; Vallone, Giuseppe; Villoresi, Paolo

2017-02-10

Secure random numbers are a fundamental element of many applications in science, statistics, cryptography and more in general in security protocols. We present a method that enables the generation of high-speed unpredictable random numbers from the quadratures of an electromagnetic field without any assumption on the input state. The method allows us to eliminate the numbers that can be predicted due to the presence of classical and quantum side information. In particular, we introduce a procedure to estimate a bound on the conditional min-entropy based on the entropic uncertainty principle for position and momentum observables of infinite dimensional quantum systems. By the above method, we experimentally demonstrated the generation of secure true random bits at a rate greater than 1.7 Gbit/s.

Random motor generation in a finger tapping task: influence of spatial contingency and of cortical and subcortical hemispheric brain lesions

PubMed Central

Annoni, J.; Pegna, A.

1997-01-01

OBJECTIVE—To test the hypothesis that, during random motor generation, the spatial contingencies inherent to the task would induce additional preferences in normal subjects, shifting their performances farther from randomness. By contrast, perceptual or executive dysfunction could alter these task related biases in patients with brain damage.
METHODS—Two groups of patients, with right and left focal brain lesions, as well as 25 right handed subjects matched for age and handedness were asked to execute a random choice motor task—namely, to generate a random series of 180 button presses from a set of 10 keys placed vertically in front of them.
RESULTS—In the control group, as in the left brain lesion group, motor generation was subject to deviations from theoretical expected randomness, similar to those when numbers are generated mentally, as immediate repetitions (successive presses on the same key) are avoided. However, the distribution of button presses was also contingent on the topographic disposition of the keys: the central keys were chosen more often than those placed at extreme positions. Small distances were favoured, particularly with the left hand. These patterns were influenced by implicit strategies and task related contingencies.
 By contrast, right brain lesion patients with frontal involvement tended to show a more square distribution of key presses—that is, the number of key presses tended to be more equally distributed. The strategies were also altered by brain lesions: the number of immediate repetitions was more frequent when the lesion involved the right frontal areas yielding a random generation nearer to expected theoretical randomness. The frequency of adjacent key presses was increased by right anterior and left posterior cortical as well as by right subcortical lesions, but decreased by left subcortical lesions.
CONCLUSIONS—Depending on the side of the lesion and the degree of cortical-subcortical involvement, the deficits take on a different aspect and direct repetions and adjacent key presses have different patterns of alterations. Motor random generation is therefore a complex task which seems to necessitate the participation of numerous cerebral structures, among which those situated in the right frontal, left posterior, and subcortical regions have a predominant role.

 PMID:9408109

Demonstration of Numerical Equivalence of Ensemble and Spectral Averaging in Electromagnetic Scattering by Random Particulate Media

NASA Technical Reports Server (NTRS)

Mishchenko, Michael I.; Dlugach, Janna M.; Zakharova, Nadezhda T.

2016-01-01

The numerically exact superposition T-matrix method is used to model far-field electromagnetic scattering by two types of particulate object. Object 1 is a fixed configuration which consists of N identical spherical particles (with N 200 or 400) quasi-randomly populating a spherical volume V having a median size parameter of 50. Object 2 is a true discrete random medium (DRM) comprising the same number N of particles randomly moving throughout V. The median particle size parameter is fixed at 4. We show that if Object 1 is illuminated by a quasi-monochromatic parallel beam then it generates a typical speckle pattern having no resemblance to the scattering pattern generated by Object 2. However, if Object 1 is illuminated by a parallel polychromatic beam with a 10 bandwidth then it generates a scattering pattern that is largely devoid of speckles and closely reproduces the quasi-monochromatic pattern generated by Object 2. This result serves to illustrate the capacity of the concept of electromagnetic scattering by a DRM to encompass fixed quasi-random particulate samples provided that they are illuminated by polychromatic light.

Advances in plant gene-targeted and functional markers: a review

PubMed Central

2013-01-01

Public genomic databases have provided new directions for molecular marker development and initiated a shift in the types of PCR-based techniques commonly used in plant science. Alongside commonly used arbitrarily amplified DNA markers, other methods have been developed. Targeted fingerprinting marker techniques are based on the well-established practices of arbitrarily amplified DNA methods, but employ novel methodological innovations such as the incorporation of gene or promoter elements in the primers. These markers provide good reproducibility and increased resolution by the concurrent incidence of dominant and co-dominant bands. Despite their promising features, these semi-random markers suffer from possible problems of collision and non-homology analogous to those found with randomly generated fingerprints. Transposable elements, present in abundance in plant genomes, may also be used to generate fingerprints. These markers provide increased genomic coverage by utilizing specific targeted sites and produce bands that mostly seem to be homologous. The biggest drawback with most of these techniques is that prior genomic information about retrotransposons is needed for primer design, prohibiting universal applications. Another class of recently developed methods exploits length polymorphism present in arrays of multi-copy gene families such as cytochrome P450 and β-tubulin genes to provide cross-species amplification and transferability. A specific class of marker makes use of common features of plant resistance genes to generate bands linked to a given phenotype, or to reveal genetic diversity. Conserved DNA-based strategies have limited genome coverage and may fail to reveal genetic diversity, while resistance genes may be under specific evolutionary selection. Markers may also be generated from functional and/or transcribed regions of the genome using different gene-targeting approaches coupled with the use of RNA information. Such techniques have the potential to generate phenotypically linked functional markers, especially when fingerprints are generated from the transcribed or expressed region of the genome. It is to be expected that these recently developed techniques will generate larger datasets, but their shortcomings should also be acknowledged and carefully investigated. PMID:23406322

N-state random switching based on quantum tunnelling

NASA Astrophysics Data System (ADS)

Bernardo Gavito, Ramón; Jiménez Urbanos, Fernando; Roberts, Jonathan; Sexton, James; Astbury, Benjamin; Shokeir, Hamzah; McGrath, Thomas; Noori, Yasir J.; Woodhead, Christopher S.; Missous, Mohamed; Roedig, Utz; Young, Robert J.

2017-08-01

In this work, we show how the hysteretic behaviour of resonant tunnelling diodes (RTDs) can be exploited for new functionalities. In particular, the RTDs exhibit a stochastic 2-state switching mechanism that could be useful for random number generation and cryptographic applications. This behaviour can be scaled to N-bit switching, by connecting various RTDs in series. The InGaAs/AlAs RTDs used in our experiments display very sharp negative differential resistance (NDR) peaks at room temperature which show hysteresis cycles that, rather than having a fixed switching threshold, show a probability distribution about a central value. We propose to use this intrinsic uncertainty emerging from the quantum nature of the RTDs as a source of randomness. We show that a combination of two RTDs in series results in devices with three-state outputs and discuss the possibility of scaling to N-state devices by subsequent series connections of RTDs, which we demonstrate for the up to the 4-state case. In this work, we suggest using that the intrinsic uncertainty in the conduction paths of resonant tunnelling diodes can behave as a source of randomness that can be integrated into current electronics to produce on-chip true random number generators. The N-shaped I-V characteristic of RTDs results in a two-level random voltage output when driven with current pulse trains. Electrical characterisation and randomness testing of the devices was conducted in order to determine the validity of the true randomness assumption. Based on the results obtained for the single RTD case, we suggest the possibility of using multi-well devices to generate N-state random switching devices for their use in random number generation or multi-valued logic devices.

Fatigue crack growth model RANDOM2 user manual, appendix 1

NASA Technical Reports Server (NTRS)

Boyce, Lola; Lovelace, Thomas B.

1989-01-01

The FORTRAN program RANDOM2 is documented. RANDOM2 is based on fracture mechanics using a probabilistic fatigue crack growth model. It predicts the random lifetime of an engine component to reach a given crack size. Included in this user manual are details regarding the theoretical background of RANDOM2, input data, instructions and a sample problem illustrating the use of RANDOM2. Appendix A gives information on the physical quantities, their symbols, FORTRAN names, and both SI and U.S. Customary units. Appendix B includes photocopies of the actual computer printout corresponding to the sample problem. Appendices C and D detail the IMSL, Ver. 10(1), subroutines and functions called by RANDOM2 and a SAS/GRAPH(2) program that can be used to plot both the probability density function (p.d.f.) and the cumulative distribution function (c.d.f.).

Knowledge-based design of generate-and-patch problem solvers that solve global resource assignment problems

NASA Technical Reports Server (NTRS)

Voigt, Kerstin

1992-01-01

We present MENDER, a knowledge based system that implements software design techniques that are specialized to automatically compile generate-and-patch problem solvers that satisfy global resource assignments problems. We provide empirical evidence of the superior performance of generate-and-patch over generate-and-test: even with constrained generation, for a global constraint in the domain of '2D-floorplanning'. For a second constraint in '2D-floorplanning' we show that even when it is possible to incorporate the constraint into a constrained generator, a generate-and-patch problem solver may satisfy the constraint more rapidly. We also briefly summarize how an extended version of our system applies to a constraint in the domain of 'multiprocessor scheduling'.

Very late stent thrombosis with second generation drug eluting stents compared to bare metal stents: Network meta-analysis of randomized primary percutaneous coronary intervention trials.

PubMed

Philip, Femi; Stewart, Susan; Southard, Jeffrey A

2016-07-01

The relative safety of drug-eluting stents (DES) and bare-metal stents (BMS) in primary percutaneous coronary intervention (PPCI) in ST elevation myocardial infarction (STEMI) continues to be debated. The long-term clinical outcomes between second generation DES and BMS for primary percutaneous coronary intervention (PCI) using network meta-analysis were compared. Randomized controlled trials comparing stent types (first generation DES, second generation DES, or BMS) were considered for inclusion. A search strategy used Medline, Embase, Cochrane databases, and proceedings of international meetings. Information about study design, inclusion criteria, and sample characteristics were extracted. Network meta-analysis was used to pool direct (comparison of second generation DES to BMS) and indirect evidence (first generation DES with BMS and second generation DES) from the randomized trials. Twelve trials comparing all stents types including 9,673 patients randomly assigned to treatment groups were analyzed. Second generation DES was associated with significantly lower incidence of definite or probable ST (OR 0.59, 95% CI 0.39-0.89), MI (OR 0.59, 95% CI 0.39-0.89), and TVR at 3 years (OR 0.50: 95% CI 0.31-0.81) compared with BMS. In addition, there was a significantly lower incidence of MACE with second generation DES versus BMS (OR 0.54, 95% CI 0.34-0.74) at 3 years. These were driven by a higher rate of TVR, MI and stent thrombosis in the BMS group at 3 years. There was a non-significant reduction in the overall and cardiac mortality [OR 0.83, 95% CI (0.60-1.14), OR 0.88, 95% CI (0.6-1.28)] with the use of second generation DES versus BMS at 3 years. Network meta-analysis of randomized trials of primary PCI demonstrated lower incidence of MACE, MI, TVR, and stent thrombosis with second generation DES compared with BMS. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Constructing high complexity synthetic libraries of long ORFs using in vitro selection

NASA Technical Reports Server (NTRS)

Cho, G.; Keefe, A. D.; Liu, R.; Wilson, D. S.; Szostak, J. W.

2000-01-01

We present a method that can significantly increase the complexity of protein libraries used for in vitro or in vivo protein selection experiments. Protein libraries are often encoded by chemically synthesized DNA, in which part of the open reading frame is randomized. There are, however, major obstacles associated with the chemical synthesis of long open reading frames, especially those containing random segments. Insertions and deletions that occur during chemical synthesis cause frameshifts, and stop codons in the random region will cause premature termination. These problems can together greatly reduce the number of full-length synthetic genes in the library. We describe a strategy in which smaller segments of the synthetic open reading frame are selected in vitro using mRNA display for the absence of frameshifts and stop codons. These smaller segments are then ligated together to form combinatorial libraries of long uninterrupted open reading frames. This process can increase the number of full-length open reading frames in libraries by up to two orders of magnitude, resulting in protein libraries with complexities of greater than 10(13). We have used this methodology to generate three types of displayed protein library: a completely random sequence library, a library of concatemerized oligopeptide cassettes with a propensity for forming amphipathic alpha-helical or beta-strand structures, and a library based on one of the most common enzymatic scaffolds, the alpha/beta (TIM) barrel. Copyright 2000 Academic Press.

Electric and magnetic microfields inside and outside space-limited configurations of ions and ionic currents

NASA Astrophysics Data System (ADS)

Romanovsky, M. Yu; Ebeling, W.; Schimansky-Geier, L.

2005-01-01

The problem of electric and magnetic microfields inside finite spherical systems of stochastically moving ions and outside them is studied. The first possible field of applications is high temperature ion clusters created by laser fields [1]. Other possible applications are nearly spherical liquid systems at room-temperature containing electrolytes. Looking for biological applications we may also think about a cell which is a complicated electrolytic system or even a brain which is a still more complicated system of electrolytic currents. The essential model assumption is the random character of charges motion. We assume in our basic model that we have a finite nearly spherical system of randomly moving charges. Even taking into account that this is at best a caricature of any real system, it might be of interest as a limiting case, which admits a full theoretical treatment. For symmetry reasons, a random configuration of moving charges cannot generate a macroscopic magnetic field, but there will be microscopic fluctuating magnetic fields. Distributions for electric and magnetic microfields inside and outside such space- limited systems are calculated. Spherical systems of randomly distributed moving charges are investigated. Starting from earlier results for infinitely large systems, which lead to Holtsmark- type distributions, we show that the fluctuations in finite charge distributions are larger (in comparison to infinite systems of the same charge density).

A Comparative Study of Randomized Constraint Solvers for Random-Symbolic Testing

NASA Technical Reports Server (NTRS)

Takaki, Mitsuo; Cavalcanti, Diego; Gheyi, Rohit; Iyoda, Juliano; dAmorim, Marcelo; Prudencio, Ricardo

2009-01-01

The complexity of constraints is a major obstacle for constraint-based software verification. Automatic constraint solvers are fundamentally incomplete: input constraints often build on some undecidable theory or some theory the solver does not support. This paper proposes and evaluates several randomized solvers to address this issue. We compare the effectiveness of a symbolic solver (CVC3), a random solver, three hybrid solvers (i.e., mix of random and symbolic), and two heuristic search solvers. We evaluate the solvers on two benchmarks: one consisting of manually generated constraints and another generated with a concolic execution of 8 subjects. In addition to fully decidable constraints, the benchmarks include constraints with non-linear integer arithmetic, integer modulo and division, bitwise arithmetic, and floating-point arithmetic. As expected symbolic solving (in particular, CVC3) subsumes the other solvers for the concolic execution of subjects that only generate decidable constraints. For the remaining subjects the solvers are complementary.

Megahertz-Rate Semi-Device-Independent Quantum Random Number Generators Based on Unambiguous State Discrimination

NASA Astrophysics Data System (ADS)

Brask, Jonatan Bohr; Martin, Anthony; Esposito, William; Houlmann, Raphael; Bowles, Joseph; Zbinden, Hugo; Brunner, Nicolas

2017-05-01

An approach to quantum random number generation based on unambiguous quantum state discrimination is developed. We consider a prepare-and-measure protocol, where two nonorthogonal quantum states can be prepared, and a measurement device aims at unambiguously discriminating between them. Because the states are nonorthogonal, this necessarily leads to a minimal rate of inconclusive events whose occurrence must be genuinely random and which provide the randomness source that we exploit. Our protocol is semi-device-independent in the sense that the output entropy can be lower bounded based on experimental data and a few general assumptions about the setup alone. It is also practically relevant, which we demonstrate by realizing a simple optical implementation, achieving rates of 16.5 Mbits /s . Combining ease of implementation, a high rate, and a real-time entropy estimation, our protocol represents a promising approach intermediate between fully device-independent protocols and commercial quantum random number generators.

Secure self-calibrating quantum random-bit generator

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fiorentino, M.; Santori, C.; Spillane, S. M.

2007-03-15

Random-bit generators (RBGs) are key components of a variety of information processing applications ranging from simulations to cryptography. In particular, cryptographic systems require 'strong' RBGs that produce high-entropy bit sequences, but traditional software pseudo-RBGs have very low entropy content and therefore are relatively weak for cryptography. Hardware RBGs yield entropy from chaotic or quantum physical systems and therefore are expected to exhibit high entropy, but in current implementations their exact entropy content is unknown. Here we report a quantum random-bit generator (QRBG) that harvests entropy by measuring single-photon and entangled two-photon polarization states. We introduce and implement a quantum tomographicmore » method to measure a lower bound on the 'min-entropy' of the system, and we employ this value to distill a truly random-bit sequence. This approach is secure: even if an attacker takes control of the source of optical states, a secure random sequence can be distilled.« less

The random energy model in a magnetic field and joint source channel coding

NASA Astrophysics Data System (ADS)

Merhav, Neri

2008-09-01

We demonstrate that there is an intimate relationship between the magnetic properties of Derrida’s random energy model (REM) of spin glasses and the problem of joint source-channel coding in Information Theory. In particular, typical patterns of erroneously decoded messages in the coding problem have “magnetization” properties that are analogous to those of the REM in certain phases, where the non-uniformity of the distribution of the source in the coding problem plays the role of an external magnetic field applied to the REM. We also relate the ensemble performance (random coding exponents) of joint source-channel codes to the free energy of the REM in its different phases.

40 CFR 761.308 - Sample selection by random number generation on any two-dimensional square grid.

Code of Federal Regulations, 2013 CFR

2013-07-01

... generation on any two-dimensional square grid. 761.308 Section 761.308 Protection of Environment... § 761.79(b)(3) § 761.308 Sample selection by random number generation on any two-dimensional square grid. (a) Divide the surface area of the non-porous surface into rectangular or square areas having a...

40 CFR 761.308 - Sample selection by random number generation on any two-dimensional square grid.

Code of Federal Regulations, 2011 CFR

2011-07-01

... generation on any two-dimensional square grid. 761.308 Section 761.308 Protection of Environment... § 761.79(b)(3) § 761.308 Sample selection by random number generation on any two-dimensional square grid. (a) Divide the surface area of the non-porous surface into rectangular or square areas having a...

40 CFR 761.308 - Sample selection by random number generation on any two-dimensional square grid.

Code of Federal Regulations, 2010 CFR

2010-07-01

... generation on any two-dimensional square grid. 761.308 Section 761.308 Protection of Environment... § 761.79(b)(3) § 761.308 Sample selection by random number generation on any two-dimensional square grid. (a) Divide the surface area of the non-porous surface into rectangular or square areas having a...

40 CFR 761.308 - Sample selection by random number generation on any two-dimensional square grid.

Code of Federal Regulations, 2014 CFR

2014-07-01

... generation on any two-dimensional square grid. 761.308 Section 761.308 Protection of Environment... § 761.79(b)(3) § 761.308 Sample selection by random number generation on any two-dimensional square grid. (a) Divide the surface area of the non-porous surface into rectangular or square areas having a...

40 CFR 761.308 - Sample selection by random number generation on any two-dimensional square grid.

Code of Federal Regulations, 2012 CFR

2012-07-01

... generation on any two-dimensional square grid. 761.308 Section 761.308 Protection of Environment... § 761.79(b)(3) § 761.308 Sample selection by random number generation on any two-dimensional square grid. (a) Divide the surface area of the non-porous surface into rectangular or square areas having a...

Accelerating Pseudo-Random Number Generator for MCNP on GPU

NASA Astrophysics Data System (ADS)

Gong, Chunye; Liu, Jie; Chi, Lihua; Hu, Qingfeng; Deng, Li; Gong, Zhenghu

2010-09-01

Pseudo-random number generators (PRNG) are intensively used in many stochastic algorithms in particle simulations, artificial neural networks and other scientific computation. The PRNG in Monte Carlo N-Particle Transport Code (MCNP) requires long period, high quality, flexible jump and fast enough. In this paper, we implement such a PRNG for MCNP on NVIDIA's GTX200 Graphics Processor Units (GPU) using CUDA programming model. Results shows that 3.80 to 8.10 times speedup are achieved compared with 4 to 6 cores CPUs and more than 679.18 million double precision random numbers can be generated per second on GPU.

Behavioral Family Intervention for Children with Developmental Disabilities and Behavioral Problems

ERIC Educational Resources Information Center

Roberts, Clare; Mazzucchelli, Trevor; Studman, Lisa; Sanders, Matthew R.

2006-01-01

The outcomes of a randomized clinical trial of a new behavioral family intervention, Stepping Stones Triple P, for preschoolers with developmental and behavior problems are presented. Forty-eight children with developmental disabilities participated, 27 randomly allocated to an intervention group and 20 to a wait-list control group. Parents…

General stochastic variational formulation for the oligopolistic market equilibrium problem with excesses

NASA Astrophysics Data System (ADS)

Barbagallo, Annamaria; Di Meglio, Guglielmo; Mauro, Paolo

2017-07-01

The aim of the paper is to study, in a Hilbert space setting, a general random oligopolistic market equilibrium problem in presence of both production and demand excesses and to characterize the random Cournot-Nash equilibrium principle by means of a stochastic variational inequality. Some existence results are presented.

Effectiveness of a Parent Training Program in (Pre)Adolescence: Evidence from a Randomized Controlled Trial

ERIC Educational Resources Information Center

Leijten, Patty; Overbeek, Geertjan; Janssens, Jan M. A. M.

2012-01-01

The present randomized controlled trial examined the effectiveness of the parent training program Parents and Children Talking Together (PCTT) for parents with children in the preadolescent period who experience parenting difficulties. The program is focused on reducing child problem behavior by improving parents' communication and problem solving…

Digital-Analog Hybrid Scheme and Its Application to Chaotic Random Number Generators

NASA Astrophysics Data System (ADS)

Yuan, Zeshi; Li, Hongtao; Miao, Yunchi; Hu, Wen; Zhu, Xiaohua

2017-12-01

Practical random number generation (RNG) circuits are typically achieved with analog devices or digital approaches. Digital-based techniques, which use field programmable gate array (FPGA) and graphics processing units (GPU) etc. usually have better performances than analog methods as they are programmable, efficient and robust. However, digital realizations suffer from the effect of finite precision. Accordingly, the generated random numbers (RNs) are actually periodic instead of being real random. To tackle this limitation, in this paper we propose a novel digital-analog hybrid scheme that employs the digital unit as the main body, and minimum analog devices to generate physical RNs. Moreover, the possibility of realizing the proposed scheme with only one memory element is discussed. Without loss of generality, we use the capacitor and the memristor along with FPGA to construct the proposed hybrid system, and a chaotic true random number generator (TRNG) circuit is realized, producing physical RNs at a throughput of Gbit/s scale. These RNs successfully pass all the tests in the NIST SP800-22 package, confirming the significance of the scheme in practical applications. In addition, the use of this new scheme is not restricted to RNGs, and it also provides a strategy to solve the effect of finite precision in other digital systems.

The Development of an Internet-Based Treatment for Problem Gamblers and Concerned Significant Others: A Pilot Randomized Controlled Trial.

PubMed

Nilsson, Anders; Magnusson, Kristoffer; Carlbring, Per; Andersson, Gerhard; Gumpert, Clara Hellner

2018-06-01

Problem gambling creates significant harm for the gambler and for concerned significant others (CSOs). While several studies have investigated the effects of individual cognitive behavioral therapy (CBT) for problem gambling, less is known about the effects of involving CSOs in treatment. Behavioral couples therapy (BCT) has shown promising results when working with substance use disorders by involving both the user and a CSO. This pilot study investigated BCT for problem gambling, as well as the feasibility of performing a larger scale randomized controlled trial. 36 participants, 18 gamblers and 18 CSOs, were randomized to either BCT or individual CBT for the gambler. Both interventions were Internet-delivered self-help interventions with therapist support. Both groups of gamblers improved on all outcome measures, but there were no differences between the groups. The CSOs in the BCT group lowered their scores on anxiety and depression more than the CSOs of those randomized to the individual CBT group did. The implications of the results and the feasibility of the trial are discussed.

Data Assimilation by Ensemble Kalman Filter during One-Dimensional Nonlinear Consolidation in Randomly Heterogeneous Highly Compressible Aquitards

NASA Astrophysics Data System (ADS)

Zapata Norberto, B.; Morales-Casique, E.; Herrera, G. S.

2017-12-01

Severe land subsidence due to groundwater extraction may occur in multiaquifer systems where highly compressible aquitards are present. The highly compressible nature of the aquitards leads to nonlinear consolidation where the groundwater flow parameters are stress-dependent. The case is further complicated by the heterogeneity of the hydrogeologic and geotechnical properties of the aquitards. We explore the effect of realistic vertical heterogeneity of hydrogeologic and geotechnical parameters on the consolidation of highly compressible aquitards by means of 1-D Monte Carlo numerical simulations. 2000 realizations are generated for each of the following parameters: hydraulic conductivity (K), compression index (Cc) and void ratio (e). The correlation structure, the mean and the variance for each parameter were obtained from a literature review about field studies in the lacustrine sediments of Mexico City. The results indicate that among the parameters considered, random K has the largest effect on the ensemble average behavior of the system. Random K leads to the largest variance (and therefore largest uncertainty) of total settlement, groundwater flux and time to reach steady state conditions. We further propose a data assimilation scheme by means of ensemble Kalman filter to estimate the ensemble mean distribution of K, pore-pressure and total settlement. We consider the case where pore-pressure measurements are available at given time intervals. We test our approach by generating a 1-D realization of K with exponential spatial correlation, and solving the nonlinear flow and consolidation problem. These results are taken as our "true" solution. We take pore-pressure "measurements" at different times from this "true" solution. The ensemble Kalman filter method is then employed to estimate ensemble mean distribution of K, pore-pressure and total settlement based on the sequential assimilation of these pore-pressure measurements. The ensemble-mean estimates from this procedure closely approximate those from the "true" solution. This procedure can be easily extended to other random variables such as compression index and void ratio.

Anonymous authenticated communications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Beaver, Cheryl L; Schroeppel, Richard C; Snyder, Lillian A

2007-06-19

A method of performing electronic communications between members of a group wherein the communications are authenticated as being from a member of the group and have not been altered, comprising: generating a plurality of random numbers; distributing in a digital medium the plurality of random numbers to the members of the group; publishing a hash value of contents of the digital medium; distributing to the members of the group public-key-encrypted messages each containing a same token comprising a random number; and encrypting a message with a key generated from the token and the plurality of random numbers.

Waste management with recourse: an inexact dynamic programming model containing fuzzy boundary intervals in objectives and constraints.

PubMed

Tan, Q; Huang, G H; Cai, Y P

2010-09-01

The existing inexact optimization methods based on interval-parameter linear programming can hardly address problems where coefficients in objective functions are subject to dual uncertainties. In this study, a superiority-inferiority-based inexact fuzzy two-stage mixed-integer linear programming (SI-IFTMILP) model was developed for supporting municipal solid waste management under uncertainty. The developed SI-IFTMILP approach is capable of tackling dual uncertainties presented as fuzzy boundary intervals (FuBIs) in not only constraints, but also objective functions. Uncertainties expressed as a combination of intervals and random variables could also be explicitly reflected. An algorithm with high computational efficiency was provided to solve SI-IFTMILP. SI-IFTMILP was then applied to a long-term waste management case to demonstrate its applicability. Useful interval solutions were obtained. SI-IFTMILP could help generate dynamic facility-expansion and waste-allocation plans, as well as provide corrective actions when anticipated waste management plans are violated. It could also greatly reduce system-violation risk and enhance system robustness through examining two sets of penalties resulting from variations in fuzziness and randomness. Moreover, four possible alternative models were formulated to solve the same problem; solutions from them were then compared with those from SI-IFTMILP. The results indicate that SI-IFTMILP could provide more reliable solutions than the alternatives. 2010 Elsevier Ltd. All rights reserved.

Markov random field based automatic image alignment for electron tomography.

PubMed

Amat, Fernando; Moussavi, Farshid; Comolli, Luis R; Elidan, Gal; Downing, Kenneth H; Horowitz, Mark

2008-03-01

We present a method for automatic full-precision alignment of the images in a tomographic tilt series. Full-precision automatic alignment of cryo electron microscopy images has remained a difficult challenge to date, due to the limited electron dose and low image contrast. These facts lead to poor signal to noise ratio (SNR) in the images, which causes automatic feature trackers to generate errors, even with high contrast gold particles as fiducial features. To enable fully automatic alignment for full-precision reconstructions, we frame the problem probabilistically as finding the most likely particle tracks given a set of noisy images, using contextual information to make the solution more robust to the noise in each image. To solve this maximum likelihood problem, we use Markov Random Fields (MRF) to establish the correspondence of features in alignment and robust optimization for projection model estimation. The resulting algorithm, called Robust Alignment and Projection Estimation for Tomographic Reconstruction, or RAPTOR, has not needed any manual intervention for the difficult datasets we have tried, and has provided sub-pixel alignment that is as good as the manual approach by an expert user. We are able to automatically map complete and partial marker trajectories and thus obtain highly accurate image alignment. Our method has been applied to challenging cryo electron tomographic datasets with low SNR from intact bacterial cells, as well as several plastic section and X-ray datasets.

NullSeq: A Tool for Generating Random Coding Sequences with Desired Amino Acid and GC Contents.

PubMed

Liu, Sophia S; Hockenberry, Adam J; Lancichinetti, Andrea; Jewett, Michael C; Amaral, Luís A N

2016-11-01

The existence of over- and under-represented sequence motifs in genomes provides evidence of selective evolutionary pressures on biological mechanisms such as transcription, translation, ligand-substrate binding, and host immunity. In order to accurately identify motifs and other genome-scale patterns of interest, it is essential to be able to generate accurate null models that are appropriate for the sequences under study. While many tools have been developed to create random nucleotide sequences, protein coding sequences are subject to a unique set of constraints that complicates the process of generating appropriate null models. There are currently no tools available that allow users to create random coding sequences with specified amino acid composition and GC content for the purpose of hypothesis testing. Using the principle of maximum entropy, we developed a method that generates unbiased random sequences with pre-specified amino acid and GC content, which we have developed into a python package. Our method is the simplest way to obtain maximally unbiased random sequences that are subject to GC usage and primary amino acid sequence constraints. Furthermore, this approach can easily be expanded to create unbiased random sequences that incorporate more complicated constraints such as individual nucleotide usage or even di-nucleotide frequencies. The ability to generate correctly specified null models will allow researchers to accurately identify sequence motifs which will lead to a better understanding of biological processes as well as more effective engineering of biological systems.

Frequent users of rural primary care: comparisons with randomly selected users.

PubMed

Mehl-Madrona, L E

1998-01-01

Frequent users of primary care have not been adequately characterized. The unique characteristics of this population was sought--why they come so often, what their care costs, and whether psychosocial factors play a role in their high utilization of health care. The billing system of a rural primary care clinic was used to find the frequency of visits for all patients attending the clinic for the previous 12 months. The 211 most frequent visitors were selected. A comparison group of 250 patients was drawn from the billing records using a random number generator. Charts were reviewed to compare diagnoses (by frequency), number of procedures, amount billed for care, amount received from those billings, number of psychotropic medications prescribed, and response to medication. A subgroup of each group was interviewed to confirm chart review findings and to inquire about personal reasons for coming to the clinic. Compared with patients who were random users, patients who were frequent users were more likely to come from the younger and older age groups, they averaged significantly more emergency department visits and visits to other specialists (P < 0.0001), and they had more mental health problems diagnosed (P < 0.01). Significantly more frequent users were insured by Medicaid and fewer were insured by Medicare. They had more detailed office visits and more laboratory tests. They received twice as much psychotherapy and had a higher percentage of problem-focused office visits. Chart audits and interviews of selected patients revealed that many nonmedical reasons were related to visits in addition to psychosocial stressors. Nonmedical factors are important among the most frequent users of a primary care clinic. Proposals to improve care for frequent users should consider the psychosocial needs of this population.

Compressive sensing based wireless sensor for structural health monitoring

NASA Astrophysics Data System (ADS)

Bao, Yuequan; Zou, Zilong; Li, Hui

2014-03-01

Data loss is a common problem for monitoring systems based on wireless sensors. Reliable communication protocols, which enhance communication reliability by repetitively transmitting unreceived packets, is one approach to tackle the problem of data loss. An alternative approach allows data loss to some extent and seeks to recover the lost data from an algorithmic point of view. Compressive sensing (CS) provides such a data loss recovery technique. This technique can be embedded into smart wireless sensors and effectively increases wireless communication reliability without retransmitting the data. The basic idea of CS-based approach is that, instead of transmitting the raw signal acquired by the sensor, a transformed signal that is generated by projecting the raw signal onto a random matrix, is transmitted. Some data loss may occur during the transmission of this transformed signal. However, according to the theory of CS, the raw signal can be effectively reconstructed from the received incomplete transformed signal given that the raw signal is compressible in some basis and the data loss ratio is low. This CS-based technique is implemented into the Imote2 smart sensor platform using the foundation of Illinois Structural Health Monitoring Project (ISHMP) Service Tool-suite. To overcome the constraints of limited onboard resources of wireless sensor nodes, a method called random demodulator (RD) is employed to provide memory and power efficient construction of the random sampling matrix. Adaptation of RD sampling matrix is made to accommodate data loss in wireless transmission and meet the objectives of the data recovery. The embedded program is tested in a series of sensing and communication experiments. Examples and parametric study are presented to demonstrate the applicability of the embedded program as well as to show the efficacy of CS-based data loss recovery for real wireless SHM systems.

Generation of Aptamers from A Primer-Free Randomized ssDNA Library Using Magnetic-Assisted Rapid Aptamer Selection

NASA Astrophysics Data System (ADS)

Tsao, Shih-Ming; Lai, Ji-Ching; Horng, Horng-Er; Liu, Tu-Chen; Hong, Chin-Yih

2017-04-01

Aptamers are oligonucleotides that can bind to specific target molecules. Most aptamers are generated using random libraries in the standard systematic evolution of ligands by exponential enrichment (SELEX). Each random library contains oligonucleotides with a randomized central region and two fixed primer regions at both ends. The fixed primer regions are necessary for amplifying target-bound sequences by PCR. However, these extra-sequences may cause non-specific bindings, which potentially interfere with good binding for random sequences. The Magnetic-Assisted Rapid Aptamer Selection (MARAS) is a newly developed protocol for generating single-strand DNA aptamers. No repeat selection cycle is required in the protocol. This study proposes and demonstrates a method to isolate aptamers for C-reactive proteins (CRP) from a randomized ssDNA library containing no fixed sequences at 5‧ and 3‧ termini using the MARAS platform. Furthermore, the isolated primer-free aptamer was sequenced and binding affinity for CRP was analyzed. The specificity of the obtained aptamer was validated using blind serum samples. The result was consistent with monoclonal antibody-based nephelometry analysis, which indicated that a primer-free aptamer has high specificity toward targets. MARAS is a feasible platform for efficiently generating primer-free aptamers for clinical diagnoses.

A Comparative Study of Random Patterns for Digital Image Correlation

NASA Astrophysics Data System (ADS)

Stoilov, G.; Kavardzhikov, V.; Pashkouleva, D.

2012-06-01

Digital Image Correlation (DIC) is a computer based image analysis technique utilizing random patterns, which finds applications in experimental mechanics of solids and structures. In this paper a comparative study of three simulated random patterns is done. One of them is generated according to a new algorithm, introduced by the authors. A criterion for quantitative evaluation of random patterns after the calculation of their autocorrelation functions is introduced. The patterns' deformations are simulated numerically and realized experimentally. The displacements are measured by using the DIC method. Tensile tests are performed after printing the generated random patterns on surfaces of standard iron sheet specimens. It is found that the new designed random pattern keeps relatively good quality until reaching 20% deformation.

Research on vehicle routing optimization for the terminal distribution of B2C E-commerce firms

NASA Astrophysics Data System (ADS)

Zhang, Shiyun; Lu, Yapei; Li, Shasha

2018-05-01

In this paper, we established a half open multi-objective optimization model for the vehicle routing problem of B2C (business-to-customer) E-Commerce firms. To minimize the current transport distance as well as the disparity between the excepted shipments and the transport capacity in the next distribution, we applied the concept of dominated solution and Pareto solutions to the standard particle swarm optimization and proposed a MOPSO (multi-objective particle swarm optimization) algorithm to support the model. Besides, we also obtained the optimization solution of MOPSO algorithm based on data randomly generated through the system, which verified the validity of the model.

Distorted estimates of implicit and explicit learning in applications of the process-dissociation procedure to the SRT task.

PubMed

Stahl, Christoph; Barth, Marius; Haider, Hilde

2015-12-01

We investigated potential biases affecting the validity of the process-dissociation (PD) procedure when applied to sequence learning. Participants were or were not exposed to a serial reaction time task (SRTT) with two types of pseudo-random materials. Afterwards, participants worked on a free or cued generation task under inclusion and exclusion instructions. Results showed that pre-experimental response tendencies, non-associative learning of location frequencies, and the usage of cue locations introduced bias to PD estimates. These biases may lead to erroneous conclusions regarding the presence of implicit and explicit knowledge. Potential remedies for these problems are discussed. Copyright © 2015 Elsevier Inc. All rights reserved.

Exploration Opportunity Search of Near-earth Objects Based on Analytical Gradients

NASA Astrophysics Data System (ADS)

Ren, Yuan; Cui, Ping-Yuan; Luan, En-Jie

2008-07-01

The problem of search of opportunity for the exploration of near-earth minor objects is investigated. For rendezvous missions, the analytical gradients of the performance index with respect to the free parameters are derived using the variational calculus and the theory of state-transition matrix. After generating randomly some initial guesses in the search space, the performance index is optimized, guided by the analytical gradients, leading to the local minimum points representing the potential launch opportunities. This method not only keeps the global-search property of the traditional method, but also avoids the blindness in the latter, thereby increasing greatly the computing speed. Furthermore, with this method, the searching precision could be controlled effectively.

Search of exploration opportunity for near earth objects based on analytical gradients

NASA Astrophysics Data System (ADS)

Ren, Y.; Cui, P. Y.; Luan, E. J.

2008-01-01

The problem of searching for exploration opportunity of near Earth objects is investigated. For rendezvous missions, the analytical gradients of performance index with respect to free parameters are derived by combining the calculus of variation with the theory of state-transition matrix. Then, some initial guesses are generated random in the search space, and the performance index is optimized with the guidance of analytical gradients from these initial guesses. This method not only keeps the property of global search in traditional method, but also avoids the blindness in the traditional exploration opportunity search; hence, the computing speed could be increased greatly. Furthermore, by using this method, the search precision could be controlled effectively.

Seizure Forecasting and the Preictal State in Canine Epilepsy.

PubMed

Varatharajah, Yogatheesan; Iyer, Ravishankar K; Berry, Brent M; Worrell, Gregory A; Brinkmann, Benjamin H

2017-02-01

The ability to predict seizures may enable patients with epilepsy to better manage their medications and activities, potentially reducing side effects and improving quality of life. Forecasting epileptic seizures remains a challenging problem, but machine learning methods using intracranial electroencephalographic (iEEG) measures have shown promise. A machine-learning-based pipeline was developed to process iEEG recordings and generate seizure warnings. Results support the ability to forecast seizures at rates greater than a Poisson random predictor for all feature sets and machine learning algorithms tested. In addition, subject-specific neurophysiological changes in multiple features are reported preceding lead seizures, providing evidence supporting the existence of a distinct and identifiable preictal state.

SEIZURE FORECASTING AND THE PREICTAL STATE IN CANINE EPILEPSY

PubMed Central

Varatharajah, Yogatheesan; Iyer, Ravishankar K.; Berry, Brent M.; Worrell, Gregory A.; Brinkmann, Benjamin H.

2017-01-01

The ability to predict seizures may enable patients with epilepsy to better manage their medications and activities, potentially reducing side effects and improving quality of life. Forecasting epileptic seizures remains a challenging problem, but machine learning methods using intracranial electroencephalographic (iEEG) measures have shown promise. A machine-learning-based pipeline was developed to process iEEG recordings and generate seizure warnings. Results support the ability to forecast seizures at rates greater than a Poisson random predictor for all feature sets and machine learning algorithms tested. In addition, subject-specific neurophysiological changes in multiple features are reported preceding lead seizures, providing evidence supporting the existence of a distinct and identifiable preictal state. PMID:27464854

The challenges of female adolescents' health needs.

PubMed

Shahhosseini, Z; Simbar, M; Ramezankhani, A; Alavi Majd, H; Moslemizadeh, Narges

2013-12-01

Due to adolescents' future crucial roles, their health needs should be included in the national health system policy. In this cross-sectional study 2010 female adolescents were recruited from randomly selected schools in Iran. To obtain their health needs, the participants completed a self-administrated questionnaire. It was revealed that emotional needs were the most important health needs of adolescents. Furthermore, there was a meaningful relationship between health needs' score with the adolescents' age and their mothers' education level. Finally, the mean score of health needs was significantly higher in urban adolescents. Therefore, it is suggested that adolescents' emotional health needs to be paid attention; otherwise irrecoverable serious problems may occur in the next generation's health.

Toward a Better Understanding of the Relationship between Belief in the Paranormal and Statistical Bias: The Potential Role of Schizotypy

PubMed Central

Dagnall, Neil; Denovan, Andrew; Drinkwater, Kenneth; Parker, Andrew; Clough, Peter

2016-01-01

The present paper examined relationships between schizotypy (measured by the Oxford-Liverpool Inventory of Feelings and Experience; O-LIFE scale brief), belief in the paranormal (assessed via the Revised Paranormal Belief Scale; RPBS) and proneness to statistical bias (i.e., perception of randomness and susceptibility to conjunction fallacy). Participants were 254 volunteers recruited via convenience sampling. Probabilistic reasoning problems appeared framed within both standard and paranormal contexts. Analysis revealed positive correlations between the Unusual Experience (UnExp) subscale of O-LIFE and paranormal belief measures [RPBS full scale, traditional paranormal beliefs (TPB) and new age philosophy]. Performance on standard problems correlated negatively with UnExp and belief in the paranormal (particularly the TPB dimension of the RPBS). Consideration of specific problem types revealed that perception of randomness associated more strongly with belief in the paranormal than conjunction; both problem types related similarly to UnExp. Structural equation modeling specified that belief in the paranormal mediated the indirect relationship between UnExp and statistical bias. For problems presented in a paranormal context a framing effect occurred. Whilst UnExp correlated positively with conjunction proneness (controlling for perception of randomness), there was no association between UnExp and perception of randomness (controlling for conjunction). PMID:27471481

Toward a Better Understanding of the Relationship between Belief in the Paranormal and Statistical Bias: The Potential Role of Schizotypy.

PubMed

Dagnall, Neil; Denovan, Andrew; Drinkwater, Kenneth; Parker, Andrew; Clough, Peter

2016-01-01

The present paper examined relationships between schizotypy (measured by the Oxford-Liverpool Inventory of Feelings and Experience; O-LIFE scale brief), belief in the paranormal (assessed via the Revised Paranormal Belief Scale; RPBS) and proneness to statistical bias (i.e., perception of randomness and susceptibility to conjunction fallacy). Participants were 254 volunteers recruited via convenience sampling. Probabilistic reasoning problems appeared framed within both standard and paranormal contexts. Analysis revealed positive correlations between the Unusual Experience (UnExp) subscale of O-LIFE and paranormal belief measures [RPBS full scale, traditional paranormal beliefs (TPB) and new age philosophy]. Performance on standard problems correlated negatively with UnExp and belief in the paranormal (particularly the TPB dimension of the RPBS). Consideration of specific problem types revealed that perception of randomness associated more strongly with belief in the paranormal than conjunction; both problem types related similarly to UnExp. Structural equation modeling specified that belief in the paranormal mediated the indirect relationship between UnExp and statistical bias. For problems presented in a paranormal context a framing effect occurred. Whilst UnExp correlated positively with conjunction proneness (controlling for perception of randomness), there was no association between UnExp and perception of randomness (controlling for conjunction).

Linking search space structure, run-time dynamics, and problem difficulty : a step toward demystifying tabu search.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Whitley, L. Darrell; Howe, Adele E.; Watson, Jean-Paul

2004-09-01

Tabu search is one of the most effective heuristics for locating high-quality solutions to a diverse array of NP-hard combinatorial optimization problems. Despite the widespread success of tabu search, researchers have a poor understanding of many key theoretical aspects of this algorithm, including models of the high-level run-time dynamics and identification of those search space features that influence problem difficulty. We consider these questions in the context of the job-shop scheduling problem (JSP), a domain where tabu search algorithms have been shown to be remarkably effective. Previously, we demonstrated that the mean distance between random local optima and the nearestmore » optimal solution is highly correlated with problem difficulty for a well-known tabu search algorithm for the JSP introduced by Taillard. In this paper, we discuss various shortcomings of this measure and develop a new model of problem difficulty that corrects these deficiencies. We show that Taillard's algorithm can be modeled with high fidelity as a simple variant of a straightforward random walk. The random walk model accounts for nearly all of the variability in the cost required to locate both optimal and sub-optimal solutions to random JSPs, and provides an explanation for differences in the difficulty of random versus structured JSPs. Finally, we discuss and empirically substantiate two novel predictions regarding tabu search algorithm behavior. First, the method for constructing the initial solution is highly unlikely to impact the performance of tabu search. Second, tabu tenure should be selected to be as small as possible while simultaneously avoiding search stagnation; values larger than necessary lead to significant degradations in performance.« less

Social problem solving in carers of young people with a first episode of psychosis: a randomized controlled trial.

PubMed

McCann, Terence V; Cotton, Sue M; Lubman, Dan I

2017-08-01

Caring for young people with first-episode psychosis is difficult and demanding, and has detrimental effects on carers' well-being, with few evidence-based resources available to assist carers to deal with the problems they are confronted with in this situation. We aimed to examine if completion of a self-directed problem-solving bibliotherapy by first-time carers of young people with first-episode psychosis improved their social problem solving compared with carers who only received treatment as usual. A randomized controlled trial was carried out through two early intervention psychosis services in Melbourne, Australia. A sample of 124 carers were randomized to problem-solving bibliotherapy or treatment as usual. Participants were assessed at baseline, 6- and 16-week follow-up. Intent-to-treat analyses were used and showed that recipients of bibliotherapy had greater social problem-solving abilities than those receiving treatment as usual, and these effects were maintained at both follow-up time points. Our findings affirm that bibliotherapy, as a low-cost complement to treatment as usual for carers, had some effects in improving their problem-solving skills when addressing problems related to the care and support of young people with first-episode psychosis. © 2015 The Authors. Early Intervention in Psychiatry published by Wiley Publishing Asia Pty Ltd.

SNP selection and classification of genome-wide SNP data using stratified sampling random forests.

PubMed

Wu, Qingyao; Ye, Yunming; Liu, Yang; Ng, Michael K

2012-09-01

For high dimensional genome-wide association (GWA) case-control data of complex disease, there are usually a large portion of single-nucleotide polymorphisms (SNPs) that are irrelevant with the disease. A simple random sampling method in random forest using default mtry parameter to choose feature subspace, will select too many subspaces without informative SNPs. Exhaustive searching an optimal mtry is often required in order to include useful and relevant SNPs and get rid of vast of non-informative SNPs. However, it is too time-consuming and not favorable in GWA for high-dimensional data. The main aim of this paper is to propose a stratified sampling method for feature subspace selection to generate decision trees in a random forest for GWA high-dimensional data. Our idea is to design an equal-width discretization scheme for informativeness to divide SNPs into multiple groups. In feature subspace selection, we randomly select the same number of SNPs from each group and combine them to form a subspace to generate a decision tree. The advantage of this stratified sampling procedure can make sure each subspace contains enough useful SNPs, but can avoid a very high computational cost of exhaustive search of an optimal mtry, and maintain the randomness of a random forest. We employ two genome-wide SNP data sets (Parkinson case-control data comprised of 408 803 SNPs and Alzheimer case-control data comprised of 380 157 SNPs) to demonstrate that the proposed stratified sampling method is effective, and it can generate better random forest with higher accuracy and lower error bound than those by Breiman's random forest generation method. For Parkinson data, we also show some interesting genes identified by the method, which may be associated with neurological disorders for further biological investigations.

CALL FOR PAPERS: Special issue on the random search problem: trends and perspectives

NASA Astrophysics Data System (ADS)

da Luz, Marcos G. E.; Grosberg, Alexander Y.; Raposo, Ernesto P.; Viswanathan, Gandhi M.

2008-11-01

This is a call for contributions to a special issue of Journal of Physics A: Mathematical and Theoretical dedicated to the subject of the random search problem. The motivation behind this special issue is to summarize in a single comprehensive publication, the main aspects (past and present), latest developments, different viewpoints and the directions being followed in this multidisciplinary field. We hope that such a special issue could become a particularly valuable reference for the broad scientific community working with the general random search problem. The Editorial Board has invited Marcos G E da Luz, Alexander Y Grosberg, Ernesto P Raposo and Gandhi M Viswanathan to serve as Guest Editors for the special issue. The general question of how to optimize the search for specific target objects in either continuous or discrete environments when the information available is limited is of significant importance in a broad range of fields. Representative examples include ecology (animal foraging, dispersion of populations), geology (oil recovery from mature reservoirs), information theory (automated researchers of registers in high-capacity database), molecular biology (proteins searching for their sites, e.g., on DNA ), etc. One reason underlying the richness of the random search problem relates to the `ignorance' of the locations of the randomly located `targets'. A statistical approach to the search problem can deal adequately with incomplete information and so stochastic strategies become advantageous. The general problem of how to search efficiently for randomly located target sites can thus be quantitatively described using the concepts and methods of statistical physics and stochastic processes. Scope Thus far, to the best of our knowledge, no recent textbook or review article in a physics journal has appeared on this topic. This makes a special issue with review and research articles attractive to those interested in acquiring a general introduction to the field. The subject can be approached from the perspective of different fields: ecology, networks, transport problems, molecular biology, etc. The study of the problem is particularly suited to the concepts and methods of statistical physics and stochastic processes; for example, fractals, random walks, anomalous diffusion. Discrete landscapes can be approached via graph theory, random lattices and complex networks. Such topics are regularly discussed in Journal of Physics A: Mathematical and Theoretical. All such aspects of the problem fall within the scope and focus of this special issue on the random search problem: trends and perspectives. Editorial policy All contributions to the special issue will be refereed in accordance with the refereeing policy of the journal. In particular, all research papers will be expected to be original work reporting substantial new results. The issue will also contain a number of review articles by invitation only. The Guest Editors reserve the right to judge whether a contribution fits the scope of the special issue. Guidelines for preparation of contributions We aim to publish the special issue in August 2009. To realize this, the DEADLINE for contributed papers is 15 January 2009. There is a page limit of 15 printed pages (approximately 9000 words) per contribution. For papers exceeding this limit, the Guest Editors reserve the right to request a reduction in length. Further advice on document preparation can be found at www.iop.org/Journals/jphysa. Contributions to the special issue should if possible be submitted electronically by web upload at www.iop.org/Journals/jphysa, or by email to jphysa@iop.org, quoting 'J. Phys. A Special Issue— Random Search Problem'. Please state whether the paper has been invited or is contributed. Submissions should ideally be in standard LaTeX form. Please see the website for further information on electronic submissions. Authors unable to submit electronically may send hard-copy contributions to: Publishing Administrators, Journal of Physics A, Institute of Physics Publishing, Dirac House, Temple Back, Bristol BS1 6BE, UK, enclosing electronic code on CD if available and quoting 'J. Phys. A Special Issue—Random Search Problem'. All contributions should be accompanied by a read-me file or covering letter giving the postal and e-mail addresses for correspondence. The Publishing Office should be notified of any subsequent change of address. This special issue will be published in the paper and online version of the journal. The corresponding author of each contribution will receive a complimentary copy of the issue.

Rational decisions, random matrices and spin glasses

NASA Astrophysics Data System (ADS)

Galluccio, Stefano; Bouchaud, Jean-Philippe; Potters, Marc

We consider the problem of rational decision making in the presence of nonlinear constraints. By using tools borrowed from spin glass and random matrix theory, we focus on the portfolio optimisation problem. We show that the number of optimal solutions is generally exponentially large, and each of them is fragile: rationality is in this case of limited use. In addition, this problem is related to spin glasses with Lévy-like (long-ranged) couplings, for which we show that the ground state is not exponentially degenerate.

Proceedings of the First NASA Formal Methods Symposium

NASA Technical Reports Server (NTRS)

Denney, Ewen (Editor); Giannakopoulou, Dimitra (Editor); Pasareanu, Corina S. (Editor)

2009-01-01

Topics covered include: Model Checking - My 27-Year Quest to Overcome the State Explosion Problem; Applying Formal Methods to NASA Projects: Transition from Research to Practice; TLA+: Whence, Wherefore, and Whither; Formal Methods Applications in Air Transportation; Theorem Proving in Intel Hardware Design; Building a Formal Model of a Human-Interactive System: Insights into the Integration of Formal Methods and Human Factors Engineering; Model Checking for Autonomic Systems Specified with ASSL; A Game-Theoretic Approach to Branching Time Abstract-Check-Refine Process; Software Model Checking Without Source Code; Generalized Abstract Symbolic Summaries; A Comparative Study of Randomized Constraint Solvers for Random-Symbolic Testing; Component-Oriented Behavior Extraction for Autonomic System Design; Automated Verification of Design Patterns with LePUS3; A Module Language for Typing by Contracts; From Goal-Oriented Requirements to Event-B Specifications; Introduction of Virtualization Technology to Multi-Process Model Checking; Comparing Techniques for Certified Static Analysis; Towards a Framework for Generating Tests to Satisfy Complex Code Coverage in Java Pathfinder; jFuzz: A Concolic Whitebox Fuzzer for Java; Machine-Checkable Timed CSP; Stochastic Formal Correctness of Numerical Algorithms; Deductive Verification of Cryptographic Software; Coloured Petri Net Refinement Specification and Correctness Proof with Coq; Modeling Guidelines for Code Generation in the Railway Signaling Context; Tactical Synthesis Of Efficient Global Search Algorithms; Towards Co-Engineering Communicating Autonomous Cyber-Physical Systems; and Formal Methods for Automated Diagnosis of Autosub 6000.

Regional SAR Image Segmentation Based on Fuzzy Clustering with Gamma Mixture Model

NASA Astrophysics Data System (ADS)

Li, X. L.; Zhao, Q. H.; Li, Y.

2017-09-01

Most of stochastic based fuzzy clustering algorithms are pixel-based, which can not effectively overcome the inherent speckle noise in SAR images. In order to deal with the problem, a regional SAR image segmentation algorithm based on fuzzy clustering with Gamma mixture model is proposed in this paper. First, initialize some generating points randomly on the image, the image domain is divided into many sub-regions using Voronoi tessellation technique. Each sub-region is regarded as a homogeneous area in which the pixels share the same cluster label. Then, assume the probability of the pixel to be a Gamma mixture model with the parameters respecting to the cluster which the pixel belongs to. The negative logarithm of the probability represents the dissimilarity measure between the pixel and the cluster. The regional dissimilarity measure of one sub-region is defined as the sum of the measures of pixels in the region. Furthermore, the Markov Random Field (MRF) model is extended from pixels level to Voronoi sub-regions, and then the regional objective function is established under the framework of fuzzy clustering. The optimal segmentation results can be obtained by the solution of model parameters and generating points. Finally, the effectiveness of the proposed algorithm can be proved by the qualitative and quantitative analysis from the segmentation results of the simulated and real SAR images.

Chemical reaction networks as a model to describe UVC- and radiolytically-induced reactions of simple compounds.

PubMed

Dondi, Daniele; Merli, Daniele; Albini, Angelo; Zeffiro, Alberto; Serpone, Nick

2012-05-01

When a chemical system is submitted to high energy sources (UV, ionizing radiation, plasma sparks, etc.), as is expected to be the case of prebiotic chemistry studies, a plethora of reactive intermediates could form. If oxygen is present in excess, carbon dioxide and water are the major products. More interesting is the case of reducing conditions where synthetic pathways are also possible. This article examines the theoretical modeling of such systems with random-generated chemical networks. Four types of random-generated chemical networks were considered that originated from a combination of two connection topologies (viz., Poisson and scale-free) with reversible and irreversible chemical reactions. The results were analyzed taking into account the number of the most abundant products required for reaching 50% of the total number of moles of compounds at equilibrium, as this may be related to an actual problem of complex mixture analysis. The model accounts for multi-component reaction systems with no a priori knowledge of reacting species and the intermediates involved if system components are sufficiently interconnected. The approach taken is relevant to an earlier study on reactions that may have occurred in prebiotic systems where only a few compounds were detected. A validation of the model was attained on the basis of results of UVC and radiolytic reactions of prebiotic mixtures of low molecular weight compounds likely present on the primeval Earth.

Lack of Interaction between Sensing-Intuitive Learning Styles and Problem-First versus Information-First Instruction: A Randomized Crossover Trial

ERIC Educational Resources Information Center

Cook, David A.; Thompson, Warren G.; Thomas, Kris G.; Thomas, Matthew R.

2009-01-01

Background: Adaptation to learning styles has been proposed to enhance learning. Objective: We hypothesized that learners with sensing learning style would perform better using a problem-first instructional method while intuitive learners would do better using an information-first method. Design: Randomized, controlled, crossover trial. Setting:…

Conduct Problems and Peer Rejection in Childhood: A Randomized Trial of the Making Choices and Strong Families Programs

ERIC Educational Resources Information Center

Fraser, Mark W.; Day, Steven H.; Galinsky, Maeda J.; Hodges, Vanessa G.; Smokowski, Paul R.

2004-01-01

This article discusses the effectiveness of a multicomponent intervention designed to disrupt developmental processes associated with conduct problems and peer rejection in childhood. Compared with 41 children randomized to a wait list control condition, 45 children in an intervention condition received a social skills training program. At the…

Reducing Developmental Risk for Emotional/Behavioral Problems: A Randomized Controlled Trial Examining the Tools for Getting Along Curriculum

ERIC Educational Resources Information Center

Daunic, Ann P.; Smith, Stephen W.; Garvan, Cynthia W.; Barber, Brian R.; Becker, Mallory K.; Peters, Christine D.; Taylor, Gregory G.; Van Loan, Christopher L.; Li, Wei; Naranjo, Arlene H.

2012-01-01

Researchers have demonstrated that cognitive-behavioral intervention strategies--such as social problem solving--provided in school settings can help ameliorate the developmental risk for emotional and behavioral difficulties. In this study, we report the results of a randomized controlled trial of Tools for Getting Along (TFGA), a social…

Emotion Regulation Enhancement of Cognitive Behavior Therapy for College Student Problem Drinkers: A Pilot Randomized Controlled Trial

ERIC Educational Resources Information Center

Ford, Julian D.; Grasso, Damion J.; Levine, Joan; Tennen, Howard

2018-01-01

This pilot randomized clinical trial tested an emotion regulation enhancement to cognitive behavior therapy (CBT) with 29 college student problem drinkers with histories of complex trauma and current clinically significant traumatic stress symptoms. Participants received eight face-to-face sessions of manualized Internet-supported CBT for problem…

WWC Review of the Report "Effects of Problem Based Economics on High School Economics Instruction"

ERIC Educational Resources Information Center

What Works Clearinghouse, 2012

2012-01-01

The study described in this report included 128 high school economics teachers from 106 schools in Arizona and California, half of whom were randomly assigned to the "Problem Based Economics Instruction" condition and half of whom were randomly assigned to the comparison condition. High levels of teacher attrition occurred after…

Single-electron random-number generator (RNG) for highly secure ubiquitous computing applications

NASA Astrophysics Data System (ADS)

Uchida, Ken; Tanamoto, Tetsufumi; Fujita, Shinobu

2007-11-01

Since the security of all modern cryptographic techniques relies on unpredictable and irreproducible digital keys generated by random-number generators (RNGs), the realization of high-quality RNG is essential for secure communications. In this report, a new RNG, which utilizes single-electron phenomena, is proposed. A room-temperature operating silicon single-electron transistor (SET) having nearby an electron pocket is used as a high-quality, ultra-small RNG. In the proposed RNG, stochastic single-electron capture/emission processes to/from the electron pocket are detected with high sensitivity by the SET, and result in giant random telegraphic signals (GRTS) on the SET current. It is experimentally demonstrated that the single-electron RNG generates extremely high-quality random digital sequences at room temperature, in spite of its simple configuration. Because of its small-size and low-power properties, the single-electron RNG is promising as a key nanoelectronic device for future ubiquitous computing systems with highly secure mobile communication capabilities.

Application of firefly algorithm to the dynamic model updating problem

NASA Astrophysics Data System (ADS)

Shabbir, Faisal; Omenzetter, Piotr

2015-04-01

Model updating can be considered as a branch of optimization problems in which calibration of the finite element (FE) model is undertaken by comparing the modal properties of the actual structure with these of the FE predictions. The attainment of a global solution in a multi dimensional search space is a challenging problem. The nature-inspired algorithms have gained increasing attention in the previous decade for solving such complex optimization problems. This study applies the novel Firefly Algorithm (FA), a global optimization search technique, to a dynamic model updating problem. This is to the authors' best knowledge the first time FA is applied to model updating. The working of FA is inspired by the flashing characteristics of fireflies. Each firefly represents a randomly generated solution which is assigned brightness according to the value of the objective function. The physical structure under consideration is a full scale cable stayed pedestrian bridge with composite bridge deck. Data from dynamic testing of the bridge was used to correlate and update the initial model by using FA. The algorithm aimed at minimizing the difference between the natural frequencies and mode shapes of the structure. The performance of the algorithm is analyzed in finding the optimal solution in a multi dimensional search space. The paper concludes with an investigation of the efficacy of the algorithm in obtaining a reference finite element model which correctly represents the as-built original structure.

Random number generation in bilingual Balinese and German students: preliminary findings from an exploratory cross-cultural study.

PubMed

Strenge, Hans; Lesmana, Cokorda Bagus Jaya; Suryani, Luh Ketut

2009-08-01

Verbal random number generation is a procedurally simple task to assess executive function and appears ideally suited for the use under diverse settings in cross-cultural research. The objective of this study was to examine ethnic group differences between young adults in Bali (Indonesia) and Kiel (Germany): 50 bilingual healthy students, 30 Balinese and 20 Germans, attempted to generate a random sequence of the digits 1 to 9. In Balinese participants, randomization was done in Balinese (native language L1) and Indonesian (first foreign language L2), in German subjects in the German (L1) and English (L2) languages. 10 of 30 Balinese (33%), but no Germans, were unable to inhibit habitual counting in more than half of the responses. The Balinese produced significantly more nonrandom responses than the Germans with higher rates of counting and significantly less occurrence of the digits 2 and 3 in L1 compared with L2. Repetition and cycling behavior did not differ between the four languages. The findings highlight the importance of taking into account culture-bound psychosocial factors for Balinese individuals when administering and interpreting a random number generation test.

Comparison of the Predictive Performance and Interpretability of Random Forest and Linear Models on Benchmark Data Sets.

PubMed

Marchese Robinson, Richard L; Palczewska, Anna; Palczewski, Jan; Kidley, Nathan

2017-08-28

The ability to interpret the predictions made by quantitative structure-activity relationships (QSARs) offers a number of advantages. While QSARs built using nonlinear modeling approaches, such as the popular Random Forest algorithm, might sometimes be more predictive than those built using linear modeling approaches, their predictions have been perceived as difficult to interpret. However, a growing number of approaches have been proposed for interpreting nonlinear QSAR models in general and Random Forest in particular. In the current work, we compare the performance of Random Forest to those of two widely used linear modeling approaches: linear Support Vector Machines (SVMs) (or Support Vector Regression (SVR)) and partial least-squares (PLS). We compare their performance in terms of their predictivity as well as the chemical interpretability of the predictions using novel scoring schemes for assessing heat map images of substructural contributions. We critically assess different approaches for interpreting Random Forest models as well as for obtaining predictions from the forest. We assess the models on a large number of widely employed public-domain benchmark data sets corresponding to regression and binary classification problems of relevance to hit identification and toxicology. We conclude that Random Forest typically yields comparable or possibly better predictive performance than the linear modeling approaches and that its predictions may also be interpreted in a chemically and biologically meaningful way. In contrast to earlier work looking at interpretation of nonlinear QSAR models, we directly compare two methodologically distinct approaches for interpreting Random Forest models. The approaches for interpreting Random Forest assessed in our article were implemented using open-source programs that we have made available to the community. These programs are the rfFC package ( https://r-forge.r-project.org/R/?group_id=1725 ) for the R statistical programming language and the Python program HeatMapWrapper [ https://doi.org/10.5281/zenodo.495163 ] for heat map generation.

Random number generators tested on quantum Monte Carlo simulations.

PubMed

Hongo, Kenta; Maezono, Ryo; Miura, Kenichi

2010-08-01

We have tested and compared several (pseudo) random number generators (RNGs) applied to a practical application, ground state energy calculations of molecules using variational and diffusion Monte Carlo metheds. A new multiple recursive generator with 8th-order recursion (MRG8) and the Mersenne twister generator (MT19937) are tested and compared with the RANLUX generator with five luxury levels (RANLUX-[0-4]). Both MRG8 and MT19937 are proven to give the same total energy as that evaluated with RANLUX-4 (highest luxury level) within the statistical error bars with less computational cost to generate the sequence. We also tested the notorious implementation of linear congruential generator (LCG), RANDU, for comparison. (c) 2010 Wiley Periodicals, Inc.

An efficient algorithm for generating random number pairs drawn from a bivariate normal distribution

NASA Technical Reports Server (NTRS)

Campbell, C. W.

1983-01-01

An efficient algorithm for generating random number pairs from a bivariate normal distribution was developed. Any desired value of the two means, two standard deviations, and correlation coefficient can be selected. Theoretically the technique is exact and in practice its accuracy is limited only by the quality of the uniform distribution random number generator, inaccuracies in computer function evaluation, and arithmetic. A FORTRAN routine was written to check the algorithm and good accuracy was obtained. Some small errors in the correlation coefficient were observed to vary in a surprisingly regular manner. A simple model was developed which explained the qualities aspects of the errors.

Planification de la maintenance d'un parc de turbines-alternateurs par programmation mathematique

NASA Astrophysics Data System (ADS)

Aoudjit, Hakim

A growing number of Hydro-Quebec's hydro generators are at the end of their useful life and maintenance managers fear to face a number of overhauls exceeding what can be handled. Maintenance crews and budgets are limited and these withdrawals may take up to a full year and mobilize significant resources in addition to the loss of electricity production. In addition, increased export sales forecasts and severe production patterns are expected to speed up wear that can lead to halting many units at the same time. Currently, expert judgment is at the heart of withdrawals which rely primarily on periodic inspections and in-situ measurements and the results are sent to the maintenance planning team who coordinate all the withdrawals decisions. The degradations phenomena taking place is random in nature and the prediction capability of wear using only inspections is limited to short-term at best. A long term planning of major overhauls is sought by managers for the sake of justifying and rationalizing budgets and resources. The maintenance managers are able to provide a huge amount of data. Among them, is the hourly production of each unit for several years, the repairs history on each part of a unit as well as major withdrawals since the 1950's. In this research, we tackle the problem of long term maintenance planning for a fleet of 90 hydro generators at Hydro-Quebec over a 50 years planning horizon period. We lay a scientific and rational framework to support withdrawals decisions by using part of the available data and maintenance history while fulfilling a set of technical and economic constraints. We propose a planning approach based on a constrained optimization framework. We begin by decomposing and sorting hydro generator components to highlight the most influential parts. A failure rate model is developed to take into account the technical characteristics and unit utilization. Then, replacement and repair policies are evaluated for each of the components then strategies are derived for the whole unit. Traditional univariate policies such as the age replacement policy and the minimal repair policy are calculated. These policies are extended to build alternative bivariate maintenance policy as well as a repair strategy where the state of a component after a repair is rejuvenated by a constant coefficient. These templates form the basis for the calculation of objective function for the scheduling problem. On one hand, this issue is treated as a nonlinear problem where the objective is to minimize the average total maintenance cost per unit of time on an infinite horizon for the fleet with technical and economic constraints. A formulation is also proposed in the case of a finite time horizon. In the event of electricity production variation, and given that the usage profile is known, the influence of production scenarios is reflected on the unit's components through their failure rate. In this context, prognoses on possible resources problems are made by studying the characteristics of the generated plans. On the second hand, the withdrawals are now subjected to two decision criteria. In addition to minimizing the average total maintenance cost per unit of time on an infinite time horizon, the best achievable reliability of remaining turbo generators is sought. This problem is treated as a biobjective nonlinear optimization problem. Finally a series of problems describing multiple contexts are solved for planning renovations of 90 turbo generators units considering 3 major components in each unit and 2 types of maintenance policies for each component.

Interpretation of sucrose gradient sedimentation pattern of deoxyribonucleic acid fragments resulting from random breaks.

PubMed

Litwin, S; Shahn, E; Kozinski, A W

1969-07-01

Mass distribution in a sucrose gradient of deoxyribonucleic acid (DNA) fragments arising as a result of random breaks is predicted by analytical means from which computer evaluations are plotted. The analytical results are compared with the results of verifying experiments: (i) a Monte Carlo computer experiment in which simulated molecules of DNA were individuals of unit length subjected to random "breaks" applied by a random number generator, and (ii) an in vitro experiment in which molecules of T4 DNA, highly labeled with (32)P, were stored in liquid nitrogen for variable periods of time during which a precisely known number of (32)P atoms decayed, causing single-stranded breaks. The distribution of sizes of the resulting fragments was measured in an alkaline sucrose gradient. The profiles obtained in this fashion were compared with the mathematical predictions. Both experiments agree with the analytical approach and thus permit the use of the graphs obtained from the latter as a means of determining the average number of random breaks in DNA from distributions obtained experimentally in a sucrose gradient. An example of the application of this procedure to a previously unresolved problem is provided in the case of DNA from ultraviolet-irradiated phage which undergoes a dose-dependent intracellular breakdown. The relationship between the number of lethal hits and the number of single-stranded breaks was not previously established. A comparison of the calculated number of nicks per strand of DNA with the known dose in phage-lethal hits reveals a relationship closely approximating one lethal hit to one single-stranded break.

Hierarchical Motion Planning for Autonomous Aerial and Terrestrial Vehicles

NASA Astrophysics Data System (ADS)

Cowlagi, Raghvendra V.

Autonomous mobile robots---both aerial and terrestrial vehicles---have gained immense importance due to the broad spectrum of their potential military and civilian applications. One of the indispensable requirements for the autonomy of a mobile vehicle is the vehicle's capability of planning and executing its motion, that is, finding appropriate control inputs for the vehicle such that the resulting vehicle motion satisfies the requirements of the vehicular task. The motion planning and control problem is inherently complex because it involves two disparate sub-problems: (1) satisfaction of the vehicular task requirements, which requires tools from combinatorics and/or formal methods, and (2) design of the vehicle control laws, which requires tools from dynamical systems and control theory. Accordingly, this problem is usually decomposed and solved over two levels of hierarchy. The higher level, called the geometric path planning level, finds a geometric path that satisfies the vehicular task requirements, e.g., obstacle avoidance. The lower level, called the trajectory planning level, involves sufficient smoothening of this geometric path followed by a suitable time parametrization to obtain a reference trajectory for the vehicle. Although simple and efficient, such hierarchical decomposition suffers a serious drawback: the geometric path planner has no information of the kinematical and dynamical constraints of the vehicle. Consequently, the geometric planner may produce paths that the trajectory planner cannot transform into a feasible reference trajectory. Two main ideas appear in the literature to remedy this problem: (a) randomized sampling-based planning, which eliminates the geometric planner altogether by planning in the vehicle state space, and (b) geometric planning supported by feedback control laws. The former class of methods suffer from a lack of optimality of the resultant trajectory, while the latter class of methods makes a restrictive assumption concerning the vehicle kinematical model. We propose a hierarchical motion planning framework based on a novel mode of interaction between these two levels of planning. This interaction rests on the solution of a special shortest-path problem on graphs, namely, one using costs defined on multiple edge transitions in the path instead of the usual single edge transition costs. These costs are provided by a local trajectory generation algorithm, which we implement using model predictive control and the concept of effective target sets for simplifying the non-convex constraints involved in the problem. The proposed motion planner ensures "consistency" between the two levels of planning, i.e., a guarantee that the higher level geometric path is always associated with a kinematically and dynamically feasible trajectory. The main contributions of this thesis are: 1. A motion planning framework based on history-dependent costs (H-costs) in cell decomposition graphs for incorporating vehicle dynamical constraints: this framework offers distinct advantages in comparison with the competing approaches of discretization of the state space, of randomized sampling-based motion planning, and of local feedback-based, decoupled hierarchical motion planning, 2. An efficient and flexible algorithm for finding optimal H-cost paths, 3. A precise and general formulation of a local trajectory problem (the tile motion planning problem) that allows independent development of the discrete planner and the trajectory planner, while maintaining "compatibility" between the two planners, 4. A local trajectory generation algorithm using mpc, and the application of the concept of effective target sets for a significant simplification of the local trajectory generation problem, 5. The geometric analysis of curvature-bounded traversal of rectangular channels, leading to less conservative results in comparison with a result reported in the literature, and also to the efficient construction of effective target sets for the solution of the tile motion planning problem, 6. A wavelet-based multi-resolution path planning scheme, and a proof of completeness of the proposed scheme: such proofs are altogether absent from other works on multi-resolution path planning, 7. A technique for extracting all information about cells---namely, the locations, the sizes, and the associated image intensities---directly from the set of significant detail coefficients considered for path planning at a given iteration, and 8. The extension of the multi-resolution path planning scheme to include vehicle dynamical constraints using the aforementioned history-dependent costs approach. The future work includes an implementation of the proposed framework involving a discrete planner that solves classical planning problems more general than the single-query path planning problem considered thus far, and involving trajectory generation schemes for realistic vehicle dynamical models such as the bicycle model.

Fidelity under isospectral perturbations: a random matrix study

NASA Astrophysics Data System (ADS)

Leyvraz, F.; García, A.; Kohler, H.; Seligman, T. H.

2013-07-01

The set of Hamiltonians generated by all unitary transformations from a single Hamiltonian is the largest set of isospectral Hamiltonians we can form. Taking advantage of the fact that the unitary group can be generated from Hermitian matrices we can take the ones generated by the Gaussian unitary ensemble with a small parameter as small perturbations. Similarly, the transformations generated by Hermitian antisymmetric matrices from orthogonal matrices form isospectral transformations among symmetric matrices. Based on this concept we can obtain the fidelity decay of a system that decays under a random isospectral perturbation with well-defined properties regarding time-reversal invariance. If we choose the Hamiltonian itself also from a classical random matrix ensemble, then we obtain solutions in terms of form factors in the limit of large matrices.

Investigation of estimators of probability density functions

NASA Technical Reports Server (NTRS)

Speed, F. M.

1972-01-01

Four research projects are summarized which include: (1) the generation of random numbers on the IBM 360/44, (2) statistical tests used to check out random number generators, (3) Specht density estimators, and (4) use of estimators of probability density functions in analyzing large amounts of data.

Bi-criteria travelling salesman subtour problem with time threshold

NASA Astrophysics Data System (ADS)

Kumar Thenepalle, Jayanth; Singamsetty, Purusotham

2018-03-01

This paper deals with the bi-criteria travelling salesman subtour problem with time threshold (BTSSP-T), which comes from the family of the travelling salesman problem (TSP) and is NP-hard in the strong sense. The problem arises in several application domains, mainly in routing and scheduling contexts. Here, the model focuses on two criteria: total travel distance and gains attained. The BTSSP-T aims to determine a subtour that starts and ends at the same city and visits a subset of cities at a minimum travel distance with maximum gains, such that the time spent on the tour does not exceed the predefined time threshold. A zero-one integer-programming problem is adopted to formulate this model with all practical constraints, and it includes a finite set of feasible solutions (one for each tour). Two algorithms, namely, the Lexi-Search Algorithm (LSA) and the Tabu Search (TS) algorithm have been developed to solve the BTSSP-T problem. The proposed LSA implicitly enumerates the feasible patterns and provides an efficient solution with backtracking, whereas the TS, which is metaheuristic, will give the better approximate solution. A numerical example is demonstrated in order to understand the search mechanism of the LSA. Numerical experiments are carried out in the MATLAB environment, on the different benchmark instances available in the TSPLIB domain as well as on randomly generated test instances. The experimental results show that the proposed LSA works better than the TS algorithm in terms of solution quality and, computationally, both LSA and TS are competitive.

Spectral turning bands for efficient Gaussian random fields generation on GPUs and accelerators

NASA Astrophysics Data System (ADS)

Hunger, L.; Cosenza, B.; Kimeswenger, S.; Fahringer, T.

2015-11-01

A random field (RF) is a set of correlated random variables associated with different spatial locations. RF generation algorithms are of crucial importance for many scientific areas, such as astrophysics, geostatistics, computer graphics, and many others. Current approaches commonly make use of 3D fast Fourier transform (FFT), which does not scale well for RF bigger than the available memory; they are also limited to regular rectilinear meshes. We introduce random field generation with the turning band method (RAFT), an RF generation algorithm based on the turning band method that is optimized for massively parallel hardware such as GPUs and accelerators. Our algorithm replaces the 3D FFT with a lower-order, one-dimensional FFT followed by a projection step and is further optimized with loop unrolling and blocking. RAFT can easily generate RF on non-regular (non-uniform) meshes and efficiently produce fields with mesh sizes bigger than the available device memory by using a streaming, out-of-core approach. Our algorithm generates RF with the correct statistical behavior and is tested on a variety of modern hardware, such as NVIDIA Tesla, AMD FirePro and Intel Phi. RAFT is faster than the traditional methods on regular meshes and has been successfully applied to two real case scenarios: planetary nebulae and cosmological simulations.

A high-speed on-chip pseudo-random binary sequence generator for multi-tone phase calibration

NASA Astrophysics Data System (ADS)

Gommé, Liesbeth; Vandersteen, Gerd; Rolain, Yves

2011-07-01

An on-chip reference generator is conceived by adopting the technique of decimating a pseudo-random binary sequence (PRBS) signal in parallel sequences. This is of great benefit when high-speed generation of PRBS and PRBS-derived signals is the objective. The design implemented standard CMOS logic is available in commercial libraries to provide the logic functions for the generator. The design allows the user to select the periodicity of the PRBS and the PRBS-derived signals. The characterization of the on-chip generator marks its performance and reveals promising specifications.

Estimation of distribution algorithm with path relinking for the blocking flow-shop scheduling problem

NASA Astrophysics Data System (ADS)

Shao, Zhongshi; Pi, Dechang; Shao, Weishi

2018-05-01

This article presents an effective estimation of distribution algorithm, named P-EDA, to solve the blocking flow-shop scheduling problem (BFSP) with the makespan criterion. In the P-EDA, a Nawaz-Enscore-Ham (NEH)-based heuristic and the random method are combined to generate the initial population. Based on several superior individuals provided by a modified linear rank selection, a probabilistic model is constructed to describe the probabilistic distribution of the promising solution space. The path relinking technique is incorporated into EDA to avoid blindness of the search and improve the convergence property. A modified referenced local search is designed to enhance the local exploitation. Moreover, a diversity-maintaining scheme is introduced into EDA to avoid deterioration of the population. Finally, the parameters of the proposed P-EDA are calibrated using a design of experiments approach. Simulation results and comparisons with some well-performing algorithms demonstrate the effectiveness of the P-EDA for solving BFSP.

Nonconvex Sparse Logistic Regression With Weakly Convex Regularization

NASA Astrophysics Data System (ADS)

Shen, Xinyue; Gu, Yuantao

2018-06-01

In this work we propose to fit a sparse logistic regression model by a weakly convex regularized nonconvex optimization problem. The idea is based on the finding that a weakly convex function as an approximation of the $\\ell_0$ pseudo norm is able to better induce sparsity than the commonly used $\\ell_1$ norm. For a class of weakly convex sparsity inducing functions, we prove the nonconvexity of the corresponding sparse logistic regression problem, and study its local optimality conditions and the choice of the regularization parameter to exclude trivial solutions. Despite the nonconvexity, a method based on proximal gradient descent is used to solve the general weakly convex sparse logistic regression, and its convergence behavior is studied theoretically. Then the general framework is applied to a specific weakly convex function, and a necessary and sufficient local optimality condition is provided. The solution method is instantiated in this case as an iterative firm-shrinkage algorithm, and its effectiveness is demonstrated in numerical experiments by both randomly generated and real datasets.

One-Shot Coherence Dilution.

PubMed

Zhao, Qi; Liu, Yunchao; Yuan, Xiao; Chitambar, Eric; Ma, Xiongfeng

2018-02-16

Manipulation and quantification of quantum resources are fundamental problems in quantum physics. In the asymptotic limit, coherence distillation and dilution have been proposed by manipulating infinite identical copies of states. In the nonasymptotic setting, finite data-size effects emerge, and the practically relevant problem of coherence manipulation using finite resources has been left open. This Letter establishes the one-shot theory of coherence dilution, which involves converting maximally coherent states into an arbitrary quantum state using maximally incoherent operations, dephasing-covariant incoherent operations, incoherent operations, or strictly incoherent operations. We introduce several coherence monotones with concrete operational interpretations that estimate the one-shot coherence cost-the minimum amount of maximally coherent states needed for faithful coherence dilution. Furthermore, we derive the asymptotic coherence dilution results with maximally incoherent operations, incoherent operations, and strictly incoherent operations as special cases. Our result can be applied in the analyses of quantum information processing tasks that exploit coherence as resources, such as quantum key distribution and random number generation.

One-Shot Coherence Dilution

NASA Astrophysics Data System (ADS)

Zhao, Qi; Liu, Yunchao; Yuan, Xiao; Chitambar, Eric; Ma, Xiongfeng

2018-02-01

Manipulation and quantification of quantum resources are fundamental problems in quantum physics. In the asymptotic limit, coherence distillation and dilution have been proposed by manipulating infinite identical copies of states. In the nonasymptotic setting, finite data-size effects emerge, and the practically relevant problem of coherence manipulation using finite resources has been left open. This Letter establishes the one-shot theory of coherence dilution, which involves converting maximally coherent states into an arbitrary quantum state using maximally incoherent operations, dephasing-covariant incoherent operations, incoherent operations, or strictly incoherent operations. We introduce several coherence monotones with concrete operational interpretations that estimate the one-shot coherence cost—the minimum amount of maximally coherent states needed for faithful coherence dilution. Furthermore, we derive the asymptotic coherence dilution results with maximally incoherent operations, incoherent operations, and strictly incoherent operations as special cases. Our result can be applied in the analyses of quantum information processing tasks that exploit coherence as resources, such as quantum key distribution and random number generation.

On random field Completely Automated Public Turing Test to Tell Computers and Humans Apart generation.

PubMed

Kouritzin, Michael A; Newton, Fraser; Wu, Biao

2013-04-01

Herein, we propose generating CAPTCHAs through random field simulation and give a novel, effective and efficient algorithm to do so. Indeed, we demonstrate that sufficient information about word tests for easy human recognition is contained in the site marginal probabilities and the site-to-nearby-site covariances and that these quantities can be embedded directly into certain conditional probabilities, designed for effective simulation. The CAPTCHAs are then partial random realizations of the random CAPTCHA word. We start with an initial random field (e.g., randomly scattered letter pieces) and use Gibbs resampling to re-simulate portions of the field repeatedly using these conditional probabilities until the word becomes human-readable. The residual randomness from the initial random field together with the random implementation of the CAPTCHA word provide significant resistance to attack. This results in a CAPTCHA, which is unrecognizable to modern optical character recognition but is recognized about 95% of the time in a human readability study.

Note: The design of thin gap chamber simulation signal source based on field programmable gate array.

PubMed

Hu, Kun; Lu, Houbing; Wang, Xu; Li, Feng; Liang, Futian; Jin, Ge

2015-01-01

The Thin Gap Chamber (TGC) is an important part of ATLAS detector and LHC accelerator. Targeting the feature of the output signal of TGC detector, we have designed a simulation signal source. The core of the design is based on field programmable gate array, randomly outputting 256-channel simulation signals. The signal is generated by true random number generator. The source of randomness originates from the timing jitter in ring oscillators. The experimental results show that the random number is uniform in histogram, and the whole system has high reliability.

Note: The design of thin gap chamber simulation signal source based on field programmable gate array

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hu, Kun; Wang, Xu; Li, Feng

The Thin Gap Chamber (TGC) is an important part of ATLAS detector and LHC accelerator. Targeting the feature of the output signal of TGC detector, we have designed a simulation signal source. The core of the design is based on field programmable gate array, randomly outputting 256-channel simulation signals. The signal is generated by true random number generator. The source of randomness originates from the timing jitter in ring oscillators. The experimental results show that the random number is uniform in histogram, and the whole system has high reliability.

Random sampling of constrained phylogenies: conducting phylogenetic analyses when the phylogeny is partially known.

PubMed

Housworth, E A; Martins, E P

2001-01-01

Statistical randomization tests in evolutionary biology often require a set of random, computer-generated trees. For example, earlier studies have shown how large numbers of computer-generated trees can be used to conduct phylogenetic comparative analyses even when the phylogeny is uncertain or unknown. These methods were limited, however, in that (in the absence of molecular sequence or other data) they allowed users to assume that no phylogenetic information was available or that all possible trees were known. Intermediate situations where only a taxonomy or other limited phylogenetic information (e.g., polytomies) are available are technically more difficult. The current study describes a procedure for generating random samples of phylogenies while incorporating limited phylogenetic information (e.g., four taxa belong together in a subclade). The procedure can be used to conduct comparative analyses when the phylogeny is only partially resolved or can be used in other randomization tests in which large numbers of possible phylogenies are needed.

Physical layer one-time-pad data encryption through synchronized semiconductor laser networks

NASA Astrophysics Data System (ADS)

Argyris, Apostolos; Pikasis, Evangelos; Syvridis, Dimitris

2016-02-01

Semiconductor lasers (SL) have been proven to be a key device in the generation of ultrafast true random bit streams. Their potential to emit chaotic signals under conditions with desirable statistics, establish them as a low cost solution to cover various needs, from large volume key generation to real-time encrypted communications. Usually, only undemanding post-processing is needed to convert the acquired analog timeseries to digital sequences that pass all established tests of randomness. A novel architecture that can generate and exploit these true random sequences is through a fiber network in which the nodes are semiconductor lasers that are coupled and synchronized to central hub laser. In this work we show experimentally that laser nodes in such a star network topology can synchronize with each other through complex broadband signals that are the seed to true random bit sequences (TRBS) generated at several Gb/s. The potential for each node to access real-time generated and synchronized with the rest of the nodes random bit streams, through the fiber optic network, allows to implement an one-time-pad encryption protocol that mixes the synchronized true random bit sequence with real data at Gb/s rates. Forward-error correction methods are used to reduce the errors in the TRBS and the final error rate at the data decoding level. An appropriate selection in the sampling methodology and properties, as well as in the physical properties of the chaotic seed signal through which network locks in synchronization, allows an error free performance.

Obstacles in Using Randomization Tests in Single-Case Experimentation.

ERIC Educational Resources Information Center

Kazdin, Alan E.

1980-01-01

Problems associated with randomization tests in single- case experiments are discussed. This article follows a discussion of randomization tests in single case studies in the same issue of this journal. (See TM 505 799; 505 801).(Author/JKS)

An analysis of the effect of funding source in randomized clinical trials of second generation antipsychotics for the treatment of schizophrenia.

PubMed

Montgomery, John H; Byerly, Matthew; Carmody, Thomas; Li, Baitao; Miller, Daniel R; Varghese, Femina; Holland, Rhiannon

2004-12-01

The effect of funding source on the outcome of randomized controlled trials has been investigated in several medical disciplines; however, psychiatry has been largely excluded from such analyses. In this article, randomized controlled trials of second generation antipsychotics in schizophrenia are reviewed and analyzed with respect to funding source (industry vs. non-industry funding). A literature search was conducted for randomized, double-blind trials in which at least one of the tested treatments was a second generation antipsychotic. In each study, design quality and study outcome were assessed quantitatively according to rating scales. Mean quality and outcome scores were compared in the industry-funded studies and non-industry-funded studies. An analysis of the primary author's affiliation with industry was similarly performed. Results of industry-funded studies significantly favored second generation over first generation antipsychotics when compared to non-industry-funded studies. Non-industry-funded studies showed a trend toward higher quality than industry-funded studies; however, the difference between the two was not significant. Also, within the industry-funded studies, outcomes of trials involving first authors employed by industry sponsors demonstrated a trend toward second generation over first generation antipsychotics to a greater degree than did trials involving first authors employed outside the industry (p=0.05). While the retrospective design of the study limits the strength of the findings, the data suggest that industry bias may occur in randomized controlled trials in schizophrenia. There appears to be several sources by which bias may enter clinical research, including trial design, control of data analysis and multiplicity/redundancy of trials.

An investigation of the uniform random number generator

NASA Technical Reports Server (NTRS)

Temple, E. C.

1982-01-01

Most random number generators that are in use today are of the congruential form X(i+1) + AX(i) + C mod M where A, C, and M are nonnegative integers. If C=O, the generator is called the multiplicative type and those for which C/O are called mixed congruential generators. It is easy to see that congruential generators will repeat a sequence of numbers after a maximum of M values have been generated. The number of numbers that a procedure generates before restarting the sequence is called the length or the period of the generator. Generally, it is desirable to make the period as long as possible. A detailed discussion of congruential generators is given. Also, several promising procedures that differ from the multiplicative and mixed procedure are discussed.