Showing papers on "Randomness published in 2002"

Random matrix approach to cross correlations in financial data.

Abstract: We analyze cross correlations between price fluctuations of different stocks using methods of random matrix theory (RMT). Using two large databases, we calculate cross-correlation matrices C of returns constructed from (i) 30-min returns of 1000 US stocks for the 2-yr period 1994-1995, (ii) 30-min returns of 881 US stocks for the 2-yr period 1996-1997, and (iii) 1-day returns of 422 US stocks for the 35-yr period 1962-1996. We test the statistics of the eigenvalues lambda(i) of C against a "null hypothesis" - a random correlation matrix constructed from mutually uncorrelated time series. We find that a majority of the eigenvalues of C fall within the RMT bounds [lambda(-),lambda(+)] for the eigenvalues of random correlation matrices. We test the eigenvalues of C within the RMT bound for universal properties of random matrices and find good agreement with the results for the Gaussian orthogonal ensemble of random matrices-implying a large degree of randomness in the measured cross-correlation coefficients. Further, we find that the distribution of eigenvector components for the eigenvectors corresponding to the eigenvalues outside the RMT bound display systematic deviations from the RMT prediction. In addition, we find that these "deviating eigenvectors" are stable in time. We analyze the components of the deviating eigenvectors and find that the largest eigenvalue corresponds to an influence common to all stocks. Our analysis of the remaining deviating eigenvectors shows distinct groups, whose identities correspond to conventionally identified business sectors. Finally, we discuss applications to the construction of portfolios of stocks that have a stable ratio of risk to return. (Less)

Probabilistic models of information retrieval based on measuring the divergence from randomness

Abstract: We introduce and create a framework for deriving probabilistic models of Information Retrieval. The models are nonparametric models of IR obtained in the language model approach. We derive term-weighting models by measuring the divergence of the actual term distribution from that obtained under a random process. Among the random processes we study the binomial distribution and Bose--Einstein statistics. We define two types of term frequency normalization for tuning term weights in the document--query matching process. The first normalization assumes that documents have the same length and measures the information gain with the observed term once it has been accepted as a good descriptor of the observed document. The second normalization is related to the document length and to other statistics. These two normalization methods are applied to the basic models in succession to obtain weighting formulae. Results show that our framework produces different nonparametric models forming baseline alternatives to the standard tf-idf model.

Random and Mixed Effects

Abstract: Models with mixed effects contain both fixed and random effects. Fixed effects are what we have been considering up to now; the only source of randomness in our models arises from regarding the cases as independent random samples. Thus in regression we have an additive measurement error that we assume is independent between cases, and in a GLM we observe independent binomial, Poisson, gamma ... random variates whose mean is a deterministic function of the explanatory variables.

Randomness conductors and constant-degree lossless expanders

Abstract: The main concrete result of this paper is the first explicit construction of constant degree lossless expanders. In these graphs, the expansion factor is almost as large as possible: (1—e)D, where D is the degree and e is an arbitrarily small constant. The best previous explicit constructions gave expansion factor D/2, which is too weak for many applications. The D/2 bound was obtained via the eigenvalue method, and is known that that method cannot give better bounds.The main abstract contribution of this paper is the introduction and initial study of randomness conductors, a notion which generalizes extractors, expanders, condensers and other similar objects. In all these functions, certain guarantee on the input "entropy" is converted to a guarantee on the output "entropy". For historical reasons, specific objects used specific guarantees of different flavors. We show that the flexibility afforded by the conductor definition leads to interesting combinations of these objects, and to better constructions such as those above.The main technical tool in these constructions is a natural generalization to conductors of the zig-zag graph product, previously defined for expanders and extractors.

The production and perception of randomness.

Abstract: This article contains a discussion of the elusive nature of the concept of randomness, a review of findings from experiments with randomness production and randomness perception tasks, and a presentation of theoretical treatments of people's randomization capabilities and limitations. The importance of task instructions and the difficulty of interpreting results when instructions are vague or ambiguous are stressed. The widely held view that people are incapable of generating or recognizing randomness is shown to lack the strong experimental support that has sometimes been claimed for it.

True Random Number Generator Embedded in Reconfigurable Hardware

Abstract: This paper presents a new True Random Number Generator (TRNG) based on an analog Phase-Locked Loop (PLL) implemented in a digital Altera Field Programmable Logic Device (FPLD). Starting with an analysis of the one available on chip source of randomness - the PLL synthesized low jitter clock signal, a new simple and reliable method of true randomness extraction is proposed. Basic assumptions about statistical properties of jitter signal are confirmed by testing of mean value of the TRNG output signal. The quality of generated true random numbers is confirmed by passing standard NIST statistical tests. The described TRNG is tailored for embedded System-On-a-Programmable-Chip (SOPC) cryptographic applications and can provide a good quality true random bit-stream with throughput of several tens of kilobits per second. The possibility of including the proposed TRNG into a SOPC design significantly increases the system security of embedded cryptographic hardware.

Markov Chain Algorithms for Planar Lattice Structures

Abstract: Consider the following Markov chain, whose states are all domino tilings of a 2n× 2n chessboard: starting from some arbitrary tiling, pick a 2×2 window uniformly at random. If the four squares appearing in this window are covered by two parallel dominoes, rotate the dominoes $90^{\rm o}$ in place. Repeat many times. This process is used in practice to generate a random tiling and is a widely used tool in the study of the combinatorics of tilings and the behavior of dimer systems in statistical physics. Analogous Markov chains are used to randomly generate other structures on various two-dimensional lattices. This paper presents techniques which prove for the first time that, in many interesting cases, a small number of random moves suffice to obtain a uniform distribution.

Minimum entropy control of non-Gaussian dynamic stochastic systems

Abstract: This paper presents a new method to minimize the closed loop randomness for general dynamic stochastic systems using the entropy concept. The system is assumed to be subjected to any bounded random inputs. Using the recently developed linear B-spline model for the shape control of the system output probability density function, a control input is formulated which minimizes the output entropy of the closed-loop system. Since the entropy is the measure of randomness for a given random variable, this controller can thus reduce the uncertainty of the closed-loop system. A sufficient condition is established to guarantee the local stability of the closed-loop system. It is shown that this minimum entropy control concept generates a minimum variance control when the stochastic system is represented by an ARMAX model which is subjected to Gaussian noises. An illustrative example is utilized to demonstrate the use of the control algorithm, and satisfactory results are obtained.

Some Difficult-to-pass Tests of Randomness

Abstract: We describe three tests of randomness-- tests that many random number generators fail. In particular, all congruential generators-- even those based on a prime modulus-- fail at least one of the tests, as do many simple generators, such as shift register and lagged Fibonacci. On the other hand, generators that pass the three tests seem to pass all the tests in the Diehard Battery of Tests. Note that these tests concern the randomness of a generator's output as a sequence of independent, uniform 32-bit integers. For uses where the output is converted to uniform variates in [0,1), potential flaws of the output as integers will seldom cause problems after the conversion. Most generators seem to be adequate for producing a set of uniform reals in [0,1), but several important applications, notably in cryptography and number theory-- for example, establishing probable primes, complexity of factoring algorithms, random partitions of large integers-- may require satisfactory performance on the kinds of tests we describe here.

Pseudo-random generators for all hardnesses

Abstract: (MATH) We construct the first pseudo-random generators with logarithmic seed length that convert s bits of hardness into sΩ(1) bits of 2-sided pseudo-randomness for any s}. This improves [8] and gives a direct proof of the optimal hardness vs. randomness tradeoff in [15]. A key element in our construction is an augmentation of the standard low-degree extension encoding that exploits the field structure of the underlying space in a new way.

Ten Lectures on Random Media

Abstract: The field of random media has been the object of intensive activity over the last twenty-five years. It gathers a variety of models generally originating from physical sciences, where certain materials or substances have defects or inhomogeneities. This feature can be taken into account by letting the medium be random. Randomness in the medium turns out to cause very unexpected effects, especially in the large-scale behavior of some of these models. What in the beginning was often deemed to be a simple toy-model ended up as a major mathematical challenge. After more than twenty years of intensive research in this field, certain new paradigms and some general methods have emerged, and the surprising results on the asymptotic behavior of individual models are now better understood in more general frameworks. This monograph grew out of the DMV Lectures on Random Media held by the authors at the Mathematical Research Institute in Oberwolfach in November 1999 and tries to give an account of some of the developments in the field, especially in the area of random motions in random media and of mean-field spin glasses. It will be a valuable resource for postgraduates and researchers in probability theory and mathematical physics.

An effective criterion for ballistic behavior of random walks in random environment

Abstract: We investigate multi-dimensional random walks in random environment. Specifically, we provide an effective criterion which can be checked by inspection of the environment in a finite box, and implies a ballistic strong law of large numbers, a central limit theorem and several large deviation controls. With the help of this criterion, we provide an example of a ballistic multi-dimensional walk which does not fulfill the criterion introduced by Kalikow in [4]. The present work complements the results of [9], where a certain condition (T) was introduced, and confirms the interest of this condition.

Compression of sources of probability distributions and density operators

Abstract: We study the problem of efficient compression of a stochastic source of probability distributions. It can be viewed as a generalization of Shannon's source coding problem. It has relation to the theory of common randomness, as well as to channel coding and rate--distortion theory: in the first two subjects ``inverses'' to established coding theorems can be derived, yielding a new approach to proving converse theorems, in the third we find a new proof of Shannon's rate--distortion theorem. After reviewing the known lower bound for the optimal compression rate, we present a number of approaches to achieve it by code constructions. Our main results are: a better understanding of the known lower bounds on the compression rate by means of a strong version of this statement, a review of a construction achieving the lower bound by using common randomness which we complement by showing the optimal use of the latter within a class of protocols. Then we review another approach, not dependent on common randomness, to minimizing the compression rate, providing some insight into its combinatorial structure, and suggesting an algorithm to optimize it. The second part of the paper is concerned with the generalization of the problem to quantum information theory: the compression of mixed quantum states. Here, after reviewing the known lower bound we contribute a strong version of it, and discuss the relation of the problem to other issues in quantum information theory.

Local and occupation time of a particle diffusing in a random medium.

Abstract: We consider a particle moving in a one-dimensional potential which has a symmetric deterministic part and a quenched random part. We study analytically the probability distributions of the local time (spent by the particle around its mean value) and the occupation time (spent above its mean value) within an observation time window of size t. In the absence of quenched randomness, these distributions have three typical asymptotic behaviors depending on whether the deterministic potential is unstable, stable, or flat. These asymptotic behaviors are shown to get drastically modified when the random part of the potential is switched on, leading to the loss of self-averaging and wide sample to sample fluctuations.

A generalization of Chaitin's halting probability $\Omega$ and halting self-similar sets

Abstract: We generalize the concept of randomness in an infinite binary sequence in order to characterize the degree of randomness by a real number D > 0. Chaitin's halting probability Ω is generalized to Ω D whose degree of randomness is precisely D. On the basis of this generalization, we consider the degree of randomness of each point in Euclidean space through its base-two expansion. It is then shown that the maximum value of such a degree of randomness provides the Hausdorff dimension of a self-similar set that is computable in a certain sense. The class of such self-similar sets includes familiar fractal sets such as the Cantor set, von Koch curve, and Sierpinski gasket. Knowledge of the property of Ω D allows us to show that the self-similar subset of [0, 1] defined by the halting set of a universal algorithm has a Hausdorff dimension of one.

How good are deterministic fluid models of Internet congestion control

Abstract: Congestion control algorithms used in the Internet are difficult to analyze or simulate on a large scale, i.e., when there are large numbers of nodes, links and sources in a network. The reasons for this include the complexity of the actual implementation of the algorithm and the randomness introduced in the packet arrival and service processes due to many factors such as arrivals and departures of sources and uncontrollable short flows in the network. To make the simulation tractable, often deterministic fluid model approximations of these algorithms are used. These approximations are in the form of either deterministic delay differential equations, or more generally, deterministic functional differential equations. We justify the use of deterministic models for proportionally fair congestion controllers under a limiting regime where the number of sources in a network is large. We verify our results through simulations of window-based implementations of proportionally fair controllers and TCP.

Random coupling of chaotic maps leads to spatiotemporal synchronization.

Abstract: We investigate the spatiotemporal dynamics of a network of coupled chaotic maps, with varying degrees of randomness in coupling connections. While strictly nearest neighbor coupling never allows spatiotemporal synchronization in our system, randomly rewiring some of those connections stabilizes entire networks at x*, where x* is the strongly unstable fixed point solution of the local chaotic map. In fact, the smallest degree of randomness in spatial connections opens up a window of stability for the synchronized fixed point in coupling parameter space. Further, the coupling ∈ b i f r at which the onset of spatiotemporal synchronization occurs, scales with the fraction of rewired sites p as a power law, for 0.1

Design of an on–chip random number generator using metastability

Abstract: This paper shows that the internal noise in a bistable exhibits a Gaussian distribution, and is close to the value expected from thermal agitation. We describe a random number generator based on this property that is capable of on chip integration, and is a primary source of high entropy data at 100MHz. The device is held close to metastability by a feedback loop, and is therefore relatively insensitive to circuit asymmetries and drift. Measurements of post-processed data from this source also show a relatively high bit rate and sequences of 223bits are shown to pass stringent tests for randomness.

Stochastic resonance in a model of opinion formation on small-world networks

Abstract: We analyze the phenomenon of stochastic resonance in an Ising-like system on a small-world network. The system, which is subject to the combined action of noise and an external modulation, can be interpreted as a stylized model of opinion formation by imitation under the effects of a “fashion wave”. Both the amplitude threshold for the detection of the external modulation and the width of the stochastic-resonance peak show considerable variation as the randomness of the underlying small-world network is changed.

Random walks on trees and matchings

Abstract: We give sharp rates of convergence for a natural Markov chain on the space of phylogenetic trees and dually for the natural random walk on the set of perfect matchings in the complete graph on $2n$ vertices. Roughly, the results show that $(1/2) n \log n$ steps are necessary and suffice to achieve randomness. The proof depends on the representation theory of the symmetric group and a bijection between trees and matchings.

Statistically stable ultrasonic imaging in random media

Abstract: Analysis of array data from acoustic scattering in a random medium with a small number of isolated targets is performed in order to image and, thereby, localize the spatial position of each target. Because the host medium has random fluctuations in wave speed, the background medium is itself a source of scattered energy. It is assumed, however, that the targets are sufficiently larger and/or more reflective than the background fluctuations so that a clear distinction can be made between targets and background scatterers. In numerical simulations nonreflective boundary conditions are used so as to isolate the effects of the host randomness from those of the spatial boundaries, which can then be treated in a separate analysis. It is shown that the key to successful imaging is finding statistically stable functionals of the data whose extreme values provide estimates of scatterer locations. The best ones are related to the eigenfunctions and eigenvalues of the array response matrix, just as one might expect from prior work on array data processing in complex scattering media having homogeneous backgrounds. The specific imaging functionals studied include matched-field processing and linear subspace methods, such as MUSIC (MUtiple SIgnal Classification). But statistical stability is not characteristic of the frequency domain, which is often the province of these methods. By transforming back into the time domain after first diagonalizing the array data in the frequency domain, one can take advantage of both the time-domain stability and the frequency-domain orthogonality of the relevant eigenfunctions.

Randomness in the Primary Structure of Protein: Methods and Implications

Abstract: It is no doubt that the evolutionary process is affected by chance, but the question is to what extent the chance plays its role. The random analysis can throw light on the underlying reasoning for the primary structure of proteins. With the use of random principles, we have explored three approaches to analyse protein primary structure, i.e. the randomness in the construction of amino-acid sequences, in the follow-up amino acid, and in the distribution of amino acid/amino acids. As the results, (i) we can evaluate the impact of chance on the composition of aminoacid sequences by comparing the measured probability/frequency with the predicted probability/ frequency; (ii) we can evaluate the impact of chance on the follow-up amino acid by comparing the Markov transition probability with the predicted conditional probability; and (iii) we can evaluate the effect of chance on the distribution of amino acids by comparing the real distribution probability with the theoretical distribution probability. These approaches can be used to quantitatively analyse the primary structure of intra-protein as well as inter-proteins, thus we can get more insights into the mechanisms of protein construction, mutation, and evolutionary process. Also, these approaches may have some potential use for development of new drugs.

Large deviations for random walks on Galton–Watson trees: averaging and uncertainty

Abstract: In the study of large deviations for random walks in random environment, a key distinction has emerged between quenched asymptotics, conditional on the environment, and annealed asymptotics, obtained from averaging over environments. In this paper we consider a simple random walk {X n } on a Galton–Watson tree T, i.e., on the family tree arising from a supercritical branching process. Denote by |X n | the distance between the node X n and the root of T. Our main result is the almost sure equality of the large deviation rate function for |X n |/n under the “quenched measure” (conditional upon T), and the rate function for the same ratio under the “annealed measure” (averaging on T according to the Galton–Watson distribution). This equality hinges on a concentration of measure phenomenon for the momentum of the walk. (The momentum at level n, for a specific tree T, is the average, over random walk paths, of the forward drift at the hitting point of that level). This concentration, or certainty, is a consequence of the uncertainty in the location of the hitting point. We also obtain similar results when {X n } is a λ-biased walk on a Galton–Watson tree, even though in that case there is no known formula for the asymptotic speed. Our arguments rely at several points on a “ubiquity” lemma for Galton–Watson trees, due to Grimmett and Kesten (1984).