We explain how to apply statistical techniques to solve several language-recognition problems that arise in cryptanalysis and other domains. Language recognition is important in cryptanalysis because, among other applications, an exhaustive key search of any cryptosystem from ciphertext alone requires a test that recognizes valid plaintext. Written for cryptanalysts, this guide should also be helpful to others as an introduction to statistical inference on Markov chains. Modeling language as a finite stationary Markov process, we adapt a statistical model of pattern recognition to language recognition. Within this framework we consider four welldefined language-recognition problems: 1) recognizing a known language, 2) distinguishing a known language from uniform noise, 3) distinguishing unknown 0th-order noise from unknown 1st-order language, and 4) detecting non-uniform unknown language. For the second problem we give a most powerful test based on the Neyman-Pearson Lemma. For the oth...

Abstract. We present a trust-based mechanism for the acquisition of information from possibly unreliable sources. Our mechanism addresses the case where the acquired information cannot be verified. The idea is to intersperse questions (“challenges”) for which the correct answers are known. By evaluating the answers to these challenges, probabilistic conclusions about the correctness of the unverifiable information can be drawn. Less challenges need to be used if an information provider has shown to be trustworthy. This work focuses on three major issues of such a mechanism. First, how to estimate the correctness of the unverifiable information. Second, how to determine an optimal number of challenges. And finally, how to establish trust and use it to reduce the number of challenges. Our approach can resist collusion and shows great promise for various application areas such as distributed computing or peer-to-peer networks.

In a regression problem, one is given a d- dimensional random vector X, the components of which are called predictor variables, and a random variable, Y , called response. A regression surface describes a general relationship between variables X and Y . One nonparametric regression technique that has been successfully applied to highdimensional data is projection pursuit regression (PPR). In this method, the regression surface is approximated by a sum of empirically determined univariate functions of linear combinations of the predictors. Projection pursuit learning (PPL) proposed by Hwang et al. formulates PPR using a two-layer feedforward neural network. One of the main differences between PPR and PPL is that the smoothers in PPR are nonparametric, whereas those in PPL are based on Hermite functions of some predefined highest order R. While the convergence property of PPR is already known, that for PPL has not been thoroughly studied. In this paper, we demonstrate that PPL networks...

ABSTRACT We have used the isolated planar cortex of sea urchin eggs to examine the role of osmotic forces in exocytosis by morphological and physiological methods. Electron micrographs of rotary-shadowed replicas show an en face view of exocytosis and demonstrate fusion of cortical vesicles to the underlying oolemma upon addition of calcium. Freeze-fracture replicas of rapidly frozen cortices reveal specialized attachment sites between cortical vesicles and the oolemma, and between the cortical vesicles themselves. We describe a novel light scattering assay for the kinetics of fusion which allows rapid changes of solutions and monitors exocytosis in real time. The rate and extent of fusion are found to be calcium dependent. The removal of calcium halts exocytosis. The validation of exocytosis in this system and development of tools for kinetic analysis allowed us to test two predictions of the osmotic hypothesis of exocytosis: hyperosmotic media should inhibit exocytosis; calcium should cause vesicular swelling. Cortical vesicles were found to be permeant to sucrose, glucose, and urea. In media made hyperosmotic with 1.7 M sucrose, cortical vesicles were seen to shrink. Addition of calcium in hyperosmotic media led to a 10-fold decrease in the rate of exocytosis compared with the isotonic rate. The

A simple method for detecting periodic components of unknown periodicity in a signal is presented. The method is based on spectral decomposition of signal using orthonormal functions. Traditionally, hypothesis testing together with harmonic functions is used, but we show that the same statistical properties are obtained for other systems of orthonormal functions as well. The appropriate behavior of the method is first demonstrated with simulation studies and then tested to identify visually determined clusters of highfrequency movements which may repeat in syncrony with respiration during sleep. The good performance in the practical test suggests that an automatic identification of these clusters could be based on Walsh functions.

Redundant task allocation with majority voting is a common technique for result verification in grid computing. The technique fails though in cases where a majority of colluding clients returns collectively the same incorrect result. We therefore propose a mechanism that tries to identify collectives of colluding clients. The mechanism bases on the observation that in all cases where colluders succeed, they are together in the majority whereas all the honest clients are together in the minority. By looking at this “correlation” in voting-outcomes of any two clients we can estimate whether they are both malicious, both honest, or one is malicious and one is honest. This allows us to partition the set of all clients into clusters containing the malicious clients and clusters containing the honest clients. To substantiate the functioning of the proposed mechanism, we theoretically show that for specific collusion strategies the correlation of two clients actually is a good indicator for them having the same or different attitudes (honest/malicious).

This paper reports on an extensive Monte Carlo study of seven residual-based tests of the hypothesis of no cointegration. Critical values and the power of the tests under the alternative of fractional cointegration are simulated and compared. It turns out that the Phillips-Perron t-test when applied to regression residuals is more powerful than Geweke-Porter-Hudak tests and the Augmented Dickey-Fuller test. Only the Modified Rescaled Range test is more powerful than the Phillips-Perron test in a few situations. Moreover in large samples, the power of the Phillips-Perron test increases if a time trend is included in the cointegrating regression.

Development Department of the Inter-American Development Bank. The views and opinions expressed herein are those of the authors and do not necessarily reflect the official position of the Inter-American Development Bank. We appreciate the comments In a recent art icle published in Land Economics, Boman, Bostedt and Kriström (1999, hereafter BBK) show how nonparametric lower bound, intermediate and upper bound measures of average welfare change can be extracted from discrete-response contingent valuation survey data, building on the work of Kriström (1990) and Haab and McConnell