Provably secure robust threshold partial blind signature

Threshold digital signature and blind signature are playing important roles in cryptography as well as in practical applications such as e-cash and e-voting systems. Over the past few years, many cryptographic researchers have made considerable headway in this field. However, to our knowledge, most of existing threshold blind signature schemes are based on the discrete logarithm problem. In this paper, we propose a new robust threshold partial blind signature scheme based on improved RSA cryptosystem. This scheme is the first threshold partial blind signature scheme based on factoring, and the robustness of threshold partial blind signature is also introduced. Moreover, in practical application, the proposed scheme will be especially suitable for blind signature-based voting systems with multiple administrators and secure electronic cash systems to prevent their abuse.     

Evolutionary training of hardware realizable multilayer perceptrons

The use of multilayer perceptrons (MLP) with threshold functions (binary step function activations) greatly reduces the complexity of the hardware implementation of neural networks, provides tolerance to noise and improves the interpretation of the internal representations. In certain case, such as in learning stationary tasks, it may be sufficient to find appropriate weights for an MLP with threshold activation functions by software simulation and, then, transfer the weight values to the hardware implementation. Efficient training of these networks is a subject of considerable ongoing research. Methods available in the literature mainly focus on two-state (threshold) nodes and try to train the networks by approximating the gradient of the error function and modifying appropriately the gradient descent, or by progressively altering the shape of the activation functions. In this paper, we propose an evolution-motivated approach, which is eminently suitable for networks with threshold functions and compare its performance with four other methods. The proposed evolutionary strategy does not need gradient related information, it is applicable to a situation where threshold activations are used from the beginning of the training, as in “on-chip” training, and is able to train networks with integer weights.     

Solving Non-Linear Models with Saddle-Path Instabilities

Economic models derived from optimizing behavior are typically characterized by the properties of non-linearity and saddle-path instability. The typical solution method involves deriving the stable arm of the saddle-path and calculating suitable “jumps” to bring the path of endogenous variables onto this stable arm. The solution for the stable arm can be determined using a range of different approaches. In this paper we examine the extent to which the success of these alternative approaches can be evaluated. Any method of evaluation will be dependent upon the amount of information that is known about a particular model solution. For some deterministic models the only information known with certainty about the path of the model solution are values taken by steady-state solutions; the rest of the path must be approximated in some way based on numerical solutions derived from non-linear ordinary differential equations. In some special cases it is possible to derive a closed-form solution of the entire path. As an example of a model with a closed-form solution, we consider a simple linear model with two stable complex-valued eigenvalues and one unstable real-valued eigenvalue. The model is then employed as a benchmark to compare the properties of model solutions derived using two well-known solution algorithms. Because the model has complex-valued eigenvalues it will have cyclic dynamics and thus problems encountered in solving these dynamics will likely coincide with some of the problems that solution algorithms have in solving non-linear models. Since the entire solution path of the model is known, it is possible to derive deeper insights into the factors that are likely to ensure the success or failure of different solution approaches than would be the case if less information about the solution path was available.An earlier version of this paper was presented to the Ninth International Conference on Computing in Economics and Finance organized by the Society of Computational Economics, University of Washington at Seattle, July 11–13, 2003. Earlier versions of this paper have also been presented at seminars and workshops at the University of Oxford, at the University of Canterbury at Christchurch, and at the University of Melbourne. JEL Classifications: C63, E17     

Global convergence of Oja's PCA learning algorithm with a non-zero-approaching adaptive learning rate

A non-zero-approaching adaptive learning rate is proposed to guarantee the global convergence of Oja's principal component analysis (PCA) learning algorithm. Most of the existing adaptive learning rates for Oja's PCA learning algorithm are required to approach zero as the learning step increases. However, this is not practical in many applications due to the computational round-off limitations and tracking requirements. The proposed adaptive learning rate overcomes this shortcoming. The learning rate converges to a positive constant, thus it increases the evolution rate as the learning step increases. This is different from learning rates which approach zero which slow the convergence considerably and increasingly with time. Rigorous mathematical proofs for global convergence of Oja's algorithm with the proposed learning rate are given in detail via studying the convergence of an equivalent deterministic discrete time (DDT) system. Extensive simulations are carried out to illustrate and verify the theory derived. Simulation results show that this adaptive learning rate is more suitable for Oja's PCA algorithm to be used in an online learning situation.     

ASYMPTOTIC BEHAVIOURS OF STOCHASTIC DIFFERENTIAL DELAY EQUATIONS

Most of the existing results on stochastic stability use a single Lyapunov function, but we shall instead use multiple Lyapunov functions in this paper. We shall establish the sufficient condition, in terms of multiple Lyapunov functions, for the asymptotic behaviours of solutions of stochastic differential delay equations. Moreover, from them follow many effective criteria on stochastic asymptotic stability, which enable us to construct the Lyapunov functions much more easily in applications. In particular, the well‐known classical theorem on stochastic asymptotic stability is a special case of our more general results. These show clearly the power of our new results. Two examples are also given for illustration.     

求解一类变分不等式离散问题的迭代法

本文提出了求解变分不等式离散问题的一个迭代算法,并在合理的假设下证明了算法的单调收敛性．     

关于同时求解多项式所有零点的改进的Newton法

讨论了同时求解n次多项式所有零点的牛顿法及其改进；给出了保证它们收敛的初值应满足的一个充分条件,并证明了收敛性．数值实例的计算结果是满意的．     

Sex differences in electrophysiological and behavioral responses to NaCl taste.

We tested the hypothesis that sex differences in preference for NaCl are attributable to estrogen-mediated alterations in gustatory processing. Electrophysiological responses of the chorda tympani nerve to NaCl were blunted by estrogen treatment in ovariectomized female rats, suggesting that females are less sensitive to concentrated NaCl solutions during high estrogen conditions. In contrast, after a taste aversion was conditioned to 150-mM NaCl, estrogen- and oil-treated ovariectomized rats generalized the aversion to a lower concentration of NaCl than did males, suggesting that females are more sensitive to the taste of dilute NaCl solutions regardless of estrogen. Thus, sex differences in NaCl preferences may be attributable to differences in NaCl taste processing that involve both acute and developmental effects of estrogen. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Sex-Specific Differences in Olfactory Sensitivity for Putative Human Pheromones in Nonhuman Primates.

In humans, the volatile C19-steroids androsta-4,16-dien-3-one (AND) and estra-1,3,5(10),16-tetraen-3-ol (EST) have been shown to modulate autonomic nervous system responses, and to cause hypothalamic activation in a gender-specific manner. Using two conditioning paradigms, the authors here show that pigtail macaques and squirrel monkeys of both sexes were able to detect AND and EST at concentrations in the micromolar and mM range, respectively. Male and female spider monkeys, in contrast, differed markedly in their sensitivity to these two odorous steroids, with males not showing any behavioral responses to the highest concentrations of AND tested and females not responding to the highest concentrations of EST. These data provide the first examples of sex-specific bimodal distributions of olfactory sensitivity in a nonhuman primate species. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Physician decision making and cardiac risk: Effects of knowledge, risk perception, risk tolerance, and fuzzy processing.

Despite training, professionals sometimes make serious errors in risky decision making. The authors investigated judgments and decisions for 9 hypothetical patients at 3 levels of cardiac risk, comparing student and physician groups varying in domain-specific knowledge. Decisions were examined regarding whether they deviated from guidelines, how risk perceptions and risk tolerances determined decisions, and how the latter differed for knowledge groups. More knowledgeable professionals were better at discriminating levels of risk according to external correspondence criteria but committed similar errors in disjunctive probability judgments, violating internal coherence criteria. Also, higher knowledge groups relied on fewer dimensions of information than did lower knowledge groups. Consistent with fuzzy-trace theory, experts achieved better discrimination by processing less information and made sharper all-or-none distinctions among decision categories. (PsycINFO Database Record (c) 2010 APA, all rights reserved)