This paper shows simple dynamic programming algorithms for RNA secondary structure prediction with pseudoknots. For a basic version of the problem (i.e., maximizing the number of base pairs), this paper presents an O(n) time exact algorithm and an O(n4− ) time approximation algorithm. The latter one outputs, for most RNA sequences, a secondary structure in which the number of base pairs is at l...

In this paper, we develop efficient spatial prediction algorithms using Gaussian Markov random fields (GMRFs) under uncertain localization and sequential observations. We first review a GMRF as a discretized Gaussian process (GP) on a lattice, and justify the usage of maximum a posteriori (MAP) estimates of noisy sampling positions in making inferences. We show that the proposed approximation c...

first riccati equation with matrix variable coefficients, arising in optimal and robust control approach, is considered. an analytical approximation of the solution of nonlinear differential riccati equation is investigated using the adomian decomposition method. an application in optimal control is presented. the solution in different order of approximations and different methods of approximat...

We analytically derive the lower bound of the total conformational energy of a protein structure by assuming that the total conformational energy is well approximated by the sum of sequence-dependent pairwise contact energies. The condition for the native structure achieving the lower bound leads to the contact energy matrix that is a scalar multiple of the native contact matrix, i.e., the so-c...

In this paper the author considers an autoregressive process where the parameters of the process are unknown and try to obtain pivots for predicting future observations. If we do a probabilistic prediction with the estimated model, where the parameters are estimated by a sample of size n, we introduce an error of order n−1 in the coverage probabilities of the prediction intervals. However we ca...

We present a new limiter for the PPM method of Colella and Woodward [4] that preserves accuracy at smooth extrema. It is based on constraining the interpolated values at extrema (and only at extrema) using nonlinear combinations of various difference approximations of the second derivatives. Otherwise, we use a standard geometric limiter to preserve monotonicity away from extrema. This leads to...

Motivation: The ideal algorithm for the prediction of pseudoknotted RNA secondary structures will provide fast and accurate predictions for pseudoknots of arbitrary complexity. However, existing algorithms are typically lacking on one of these three axes. Energy-based methods suffer from the intractability of pseudoknotted structure prediction under realistic energy models, while statistical ap...

To tackle the problem with inexact, uncertainty and vague knowl- edge, constructive method is utilized to formulate lower and upper approx- imation sets. Rough set model over dual-universes in fuzzy approximation space is constructed. In this paper, we introduce the concept of rough set over dual-universes in fuzzy approximation space by means of cut set. Then, we discuss properties of rough se...

Barndorff-Nielsen and Cox (1994, p.319) modify an estimative prediction limit to obtain an improved prediction limit with better coverage properties. Kabaila and Syuhada (2008) present a simulation-based approximation to this improved prediction limit, which avoids the extensive algebraic manipulations required for this modification. We present a modification of an estimative prediction interva...

Prediction of economic variables is a basic component not only for economic models, but also for many business decisions. But it is difficult to produce accurate predictions in times of economic crises, which cause nonlinear effects in the data. Such evidence appeared in the German automobile industry as a consequence of the financial crisis in 2008/09, which influenced exchange rates and a...