Narrow Results

We examine the impact of exchange rates on foreign direct investment (FDI) inflows into the United States in the context of a model that allows for the interdependence of FDI over time. Interdependence is modeled as a two-state Markov process where the two states can be interpreted as either a favorable or an unfavorable environment for FDI in an industry. We use unbalanced industry-level panel data from the US wholesale trade sector and our analysis yields two main results. First, we find evidence that FDI is interdependent over time. Second, under a favorable FDI environment, the exchange rate has a positive and significant effect on the average rate of FDI inflows.

In this paper, an effective method based on the color quaternion wavelet transform (CQWT) for image forensics is proposed. Compared to discrete wavelet transform (DWT), the CQWT provides more information, such as the quaternion’s magnitude and phase measures, to discriminate between computer generated (CG) and photographic (PG) images. Meanwhile, we extend the classic Markov features into the quaternion domain to develop the quaternion Markov statistical features for color images. Experimental results show that the proposed scheme can achieve the classification rate of 92.70%, which is 6.89% higher than the classic Markov features.

This paper presents a new approach for the estimation of motion trajectories from image sequences. This approach is seen to be similar to both "token tracking" feature-based methods, and "block based" region-matching approaches. Initially, a parametric motion trajectory model is formulated, and the motion trajectory estimation problem is conveniently cast into a Markov Random Field (MRF) framework which allows a priori motion constraints to be expressed. A three-stage optimization algorithm is presented, and a recursive estimation approach is developed. A method for the robust detection of areas of occlusion is presented, as are comparative results to show the relative success of this motion estimation approach. Although robustly computed motion trajectories show great promise in many motion-related areas of computer vision, this paper illustrates their use in the context of motion compensated prediction as part of a video compression algorithm.

A group is Markov if it admits a prefix-closed regular language of unique representatives with respect to some generating set, and strongly Markov if it admits such a language of unique minimal-length representatives over every generating set. This paper considers the natural generalizations of these concepts to semigroups and monoids. Two distinct potential generalizations to monoids are shown to be equivalent. Various interesting examples are presented, including an example of a non-Markov monoid that nevertheless admits a regular language of unique representatives over any generating set. It is shown that all finitely generated commutative semigroups are strongly Markov, but that finitely generated subsemigroups of virtually abelian or polycyclic groups need not be. Potential connections with word-hyperbolic semigroups are investigated. A study is made of the interaction of the classes of Markov and strongly Markov semigroups with direct products, free products, and finite-index subsemigroups and extensions. Several questions are posed.

The current study uses reliability models for the improvement of the operation of a ship's "bilge — water separator" system. A "bilge-water separator" is a mechanism which cleans and inspects the ship's bilge water before it is discharged into the sea. Homogeneous continuous time Markov models have been used to record and estimate possible hazards and system failures in two different operational scenarios. If the photocell unit of the system fails, the ship may cause severe sea pollution. This study attempts to estimate the probability of sea pollution based on empirical data. In addition, the results of the model are compared with those of a system in which a second metering unit is added in an effort to to find out if this alteration improves the systems' efficiency.

A methodology for reliability and safety analysis of a certain class of repairable fault-tolerant systems is presented. The analysis leads to a closed-form approximation of the probability of an absorbing state in a state transition diagram. This expression can provide insight into the relationship between various system parameters and system reliability and/or safety. The approximation technique is based on the combination of results from the analysis of several failure mechanisms, each studied by itself, into an expression for the approximate reliability of a system. The resulting approximation error can be analyzed in order to evaluate whether or not the approximation is useful in a given situation.

R-processes are a new type of discrete stationary stochastic process. In this paper, we investigate the underlying structure of two special classes of r-processes. For each class, we will prove precisely when the associated multidigraph is perfect.

Records of physical phenomena, such as turbulence, have been successfully modeled by random multiplicative processes. The present work expands such treatments by considering the effects of memory within the random multiplicative process and its consequences on the multifractal behavior of the measure. The measure-generating multiplicative cascade treated here involves first-order, two-state, Markov multipliers. When the two self-transition Markov probabilities, pii, i = 1,2, are equal, yet different from 0.5, the average occurrence of the multipliers converges to 50% as in the memoryless case. Nevertheless, the Markov memory influences the spread of multipliers. The conservation of measure now relaxes to convergence towards a nontrivial and finite value and the shape of singularity spectrum depends to a great extent on the Markov probabilities. Application of the model to turbulence data indicates an underlying anti-persistent Markov process.

This paper presents a new approach for the estimation of motion trajectories from image sequences. This approach is seen to be similar to both “token tracking” feature-based methods, and “block based” region-matching approaches. Initially, a parametric motion trajectory model is formulated, and the motion trajectory estimation problem is conveniently cast into a Markov Random Field (MRF) framework which allows a priori motion constraints to be expressed. A three-stage optimization algorithm is presented, and a recursive estimation approach is developed. A method for the robust detection of areas of occlusion is presented, as are comparative results to show the relative success of this motion estimation approach. Although robustly computed motion trajectories show great promise in many motion-related areas of computer vision, this paper illustrates their use in the context of motion compensated prediction as part of a video compression algorithm.