The recent development of multi-agent simulations brings about a need for population synthesis. It is a task of reconstructing the entire population from a sampling survey of limited size (1% or so), supplying the initial conditions from which simulations begin. This paper presents a new kernel density estimator for this task. Our method is an analogue of the classical Breiman-Meisel-Purcell estimator, but employs novel techniques that harness the huge degree of freedom which is required to model high-dimensional nonlinearly correlated datasets: the crossover kernel, the k-nearest neighbor restriction of the kernel construction set and the bagging of kernels. The performance as a statistical estimator is examined through real and synthetic datasets. We provide an "optimization-free" parameter selection rule for our method, a theory of how our method works and a computational cost analysis. To demonstrate the usefulness as a population synthesizer, our method is applied to a household synthesis task for an urban micro-simulator.

This paper proposes a fast multi-cycle path analysis method for large sequential circuits. It determines whether or not all the paths between every flip-flop pair are multi-cycle paths. The proposed method is based on ATPG techniques, especially on implication techniques, to utilize circuit structure and multi-cycle path condition directly. The method also checks whether or not the multi-cycle path may be invalidated by static hazards in combinational logic parts. Experimental results show that our method is much faster than conventional ones.

Proposes lazy group sifting for dynamic variable reordering during state traversal of finite state machines (FSMs). The proposed method relaxes the idea of pairwise grouping of the present state variables and their corresponding next state variables. This is done to produce better variable orderings during image computation without causing BDD (binary decision diagram) size blowup in the substitution of next state variables with present state variables at the end of image computation. Experimental results show that our approach is more robust in state traversal than the approaches that either unconditionally group variable pairs or never group them.

This paper proposes a state reduction algorithm for incompletely specified FSMs. The algorithm is based on iterative improvements. When the number of compatibles is likely to be too large to handle explicitly, they are represented by a BDD. Experimental results are given to demonstrate that the algorithm described here is faster and obtains better solutions than conventional methods.

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