Turk-2, a multi-modal chess player

In this paper we present Turk-2, a hybrid multi-modal chess player with a robot arm and a screen-based talking head. Turk-2 can not only play chess, but can see and hear the opponent, can talk to him and display emotions. We were interested to find out if a simple embodiment with human-like communication capabilities enhances the experience of playing chess against a computer. First, give an overview of the development road to multi-modal communication with computers. Then we motivate our research with a hybrid system, we introduce the architecture of Turk-2, we describe the human experiments and its evaluation. The results justify that multi-modal interaction makes game playing more engaging, enjoyable – and even more effective. These findings for a specific game situation provide yet another evidence of the power of human-like interaction in turning computer systems more attractive and easier to use.     

A clonal selection algorithm for dynamic facility layout problems

The dynamic facility layout problem (DFLP) aims to minimize the sum of handling and re-layout costs by devising an individual layout for each distinctive production period. It is a complex combinatorial optimization problem. This paper proposes a clonal selection algorithm (CSA) for DFLP. The algorithm is capable of being extended to more general cases although equal area machines and standardized handling equipments with identical unit costs are assumed for the time being. The performance of the proposed algorithm is examined by using three test problems from the literature. CSA reached the best known solutions–even found better solutions for large sized problems–in 88% of the instances, while each of the former methods was successful only in a small fraction of those 50 problems. CSA also outperformed all the present methods as regards solution times.     

General distributed economic framework for dynamic spectrum allocation

The emergence of novel radio techniques enables the application of advantageous revolutionary spectrum policies. An important body of research has appeared about possible frequency management schemes, but none of them proposes solutions that meet every related criteria. In this paper we present our work on dynamic spectrum allocation and pricing that offers a distributed mechanism design, well-suited to practical employment issues. Our model handles interference effects without any restricting assumptions, provides universal scalable and incentive-compatible allocation and pricing mechanisms. We provide both analytical and numerical evaluation of the proposed framework, and in either case we prove this latter to be a suitable approach to efficient and flexible spectrum utilization.     

Models wagging the dog: are circuits constructed with disparate parameters?

In a recent article, Prinz, Bucher, and Marder (2004) addressed the fundamental question of whether neural systems are built with a fixed blueprint of tightly controlled parameters or in a way in which properties can vary largely from one individual to another, using a database modeling approach. Here, we examine the main conclusion that neural circuits indeed are built with largely varying parameters in the light of our own experimental and modeling observations. We critically discuss the experimental and theoretical evidence, including the general adequacy of database approaches for questions of this kind, and come to the conclusion that the last word for this fundamental question has not yet been spoken.     

Efficient computation of Morse-Smale complexes for three-dimensional scalar functions

The Morse-Smale complex is an efficient representation of the gradient behavior of a scalar function, and critical points paired by the complex identify topological features and their importance. We present an algorithm that constructs the Morse-Smale complex in a series of sweeps through the data, identifying various components of the complex in a consistent manner. All components of the complex, both geometric and topological, are computed, providing a complete decomposition of the domain. Efficiency is maintained by representing the geometry of the complex in terms of point sets.     

Interactive Ray Tracing of Large Models Using Voxel Hierarchies

We propose an efficient approach for interactive visualization of massive models with CPU ray tracing. A voxel‐based hierarchical level‐of‐detail (LOD) framework is employed to minimize rendering time and required system memory. In a pre‐processing phase, a compressed out‐of‐core data structure is constructed, which contains the original primitives of the model and the LOD voxels, organized into a kd‐tree. During rendering, data is loaded asynchronously to ensure a smooth inspection of the model regardless of the available I/O bandwidth. With our technique, we are able to explore data sets consisting of hundreds of millions of triangles in real‐time on a desktop PC with a quad‐core CPU.     

An implementation of the coupled perturbed Kohn-Sham equations: perturbation due to nuclear displacements

The Coupled Perturbed Kohn-Sham equations have been implemented in the Amsterdam Density Functional program package. Our implementation differs from previous ones in many ways. This program uses density fitting to calculate the Coulomb and exchange integrals. Further, all matrix elements of the Fock type matrix and its derivatives are calculated by numerical integration. The frozen core approximation is also implemented. Our implementation is approximately 10 times faster than a finite difference algorithm, and the absolute CPU times also compare favorably with other reported implementations.     

Model complexity reduction of chemical reaction networks using mixed-integer quadratic programming

The model complexity reduction problem of large chemical reaction networks under isobaric and isothermal conditions is considered. With a given detailed kinetic mechanism and measured data of the key species over a finite time horizon, the complexity reduction is formulated in the form of a mixed-integer quadratic optimization problem where the objective function is derived from the parametric sensitivity matrix. The proposed method sequentially eliminates reactions from the mechanism and simultaneously tunes the remaining parameters until the pre-specified tolerance limit in the species concentration space is reached. The computational efficiency and numerical stability of the optimization are improved by a pre-reduction step followed by suitable scaling and initial conditioning of the Hessian involved. The proposed complexity reduction method is illustrated using three well-known case studies taken from reaction kinetics literature.