Combined Activity/Travel Choice Models: Time-Dependent and Dynamic Versions

To fully understand and predict travel demand and traffic flow, it is necessary to investigate what drives people to travel. The analysis should examine why, where and when various activities are engaged in, and how activity engagement is related to the spatial and institutional organization of an urban area. In view of this, two combined activity/travel choice models are presented in this paper. The first one is a time-dependent (quasi-dynamic) model for long-term transport planning such as travel demand forecasting, while the other one is a dynamic model for short-term traffic management such as instantaneous flow analysis. The time-dependent model is formulated as a mathematical programming problem for modeling the multinomial logit activity/destination choice and the user equilibrium route choice behavior. It can further be converted to a variational inequality problem. On the other hand, the dynamic model is aimed to find a solution for equilibrium activity location, travel route and departure time choices in queuing networks with multiple commuter classes. It is formulated as a discrete-time, finite-dimensional variational inequality and then converted to an equivalent zero-extreme value minimization problem. Solution algorithms are proposed for these two models and numerical example is presented for the latter. It is shown that the proposed modeling approaches, either based on time-dependent or dynamic traffic assignment principles, provide powerful tools to a wide variety of activity/travel choice problems in dynamic domain.     

Surface reconstruction from stereoscopy and “shape from shading” in SEM images

The computational reconstruction of surface topographies from scanning electron microscope (SEM) images has been extensively investigated in the past, but fundamental image processing problems still exist. Since conventional approaches adapted from general-purpose image processing have not sufficiently met the requirements in terms of resolution and reliability, we have explored combining different methods to obtain better results.This paper presents a least-squares combination of conventional stereoscopy with shape from shading and a way of obtaining self-consistent surface profiles from stereoscopy and stereo-intrinsic shape from shading using dynamic programming techniques. Results are presented showing how this combined analysis of multi-sensorial data yields improvements of the reconstructed surface topography that cannot be obtained from individual sensor signals alone.     

Expert Estimation of Web-Development Projects: Are Software Professionals in Technical Roles More Optimistic Than Those in Non-Technical Roles?

Estimating the effort required to complete web-development projects involves input from people in both technical (e.g., programming), and non-technical (e.g., user interaction design) roles. This paper examines how the employees' role and type of competence may affect their estimation strategy and performance. An analysis of actual web-development project data and results from an experiment suggest that people with technical competence provided less realistic project effort estimates than those with less technical competence. This means that more knowledge about how to implement a requirement specification does not always lead to better estimation performance. We discuss, amongst others, two possible reasons for this observation: (1) Technical competence induces a bottom-up, construction-based estimation strategy, while lack of this competence induces a more outside view of the project, using a top-down estimation strategy. An outside view may encourage greater use of the history of previous projects and reduce the bias towards over-optimism. (2) Software professionals in technical roles perceive that they are evaluated as more skilled when providing low effort estimates. A consequence of our findings is that the choice of estimation strategy, estimation evaluation criteria and feedback are important aspects to consider when seeking to improve estimation accuracy.     

Trainable Articulatory Control Models for Visual Speech Synthesis

This paper deals with the problem of modelling the dynamics of articulation for a parameterised talking head based on phonetic input. Four different models are implemented and trained to reproduce the articulatory patterns of a real speaker, based on a corpus of optical measurements. Two of the models, (Cohen-Massaro and Öhman) are based on coarticulation models from speech production theory and two are based on artificial neural networks, one of which is specially intended for streaming real-time applications. The different models are evaluated through comparison between predicted and measured trajectories, which shows that the Cohen-Massaro model produces trajectories that best matches the measurements. A perceptual intelligibility experiment is also carried out, where the four data-driven models are compared against a rule-based model as well as an audio-alone condition. Results show that all models give significantly increased speech intelligibility over the audio-alone case, with the rule-based model yielding highest intelligibility score.     

Learning to Play Chess Using Temporal Differences

In this paper we present TDLEAF(), a variation on the TD() algorithm that enables it to be used in conjunction with game-tree search. We present some experiments in which our chess program KnightCap used TDLEAF() to learn its evaluation function while playing on Internet chess servers. The main success we report is that KnightCap improved from a 1650 rating to a 2150 rating in just 308 games and 3 days of play. As a reference, a rating of 1650 corresponds to about level B human play (on a scale from E (1000) to A (1800)), while 2150 is human master level. We discuss some of the reasons for this success, principle among them being the use of on-line, rather than self-play. We also investigate whether TDLEAF() can yield better results in the domain of backgammon, where TD() has previously yielded striking success.     

A computational method for simulating growth patterns in unicell propagation

The modelling of natural phenomena through the use of computer-generated graphics has attracted much interest recently. It is believed that such methods will lead to new breakthroughs in understanding nature. One of the most popular methods used is the cell automata method, where cells are made to propagate and form cellular patterns according to certain predefined rules. Although much of the work in this area is for recreational purposes, as in the Game of Life, there can be more serious aspects to it. One of these is in the use of such methods to predict and simulate the growth behaviour of cell clusters in real-life situations. In this study, an attempt is made to formalise certain rules for modelling the growth characteristics of unicell populations. The methodology proposed models three fundamental factors: first, the generic propagational characteristics of a cell; second, the effect of adverse factors to growth; and, third, the effect of spatial constraints. The first two factors, relating to the population of a cell colony, can be modelled mathematically; the third factor determines the visual appearance of the cell colony. Patterns resulting from some computational simulations are presented and discussed.     

An average complexity measure that yields tight hierarchies

A new definition is given for the average growth of a functionf: ^* N with respect to a probability measure on ^* This allows us to define meaningful average distributional complexity classes for arbitrary time bounds (previously, one could not guarantee arbitrary good precision). It is shown that, basically, only the ranking of the inputs by decreasing probabilities is of importance.To compare the average and worst case complexity of problems, we study average complexity classes defined by a time bound and a bound on the complexity of possible distributions. Here, the complexity is measured by the time to compute the rank functions of the distributions. We obtain tight and optimal separation results between these average classes. Also, the worst case classes can be embedded into this hierarchy. They are shown to be identical to average classes with respect to distributions of exponential complexity.     

Exact analytical solutions for some popular benchmark problems in topology optimization

The aim of this paper is to provide the exact analytical truss solutions for some benchmark problems, which are often used as test examples in both discretized layout optimization of trusses and variable topology (or generalized) shape optimization of perforated plates under plane stress.     

Mathematical modelling of processes in mechanics

Mathematical modelling processes are computed by the path following method from the literature. A new approximation is presented using parametrical optimization. The problems are solved as nonlinear programming problems by converting the problem from the function space into the ² space.     

Unitary immersions of nonlinear systems

In this paper, we define what we call a unitary immersion of a nonlinear system. We observe that, for classical Hamiltonian systems, this notion contains, in some sense, the concept of quantization. We restrict our attention to degree-zero unitary immersions, where all observation functions must be represented by operators of the type multiplication by a function. We show that the problem of classifying such degree-zero unitary immersions of a given nonlinear system is not obvious. In some cases, we solve this problem.Chargé de Recherche au CNRS.Maître de Conférences.