Tracking of non-stationary time-series using resource allocating RBF networks

In this paper, we discuss some practical implications for implementing adaptable network algorithms applied to non-stationary time series problems. Using electricity load data and training with the extended Kalman filter, we demonstrate that the dynamic model-order increment procedure of the resource allocating RBF network (RAN) is highly sensitive to the parameters of the novelty criterion. We investigate the use of system noise and forgetting factors for increasing the plasticity of the Kalman filter training algorithm, and discuss the consequences for on-line model order selection. We also find that a recently-proposed alternative novelty criterion, found to be more robust in stationary environments, does not fare so well in the non-stationary case due to the need for filter adaptability during training.

Non-rigid object tracking in complex scenes

A recently proposed colour based tracking algorithm has been established to track objects in real circumstances [Zivkovic, Z., Krose, B. 2004. An EM-like algorithm for color-histogram-based object tracking. In: Proc, IEEE Conf. on Computer Vision and Pattern Recognition, pp. 798-803]. To improve the performance of this technique in complex scenes, in this paper we propose a new algorithm for optimally adapting the ellipse outlining the objects of interest. This paper presents a Lagrangian based method to integrate a regularising component into the covariance matrix to be computed. Technically, we intend to reduce the residuals between the estimated probability distribution and the expected one. We argue that, by doing this, the shape of the ellipse can be properly adapted in the tracking stage. Experimental results show that the proposed method has favourable performance in shape adaption and object localisation.

Investigating tiredness and ad complexity effects on visual processing of advertisements:an eye-tracking study

Although advertising is pervasive in our daily, it proves to be not necessarily efficient all the times due to bad conditions or bad contexts of reception. Indeed, the communication process might be jeopardized at its very last stage because of advertising exposure quality. However critical it may be, ad exposure quality is not very much examined by researchers or practitioners. In this paper, we investigate how tiredness combined with ad complexity might influence the way consumers extract and process ad elements. Investigating tiredness is useful because it is a common daily state experienced by everyone at various moments of the day. And although it might drastically alter ad reception, it has not been studied in advertising for the moment. In this regards, we observe eye movement patterns of consumers viewing simple or complex advertisements being tired or not. We surprisingly find that tired subjects viewing complex ads don’t adopt a lessening effort visual strategy. They rather use a resource demanding one. We assume that the Sustained Attention strategy occurring is a kind of adaptive strategy allowing to deal with an anticipated lack of resource.

Practical methods of tracking of nonstationary time series applied to real-world data

In this paper, we discuss some practical implications for implementing adaptable network algorithms applied to non-stationary time series problems. Two real world data sets, containing electricity load demands and foreign exchange market prices, are used to test several different methods, ranging from linear models with fixed parameters, to non-linear models which adapt both parameters and model order on-line. Training with the extended Kalman filter, we demonstrate that the dynamic model-order increment procedure of the resource allocating RBF network (RAN) is highly sensitive to the parameters of the novelty criterion. We investigate the use of system noise for increasing the plasticity of the Kalman filter training algorithm, and discuss the consequences for on-line model order selection. The results of our experiments show that there are advantages to be gained in tracking real world non-stationary data through the use of more complex adaptive models.

Tracking and updating academic research in selling and sales management:a decade later

This study examines the state of academic research in selling and sales management (S&SM) from the years 2003-7, ten years after the data collected by Moncrief, Marshall, and Watkins (2000). Sales articles are reviewed that appeared in 19 marketing journals and evidence is provided on the state of the S&SM discipline by comparing the number of authors, authorships, and publications versus a comparable five-year period a decade ago. Of interest are the universities that produce and employ faculty in S&SM and to identify those schools and geographic regions that are publishing the majority of articles. Publication distribution trends across journals are also examined. A dramatic increase in non-U.S. authors and authorships is noted versus the prior study. Overall, the findings indicate that, perhaps contrary to some popular misconceptions, the state of S&SM research is healthy, vibrant, and evolving.