Parameter Estimation of Sigmoid Superpositions: Dynamical System Approach

Superposition of sigmoid function over a finite time interval is shown to be equivalent to the linear combination of the solutions of a linearly parameterized system of logistic differential equations. Due to the linearity with respect to the parameters of the system, it is possible to design an effective procedure for parameter adjustment. Stability properties of this procedure are analyzed. Strategies shown in earlier studies to facilitate learning such as randomization of a learning sequence and adding specially designed disturbances during the learning phase are requirements for guaranteeing convergence in the learning scheme proposed.Comment: 30 pages, 7 figure

A pragmatic approach to multi-modality and non-normality in fixation duration studies of cognitive processes

Interpreting eye-fixation durations in terms of cognitive processing load is complicated by the multimodality of their distribution. An important source of multimodality is the distinction between single and multiple fixations to the same object. Based on the distinction, we separated a log-transformed distribution made to an object in non-reading task. We could reasonably conclude that the separated distributions belong to the same, general logistic distribution, which has a finite population mean and variance. This allowed us to use the sample means as dependent variables in a parametric analysis. Six tasks were compared, which required different levels of post-perceptual processing. A no-task control condition was added to test for perceptual processing. Fixation durations differentiated task-specific perceptual, but not post-perceptual processing demands

OMPC: an Open-Source MATLAB®-to-Python Compiler

Free access to scientific information facilitates scientific progress. Open-access scientific journals are a first step in this direction; a further step is to make auxiliary and supplementary materials that accompany scientific publications, such as methodological procedures and data-analysis tools, open and accessible to the scientific community. To this purpose it is instrumental to establish a software base, which will grow toward a comprehensive free and open-source language of technical and scientific computing. Endeavors in this direction are met with an important obstacle. MATLAB®, the predominant computation tool in many fields of research, is a closed-source commercial product. To facilitate the transition to an open computation platform, we propose Open-source MATLAB®-to-Python Compiler (OMPC), a platform that uses syntax adaptation and emulation to allow transparent import of existing MATLAB® functions into Python programs. The imported MATLAB® modules will run independently of MATLAB®, relying on Python's numerical and scientific libraries. Python offers a stable and mature open source platform that, in many respects, surpasses commonly used, expensive commercial closed source packages. The proposed software will therefore facilitate the transparent transition towards a free and general open-source lingua franca for scientific computation, while enabling access to the existing methods and algorithms of technical computing already available in MATLAB®. OMPC is available at http://ompc.juricap.com