The ultimate goal of most neuromusculoskeletal modeling research is to improve the treatment movement impairments. However, even though models have become more realistic anatomically, physiologically, and neurologically over past 25 years, they yet make a positive impact on design clinical treatments for Such impairments are caused by common conditions such as stroke, osteoarthritis, Parkinson’...

We introduce a novel feature set, which we call HDRMAX features, that when included into Video Quality Assessment (VQA) algorithms designed for Standard Dynamic Range (SDR) videos, sensitizes them to distortions of High (HDR) videos are inadequately accounted by these algorithms. While features not specific HDR, and also augment the equality prediction performances VQA models on SDR content, th...

Coastal ecosystems are experiencing degradation from compound impacts of climate change and multiple anthropogenic disturbances. These pressures often act synergistically complicate designing effective conservation measures; consequently, large-scale coastal restoration actions become a wicked problem. The purpose this study was to use two different food web models in coordinated manner inform ...

We propose a new approach to density forecast optimisation and apply it to Value-at-Risk estimation. All existing density forecasting models try to optimise the distribution of the returns based solely on the predicted density at the observation. In this paper we argue that probabilistic predictions should be optimised on more than just this accuracy score and suggest that the statistical consi...

Computational models for human decision making are typically based on the properties of bistable dynamical systems where each attractor represents a different decision. A limitation of these models is that they do not readily account for the fragilities of human decision making, such as “choking under pressure”, indecisiveness and the role of past experiences on current decision making. Here we...

People are often expected to make decisions based on all of the relevant information, weighted and combined appropriately. Under many conditions, however, people use heuristic strategies that depart from this ideal. I tested the ability of two models to predict bail decisions made by judges in two courts. In both courts, a simple heuristic proved to be a better predictor of judicial decisions t...

Understanding the decoding algorithm for hidden Markov models is a difficult task for many students. A comprehensive understanding is difficult to gain from static state transition diagrams and tables of observation production probabilities. We have built a number of visualizations depicting a hidden Markov model for partof-speech tagging and the operation of the Viterbi algorithm. The visualiz...

The paper is devoted to a generalization of static and dynamic mathematical models of behavior with explicitly stated reflexive models of agents' decision-making. Reflexion is considered as agent's beliefs about nature, opponents' beliefs and opponents' decision-making principles in the framework of game theory, collective behavior theory and learning models.