Model-checking ecological state-transition graphs

Model-checking is a methodology developed in computer science to automatically assess the dynamics of discrete systems, by checking if a system modelled as a state-transition graph satisfies a dynamical property written as a temporal logic formula. The dynamics of ecosystems have been drawn as state-transition graphs for more than a century, from state-and-transition models to assembly graphs. Thus, model-checking can provide insights into both empirical data and theoretical models, as long as they sum up into state-transition graphs. While model-checking proved to be a valuable tool in systems biology, it remains largely underused in ecology. Here we promote the adoption of the model-checking toolbox in ecology through its application to an illustrative example. We assessed the dynamics of a vegetation model inspired from state-and-transition models by model-checking Computation Tree Logic formulas built from a proposed catalogue of patterns. Model-checking encompasses a wide range of concepts and available software, mentioned in discussion, thus its implementation can be fitted to the specific features of the described system. In addition to the automated analysis of ecological state-transition graphs, we believe that defining ecological concepts with temporal logics could help clarifying and comparing them.

A joint transition model for evaluating eGFR as biomarker for rejection after kidney transplantation

The estimated glomerular filtration rate (eGFR) quantifies kidney graft function and is measured repeatedly after transplantation. Kidney graft rejection is diagnosed by performing biopsies on a regular basis (protocol biopsies at time of stable eGFR) or by performing biopsies due to clinical cause (indication biopsies at time of declining eGFR). The diagnostic value of the eGFR evolution as biomarker for rejection is not well established. To this end, we built a joint model which combines characteristics of transition models and shared parameter models to carry over information from one biopsy to the next, taking into account the longitudinal information of eGFR collected in between. From our model, applied to data of University Hospitals Leuven (870 transplantations, 2 635 biopsies), we conclude that a negative deviation from the mean eGFR slope increases the probability of rejection in indication biopsies, but that, on top of the biopsy history, there is little benefit in using the eGFR profile for diagnosing rejection. Methodologically, our model fills a gap in the biomarker literature by relating a frequently (repeatedly) measured continuous outcome with a less frequently (repeatedly) measured binary indicator. The developed joint transition model is flexible and applicable to multiple other research settings.

Effects of Chord Inversion and Bass Patterns on Harmonic Expectancy in Musicians

This study tests the respective roles of pitch-class content and bass patterns within harmonic expectation using a mix of behavioral and computational experiments. In our first two experiments, participants heard a paradigmatic chord progression derived from music theory textbooks and were asked to rate how well different target endings completed that progression. The completion included the progression’s paradigmatic target, different inversions of that chord (i.e., different members of the harmony were heard in the lowest voice), and a “mismatched” target, a triad that shared its lowest pitch with the paradigmatic ending but altered other pitch-class content. Participants generally rated the paradigmatic target most highly, followed by other inversions of that chord, with lowest ratings generally elicited by the mismatched target. This suggests that listeners’ harmonic expectations are sensitive to both bass patterns and pitch-class content. However, these results did not hold in all cases. A final computational experiment was run to determine whether variations in behavioral responses could be explained by corpus statistics. To this end, n-gram chord-transition models and frequency measurements were compiled for each progression. Our findings suggest that listeners rate highly and have stronger expectations about chord progressions that occur frequently and behave consistently within tonal corpora.

Adaptive attention-based human machine interface system for teleoperation of industrial vehicle

This study proposes a Human Machine Interface (HMI) system with adaptive visual stimuli to facilitate teleoperation of industrial vehicles such as forklifts. The proposed system estimates the context/work state during teleoperation and presents the optimal visual stimuli on the display of HMI. Such adaptability is supported by behavioral models which are developed from behavioral data of conventional/manned forklift operation. The proposed system consists of two models, i.e., gaze attention and work state transition models which are defined by gaze fixations and operation pattern of operators, respectively. In short, the proposed system estimates and shows the optimal visual stimuli on the display of HMI based on temporal operation pattern. The usability of teleoperation system is evaluated by comparing the perceived workload elicited by different types of HMI. The results suggest the adaptive attention-based HMI system outperforms the non-adaptive HMI, where the perceived workload is consistently lower as responded by different categories of forklift operators.