HKUST Figure 1: Comparison of uniform timeslicing and non-uniform timeslicing using the Rugby Dataset. (a) Traditional uniform timeslicing, (b) non-uniform timeslicing. The whole stream of temporal edges are divided into 12 intervals with the sequence number marked at the top right corner of each snapshot. The top left bars and smoothed line chart show the time range of each interval (in a format of "year.month.day ") and the edge frequency distribution, respectively. The dotted blue rectangles highlight two interesting intervals and the red dotted ellipses highlight several thick edges. The teams are: ca -Cardiff, sc -Scarlets, dr -Dragons, os -Ospreys, le -Leinster, mu -Munster, ul -Ulster, co -Connacht, ed -Edinburgh, gl -Glasgow, ze -Zebre, and be -Benetton. ABSTRACTUniform timeslicing of dynamic graphs has been used due to its convenience and uniformity across the time dimension. However, uniform timeslicing does not take the data set into account, which can generate cluttered timeslices with edge bursts and empty timeslices with few interactions. The graph mining filed has explored nonuniform timeslicing methods specifically designed to preserve graph features for mining tasks. In this paper, we propose a nonuni-* form timeslicing approach for dynamic graph visualization. Our goal is to create timeslices of equal visual complexity. To this end, we adapt histogram equalization to create timeslices with a similar number of events, balancing the visual complexity across timeslices and conveying more important details of timeslices with bursting edges. A case study has been conducted, in comparison with uniform timeslicing, to demonstrate the effectiveness of our approach.

Two different computational approaches were used to predict Olympic distance triathlon race time of German male elite triathletes. Anthropometric measurements and two treadmill running tests to collect physiological variables were repeatedly conducted on eleven male elite triathletes between 2008 and 2012. After race time normalization, exploratory factor analysis (EFA), as a mathematical preselection method, followed by multiple linear regression (MLR) and dominance paired comparison (DPC), as a preselection method considering professional expertise, followed by nonlinear artificial neural network (ANN) were conducted to predict overall race time. Both computational approaches yielded two prediction models. MLR provided R² = 0.41 in case of anthropometric variables (predictive: pelvis width and shoulder width) and R² = 0.67 in case of physiological variables (predictive: maximum respiratory rate, running pace at 3-mmol·L -1 blood lactate and maximum blood lactate). ANNs using the five most important variables after DPC yielded R² = 0.43 in case of anthropometric variables and R² = 0.86 in case of physiological variables. The advantage of ANNs over MLRs was the possibility to take non-linear relationships into account. Overall, race time of male elite triathletes could be well predicted without interfering with individual training programs and season calendars.

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We announce the first best paper awards for papers published in 2019 as well as best AE awards, outline what is in store for 2021, and thank departing and incoming staff.