A Comparison Between Alignment and Integral Based Kernels for Vessel Trajectories

In this paper we present a comparison between two important types of similarity measures for moving object trajectories for machine learning from vessel movement data. These similarities are compared in the tasks of clustering, classication and outlier detection. The rst similarity type are alignment measures, such as dynamic time warping and edit distance. The second type are based on the integral over time between two trajectories. Following earlier work we dene these measures in the context of kernel methods, which provide state-of-the-art, robust algorithms for the tasks studied. Furthermore, we include the in uence of applying piecewise linear segmentation as pre-processing to the vessel trajectories when computing alignment measures, since this has been shown to give a positive eect in computation time and performance. In our experiments the alignment based measures show the best performance. Regular versions of edit distance give the best performance in clustering and classication, whereas the softmax variant of dynamic time warping works best in outlier detection. Moreover, piecewise linear segmentation has a positive eect on alignments, which seems to be due to the fact salient points in a trajectory, especially important in clustering and outlier detection, are highlighted by the segmentation and have a large in uence in the alignments

Do your troubles today seem further away than yesterday? On sleep’s role in mitigating the blushing response to a reactivated embarrassing episode

The “sleep to forget and sleep to remember hypothesis” proposes that sleep weakens the emotional tone of an experience while preserving or even enhancing its content. Prior experimental research however shows contradictory findings on how emotional reactivity changes after a period of sleep, likely explained by methodological variations. By addressing these inconsistencies, we investigated the mitigating effect of overnight sleep on emotional reactivity triggered by memory reactivation. Using a karaoke paradigm, we recorded participants’ singing of two songs, followed by exposing them to one of the recordings (rec1) to induce an embarrassing episode. After a 12-hr period of either day-time wakefulness (N = 20) or including nighttime sleep (N = 20), we assessed emotional reactivity to the previously exposed recording (rec1) and the newly exposed recording (rec2). Emotional reactivity was assessed with a physiological measure of facial blushing as the main outcome and subjective ratings of embarrassment and valence. Sleep and wake were monitored with diaries and actigraphy. The embarrassing episode was successfully induced as indicated by objective and subjective measures. After controlling for an order effect in stimulus presentation, we found a reduction in blushing response to the reactivated recording (rec1) from pre- to post-sleep compared to wakefulness. However, emotional reactivity to the reactivated recording (rec1) and the new recording (rec2) did not differ after sleep and wakefulness. This study shows that facial blushing was reduced following overnight sleep, while subjective ratings were unaffected. Whether the beneficial effect of sleep is due to changes in memory representation or rather emotion regulation remains elusive

Learning from the past with experiment databases

Thousands of Machine Learning research papers contain experimental comparisons that usually have been conducted with a single focus of interest, and detailed results are usually lost after publication. Once past experiments are collected in experiment databases they allow for additional and possibly much broader investigation. In this paper, we show how to use such a repository to answer various interesting research questions about learning algorithms and to verify a number of recent studies. Alongside performing elaborate comparisons and rankings of algorithms, we also investigate the effects of algorithm parameters and data properties, and study the learning curves and bias-variance profiles of algorithms to gain deeper insights into their behavior

Effect of hot water immersion on acute physiological responses following resistance exercise

Purpose: Hot water immersion (HWI) is a strategy theorised to enhance exercise recovery. However, the acute physiological responses to HWI following resistance exercise are yet to be determined. Methods: The effect of HWI on intramuscular temperature (IMT), muscle function, muscle soreness and blood markers of muscle cell disruption and inflammatory processes after resistance exercise was assessed. Sixteen resistance trained males performed resistance exercise, followed by either 10 min HWI at 40°C or 10 min passive recovery (PAS). Results: Post-intervention, the increase in IMT at all depths was greater for HWI compared to PAS, however this difference had disappeared by 1 h post at depths of 1 and 2 cm, and by 2 h post at a depth of 3 cm. There were no differences between groups for muscle function, muscle soreness or any blood markers. Conclusion: These results suggest that HWI is a viable means of heat therapy to support a greater IMT following resistance exercise. Recovery of muscle function and muscle soreness is independent of acute changes in IMT associated with HWI