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The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis and a "real-time" experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. A long-term tracking subchallenge has been introduced to the set of standard VOT sub-challenges. The new subchallenge focuses on long-term tracking properties, namely coping with target disappearance and reappearance. A new dataset has been compiled and a performance evaluation methodology that focuses on long-term tracking capabilities has been adopted. The VOT toolkit has been updated to support both standard short-term and the new longterm tracking subchallenges. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website 60 .

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The Visual Object Tracking challenge 2015, VOT2015, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 62 trackers are presented. The number of tested trackers makes VOT 2015 the largest benchmark on shortterm tracking to date. For each participating tracker, a short description is provided in the appendix. Features of the VOT2015 challenge that go beyond its VOT2014 predecessor are: (i) a new VOT2015 dataset twice as large as in VOT2014 with full annotation of targets by rotated bounding boxes and per-frame attribute, (ii) extensions of the VOT2014 evaluation methodology by introduction of a new performance measure. The dataset, the evaluation kit as well as the results are publicly available at the challenge website 1 .

The behavior of gene modules in complex synthetic circuits is often unpredictable1–4. Upon joining modules to create a circuit, downstream elements (such as binding sites for a regulatory protein) apply a load to upstream modules that can negatively affect circuit function1,5. Here we devise a genetic device named a load driver that mitigates the impact of load on circuit function, and we demonstrate its behavior in Saccharomyces cerevisiae. The load driver implements the design principle of time scale separation: inclusion of the load driver’s fast phosphotransfer processes restores the capability of a slower transcriptional circuit to respond to time-varying input signals even in the presence of substantial load. Without the load driver, we observe circuit behavior that suffers from 76% delay in response time and a 25% decrease in system bandwidth due to load. With the addition of a load driver, circuit performance is almost completely restored. Load drivers will serve as fundamental building blocks in the creation of complex, higher level genetic circuits.