In movies or on TV, a wheel can seem to rotate backwards, due to the temporal subsampling inherent in the recording process (the wagon wheel illusion). Surprisingly, this effect has also been reported under continuous light, suggesting that our visual system, too, might sample motion in discrete "snapshots." Recently, these results and their interpretation have been challenged. Here, we investi...

In this paper, a simple Lyapunov sampling is proposed. Contrary to a periodic fashion which samples the system uniformly in time, an event-based scheme updates the control signal only when the system trajectory sufficiently changes. Furthermore, the present triggering mechanism is based on a Lyapunov function in order to enforce the events only when required from a stability point of view. Neve...

The reduction of the number of samples is a key issue in signal processing for mobile applications. We investigate the link between the smoothness properties of a signal and the number of samples that can be obtained through a level crossing sampling procedure. The algorithm is analyzed and an upper bound of the number of samples is obtained in the worst case. The theoretical results are illust...

Œis paper introduces new techniques for sampling aŠributed networks to support standard Data Mining tasks. Œe problem is important for two reasons. First, it is commonplace to perform data mining tasks such as clustering and classi€cation of network aŠributes (aŠributes of the nodes, including social media posts). Furthermore, the extraordinarily large size of real-world networks necessitates t...

In real environments it is often difficult to obtain a collection of cases for the case base that would cover all the problem solving situations. Although it is often somewhat easier to generate potential problem cases that cover the domain tasks, acquiring the solutions for the problems captured by the cases may demand valuable time of a busy expert. This paper investigates how a Case-Based Re...

Respondent-driven sampling (RDS) is frequently used when sampling hard-to-reach and/or stigmatized communities. RDS utilizes a peer-driven recruitment mechanism where sampled individuals pass on participation coupons to at most c of their acquaintances in the community (c = 3 being a common choice), who then in turn pass on to their acquaintances if they choose to participate, and so on. This p...

Abstract. Respondent-driven sampling (RDS) is a commonly used method for acquiring data on hidden communities, i.e., those that lack unbiased sampling frames or face social stigmas that make their members unwilling to identify themselves. Obtaining accurate statistical data about such communities is important because, for instance, they often have different health burdens from the greater popul...

Visual analytics and interactive machine learning both try to leverage the complementary strengths of humans and machines to solve complex data exploitation tasks. These fields overlap most significantly when training is involved: the visualization or machine learning tool improves over time by exploiting observations of the human-computer interaction. This paper focuses on one aspect of the hu...